Optuma Blog

S&P Indices - March 2026 Quarterly Rebalancing

Darren Hawkins — Wed, 18 Mar 2026 23:45:00 GMT

March 23rd, 2026 sees the quarterly rebalancing of major S&P indices, affecting benchmarks from the Australian ASX 200 ($XJO) to the US S&P 500 ($SPX), MidCap 400 ($MID), and SmallCap 600 ($SML) indices.

When an index rebalances, it ensures that the benchmark accurately represents the current market landscape. However, as the latest data shows, representation does not always equate to stability.

In Australia, three changes were made to the ASX 200. Notably, the companies being removed were only added last year. In fact, Catapult Sports ($CAT) has seen its market value halve since joining the index less than six months ago.

A list of the ASX 200 Changes in March 2026

In the United States, the S&P 500 sees four major additions this quarter, with over $200 billion in market capitalization entering the index (50% of which is concentrated within the Technology sector).

A list of the S&P 500 Changes in March 2026

The “Index Effect”: Performance vs. Expectation

A common hypothesis in finance is that inclusion in a major index leads to outperformance due to forced buying from passive ETFs and mutual funds. However, empirical data often paints a more nuanced picture.

By utilizing the IsMember() function within Optuma to track entry and exit dates, we can analyze the “post-addition” performance of last year’s S&P 500 newcomers.

The Winners: Out of 18 companies added in 2025, only 7 have posted gains since the day they joined. Sandisk ($SNDK) leads the pack, surging 250% since late November.
The Losers: Nine additions have seen double-digit declines. The Trade Desk ($TTD) sits at the bottom, experiencing a 71% loss since its inclusion.

Performance of companies since the day they joined the S&P 500 in 2025

This data suggests that while index inclusion provides liquidity and prestige, it is not a guaranteed catalyst for price appreciation.

Quantitative Accuracy: Solving for Survivorship Bias

For researchers conducting backtests on index-linked strategies, historical accuracy is paramount. A common pitfall is failing to account for Survivorship Bias.

If you were to test a strategy on the current members of the S&P 500 over a ten-year period, your results would be artificially inflated. You would be testing companies that succeeded enough to stay in the index while ignoring those that went bankrupt or were delisted.

For instance, a valid historical test should ignore Tesla ($TSLA) signals prior to its December 2020 inclusion and instead account for the company it replaced, Apartment Investment & Management Co ($AIV). Managing these shifting memberships manually is a recipe for data errors but for those using Optuma Symbol Lists, these updates are seamless. Your linked scans, watchlists, and signal testers will automatically reflect membership changes, ensuring accuracy in your analysis. For a deeper dive on survivorship bias, see Mathew Verdouw’s article here.

How to Create a Custom Index

Darren Hawkins — Wed, 12 Feb 2025 00:00:00 GMT

Clients with Australian or US fundamental data enabled on their accounts now have access to short interest data.

What is Short Interest data?

Short selling is a trading strategy used by investors who believe that a particular security's price will decline in the future. In a short selling transaction, an investor borrows shares of a stock from a broker and sells them on the open market. The investor's goal is to buy back the shares at a lower price in the future, return them to the lender (the broker), and profit from the price difference.

Exchanges require brokers to report their short positions on a regular basis (every two weeks in the US) which are then published. We can use this data as a sentiment indicator with short interest being the total number of shares sold short that have not been repurchased. When there are too many shorts, there is a risk of a strong covering rally. Due to the high risk involved in shorts and the potential for unlimited losses, short sellers must buy to cover before losses become too great.

The Short Interest Ratio - also called Days to Cover - is an indicator that reveals how long, at current volume levels, it would take for all outstanding shorts to be covered. This is an important indicator as a high ratio can cause panic in short holders, leading to sharp and fast rallies as they seek to cover their positions to protect against inordinate losses.

Adding Data to a Chart

To add the data to a chart use the Data Field tool and select Short Interest and Short Interest Ratio. They can also be added to a watchlist column, by selecting them from the Fundamental Field section.

Here’s a list of the stocks in the Dow, showing Caterpillar $CAT with the highest Short Interest Ratio of 6.99 as at the last report at the end of February, meaning it will take 7 days of average volume to cover the 13.9 million shares that are currently short. This has risen from 8 million shares with 2.8 days to cover over the last month as the price has reached all- time highs (note the data appears stepped on a daily chart as the values are only updated twice a month in the US).

It seems like lots of bears are chasing the $CAT in anticipation of a fall, but if the price keeps rising then the short sellers will start to get nervous and start to buy shares back to cut their losses, which can quickly push the price even higher. This is known as a short squeeze.

[Image: Chart 1 - Dow Jones stocks sorted by Days to Cover ratio] Chart 1 - Dow Jones stocks sorted by Days to Cover ratio

Short Interest as a Percentage of Float

As seen above, Caterpillar has 13.9 million shares sold short, but is that a lot compared to other companies? For example, Apple has 112 million shares short, so how to compare?

By taking the company’s float value (the number of shares available for trading by the public, i.e. not including those held by insiders) we can calculate the percentage of float that has been sold short.

This requires a simple script formula using the two data fields (because Float is reported in millions and Short Interest is actual value the float value has to be multiplied):

Short = DATAFIELD(LATESTONLY=True, FEED=FD, FIELD=ShortInterest);
Float = DATAFIELD(LATESTONLY=True, FEED=FD, FIELD=Float)*1000000;

Short/Float

This watchlist of the ASX200 stocks in Australia has been sorted by the percentage of float that has been sold short, with Pilbara Minerals $PLS on top with 23%, suggesting that sentiment is very bearish.

[Image: Chart 2 - ASX200 stocks sorted by percentage of float] Chart 2 - ASX200 stocks sorted by percentage of float

Sample Workbooks

If you have the Australian or US Fundamental data enabled on your account you can download sample Short Interest workbooks from the country pages here.

To add the data, click the My Account icon on the welcome screen when you log in to Optuma, go to the Products page and click the Modify Exchanges button.

Now Available: Short Interest Data for US and Australian Equities

Darren Hawkins — Fri, 15 Mar 2024 00:00:00 GMT

Clients with Australian or US fundamental data enabled on their accounts now have access to short interest data.

What is Short Interest data?

Short selling is a trading strategy used by investors who believe that a particular security’s price will decline in the future. In a short selling transaction, an investor borrows shares of a stock from a broker and sells them on the open market. The investor’s goal is to buy back the shares at a lower price in the future, return them to the lender (the broker), and profit from the price difference.

Adding Data to a Chart

Chart 1 - Dow Jones stocks sorted by Days to Cover ratio

Short Interest as a Percentage of Float

As seen above, Caterpillar has 13.9 million shares sold short, but is that a lot compared to other companies? For example, Apple has 112 million shares short, so how to compare?

This requires a simple script formula using the two data fields (because Float is reported in millions and Short Interest is actual value the float value has to be multiplied):

<table class="rouge-table"><tbody><tr><td class="gutter gl"><pre class="lineno">1
2
3
4
</pre></td><td class="code"><pre><span class="nx">Short</span> <span class="o">=</span> <span class="nc">DATAFIELD</span><span class="p">(</span><span class="nx">LATESTONLY</span><span class="o">=</span><span class="nx">True</span><span class="p">,</span> <span class="nx">FEED</span><span class="o">=</span><span class="nx">FD</span><span class="p">,</span> <span class="nx">FIELD</span><span class="o">=</span><span class="nx">ShortInterest</span><span class="p">);</span>
<span class="nx">Float</span> <span class="o">=</span> <span class="nc">DATAFIELD</span><span class="p">(</span><span class="nx">LATESTONLY</span><span class="o">=</span><span class="nx">True</span><span class="p">,</span> <span class="nx">FEED</span><span class="o">=</span><span class="nx">FD</span><span class="p">,</span> <span class="nx">FIELD</span><span class="o">=</span><span class="nx">Float</span><span class="p">)</span><span class="o">*</span><span class="mi">1000000</span><span class="p">;</span>

<span class="nx">Short</span><span class="o">/</span><span class="nx">Float</span>
</pre></td></tr></tbody></table>

Chart 2 - ASX200 stocks sorted by percentage of float

Sample Workbooks

If you have the Australian or US Fundamental data enabled on your account you can download sample Short Interest workbooks from the country pages here.

To add the data, click the My Account icon on the welcome screen when you log in to Optuma, go to the Products page and click the Modify Exchanges button.

CMT Association Summit - Dubai 2024

Darren Hawkins — Fri, 08 Mar 2024 00:00:00 GMT

Last week, Mathew, Darren and Jairus attended the CMT Association Summit in Dubai, the first held outside of New York.

Optuma has been sponsoring CMTA events for over 10 years now, and it’s always a great opportunity to meet some of the best technicians from around the world, with many of them being clients. This year was no exception, with presentations from John Bollinger, Bloomberg’s Global Head of Portfolio Strategy Gina Martin Adams, and RRG’s Julius de Kempenaur. Optuma’s Founder & CEO Mathew Verdouw presented alongside one of our clients, portfolio manager David Cox of Raymond James in Canada where they discussed the need to balance quantitative analysis with a discretionary approach.

Mathew Verdouw and David Cox Presenting at the CMT Association Summit

Overall the mood was generally bullish, with Gina Martin Adams in particular believing the bull market is just getting going, and she sees nothing in the near future to make her nervous.

Dubai aims to become a major financial hub and already the region has many aspiring market technicians, as seen in the number of attendees who wanted to take selfies with Mathew after gaining their CMT designation with the help of our CMT Prep classes.

In addition the CMTA signed an agreement with the Dubai International Financial Centre Academy to develop training programs in technical analysis, behavioral finance, and market strategy, so hopefully we will be back!

If you’ve never been to a CMTA event, and you have a professional approach to your Technical Analysis, make sure you consider coming along, they’re a great way to learn more and connect with other professionals.

Darren Hawkins and John Bollinger

Museum Of The Future - Dubai

Catching a Falling Knife

Mathew Verdouw — Tue, 27 Feb 2024 00:00:00 GMT

Mean Reverting Signals

So far in this series, we’ve been looking at long term general market conditions with an overall bullish sentiment coming through. Today, I want to shift gears and review a mean-reversion technical indicator that we’ve created and look at some short-term signals.

Mean reverting trading is not for the faint hearted. Some liken it to catching a falling knife - you may do it successfully many times but you can also get cut up really badly if you don’t know what you’re doing. They are short-term trading signals, not long-term allocations to a portfolio. Although if the decision has already been made to add a name to your portfolio, a mean-reverting signal is a great entry to ensure the position is profitable from the start.

One way to alleviate the risk is to make sure that we stick to the foundations of Technical Analysis. One of the of the most enduring and important foundations is Dow Theory and two rules that are relevant to mean-reversion trading are:

Always trade in the direction of the Primary Trend
Use volume to confirm if a move against the Primary Trend is a temporary correction or a change in the Primary Trend.

The CMT program is a great way to get these core foundations locked in to all you do.

Rule 1 will tell us that we only take the mean reverting signal if we are confident that the Primary Trend is still valid.

Rule 2 tells us that we can look at volume to determine if there is growing support for a change in trend. If volume was growing into a mean-reversion signal, it could be confirming a change in trend and we’d need to be very careful. What we want to see is a general declining of volume into the signal.

Optex Bands

Optex Bands is an indicator that we built to capture high probability mean-reversion trades. (Optex was a shortening of “Optuma Extremes” since we were capturing extreme deviations).

They are built using Market Profile - a concept that has been in use since the 1980s. We have a full explanation of the calculation at https://optuma.com/optex

You can see the Optex Bands in Chart 1. The black line is the “Ratio” line and it is a volatility adjusted measure of how far price is deviating away from the point of balance on the chart. The dynamic zones are the extreme zones. They are also calculated using Market Profile techniques.

The theory here is that an extreme move in any direction has a high probability of reverting. The more extreme the move, the higher the probability.

Chart 1: Optex Bands. Courtesy Optuma.com

In Chart 1, I have also added Green Arrows when the Optex Ratio enters the Blue extreme over-sold zone and Red Arrows when the Optex Ratio enters the Red extreme over-bought zone.

They are not perfect signals - no signal ever is - but when coupled with Dow’s rules of “Trade with the Primary Trend” they do yield some good results.

Russell 3000 Optex Signals

While it is great practice to go through charts looking for trading candidates, it’s so much better when we can have the computer do the work for us. In Chart 3 we have scanned for any name in the Russell 3000 that has an Optex Long Signal (as of Feb 21st).

I’ve also included in the Watchlist the monthly change in price (which as you’d expect is down) and the yearly change. I really prefer to see positive yearly numbers as it give me more confidence that the security is in an uptrend giving a higher probability that this will be a good mean-reversion trade. If the monthly is also up (eg Bellring Brands BRBR, and SiriusPoint SPNT) then those really interest me.

Finding the candidates is only step one. We would need to review the chart and make sure that we are confident that we are not breaking Dow’s rules and that we are willing to take on the risk associated with this trade.

Remember! No technical signal is a guarantee of success but as a concept, this type of Mean Reverting signal can be helpful to find trading candidates. Watch these names over the coming days and weeks and see what you think.

Chart 2: Russell 3000 Optex Signals. Courtesy Optuma.com

RRG Confirmation

Relative Rotation Graphs are a great way to visualise relative strength of a universe of stocks against a benchmark ( you can learn more about RRGs here ).

In Chart 3, I have added these scan results (those names with an Optex Signal) to a daily (on the left) and monthly RRG.

As you would expect, the daily RRG has all these names under-performing the benchmark. The Yearly RRG, however, is highlighting that some of the names are in solid out-performance territory and those are the one that I am most interested in.

Chart 3: RRG Confirmation. Courtesy Optuma.com

Low Vix Analysis

Mathew Verdouw — Tue, 06 Feb 2024 00:30:00 GMT

VIX Remaining Low

Despite all that is going on in the world (wars, inflation, natural disasters, & Taylor Swift’s latest album), Stock Markets around the world have just continued on their merry way seemingly oblivious to everything. Confidence is so high in the stability of the market that the VIX is continuing its general downtrend and is now approaching all time low levels.

Remember that the VIX is a measure volatility based on 30-day Options. When “old” funds cannot sell because of the tax that sale would attract, they add Put Options to their portfolio to hedge against the risk that the market will fall. It is the increased buying of Options that causes the VIX to rise.

The low VIX values tell us that there are not a lot of options being added to Portfolios right now (it’s a little more complicated than that, but the general principle holds). You can see quite clearly in Chart 1 that the trend of the S&P500 is up while the trend in the VIX is down.

Chart 1: S&P 500 & CBOE S&P 500 Volatility Index. Courtesy Optuma.com

Low VIX Expectations

What can we expect from this environment of low VIX? Well, one thing is for sure, at some point in the future we’re going to have a spike in VIX as the market falls again—that’s inevitable. But we have no idea how long that will be away. This is when we look to history to give us a guide (not a prediction).

It is always interesting to look at the past to glean some idea of what we can expect in the future. When VIX falls to low levels, what can we expect?

In the short term, the signs are looking good for Stock Markets, values are increasing, volatility is falling, interest rates are also falling (or at least holding steady).

In Chart 2, I’ve added arrows to all the times that the VIX had been above 30 and then crossed below 14 for the first time. Just by looking at the chart you can see that it’s a nice signal that gives us confidence in the short-term outlook.

Chart 2: VIX Crossing Below 13. Courtesy Optuma.com

VIX Is not a Sell Signal

You may, like most people, be tempted to think about the VIX in terms of a sell signal. Eg, when VIX spikes, SELL! Yet I’m presenting it as a Buy (or at least Hold) signal.

We’ve done a lot of quantitative testing on this, and we cannot find a single scenario where selling on rising VIX works.

The problem is that the Option buying is a reaction to a fall in price. So the spike in the VIX happens after the fall in the S&P500 and by then, it’s too late to sell.

A Better VIX Signal

While we can find no suitable quantitative signal on rising VIX, there are some interesting Signals on falling VIX that you should lock away. It may be a few years before you can use these ones. Firstly, we can use the VIX above 25 as a filter. When it’s above, we turn off any long equity strategies and look for Shorts. When it’s below, we turn back on our long equity strategy. So rather than being a “Sell” signal, it becomes a “Don’t Buy” signal.

Secondly, When VIX rises above 45 (major panic) and then Crosses Below 30 as things settle down again, we get the signals you can see in Chart 3. They don’t happen often, and we can see in 1990 and 2000 there was still some downward movement after the signal, but how many times have you wondered when is the right time to get back into Stocks after a major crash? This VIX signal is something you should pay attention to.

Chart 3: VIX Crossing Below 35. Courtesy Optuma.com

Resource links

All full size charts can be viewed online at VIX Analysis Charts - Investopedia Chart Advisor - Feb 7 | Optuma Publishing

Presidential Cycles

Mathew Verdouw — Tue, 06 Feb 2024 00:20:00 GMT

Presidential Cycle

The Presidential Cycle is a four-year cycle where the US Stock Market seems to make similar returns in each of the four years. I.e. We compare all the years of the election (2020, 2016, 2012…) and the results seem to align.

The most common theory behind the cycle is that there is more spending in years leading up to a Presidential Election and that leads to Stock Market gains. Another reason is that fund managers tend to take a “wait and see” approach when there is a change in administration, so the buying pressure is lower.

Regardless, there is a general pattern that we can take advantage of if we dig in and have a look at what history has to tell us.

Chart 1: S&P 500 with Presidential Years Highlighted. Courtesy Optuma.com

In Chart 1, we can see the past 70 years of the S&P 500. On the chart I have highlighted each of the cycle years. This is a great visual, but there is not much we can do with this on its own.

Instead, we can do a small quantitative analysis to determine the average returns for each of these years and also the Probability that the year will end up (positive).

Let’s look at the four years in the cycle:

Year 1: Post-Election Year Average Return is 4.7% Probability of a Positive Year is 56%

Year 2: Mid-Term Year Average Return is 2.3% Probability of a Positive Year is 65%

Year 3: The Pre-Election Year. Average Return is 11% Probability of a Positive Year is 88%

Year 4: Election year (we are here now!) Average Return is 7.4% Probability of a Positive Year is 83%

Election Years

It’s always good to look inside the numbers and view what made up the average. In Chart 2, we look at all the Election years on a Seasonality Chart and we can see how the majority of Election years end with the SPX higher.

Chart 2: Return of each Election Year. Courtesy Optuma.com

What jumps straight out is that there have only been three bad Election Years in the past 70 years. What is also interesting is those three elections saw a change in which party won the White House:

1960 : Kennedy defeated Nixon 2000 : Bush defeated Gore 2008 : Obama defeated McCain

So there are two conclusions:

Parties continue spending in the Election Year and the Stock Market benefits from that.
If the Stock Market does not rise, a change in the White House is highly likely.

Of course, there are many caveats with any conclusions like this. As Technical Analysts we do not observe something and assume that it is an immutable fact. Rather we see it, take note, and consider it in light of our other analysis.

Quant Testing

One way that we can dig in further is to examine the average path taken during all the Election Years. Chart 3 shows an Optuma Signal Test result. The main plot is the average of each of the Election Years from the start of the year to the last trading day (252 trading days in a year).

Chart 3: Average Election Year Returns. Courtesy Optuma.com

The average behavior is for the market to be sideways for the first 60 days and then start making gains until the Election is getting close. There is uncertainty about who will win and most large investors are minimizing exposure to potential volatility around the election day.

In the final 60 days, after the election until the end of the year, the certainty of what is coming gives investors the confidence to jump back into the market and we usually see a final rally into Christmas.

Component Years

So far this year, the S&P 500 is up 4.6%. That’s considerably more than the average flat 60 days that we saw in Chart 3. So what can we expect from here?

To get an idea, we can have a look at the Individual Component Years in Chart 4. It’s just a different version of Chart 3 but instead of showing the combined average, we are seeing each of the years that made up the average.

Chart 4 Component Election Year Returns. Courtesy Optuma.com

1980, 2008, & 2024

What jumps out is that those years that started well finished well. Look at the Red line in Chart 4 (1980), it was at 8.8% by this same time in the year and it finished at 28% gain. In Contrast, 2008 started down -3.5% and finished the year nearly 40% down.

I’ve repeated those two years with 2024 in Chart 5. Again, while there are no guarantees in financial analysis — we deal in probabilities based on past observations — based on the historical results, the signs are good for 2024 to finish the year strongly.

Chart 5. SPX Yearly Return for Years 1980, 2008, 2024

Resource links

All full size charts can be viewed online at Presidential Cycle Charts - Investopedia Chart Advisor - Feb 6 | Optuma Publishing

Want to know a better way to determine Trend?

Mathew Verdouw — Mon, 30 Oct 2023 00:20:00 GMT

A few years ago I picked up an old copy of a book by Michael Gur called The Symmetry Wave Trading Method. Gur introduced the concept of using a series of swings based on the Average True Range of a chart, or the ATR. This was one of those “ah-ha” moments. I’ve always liked swing charts but knew that we needed to do something to factor in volatility if we want to achieve consistent swing-based rules across multiple securities.

Basically, a Swing Chart shows up and down price movement of a minimum size regardless of the time it takes to move - much like the trend of the market. In fact, WD Gann called his Swing Chart the “Master Trend Detector”. There are a variety of swing charting methods used. Where each of the methods differ is in how they define when a change in the swing’s direction should occur. There are some that are obvious – like Point Swings that require a retracement of a number of points for the swing to turn, or Percent Swings that measure the retracement in terms of percentage change. Others are not so obvious. Gann Swings look at the relationship between successive bars on a bar chart to determine when the swings should change.

Gur suggested in his course that the retracement determining when the swings change should be measured in terms of the number of ATRs. The Average True Range has become recognised as a reliable measure of volatility – measuring the average extreme movement of price from one day to the next. This means a security that oscillates wildly will have a higher volatility measure compared to one that has much smaller movements.

The following two charts of Coca-Cola (KO) and Harley Davidson (HOG) highlight this. They both are valued in the $45 range, but they have very different volatility values (0.58 vs 2.63). Under each chart you can see the ATR indicator. You will notice on the right that Harley Davidson had a major spike in its share price which led to a higher ATR value.

For a major Swing Gur would use 4 ATRs, meaning that the price would need to retrace by 4 times the value of the ATR for that day. Again, taking the two charts above, what would the price need to fall to for the swing to turn down?

With Coca-Cola at an ATR value of 0.58, it would need to retrace by $2.32 from the high before the swing would turn down. With Harley Davidson at an ATR value of 2.63, it would need to retrace by $10.52 from the high for the swing to turn down.

You can see these two similarly priced stocks have very different thresholds for when the swing turns. And rightly so. If we had just used Points, we would be hard pressed to find a point interval that’s suitable for both. By using the ATRs, we’re able to ignore the “typical” noise of the security and only respond when there has been a significant change of trend.

An Extra Bonus

Because the ATR value is adaptive, the ATR will drop in value as the market goes sideways. As the ATR drops, so does the size of the required retracement for the swing change. This means that as the market trades sideways the swing may turn.

Numbering Swings

One of the great concepts that Elliott brought to Technical Analysis is the concept of numbering waves. Gur did the same thing, although there is no limit to how high the count can go.

In this image you can see the end of the swings are numbered. There are a number of rules that Gur introduced in his course:

Odd-numbered swings are in the direction of the main trend.
Successive odd-numbered swings MUST extend past the previous odd-numbered swing. E.g. in the chart above, Swing 3 must end higher than Swing 1, etc.
If an odd swing fails to extend past the last odd swing, it is called a “Failed” swing and gets an “F” prefixed.

If an even swing extends part the last even swing, then it’s the start of a new number sequence in the opposite direction. In this image you can see that what would have been Swing 8 went lower than Swing 6, and so it became a new Swing 1 going down.

When a swing reaches the required ATR measure to change direction, but may not yet have passed the previous odd swing, it is called a “Potential” Swing. E.g. in this image of Visa we have a P-11 - meaning that it has retraced by the right number of ATRs but to be a “real” Swing 11, it needs to extend past Swing 9. If it fails and turns down, it will become a Failed Swing 11 (F-11).

My goal was to see if we could find opportunities that had a high probability of identifying successful trades. This was all done before we built the new Signal Tester in Optuma. It’s an area that we want to get back to, but two early observations we noticed are:

A failed Swing 3 (essentially a lower top) has a very high probability of indicating the start of a down move.
It’s rare to get sets of swings that go beyond Swing 5. As the counts get higher, the probability of a change in trend is getting higher (so we'll be keeping an eye on Visa!).

These swings are a great way to identify the direction of the trend while considering the volatility of the security.

We can even add the Volatility Swing as a filter in other quantitative work that we are doing. Here I added it as a true/false column in my Watchlist—depending if the swing is up or not—using the following script:

VOLATILITYSWINGS(ATRS=4.00) IsUp

The second column shows the current swing count:

VOLATILITYSWINGS(ATRS=4.00).BarLabels

We could even use them as an ingredient in a Market Breadth measure. That is, how many stocks in the S&P500 have Volatility Swings that are heading up? Here we have the S&P500 chart with the percent of those stocks with a Volatility Swing pointed up in the bottom window.

You can see there are many uses for Volatility Swings, and more that we want to do with them. I still think the most important use is as a way to determine the trend of the security, taking that security’s volatility into consideration. If you’d like to try any of these out, make sure you let us know and we will help you get set up.

Now Available: World Commodities Data

Darren Hawkins — Thu, 19 Oct 2023 23:54:00 GMT

As well as recent improvements to our Breadth, Economic, and US Mutual Fund data{: target="_blank"}, we now have pricing for over 75 global commodities. This includes coverage of energy, metals, industrials, agriculture, and livestock. Click the image to see what’s available:

Adding the data to your account

To access your account page, click the My Account icon on the welcome screen when you log in to Optuma, or manually log in via the following link and use your Optuma username and password:

https://portal.optuma.com/myaccount/products

Click on the Modify Exchanges button in the Products section, and select World Commodities under Global Data. Scroll down and click the Save button (beneath your monthly subscription total and billing date). The data will be available when Optuma is restarted.

If you would like to replace one of your existing data selections with World Commodities rather than adding it as an extra option please contact support@optuma.com.

Download Sample Workbook

Once the data has been enabled on your account, click this button to save and open a workbook containing a watchlist of all the commodities.

Additional Data in World Indices

On the second tab of the workbook you will find charts for five new indices that have been added to World Indices:

London Metals Exchange Index - symbol LME EU Carbon Permits - EUCP Nuclear Energy Index - NEIX Solar Energy Index - SEIX Wind Energy Index - WEIX

If you have questions about the new data or any aspect of Optuma please let us know, or try the chat icon on the KnowledgeBase to use our AI assistant{: target="_blank"}.

Price Return vs Total Return Data

Darren Hawkins — Fri, 22 Sep 2023 00:20:00 GMT

Price return and total return are two different ways to measure the performance of stocks, bonds, and ETFs. Here are the key differences:

Price Return

Price return, also known as capital return, only considers the change in the asset's price or market value over a specific period.
It does not take into account income generated from the asset, such as dividends for stocks or coupon payments for bonds.
Price return is a simple measure of how the market value of the asset has changed, excluding any income received.

Total Return

Total return includes not only the price appreciation of the asset but also any income generated from it, such as dividends, interest, or distributions.
It provides a more comprehensive view of an investment's performance as it accounts for all sources of return.
Total return is often considered a more accurate measure of an investment's true profitability.

What data should I be using?

Generally, technical analysts use Price Return data because those are the prices at which the stocks actually traded (ignoring stock splits!). Total Return data changes the historical prices to take into account the extra income from the dividend or interest payments. By default, the charts in Optuma are Price Returns, but - if using our end-of-day data - it's possible to display the Total Return data by clicking on the chart header:

Here's an example showing the difference with IBM. The Price Return in black has been overlaid with the Total Return chart (green):

As you can see, the further back you go the bigger the disparity between the lines because more dividends have been paid so the more the historical prices drop. Recent prices will converge, and will be the same since the last payout.

On a price return basis IBM is currently at $147, way below its 2013 all-time high of $205, but when the dividend payouts are included the equivalent price at that time was $130. This means that if you bought (and held!) IBM back then and received all the dividends you would be up about $17 per share - although I'm not sure how many of you would have held on until the 2016 low!

Comparing historical returns

It's possible to compare the total returns in Optuma's watchlists. Below are two identical lists with the same column formulas. By changing the Price Adjustment property in the bottom watchlist to Total Returns, all the column calculations will use the historically adjusted data. So BHP on the ASX has gained 22% over the last two years when taking into account dividends, versus only 18.8% on a price return basis. The other examples are for a Fidelity US Mutual fund, a stock, and a bond ETF:

In summary, price return focuses solely on changes in the market price of an asset, while total return provides a measure of the returns you would have achieved from holding the security by considering both price changes and income generated by the asset, giving a more accurate representation of an investor's actual gains or losses.

GoNoGo Evidence-Based Investing - Part 6

Alex Cole — Wed, 30 Aug 2023 02:37:25 GMT

This sixth and final video in the GoNoGo Charts educational series brings all the concepts together into a practical approach to market analysis and trading decisions. By answering just three simple questions, investors glean a complete technical perspective on any investment decision. In addition to absolute trends for securities, sectors and indices, GoNoGo Charts can be used to understand relative strength trends as well. By applying the same tools to ratio charts, investors gain a key perspective on asset allocation and security selection within the strongest areas of the market.

Better charts. Better decisions.

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View Part 1 | GoNoGo Trend Identification - Part 1 View Part 2 | GoNoGo Trend Identification - Part 2 View Part 3 | GoNoGo Oscillator - Momentum Concepts in Technical Analysis - Part 3 View Part 4 | GoNoGo Trend Re-Entry - Part 4 View Part 5 | GoNoGo Embracing Volatility - Part 5

New Data Options: Improved Market Breadth, Economic, and US Mutual Funds

Darren Hawkins — Thu, 24 Aug 2023 23:25:00 GMT

A lot has been going on in the background here at Optuma in Brisbane. As well as finishing off the Optuma 2.2 update (Beta out now!), we have also been busy with improvements to our data options. As well as improvements to the Sector and Industry classification (you can read about that here{:target="_blank"}), we have also made significant changes to the following Data Products in Optuma:

Market Breadth
Economic Data
US Mutual Funds

We’ll dive deeper into the details over the coming weeks, but here’s an overview of what’s available.

Market Breadth

Over many years, we added different breadth measures but there was no consistency when it came to names, symbols, and even location. One of the drivers of the Market Breadth overhaul was to create consistent names and symbols.

The other issue we had was that the power and speed of Optuma itself was not available in our Data Servers. This meant that we had to rewrite everything in a scripting language that was really slow.

With the constant development towards our web-based products, we have now built a web-based version of the Optuma engine and that is what has allowed us to greatly increase the number and complexity of what we can include in the Breadth Measures Data Product.

Following the significant upgrade to our breadth engine, we now have over 50 daily measures for these major global indices:

Australia: ASX 200, ASX 300, ASX All Ords indices
US: Dow Jones, Nasdaq 100, Russell 2000, Russell 3000, S&P 400, S&P 500, S&P 600, S&P 1500, - plus Nasdaq & NYSE exchanges (equities only)
Europe: EuroStoxx 600, FTSE100, FTSE350
India: NIFTY 500
Canada: TSX Composite.

The symbology has also been standardised, so that they are consistent across all groups. Here’s a list of the available Russell 3000 measures, which all start with R3K- (S&P500 measures will start with SPX-, ASX 200 ASX2-, etc).

Breadth Data

Economic Data

The new Economic Data - which is not to be confused with the FRED Economic Reserve Data{:target="_blank"} - now has access to over 100 global macroeconomic measures from Australia, Canada, Euro Area, Germany, India, Singapore, Switzerland, UK, and US. This includes historical data for things such as GDP, Unemployment Rate, Inflation, Consumer Confidence, and government bond yields.

For example, here’s a comparison of inflation rates around the world:

Global Inflation Rates

US Mutual Funds

This new data product replaces our old mutual fund data, with the updated version now including dividends for thousands of US Mutual Funds, as well as the Net Asset Value. To see dividend-adjusted charts click on the Price Returns label in the chart header and change it to Total Returns.

Here’s an example of the Vanguard Total Bond Market Index Fund $VBTLX, with total returns on the right:

Mutual Funds

Contact support if you have any questions, or would like the new data added to your account. Also, be sure to visit our Sample Workbooks Knowledge Base{:target="_blank"} page where you can download and open lots of chart examples in your copy of Optuma, including ones using these new data options.

GoNoGo Embracing Volatility - Part 5

Alex Cole — Tue, 22 Aug 2023 23:37:00 GMT

Volatility compression rounds out the concepts blended into GoNoGo Charts®. Visualizing behavior in markets is the essence of all technical analysis tools, and those moments where a "tug of war" exists between buyers and sellers are important to highlight on our charts. In this fifth session of the GoNoGo Charts educational series, Alex Cole and Tyler Wood, CMT review familiar tools such as Bollinger Bands and Keltner Channels and return to the original challenge of capturing the information without crowding our charts. The GoNoGo Squeeze® was developed to highlight periods of volatility compression right inside the GoNoGo Oscillator® panel with a climbing amber grid. As experienced money managers know, trading in the direction of the break can help capture high velocity price moves which often accompany expanding volatility. Alex and Tyler show examples of volatility compression in trend continuation and trend reversal circumstances.

Volatility doesn’t have to be a four-letter word. Investors can embrace the opportunity by understanding the concept and remaining objective in their approach.

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GoNoGo Trend Re-Entry - Part 4

Alex Cole — Tue, 15 Aug 2023 22:00:00 GMT

Trend following investors lean heavily on money management practices that let winners run and cut losses short. While that is simple to say, it is very difficult to execute. It requires the investor to distinguish between turning points and pullback opportunities. Irresponsible enthusiasts in bull markets will shout to “BTFD!” or post diamond hands that defy sensible risk management practices. This fourth video in the GoNoGo Charts® educational series helps traders, analysts, and investors understand the interplay between price trends and momentum signals. Alex Cole, and Tyler Wood, CMT show practical examples of using momentum concepts within trending markets to lean into moments when momentum surges in the direction of the price trend.

In practice, using the GoNoGo Oscillator® with an objective neutral level at the zero line can provide investors with a rules-based approach to entering trends after they have already been confirmed, sticking with trending securities for the full length of the move, and potentially increasing position size at logical points to lean into durable trends.

Momentum indicators are often misused in trending markets. And, a responsible approach to “buying dips” can assist every investor.

Better charts. Better decisions.

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New in Optuma: Updated Exchange Sector & Industry Groups

Darren Hawkins — Thu, 10 Aug 2023 22:45:00 GMT

If you have logged in to Optuma this week then you may have noticed a change to the Sector and Industry tree structure in the Security Selection window for a number of the equity exchanges, including ASX, US, and Canada.

Previously they were grouped by the GICS classifications but we stopped getting that data several years ago and the groupings were no longer accurate.

We now get fundamental data{: target="_blank"} from FactSet, including their sector classifications. As such, the exchanges are now grouped by their 12 sector, 20 industry, and 120+ sub-industry classifications, as in this example for the ASX:

Security Selection

Click the + to expand the sectors into industries and sub-industries, as below for HealthCare which has two industry groups (Health Services and Health Technology). Clicking on the Biotechnology sub-industry will update the list on the right. Click the Open List As… button to choose how you would like to view those stocks, such as a Watchlist, using a previously saved Page Layout, or as a Relative Rotation Graph:

Open List As

One thing to be aware of is that the groups may include delisted or inactive stocks. This is because if you are running historical backtests on these sectors then for accuracy you will want these stocks to be included in the results.

However, if you are using the Scanning Manager, you can select the sector or industry from the Exchanges tab under Codes to Scan, and on a Last Bar or Last Week Date Range only stocks trading in that time period will show in the results.

Select Industry & Sector

US GICS Index Data

Whilst we no longer have GICS classification data for individual stocks (unless you have connected Optuma to a Bloomberg datafeed) we still have the US GICS sector index data for all four classification levels. These are included in the US Indices data selection and you can enable it on your account. If you have that data enabled, you can download the GICS Sector workbook from the Sample US Workbooks{: target="_blank"} page.

US GICS Index Data Watchlist

GoNoGo Oscillator - Momentum Concepts in Technical Analysis - Part 3

Alex Cole — Thu, 03 Aug 2023 00:20:00 GMT

Momentum is the next step for Technical Analysts after identifying trend. This third video in the GoNoGo Charts® educational series helps traders, analysts, and investors understand how momentum studies are calculated and what they seek to provide. Alex Cole, and Tyler Wood, CMT discuss the concept of confirmation and divergence with respect to price action. Equally important, they describe how many of the traditional oscillators are designed to highlight overbought and oversold conditions in market activity but can leave a lot up to interpretation.

Better charts. Better decisions.

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View Part 1 | GoNoGo Trend Identification - Part 1 View Part 2 | GoNoGo Trend Identification - Part 2 View Part 4 | GoNoGo Trend Re-Entry - Part 4 View Part 5 | GoNoGo Embracing Volatility - Part 5 View Part 6 | GoNoGo Evidence-based Investing - Part 6

GoNoGo Trend Identification - Part 2

Alex Cole — Thu, 27 Jul 2023 02:20:00 GMT

In this second installment of this educational series from GoNoGo Charts, Alex and Tyler expand upon the concept of a rules-based approach to trend identification. This video offers real-time examples of trend-following concepts and covers multiple timeframes as well as the application across all asset classes.

Better charts. Better decisions.

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View Part 1 | GoNoGo Trend Identification - Part 1 View Part 3 | GoNoGo Oscillator - Momentum Concepts in Technical Analysis - Part 3 View Part 4 | GoNoGo Trend Re-Entry - Part 4 View Part 5 | GoNoGo Embracing Volatility - Part 5 View Part 6 | GoNoGo Evidence-based Investing - Part 6

The Secret Wealth Advantage: How You Can Profit from the Economy's Hidden Cycle

Mathew Verdouw — Tue, 25 Jul 2023 01:17:00 GMT

The Secrect Wealth Advantage By Akhil Patel

For nearly 20 years, I've had the pleasure of working with Phil Anderson and Akhil Patel. In that time I've seen them use the real estate cycle to accurately forecast not only real estate prices but also the impact on stock markets and the economy in general. Their analysis of this 18.6-year cycle is amazing and I have a lot of respect for the work that they do.

The difficulty with all long-term cycles has always been how to know what to expect at each stage of the cycle. Well, we do not have to struggle with this anymore. In his new book "The Secret Wealth Advantage: How You Can Profit from the Economy's Hidden Cycle", Akhil deals with each year of the cycle and looking through the lens of history, he unpacks what we can expect. Spoiler Alert: We are heading to a climactic end to this cycle and this information is as important now as any other time in the cycle.

The book's focus is on the US market, but in our connected financial world, the implications are applicable to us all.

As a rule, we do not promote other products and we are not receiving any compensation for this, but I wanted to let you know about this book because I respect Phil & Akhil's work so much and I want you to survive in the market.

Here is the publisher's blurb on the book:

{:class="smallquote"}

Why did the recent banking crisis, involving some of the largest bailouts in American history not crash the American economy? Why is the American economy still powering ahead despite a record pace of interest rate rises, and not falling into a recession as many had feared at the start of the year? Why have the stocks of some US housebuilders rivalled US tech firms in the speed of share price growth in since March of this year? These are puzzling questions and one which many learned commentators and large investors have been puzzled by in recent months. Why? Because they remain ignorant of the 18-year economic cycle, how it works, and plays out; they do not understand the fundamental law of economics, the law of economic rent, that drives it. The cycle is responsible for all of the periods of wild speculation and spectacular collapse that have been a feature of the modern economy for well over 200 years. At the heart of this cycle is the land market. In his new book, The Secret Wealth Advantage: how to profit from the economy’s hidden cycle, Akhil Patel, explains all that in a highly readable account of the cycle. He takes the reader through an 18-year journey through a full 18-year cycle. He illustrates each of the cycle’s nine stages with a different historical episode. Along the way, he also explains why it happens and why it repeats, how money and banking fit in and much more. Akhil is himself a long-term user of Optuma and a student of Gann and market cycles. He came to the study of cycles when his family’s business was badly affected by the 2008 crisis because banks called in loans from small businesses, thus making economic problems much greater. He decided he wanted to find out why there had been no warning that this was coming, and he wanted to know what to be prepared for the next one. The book is the result of that effort. At its heart, the book is a practical guide to the cycle so that investors and traders can take advantage of the long-term dynamics that drive our economies through periods of boom and bust. Each of the book’s 18 chapters ends with a section that makes up a book within a book, The Handbook of Wealth Secrets. The book can be bought at all major online retailers and is available in paperback, ebook and audio book formats. You can find a selection of retailers at this link here - [https://tr.ee/StmsiugYlV](https://tr.ee/StmsiugYlV){: target="\_blank"}

GoNoGo Trend Identification - Part 1

Alex Cole — Wed, 19 Jul 2023 01:45:00 GMT

This short video explains the problem with technical analysis. As investors/traders, our goal is to remove emotion from our decision making. However, with a bounty of powerful indicators and tools, we quickly introduce "indicator overload" and "analysis paralysis." GoNoGo Charts® seek to capture the responsible checklist with multiple indicators but calculate the weight of the evidence in the background, so the charts remain clean, elegant and focused on price action. This is the first video in a multi-part educational series from Tyler Wood, CMT and Alex Cole, co-founders of GoNoGo Charts®.

Better charts. Better decisions.

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View Part 2 | GoNoGo Trend Identification - Part 2 View Part 3 | GoNoGo Oscillator - Momentum Concepts in Technical Analysis - Part 3 View Part 4 | GoNoGo Trend Re-Entry - Part 4 View Part 5 | GoNoGo Embracing Volatility - Part 5 View Part 6 | GoNoGo Evidence-based Investing - Part 6

Using Optuma Symbol Lists

Darren Hawkins — Thu, 22 Jun 2023 03:45:00 GMT

In this short video you will learn how to use Optuma Symbol Lists to automatically access the current index constituents of major indices, such as the ASX200, FTSE250, and the S&P500 and its sectors.

To import or create your own symbol lists see here

The Birth of Ralph Acampora's Brainchild: ARGON

Julius De Kempenaer — Tue, 09 May 2023 05:00:00 GMT

This journey started back in October 2022. After three years of no traveling and four years not having been to the US, it was finally possible to have an in-person event again, or at least partially.

StockCharts.com has been hosting its bi-annual conference, "ChartCon," since 2011. My first attendance was in 2014, shortly after Relative Rotation Graphs were introduced on the site. At that time, it was still an all-in-person event.

In 2016 ChartCon switched to a hybrid format where attendees were able to attend online with a small group on-site where all presenters would also be to deliver their talks. Due to the Corona Pandemic, it was not possible to have a conference in 2020, but as soon as things settled down and the world opened up again, the team at StockCharts got to work and put together ChartCon 2022.

Among the elite group of speakers was Ralph Acampora, the Godfather of Technical Analysis, who needs no further introduction for those of you who haven't seen the documentary about Ralph's barn. Please do yourself a favour and watch it here on StockCharts Television.

After two intensive days of putting together a great conference, all of us got together for drinks and a bit of dinner, and of course, a chat.

I ended up in a setting with Ralph, his nephew Jay Woods, and a few others when an engaging conversation emerged.

You have to know that Ralph and I go back more than 30 years now. The first time I met him was in 1992 at the IFTA conference in Dublin, Ireland. Ever since, it's "Hey, big guy, how are you doing?" every time we meet.

The discussion revolved around old vs. new and how computers had taken over trading and order execution. Jay was chipping in from his experience as an NYSE floor governor, and I came in from my experience with institutional investors as I was working on trading floors of investment banks. It was a bit of picking and making fun of "the old guy" in a friendly setting.

As the chat continued, the debate turned to "outperformance" and how institutional investors nowadays are ruled by benchmarks. They need to beat them, but they are not allowed to diverge too much because that means too much risk in the portfolio, and that's not allowed by the guidelines of the mandate, etc., etc.

Then Ralph began telling a story about his experience in the time he was working with Prudential Securities.

Ralph talks

At PruSec, I not only had to deal with institutional clients but also to drop into the firm's local retail investor's offices. In other words, I was always interacting with clients that had completely different investment objectives. For me, personally and professionally, this was not only challenging but was a very rewarding experience.

Sometime in 1995, during a retail office visit, I presented the audience with my assessment of those sectors and stocks that were outperforming the stock market (S&P 500). And it was during dinner that two investors came up to me seeking a more detailed explanation of exactly what I meant by "performance". I then took out my pad and explained in more detail exactly what comparative relative strength is and how it is calculated.

I took the current price of General Motors and divided it by the current price of the S&P 500, which was the ratio. I stated that professional portfolio managers had to "beat the market." In other words, the ideal trend of this ratio, which I sketched on the pad, should be moving up on a chart, indicating that your individual stock/sector was outperforming the market; a neutral trend in this ratio meant that the sector or stock was "even with the market." And lastly, a downward trend in this ratio meant that your stock/sector was "underperforming the market."

Ralph Talks

One of these two investors then said: "Wait a minute, if my stock is up 25% and the S&P 500 is up 30%, you would then say that this is bad news because I am not beating the market? Who cares what the S&P 500 is doing? I am making a profit. And that's what is most important to me as an individual investor."

And the second investor chimed in, saying: "I have another example that doesn't sound quite right to me. Let's say my stock is down 25% and the S&P 500 is down 30 %; you would then tell me that this is good news because I am outperforming the stock market. Are you crazy? I am losing a ton of money – who cares what the S&P 500 is doing?

Honestly, I never looked at comparative relative strength through the eyes of an individual investor before. Now I had a problem, which set of glasses would I use when looking at comparative relative strength – it is quite obvious that institutional investors need to beat the market in order to stay in business, while individual investors seek only to make a profit.

When I returned to my office in New York City, I could not get this conflict out of my mind. So, I started scribbling lines on paper in order to make sense of the true meaning of performance. It took time, but eventually, I realized that there are actually nine versions of price trends versus comparative relative strength trends. And this is what I came up with.

Explaining Something Without Pen and Paper

Then Ralph started drawing in the air, explaining how he viewed relative performance against price performance. Essentially showing the grid as we know it from the game noughts and crosses.

Explaining Something Without Pen and Paper

Over the years, I shared these nine combinations with my students and some of my institutional investors but never wrote about it in any of my formal technical reports.

Later I started to do a weekly review of these nine combinations for all thirty Dow Jones Industrial Average stocks. Again, I didn't publish these results, but they were very interesting, especially at turning points in the overall stock market.

Relative Strength, Grids, and Visualization

Ralph told us that he had shared his results with some of his colleagues in the industry, but none seemed too interested.

Can you believe it? Relative, strength, grids, and visualization. Clearly, that got me interested.

After Ralph finished his explanation, Jay and I started talking about how that approach could be "computerized" and made more "quantitative." Of course, we also made the odd joke that having a computer do all the work would allow Ralph to enjoy his Merlot on Sunday evening.

We also needed to get a good name for this "thing"; Ralph's Nine, The Nine of Acampora, Acampora's Nine, Ralph's 9 Grid or R9G, and many more were thrown up in the air, but none really kept floating.

As the discussion and the evening drew to an end, I promised Ralph to write something about it (this article) and see if I could prototype the 9-grid so we could do more research.

After I returned to my farm near Amsterdam and Ralph to his in Minnesota, I started brainstorming this, and emails started flying. Since October 19 (nice date 😉 ), about 25 emails have been sent, and two video calls have been held.

Eye Balling vs. Bits & Bytes

One of the first things we ran into was the fact that Ralph is/was eye-balling these trends rather than using some hard-coded formula to determine the trend. Clearly, the computer needs a few rules to determine when a trend is moving up, down, or sideways.

In modern technical analysis, there are many ways to accomplish such an assessment. To keep things simple, we opted to use the classic dual-moving average approach. A short and a long MA, and we require the short MA AND the close to be above the long MA for an uptrend. A downtrend requires the short MA AND the close to be below the long MA. Any other combination is labeled as sideways.

So far, so good.

But when I started to crank out some results and we started comparing notes. My "computerized" results were nowhere near his master's visual observations. And although I was expecting some discrepancies, this was way off.

It wasn't until I started quizzing Ralph on how he was labelling the buckets on the grid that I realized he started counting in the top-left corner of the grid and then going row by row. This meant that the X-axis, which holds the price trend, was flipped upside down... So I had to be the bearer of bad news and bring the message:

Ralph, computers don't like that 😉

Once I stylized the grid going from down, to sideways, to up on both scales, things started to make more sense.

Eye Balling vs. Bits & Bytes

A Sticky Name

Meanwhile, the brainstorming on a good name continued. After realizing that Ralph is way more handsome than George and definitely way smarter, we decided to ditch the movie associations, and eventually, we came up with the following acronym:

"Acampora's Relative Grid of Nine - ARGON"

The next problem I faced was the fact that the StockCharts system does not allow (yet, we are working on it), the level of custom coding that is needed to achieve the necessary results. So, I coded up this approach in the software of our friends down under at Optuma and used the scatter-plot visualization to create the nine-grid. With a bit of tweaking, I managed to get the ticker symbols plotted in the various buckets (there's a limit to how many symbols can show up in the same bucket, but for now, it's enough to show the idea).

Scatter-plot Visualization

In a live implementation, you can drag the History Slider of the scatter-plot to make the symbols move through the buckets over time and see how they move as a group.

Things could have ended here, as this in itself is a very simple but very effective display of relative vs. price trends. And as Ralph does this only for the DJ Industrial stocks, he can do it within two glasses of his beloved Merlot every Sunday night.

Bigger Index = More Difficult

When you would try to do this for larger indexes like the S&P 500, this would be much more difficult, if not impossible. Ralph would need at least two bottles of Merlot every weekend, which could cause unwanted side effects...

So here comes the computer and some help from Mathew Verdouw at Optuma.com, who kindly has set up the following page:

https://app.optuma.com/argon

Here you can see ARGON for a few larger universes like the S&P 500, ASX 200, Nasdaq 100, and the FTSE 100.

Bigger Index equals More Difficult

We'd like to emphasize that this is very much a showcase setup to get the idea behind ARGON and is nowhere near a finished product. People may want to experiment with their own trend measures, their own investment horizons, etc.

A Use Case For Market Breadth

Finally, Ralph told us that he got a great sense of market breadth by looking at the distribution of the symbols on the grid. His rough estimate was that when more than 50% of the stocks are in the upper echelon of the market (i.e. in the 7, 8, or 9 buckets), then the index is in an uptrend.

Obviously, it all depends on the investment horizon and the definition of a trend that is used. Everybody can tailor that to their own liking. Longer term, shorter term, different metrics for trend measurement, etc., etc.

When creating a "breadth indicator," you need the historical data for all the rankings, and you need to be able to count / summarize these values over time. Optuma have calculated these values historically for the S&P500, ASX 200, Dow Jones, and Nasdaq 100 indices, as in the images below. This will be available in their new breadth data which is about to be released (contact support if you would like early access!).

SPX - Argon Breadth

DJI - Argon Breadth

XJO - Argon Breadth

With this, Ralph's brainchild ARGON is now live and shared with the industry in which he has been instrumental for many analysts.

There is much more research that can be done to fine-tune the parameters and find use cases. You are encouraged to take this approach and tailor it to your own needs and ideas. The only thing we ask is to respect Ralph's idea and keep the acronym ARGON as a reference to his work.

Here's Ralph (front row, third from the left) with the CMT Association ringing the closing bell at the NYSE. Optuma Founder & CEO Mathew Verdouw is standing far-right.

NYSE Closing Bell

Momentum and the GoNoGo Oscillator

Alex Cole — Fri, 31 Mar 2023 04:46:00 GMT

What is Momentum and how is it useful?

To answer this question in the context of technical analysis I would say that it is the acceleration of price movement. Momentum can help the trader understand the velocity of price change and get a sense of the strength of price trends. It can give clues as to whether price will continue in the current direction or if the trend is at risk of stalling.

When I teach the concept of momentum to new technicians, I talk cars. Being from the UK, I grew up in a strong car culture. Formula One, and the World Rally Championship were events that had me glued to the TV. Using a car’s acceleration as a proxy for price momentum resonates with students and brings an understanding of the value in studying these technical indicators.

Imagine driving a car accelerating on a ramp to join a highway. As you increase speed to join the highway the car will increase its speed quickly. You may go from 10-20 mph, 20-30 mph, and 30-40 mph and each increase of pace might take a similar amount of time. In each of these speed increases, the rate of change of speed is the same. As you approach the highway speed limit, which here in New Jersey is 50mph, you will continue to get faster but most likely at a slower rate. You might go from 40-45 mph, 45-48 mph, and 48-50 mph. At 50 mph, unless you are willing to risk the long arm of the law you are going to stop accelerating. As you got close to top speed, you were still getting faster, but you were getting faster more slowly! The car’s decreasing rate of acceleration (falling momentum) tipped us off to the fact that we were approaching the highest speed we could go. Our next move is likely to slow down when we approach our exit.

So it is with price momentum. With each price increase, we would like to see that price increase with the same velocity, the same momentum. If price is making higher highs, we would expect similar or higher highs in momentum in a strong trend. If price is making higher highs, but momentum is less on each high (making lower highs) then it suggests that we may be approaching top speed! There is a chance that we will not see new highs, and even the chance that price may fall.

Different methods of calculation and popular indicators

Momentum indicators are an attempt to measure the speed at which price has arrived at its current level. They will involve a look back period to compare current price in some way to prior prices. Below are just a few of many.

ROC (Rate of Change)

One of the simplest ways to get a sense of the speed of price change is to use the Rate of Change indicator. This study compares price to price N bars ago and then divides by price N bars ago. In this way it makes the change a percentage and so gives a reading that can be compared across securities.

Optuma SPY ROC

CCI (Commodity Channel Index)

The CCI is another well used momentum indicator. Whereas ROC compared price to a single price a set number of periods ago, CCI looks at price and compares it to an average of its prices over a set time frame. In layman’s terms it compares price to an historical average of price, and then divides by a standard deviation of price (the price used is the “typical price” or (H + L + C) / 3).

Optuma SPY CCI

RSI (Relative Strength Index)

Perhaps the most used momentum indicator in our industry, RSI has a different calculation. This momentum study looks back over a set number of periods and evaluates whether the bar closed up or down. It then divides the number of higher closes by lower closes and converts this idea into an oscillator that moves between 0 – 100. This concept has value because one would expect that as prices move higher there will be more higher closes relative to lower closes.

Optuma SPY RSI

Powerful Concepts

Extremes of overbought and oversold

Remember, momentum indicators measure the velocity of price movement. If an asset has moved to quickly in one direction it can move away from intrinsic value. In other words, it becomes overbought or oversold. In the short term, a trader may expect price to mean revert. If we use RSI as an example in the chart below, when the indicator moves into overbought territory above 70 it is considered overbought. When it crosses back below 70 into neutral territory, one might expect price to move lower in the short term (see the arrows in the image below).

Divergence from price

Think back to our car accelerating getting quicker more slowly. That is the concept of divergence. If price makes a higher high but momentum makes a lower high this tells us that there is not as much enthusiasm behind this higher high. Perhaps our security has hit its top speed and we will look for it to move lower in the short term. Again, see the example with trendlines on the RSI.

Optuma RSI SPY divergence

Analysis Paralysis

In our recent blog discussing the importance of trend identification (click here), we saw how adding valuable indicators and concepts to our chart could cloud our judgement and leave us more confused than when we started. The same is true here. These three studies attack the problem of momentum differently and each bring something of value to our process. If we add only these three momentum studies to our trend identification chart we can see how difficult we have made our research.

Optuma SPY with everything

The GoNoGo Oscillator

Years of working with professional technicians has taught us that having the information from momentum indicators is extremely valuable. Therefore, we need to ensure we have the insight but without the indecision. With GoNoGo Oscillator we take the same approach that we did with GoNoGo Trend. Behind the scenes, GoNoGo Oscillator calculates several of the most robust momentum concepts into one oscillator that can be added to our chart but still allows us to use momentum to inform our understanding of the price activity. With GoNoGo Oscillator, we still can identify areas of overbought and oversold extremes (highlighted by arrows on the below chart). We also can quickly see divergences (marked with trendlines). Adding GoNoGo Oscillator to our chart we get a sound understanding of momentum analysis. Keeping it all in one panel we ensure simplicity and remove complication.

Optuma SPY with oscillator

Scanning using the Oscillator

Optuma clients with the suite of GoNoGo tools enabled on their account are able to scan on the GoNoGo Oscillator using the GNGOSC() function. For example, to find stocks that are coming out of oversold (eg crossing above -4) use the following:

GNGOSC() CrossesAbove -4

The results below show 14 stocks moved from oversold, with three ($CNSL, $OFIX, and $CBRE) making strong moves from -5 to -1.

GoNoGo Scan Results

To add the suite of GoNoGo tools to your account (including the Oscillator, Squeeze, and Trend tools) is US$125 per month.

Contact support for a free trial, or click here to sign up.

S&P Indices - March 2023 Quarterly Rebalancing

Darren Hawkins — Wed, 15 Mar 2023 23:45:00 GMT

Monday, March 20, 2023 sees the quarterly rebalancing of the major S&P indices. Included in the update are the Australian ASX200 ($XJO), ASX300, and in the USA the S&P500 ($SPX), S&P400, and S&P600 indices.

If you have access to the Optuma Symbol lists, the members of the indices will automatically update for you. Any scan or watchlist you have linked to the symbol list will use the new membership.

Four changes have been made to the top 200 Australian companies, with one change to the ASX20 (South32 $S32 replaces James Hardie $JHX). The watchlist below shows the changes in the ASX200. It's interesting to note the four added have all been in the index before - in fact Life360 was only removed last September - and so far this year have, on average, significantly underperformed those they are replacing:

A list of the ASX 200 Changes in March 2023

There was only one change to the U.S. S&P500 based on rebalancing, but there were two other changes this week following the collapse of Silicon Valley Bank ($SIVB) and Signature Bank ($SBNY).

For the rebalancing, Fair Isaac & Co ($FICO) replaced Lumen Technologies ($LUMN) which is the worst-performing member of the index this year, down 80% from its one-year high.

The two banks were replaced on March 15th by healthcare company Insulet ($PODD) and Bunge ($BG), an agribusiness company.

Before these recent changes there were 16 companies that joined the S&P500 since the beginning of 2022. The watchlist below uses a couple of simple scripts to display the date they joined and the relative performance versus the index since the day they joined:

A list of the SPX Additions in 2022

Date joined (with the Column Type set to Date)

V1=ISMEMBER(SYMBOLLIST=S&P 500) ChangeTo 1;  
D1=BARDATE();  
VALUEWHEN(D1, V1==1)

Relative % to SPX (Column Type set to Percentage)

V1=ISMEMBER(SYMBOLLIST=S&P 500) ChangeTo 1;  
D1=BARDATE();  
$D2=VALUEWHEN(D1, V1[-1]==1);  
RIC(DATESEL=User Defined, START_DATE=$D2, ZEROBASED=True)/100

On average they have outperformed the index by 7.5%, with Constellation Energy $CEG beating the index by 86% since it was added to the index in February 2022. The worst performer has been $EQT which was added in October and is down 33% since then on a relative basis.

Whilst being added to the index is by no means a guarantee of success (see this article for more index membership analysis) it makes sense those being added to the index as a result of rebalancing are likely to perform better than those being removed (usually for poor performance).

If we take the case of this rebalance with $FICO replacing $LUMN you can see in the charts below how the two have fared over the last year, $FICO gaining 42% and $LUMN losing 77%. The weekly Relative Rotation Graph® in the bottom-right shows $LUMN started to lag the S&P500 index when it crossed into the red quadrant in early September 2022, with $FICO joining the Leading green quadrant in July (to learn more about RRGs see here).

Charts comparing the performance of $FICO vs $LUMN

When testing on our S&P500 historical data, $LUMN will be included in the results up to this week, with $FICO only included from this coming Monday. The concept of testing on historical index members is discussed in detail in Mathew Verdouw's article on the importance of Survivorship Bias-free data.

Is 2023 shaping up to become the year of A.I.?

Matthew Humphreys — Fri, 03 Mar 2023 01:42:00 GMT

The world was introduced to ChatGPT, the next generation of Artificial Intelligence (A.I.) platforms, in November of 2022. This natural language model using A.I. machine learning algorithms revolutionised the way people interacted with their computers. Within two months of its initial release, ChatGPT became the fastest-growing site in history. It quickly hit over 100 million daily users, faster than any other platform before it. In the same way Google redefined the search engine platform (to the point Googling entered the zeitgeist), ChatGPT is doing the same for A.I. based user-interactions.

What is ChatGPT and why should I care?

Good question, I'll ask ChatGPT to describe what it is and how it works:

{:class="smallquote"}

Hello! I'm ChatGPT, a language model created using Artificial Intelligence (AI) techniques. Put simply, I'm a computer program that can understand and generate human-like language. To create me, my creators fed me with a massive amount of text from various sources such as books, articles, and websites. I used this data to learn patterns in language and gain an understanding of the way words, sentences, and paragraphs are used to convey meaning. So now, when you ask me a question or give me a prompt, I use the knowledge I've gained from that data to generate a response that's hopefully helpful and makes sense. I can handle a wide range of topics and can converse with you on various subjects. In a nutshell, I'm a computer program that can understand and generate human-like language, and I work by using the vast amount of data that I've been trained on to generate responses to your questions and prompts. <cite>ChatGPT' Response</cite>

Based on my own use of the system over the last few months I can tell you it’s being modest here. The scope of the things ChatGPT can do is astounding. It can review text and provide suggested improvements. It can learn your writing style and provide additional content. It can code in popular languages such as python or java. It will find and give several sources on a topic or write entire assessments. It's not unreasonable to say this technology will see many areas (workplaces, schools, universities, etc.) having to adapt quickly to ensure it's used effectively and ethically.

Think of ChatGPT as an artificial brain with perfect recall, but it’s still learning what to do with all that information.

This is an Optuma blog, what does this have to do with me?

A.I. is still in its early stages of development. What we’re seeing now is a beta version of A.I. from a single developer. Google has begun to roll out its own A.I. platform called Bard in a closed beta, and there are more advanced versions being developed right now.

We don’t yet know what this will mean for people trading the markets, but it would be crazy to assume A.I. won’t be used or have an effect in our industry. We’re already starting to see the early stages of this such as the A.I.-managed ETF AIEQ.

AIEQ, an AI-powered stock market fund, has had an interesting journey since its launch a few years ago. Initially the fund underperformed against the US market, but it gradually caught up and finally surpassed it in 2020 by at least seven percentage points. This was an impressive achievement for the fund. It demonstrated that AI technology can be effective in the stock market.

However, AIEQ has also faced challenges along the way. For example, its higher weightings in the tech and healthcare sectors saw a temporary dip in performance in Q2 2021. Despite these setbacks, AIEQ has had a good start to 2023, with an increase of 6.56% YTD, compared to 3.69% for the S&P500.

AIEQ’s performance vs the S&P500 Index

There have been examples of others using ChatGPT to summarise company financials or provide rebalancing suggestions based on an established portfolio.

Where A.I. will really shine will be its ability to search large swathes of information (the Optuma forum and Knowledge Base for example) and give you a detailed response. There’s also great potential for an A.I. platform like this to learn Optuma scripting language and assist in building a script based on a set of requirements listed in plain text. There’s a lot of work to do before this is possible, but our initial investigations have been exciting and we hope to be incorporating this in 2023.

Can managing a portfolio really be boiled down to a series of 1’s and 0’s?

Can I retire now and let A.I. trade my Portfolio?

Well, you can, but it’s not something I’d be doing anytime soon. As amazing as this new technology is, there are still gaping flaws that have yet to be overcome. In the example of ChatGPT, the responses are always well-written, and provided with confidence, even when the information is completely and utterly incorrect. It’s still very much a “user beware” situation, and any information provided needs to be verified before being acted on.

The developers of ChatGPT (OpenAI) are also aware of the dangers associated with stock picking and financial advice. Any response in that area always comes with the note that a professional should be consulted for any investment decisions.

Business man reclining on an office chart, around him robots and computers are doing all his tasks for him

How far are we from being able to relax while A.I. does everything for us? I’m not sure, but all the non-chart images used in this article are originals, generated by an A.I. service called Dall-E with a few simple sets of directions to work from. Just like ChatGPT, it's impressive but misses the mark in some areas. Another A.I. driven tech I'm excited to see evolve over the coming years.

This hasn’t stopped people from building Portfolios from ChatGPT interactions. Here's an example of an experiment I ran where I provided ChatGPT with the following directions:

{:class="smallquote"}

Using the stocks listed on the Australian stock market, build a portfolio of approximately 20 stocks that have low risk but offer good returns, take dividends into account, avoid stocks in the finance sector especially Buy Now Pay Later companies, and have a higher weighting towards emerging technologies and renewables. Provide each stock with a weighting based on historical volatility, dividend yield, and ensure one sector is not overrepresented. List the constituents and weightings of each stock in a table using an initial portfolio balance of $85K. <cite>Matthew Humphreys prompting ChatGPT</cite>

Within seconds ChatGPT was happy to provide an example of 20 ASX stocks and their suggested weighting in the portfolio. However, as it was based on a 2021 database, the information was stale, and some BNPL companies were still included. Other suggested stocks were no longer listed.

The fact an A.I. is capable of producing results based on a set of conversational instructions like that is the part that's impressive. The final product still needs a lot of work, and there's no way I’d be using it in day-to-day analysis at this point in time.

For now, as exciting as the world of A.I. is, it's still no match for an educated analyst working off a tested and proven trading plan.

Introducing GoNoGo: Avoiding Analysis Paralysis

Alex Cole — Mon, 27 Feb 2023 14:00:00 GMT

"We are what we repeatedly do" <cite>Aristotle.</cite>

As a trader or investor, it is essential to develop a process. Using the same checklist or rules by which you determine position entry and exit creates a repeatable discipline for trading that helps avoid behavioural pitfalls. Technical Analysis provides a broad toolkit of indicators that, when used in combination, give the trader a weight of the evidence perspective that can be used across timeframes and in any market regime. While the composite blend of information is powerful, the complexity of a price chart can lead to Analysis Paralysis.

Developing a Process

As a metaphor for portfolio management, imagine you are a young person learning to drive. Safety (risk management) and efficiency (profitability) require that you master many different functions of the vehicle and be able to interpret in real time the many challenges that await you on the roadways (markets). Driving can be fun, but involves inherent risk, and the many functions of the car can be overwhelming. As a young adult you learn that every single time you get into your vehicle it is not only sensible but potentially lifesaving to follow a "new driver checklist". It could look like this:

Adjust the seat and steering wheel positions to reach gas, brake, clutch
Fasten seat belt
Adjust mirrors and check for obstacles at the rear and sides of the vehicle
Check all lights are functional
Engage brake when starting engine
Signal before driving off

And, while driving there is a myriad of information to process and react to in real-time, speed, distance from other cars, navigation, fuel levels, gear shifting, etc… The disciplined approach to driving makes one a more responsible and importantly, a safer driver. We all have a checklist to follow even though it may be largely subconscious for experienced drivers. The same approach applies to using technical analysis for investing in markets. A disciplined process that applies the same checklist to every trade ensures we don’t make irrational decisions based on our own fear or greed. A diversified blend of technical indicators adds probabilistic rigour to identifying trends and their reversals. As investors, we seek to participate in the majority of a security’s price move while avoiding the hazards of the financial markets. We seek an intelligent framework in which to invest; one where we can make fast, effective decisions based on objective information. A technical analysis checklist might include the use of:

Trend lines
Moving averages
Momentum analysis
Volume analysis
Price patterns
Dow Theory

Sophisticated traders and portfolio managers develop their individual technical checklist over time. They have faith in their process and adhere to the rules to therefore remove uncertainty in the decision-making process for each and every trade. This allows an investor to take deliberate action rather than succumb to the emotional reactions we have to the daily barrage of storytelling and misinformation in headline news. To extend the driving metaphor into the highest arena of skill and danger think of a Formula One race. While it might be helpful for a driver to understand the physics of internal combustion engines or fluid dynamics, it is not necessary for his ability to pilot the car around the racetrack. One of the incredible advantages of technical analysis is that you can understand the price action of any security over any timeframe without a deep understanding of the fundamentals of that particular industry, intimate knowledge of corporate actions, the private politics of management teams, or expertise in emerging technologies. As market participants we need to pilot our investments around the racetrack. In this way, by sticking to a rules-based process which follows a sensible set of guidelines, we can remove subjectivity or uncertainty and be able to invest in any area of the market.

A Sensible Technical Checklist

To demonstrate this idea, let’s build a sensible checklist of technical tools. Identifying a trend is arguably the most important concept in technical analysis. Investors who can successfully identify market trends can profit from the majority of the move. Nicolas Darvas exclaimed, “Buy high and sell higher.” This is a simple concept, but not easy to execute. A checklist, a disciplined process is required to allow for confident recognition of a price trend as early as possible. How might an analyst do this? The following is an example of a responsible (yet rudimentary) checklist of trend identification using current price data from the FTSE All Share Index, $ASX as our example. Let's start with the very basics. Can you visually identify a trend? Are you seeing higher highs and higher lows, the definition of a trend? In other words, are you seeing prices move in a discernible direction? For an uptrend, you will be looking for price to be moving from bottom left to top right. Consider Figure #1 below.

Figure #1 – FTSE all share index, $ASX daily bar chart: “higher highs and higher lows”

At this point, it would make sense for the analyst to automate the identification of highs and lows, to remove subjectivity. A technical indicator such as Donchian Channels may be used for this. This study compares current price to the highs and lows of price action over a defined period. For example, if the current price is the 20-period high, then the upper line of the channel (red) will move up with price. An equivalent formula is used to identify higher lows (green) This way, you can see the price trend plotted via channels on the chart. So prevalent is the use of simple moving averages on financial charts, the joke among technicians is that the simple moving average doesn’t even count as technical analysis.

Many portfolio managers who condemn technical analysis still insist on having a moving average on every chart! Comparing price to its historical average gives us vital information. It is inherently bullish when a security is trading above its moving average. Securities trading higher than they have in the past reflects the market’s perceived value for that security. Next, you could add Bollinger Bands to identify breakouts.

A faint memory from a distant mathematics class may remind you that 95% of all data points in a set of normally distributed data will fall within two standard deviations of the mean. Bollinger Bands work off that principle, bands formed two standard deviations away from a mean tells us to pay attention to any data that falls outside of those two standard deviations. Typically, the mean used in Bollinger Bands is the 20-period moving average. Therefore, if price is breaking out of the bounds of a Bollinger Band, something important is happening. When the bands are narrow, volatility is compressed. The start of a new trend can often be identified in the direction of the break. Figure #2 below shows these three indicators overlaid on our chart of the FTSE All Share Index, $ASX

Figure #2 – FTS all share index, $ASX daily bar chart: Donchian Channels with a 50-day simple moving average and Bollinger Bands

Our analysis is not complete. To get a sense of market participation, one might add volume to the chart. Volume is the original confirmatory indicator, one used by the founder of western technical analysis Charles H Dow. Volume can provide confirmation when looking at price action. Significant price action when volume is heavier than usual informs our understanding of the strength of the price move. For example, a trader may want to see market enthusiasm (heavy volume) on a price breakout. To keep this example simple let’s add just one more indicator to our chart: the Moving Average Convergence Divergence (MACD) indicator. This study is often thought of as a blend of trend and momentum ideas. The MACD looks at the difference between two exponential moving averages. This gives the user a sense of how short-term prices are moving relative to longer-term prices. Then a further exponential average is used to smooth MACD and give a signal line. Traditionally, when the MACD line crosses above its signal that is a bullish sign because the short-term average of price is crossing above the longer-term average.

Figure #3 – FTSE all share index, $ASX daily bar chart: Donchian Channels with Bollinger Bands, 50-day Simple Moving Average, Volume Histogram, and MACD – Interpreting start of uptrend

At this stage we may pause, take a step back and try to make sense of the chart we have built. Assessing all the information we have added to the chart of $ASX we will ask ourselves at what point on this chart can we be confident that we are able to identify the beginnings of a new trend upwards?

Note the area inside the yellow rectangle in figure #3. Somewhere during this timeframe, we see all of our trend identification criteria being met. MACD first crosses above its signal. A few bars later price breaks out of the upper Bollinger Band. That is followed by the red Donchian upper channel line moving higher indicating a new 20-period high in price. Several bars later, price climbs above its moving average. Finally, volume rises as the market shows enthusiasm for the strength of the price move.

We are now faced with a problem… As the checklist grows and the number of valid technical concepts are added we can overwhelm our own analysis and make it difficult to see price action itself. Using this relatively simple checklist can introduce uncertainty. How many elements of our checklist must be present in order to enter the trade? What if we are fighting against an otherwise bearish thesis, could we find evidence to argue staying out of the trade? Analysis paralysis creeps into our process and we fall prey to our behavioural biases.

Many professionals will have a much more complex process than the one we outlined above and will use many more indicators than represented here. This introduces redundancy or even conflicting signals. With enough lines on the chart, the human mind can convince itself of almost anything. Too many components on the chart can obscure price action – our most important indicator of all! Rather than arriving at a sensible conclusion quickly that moves the analyst to act, we are left with more questions and uncertainty.

The Solution

Our challenge is to retain all the information from our trend identification checklist yet remove the complexity of the data displayed on our chart. We want to separate signals from noise to gain a clear picture of trend while keeping the focus on price.

GoNoGo Trend® blends all the foundational studies used by industry professionals into an indicator that colour codes the price bar based on the strength of trend. Handling all the complex technical analysis and computing the heavy mathematics in the background, we arrive at a chart that uses a weight of the evidence approach to show trend direction and intensity.

Borrowing terminology from a NASA shuttle launch, we divide trend direction into “Go” or “NoGo” giving us a simple pass/fail test for long positions. Building sensitivity into the study, we provide both a weak and strong form colour for both “Go” and “NoGo” trends. Strong blue bars indicate the most bullish trend conditions. Paler aqua bars represent a weaker form of a “Go” trend. A strong “NoGo” trend is painted with purple bars and a weaker “NoGo” trend is displayed with pink bars.

A “NoGo” trend tells us that we are in a bearish technical environment. The checklist has been broken, helping us objectively exit long positions and potentially evaluate short positions in the same security. Amber bars indicate neutral readings on the composite of trend indicators and are named “Go Fish” bars. Jesse Livermore is famously quoted as saying “in markets, there is a time to go long, a time to go short and a time to go fishing”. When we do not have enough criteria being met for trend direction, GoNoGo Trend tells us to “Go Fish” and paints the bar amber.

Figure #4 represents the same $ASX chart with the GoNoGo Trend indicator applied. You can see where the new upward trend is identified with the “Go” flag. We can compare this with the vague area of trend identification in the Figure #3. The government wouldn’t launch billions of dollars of hardware into space unless the conditions were a “Go”. The decision to launch an investment should be just as clear.

Figure #4 – FTSE all share index, $ASX daily GgoNoGo chart

For more information about GoNoGo Charts, visit www.gonogocharts.com

To celebrate the release of the GoNoGo add-on in Optuma, for one week only you can save 30% off the usual subscription price of US$125 per month (or US$1,500 per year). With this discount, you will only pay US$87.50 for the first month's subscription (or US$1,050 for up to a year).

Click here to sign up!

Scripting and Anchored VWAPs

Darren Hawkins — Wed, 22 Feb 2023 23:33:00 GMT

The Anchored Volume-Weighted Average Price tool is a great way to determine potential support and resistance levels for any stock. Brian Shannon, CMT believes that it is because of “anchoring bias” - the human tendency to rely heavily on the first piece of information encountered when making decisions and then use that initial piece of information to make future decisions.

To learn more about AVWAPs and how they can be used we highly recommend Brian’s fantastic new book: Maximum Trading Gains With Anchored VWAP - The Perfect Combination of Price, Time & Volume. Click here to order.

Significant highs and lows are suitable starting points from which to calculate the average price paid. In the following example of Microsoft ($MSFT), the tool has been placed on the November 2021 high (in green) and the November 2022 low (in blue). It’s telling us the average price paid for Microsoft since the high is $273.02, but only $245.28 since the low, and the price is currently trading between these levels after failing to break above the green line three times:

Anchored VWAP

With Optuma’s scripting language you can use the AVWAP() function in a number of ways to automatically find these AVWAP levels to really speed up your analysis.

For example, in a watchlist column you can use the following to find how far the current price is from a VWAP anchored to a fixed date, such as the first trading day of the year:

V1=AVWAP(BACKTYPE=Fixed, DATE=2023-01-03);
DIFFPCT(CLOSE(), V1)

It’s good practice to click on the text in the Scripting Manager to open a pop-up window where you can change the parameters of the function (this is better than typing the text and risking using the wrong syntax ).

Script Editor

You could also scan for when the price crosses a VWAP anchored to a 3-month lookback. It might be useful to run this at the end of a month:

CLOSE() Crosses AVWAP(BACKTYPE=Month, BARS=3))

Variable Lookback Dates

These formulas are great for anchoring to a specific date, but of course not all stocks have highs and lows on the same day. This is a bit trickier, but it can be done! The first part of the following formula identifies the respective 3-month high date for each stock. It uses the high or low day over that period from which to start the AVWAP calculation. It will return a true result when the closing price crosses above the AVWAP level, with the caveat that the high must have occurred more than 10 days ago:

//Set highest high lookback period in bars eg 63 bars for 3 months;
Start = BARINDEX()==LAST(BARINDEX())-63;
//Find when high occurred;
Sig = HIGH() == HIGHESTSINCE(Start);
//Find when low occurred;
//Sig = LOW() == LOWESTSINCE(Start);
//Remove Non Zero results showing most recent result as latest value;
$DATE = BarDate(NonZero(Sig));
//Calculate VWAP from Signal date;
R1=AVWAP(BACKTYPE=Fixed, DATE=$DATE);
//Signal when price crosses above the VWAP if high occurred > 10 days ago;
TIMESINCESIGNAL(Sig)>10 and CLOSE() CrossesAbove R1

NOTE: To change it from the 3-month high to the low, comment out line 5 (add // to the start of the line) and enable line 7 (delete the //).

AVWAP Scan

Automatically applying AVWAPs to a chart

By modifying the scan formula used above we can use a Show Plot tool to automatically apply the tool to an important high or low, instead of manually drawing it on the chart. The following will apply the tool to the 6-month low.

//Set lowest low lookback period in bars eg 126 bars for 6 months;
Start = BARINDEX()==LAST(BARINDEX())-126;
//Find when low occurred;
Sig = LOW() == LOWESTSINCE(Start);
//Find when high occurred;
//Sig = HIGH() == HIGHESTSINCE(Start);
//Remove Non Zero results showing most recent result as latest value;
$DATE = BarDate(NonZero(Sig));
//Calculate VWAP from Signal date;
R1=AVWAP(BACKTYPE=Fixed, DATE=$DATE);
R1

TIP: In the Script Editor window save the script formula as an indicator to the same view to add it to your toolbox.

As you scroll down the watchlist the tool will automatically be drawn from the respective turning dates on the chart:

Show Plot Tool

Using AVWAP from the IPO date

As Brian writes in his book, the AVWAP from a stock’s IPO date can be an important level to keep on your chart. To do this automatically, use the following in a Show Plot tool to draw the AVWAP on the chart from the first trading day:

Get IPO Date;
$IPO=FIRST(BARDATE());
//Draw VWAP from IPO Date;
AVWAP(BACKTYPE=Fixed, DATE=$IPO)

From there it’s a small adjustment if you wish to find the percentage the price is from the IPO level, or to get a signal when it crosses, as per this example of Airbnb ($ABNB):

IPO AVWAP

As you can see, scripting for AVWAP levels and price proximity in Optuma is very flexible. I’m sure you have more ideas. Feel free to share or post queries on this forum thread, and Optuma clients can click the buttons below to download and open Australian and US watchlists featuring the AVWAP scripting.

Next time we’ll look at some quantitative tests using AVWAP levels.

Optuma on the Cloud

Matthew Humphreys — Thu, 09 Feb 2023 01:33:00 GMT

Cloud drives (OneDrive, Dropbox, Google Drive, etc) are nothing new. They’ve been around for years now. They can provide peace of mind, ensuring important files remain intact in the event of a hard disk malfunction.

In the past, unless you were committed to a manual backup routine, it was unlikely you’d be able to retrieve any of your lost files. Cloud drives have made protecting your important files much easier. They’ve become an invaluable part of our setups.

If you use Optuma, this means your Workbooks, Scans, Scripts, Custom Tools, etc. can all be saved on a cloud drive as an extra level of protection. You won’t need a USB stick or network drive to store your backups off your main system’s hard disk.

While this is a terrific time saver, there are some important limitations to be aware of to ensure your white fluffy cloud doesn’t turn into a dark grey storm of destruction.

PC weather report: cloudy with a chance of total destruction

You may be using a Cloud Drive without knowing it

For more than a year Microsoft has been expanding the reach of OneDrive. For most new Windows installs, OneDrive will be set up to automatically backup your Library folders (Documents, Photos, etc). Optuma stores your user files (such as workbooks) in a sub-folder of Documents by default, meaning your Optuma work may be part of your OneDrive cloud without you even knowing about it.

I’ve heard a few audible sighs of relief from people over the years who encountered a hard drive malfunction thinking they had lost all their Optuma work, only to find them safe and sound in OneDrive’s cloud.

This change can also be made to your system as part of a Windows update with the initial results making it appear as though your Optuma work has been lost.

If you find after a Windows update that your copy of Optuma appears like a new installation (no saved login, no saved files like workbooks), there’s a good chance your Documents folder has been moved to OneDrive as part of that update.

We have a quick guide on how to fix this here.

The fastest way to check if a folder is syncing to the cloud is to view it in Windows File Explorer. You’ll see the folder either has a green tick on the folder icon or has a sync status icon next to it.

Folders syncing to the cloud will generally display a sync icon

The Cloud isn’t a “one size fits all” solution

Cloud folders are easy to use and for the most part their activities occur automatically in the background. For keeping your Workbooks safe, it’s a great option. An exception is the Optuma Data Folder where we store all the price data used to draw charts, scan, & run tests. I’ll explain why you never want this on your cloud drive.

Any file stored on your cloud drive needs to be uploaded to a server for storage. Depending on your connection speed, this can be a slow process to complete. Most cloud drives have a limited space allotment (5gb for OneDrive’s free version as an example), meaning it can be easy to fill.

Depending on how many data exchanges you have, your Optuma data folder will easily be 10 – 30gb with tens of thousands of files.

For this reason alone, the Optuma Data folder is not suitable to be stored on your cloud folder.

The cloud is amazing for redundancy but is not suitable for all files.

NOTE: The default location for Optuma Data files is:

C:\ProgramData\Optuma\Data\

The folder can be stored on any drive or folder path, as long as it’s not a cloud drive.

There are many times when clients will report issues with Optuma (slow speeds for data updates and scans, file access errors as they are in a read-only state, etc.). A quick examination of their system tells us they moved their Data folders to OneDrive. Don’t do that!

For the most part, nothing in that folder needs to be backed up (extensive intraday historical data would be an exception to this. If you want to keep a backup, it needs to be done using a USB Stick or Network Drive, not a cloud drive).

The bane of cloud apps…duplicate files

The issues that can occur when the Optuma Data folder is moved to a cloud dive are bad enough when we’re dealing with End of Day files only. They become worse if Intraday data has been setup as well.

Files saved on a cloud drive follow the same rules as Highlander…

This guy gets it.

If a file is stored on a cloud drive it becomes locked during the sync process.

If a program (like Optuma) tries to open and modify that same file during the sync, it will be unable to complete the process, and can create a duplicate / conflict file instead.

Optuma has no reference to this new duplicate file the cloud app has made, running the risk of your intraday data cache becoming fragmented.

The sync process also tends to struggle with files that are constantly updated. Your PC spends all of its processing power trying to store files on the cloud. After a while this can cause the entire cloud application to stall. Once that happens all the benefits the cloud drive offers stop.

When left too long in this state, the number of duplicate / conflict files can create a real mess on the cloud drive (untangling Christmas lights can be an easier task than untangling a drive full of duplicates!).

All this to say, if you have your Optuma Data folder currently stored on a cloud drive, I’d highly recommend you take the weekend to work on moving this folder to an alternative location. Especially if you also use live data. The performance improvements this can achieve to your system alone are worth it.

Article: Optuma File Location Settings

By following the above recommendation and keeping your Optuma Data folder off of the cloud you can avoid common issues. You’ll benefit from a ‘set and forget’ backup process for your important Optuma user files.

Pressure is on the January Barometer

Darren Hawkins — Wed, 01 Feb 2023 05:01:35 GMT

The January Barometer is an investing theory that suggests that the performance of the stock market in the month of January can be used to predict the performance of the stock market for the rest of the year. According to the theory – first posited by Yale Hirsch in 1972 - if the stock market rises in January, then it is likely to rise for the rest of the year. Conversely, if the stock market falls in January, then it is likely to fall for the rest of the year. The theory is based on the idea that investors are optimistic at the start of the year and that their optimism carries over into the rest of the year.

With January 2023 gaining 6.2% - the best start to a year since 2019 - should we expect the rest of the year to be positive? <cite>Darren Hawkins</cite>

We can visualise this on a monthly chart of the S&P500 index with two Show Bar tools showing a T when the theory is true (i.e. January was positive (negative) and the index closed higher (lower) 11 months later) and an F where it failed. Last year the theory passed - the first time since 2019 - because in January the index fell -5% and the year ended negative (-19%).

The Show View indicators below the chart count the true / false events, so since 1950 the theory is twice as likely to be true (48 times versus 24). However, before 2009 the ratio was 2.9 but over the last 14 years it has only been 0.6 (5 passes versus 9 failures). Of course, we’ll have to wait another 11 months to see how this year turns out - if it fails then the pressure will really be on!

January Barometer

If you are interested to see how these values were calculated, the script formulas are listed at the end of the article.

That’s OK for the index, but how do individual stocks fare? For that analysis we can use the Signal Tester. Using a script formula, we can calculate performance for the rest of the year when January performance was positive and compare it with when it was negative.

The test uses the historical membership of the S&P500 since 2000, so stocks are only included when they are in the index (for example $TSLA wasn't considered until January 2021). These are the results of the 5,000+ events following a positive January:

Avg Returns following Positive January

Not bad! The -21 area on the left of the chart represents the average January performance (ie sloping up because it was positive), and we then measure 231 days after – approximately 11 months – to take us to the end of the year. The Probability of Gain is 63%, with a mean return from the end of January to the end of the year of 8.65%.

Now let’s look at when January is negative. If the barometer theory holds, you would expect that the returns for those events would also be negative, but it’s not true!

With slightly more trades and a similar Probability of Gain, those stocks closing down in January actually end the year – on average – gaining 11%! February tends to also be weak, and then performance improves in March, and especially in the 4th quarter:

Avg Returns following Negative January

This is telling us that no matter what happens in January, stocks will, on average, perform positively for the rest of the year – and even better if they are down in January! This year there were 106 members of the S&P500 index which lost value in January, including UnitedHealth ($UNH), Chevron ($CVX), Coca-Cola ($KO), and IBM ($IBM). It will be interesting to see how they fare the rest of the year.

As analysts, we need to know that our theories have a sound statistical basis. In only a few minutes, we’ve been able to test a theory and now know with certainty if it is something that we should believe or not.

Script Formulas

True Barometer

// To display in a Show Bar set to display as text;
// Define data for January;
V1=MONTHNUM()==1;

// Calculate performance direction for January and 11 months after;
V1 and ((PERFORMANCE(PERIODAMT=1)<0 and PERFORMANCE(DIRECTION=Forward, PERIODAMT=11)<0)
or (PERFORMANCE(PERIODAMT=1)>0 and PERFORMANCE(DIRECTION=Forward, PERIODAMT=11)>0))

False Barometer

// To display in a Show Bar set to display as text;
// Define data for January;
V1=MONTHNUM()==1;

// Calculate performance direction for January and 11 months after;
V1 and ((PERFORMANCE(PERIODAMT=1)<0 and PERFORMANCE(DIRECTION=Forward, PERIODAMT=11)>0)
or (PERFORMANCE(PERIODAMT=1)>0 and PERFORMANCE(DIRECTION=Forward, PERIODAMT=11)<0))

True / False counts

Take the above formulas and wrap the result in an ACC() accumulation function in a Show View.

Signal Test – Performance After a Positive January

// Get monthly data;
M1 = MONTH();

// Signal when month changes to Feb, and Jan is up for historical index members only;
V1 = MONTHNUM() ChangeTo 2 and M1[1] IsUp and IsMember();

// offset by -1 to get the January close;
V1[-1]

Signal Test – Performance After a Negative January

// Get monthly data;
M1 = MONTH();

// Signal when month changes to Feb, and Jan is down for historical index members only;
V1 = MONTHNUM() ChangeTo 2 and M1[1] IsDown and IsMember();

// offset by -1 to get the January close;
v1[-1]

IPO Performance

Darren Hawkins — Fri, 09 Dec 2022 04:15:00 GMT

2022 has been rather a quiet year for US IPOs, with the number of new listings being down over 80% from last year. Of the 76 new listings - not including Special Purpose Acquisition Companies (SPACs) - as of December 8th only 14 are trading above their first day opening price (not offering price). In fact, the median stock in the list is 59% below their opening price, and 75% below their all-time high.

The best performer has been Belite Bio Inc, which is currently +147% above April's opening price, outperforming the S&P500 index by 204% since listing, but is 31% off its high.

US IPOs 2022

Clients with US data can download the watchlist above by clicking the button below, and it will also include a tab with the statistics of the holdings of the Renaissance IPO ETF ($IPO). At 9.5%, the largest holding in the IPO is Airbnb ($ABNB) which listed almost 2 years ago. It reached a high of $220 a couple of months after listing - almost a 50% gain on its opening price of $146 - but has since slumped to below $100. Over that 2 year period, it has underperformed the $SPX index by 40%:

IPO ETF Holdings

In Australia, there has been a similar number of IPOs this year - 75 - less than half of 2021. The standout performer has been WA1 Resources which started trading in February at $0.25 and did nothing until October when it jumped over 400% in one day! It's currently at $1.86 - a 652% gain over the ASX All Ordinaries index ($XAO).

The biggest IPO of the year was The Lottery Corporation ($TLC) after its demerger from Tabcorp in May. After a brief gain, it soon fell below the opening price of $4.61, which it couldn't break above despite several attempts. It wasn't until a few days ago that it managed to close - and stay - above that level.

ASX - IPOs - 2022

Clients with access to our US or ASX data can click the buttons below to save and open the workbooks. Please contact us at support@optuma.com, and if you have any queries about the formulas for the watchlist columns or tools please reply to this forum thread.

How to Calculate Quarterly Performance in Optuma Watchlists

Darren Hawkins — Fri, 07 Oct 2022 02:00:00 GMT

As you may know, Optuma watchlists allow you to add custom columns containing any tool or indicator values (eg daily/weekly RSI, percent from a moving average or 52 week high) or true/false results based on any condition (is the close above the 34 exponential moving average, or is the Gann Swing trending up?). It's also possible to create performance statistics based on time frames (eg over the last month, year-to-date, or from a specific date), but you can also create quarterly (or monthly) returns. The following article will show you how to add these types of calculations to a watchlist - or you can skip to the bottom to download a workbook!

The example below shows the quarterly performance of the Australian ASX 200 index and sectors for 2022, along with year-to-date and the 3 year annual rate of return. Sorted by Q3 performance, it shows Health Care $XHJ gaining 2.64%, following Q2's -2% and Q1's -10.65%. On a year-to-date basis, the clear winner is Energy $XEJ at +43% to Friday's close (click images to enlarge).

XJO Quarterly Performance

Here's how to calculate these values in any watchlist by combining the following functions to specify the periods:

MONTHNUM() - assigns a number for each month: 1 = January, 2 = February, etc

YEARNUM() - eg 2022

VALUEWHEN() - returns the required value (the close by default) when a certain condition has been met.

So to get the closing value at the end of the third quarter in 2022 we need to create a variable (M1) for the end of September 2022:

M1 = MONTHNUM() == 9 and YEARNUM() == 2022;

Note: in scripting a single ‘=’ sign is used to assign a variable so when looking for equality in a formula we need to use the double ‘==’ sign.

Next we use VALUEWHEN to get the closing value of M1 by placing it in the parentheses:

V1 = VALUEWHEN(M1);

So now we have June’s closing values (V1) we need to compare it to the end of June 2022 by creating two more variables:

M2 = MONTHNUM() == 6 and YEARNUM() == 2022;

V2 = VALUEWHEN(M2);

With these two closing values (V1 and V2) we can now calculate the percentage change between them using the DIFFPCT() function:

DIFFPCT(V1,V2)/100

NOTE: the last lines have been divided by 100 to display the watchlist columns in percentage format (as per the image and workbook examples below). In the watchlist, right-click on the column header and change the Column Type to Percentage.

Putting it all together for the Q3 2022 column:

M1 = MONTHNUM() == 9 and YEARNUM() == 2022;
M2 = MONTHNUM() == 6 and YEARNUM() == 2022;
V1 = VALUEWHEN(M1);
V2 = VALUEWHEN(M2);
DIFFPCT(V1,V2)/100

Current Quarter-to-Date (QTD)

To calculate the performance for the current quarter we can change the M1 variable to CLOSE() so it will use the latest closing price in the calculation, and will therefore update every day.

M1 = CLOSE();

YTD

The year-to-date column is quite straight forward, using the Rate of Change function set to use yearly data, so a 1 bar change from the end of the previous year. NOTE: This will calculate the performance to the latest date downloaded, not the end of the quarter and will update every day.

ROC(Year(PERIODAMOUNT=1), BARS=1)/100

2021 Performance

To calculate the column for 2021's performance we just use the same formula as the YTD column above but offset it by 1 - the number in the square brackets. Increase the offset value for earlier years, ie [2] for 2020.

Y1=ROC(Year(PERIODAMOUNT=1), BARS=1);
Y1[1]/100
{% endhighlight js %}

## Annual Rate of Return

This is calculated using the [ARR()](https://help.optuma.com/kb/faq.php?id=1112) function, set to 3 years in this example:

{% highlight js %}
ARR(PERIODAMT=3)/100

So the best performing sector over the last 3 years to date (ie from October 2019 to October 2022) has been Materials $XMJ, gaining 7.28% per year for the last 3 years. Real Estate $XRE has lost 9% per year.

Custom Column Colours

To change the watchlist colours to green for positive and red for negative right-click on the column heading and select Custom Labels, and select green > 0 and red > 0 (delete the Label text as it's not required in this example). Once changed, click on Clone Column to duplicate the column and then Edit Column to open the Script Editor window and change the formula for subsequent columns as required.

Colours

Workbooks

Optuma clients can click the buttons below to save a workbook with examples for Australian and US sectors, but if you would like to apply these columns to other watchlists click the No Layout label in the header and give it a name and then apply the Watchlist Layout to another list.

Of course, columns can be added, removed, or dragged to a new position to your watchlists as required. If you have any questions or comments please let us know, or visit our Scripting Forum for help and more watchlist ideas.

S&P Indices - Quarterly Rebalancing

Darren Hawkins — Fri, 23 Sep 2022 00:01:00 GMT

Earlier this week saw the quarterly rebalancing of the major S&P indices come in to effect - including the Australian ASX200 ($XJO) and US S&P500 ($SPX) indices.

If you have access to the Optuma Symbol lists then the members of the indices have automatically been updated for you, so any scan or watchlist you have linked to the symbol list will now be using the new membership.

The quarterly reviews are based on market cap, liquidity, and performance, with the worst performing members being replaced (see this S&P article for a detailed methodology). Here are the recent changes made to the ASX200 index, with - at the time of the changes before the open on Monday 19th - the eight new members averaged an 8% gain year-to-date, versus -50% for the ones that have been removed (the ASX200 index had lost 9%).

XJO Changes

We keep track on the historical index membership and the dates they join and leave the index using the IsMember() function so you can see when the stocks are included in the index - this is especially important when backtesting and why the Historical membership option should be used to avoid survivorship bias.

In the example below, the IsMember() function has been used in a Show View in a chart of US-listed Owens-Illinois ($OI). The highlighted green areas show the two periods they were included in the index: 1997 to 2000 (when they were removed at the low!) and from 2008 to 2016.

Member of $SPX

NOTE: For more information on the concept of ‘survivorship bias’ in indices see Mathew Verdouw’s article here.

US Indices

Here are the Q3 2022 changes to the S&P500, S&P MidCap 400 ($MID), and S&P SmallCap 600 ($SML) indices:

S&P Changes

How does being added to the index impact performance?

As mentioned, our symbol lists for these indices have been updated to reflect these changes. Using the IsMember() function it’s possible to create studies on performance when a stock is added to (or removed from) the index.

For example, since 2020 there have been 41 companies join the S&P500 index (not including the two changes this week). Of those, only 15 are out-performing the index (calculated from the day they were added to the close on September 20th 2022). Here’s the list, sorted by current market capitalization, showing that on average they are underperforming the index by 7%:

S&P List

Tesla ($TSLA) - by far the biggest company to join - was added to the index on December 21st, 2020. Since then, it has gained 30% - out-performing the SPX index by 27% (although those gains have come since July 2022).

Tesla

As the watchlist shows, just because a company gets added to the index it by no means guarantees that it will perform well. For instance, PENN Entertainment ($PENN) joined the S&P500 index in March 2021 just a few days after what turned out to be it’s all-time high, and it immediately started to fall. When it was removed this week the price had fallen 74% since the day it was added.

ASX Performance

Life360 ($360) is an Australian software company with a similar story to PENN - but they only lasted nine months in the ASX200 index! It was added in December 2021 after gaining 300% during the year. Six months after joining the price had fallen 80%, before rallying off June’s lows. But it was too late, and the S&P removed it from the index in the most recent review.

Life 360

Before the latest rebalance, there had been 48 changes to the ASX200 index since the beginning of 2020 and only 10 of those are outperforming the index since joining - and one of those (Uniti Group $UWL) has since been delisted. In fact, the turnover of the top 200 stocks is quite high: 13 of those 48 have already dropped down to the ASX300.

XJO List

Optuma clients interested in this analysis can download the attached ASX and S&P workbooks which contain the formulas that have been used in the examples above. For example, this is the formula to get the date when joining the index:

//Select Index list;
V1=ISMEMBER(SYMBOLLIST=[SELECT INDEX]) ChangeTo 1;
//Get date when IsMember becomes true;
D1=BARDATE();
VALUEWHEN(D1, V1==1)

As always, any questions please let us know - and any scripting queries can be posted to the forum.

ETF Net Fund Flows

Darren Hawkins — Sun, 21 Aug 2022 22:37:00 GMT

Following on from the additional ETF data that was recently added to our database, we're pleased to announce that we are now able to provide net fund flow data for ETFs listed on a number of exchanges. Clients with Equity and Fundamental data enabled on their account (e.g. US, London - we're working on ASX) will have access to these net flow timeframes:

What are Net Fund Flows?

Investors and analysts watch fund flows as investor sentiment within specific asset classes, sectors, or the market as a whole. For instance, if net fund flows for bond funds during a given month are negative by a large amount, this signals broad-based pessimism over the fixed-income markets.

A couple of things to note: fund flows do not reflect the performance of the investment, only how investors move their money. Outflows reflect redemptions - or when investors take their money out of a fund - and fund expenses, while inflows reflect fund purchases. Net fund flows are the difference between the inflows and outflows.

In this example, three net flow timeframes have been added to a watchlist of the SPDR sector and bond ETFs:

Net Flows

So far this year, the SPDR S&P 500 ETF ($SPY) has had $20.6 billion pulled out of the fund (see the Net Flows YTD column). Of the sectors, Financials ($XLF) has lost $7.8 billion year-to-date, with Health Care ($XLV) gaining $6.2 billion. Over the last month, Technology ($XLK) has gained $1.4 billion and is now positive year-to-date. Not surprisingly given the equity market conditions for the majority of the year, more money has flowed in to bonds, with $27.6 billion flowing in to the seven listed ( a third of that in to iShares 20+ Year Treasury ETF ($TLT) ).

Note: to access this ETF data requires a subscription to both the US Equities and US Fundamental data. Log in to your account page to view your and modify your data options, or contact Support for help.

Of course, some funds are bigger than others so one way to compare net flows across funds is to calculate it as a percentage of Assets Under Management. This can be calculated in a watchlist column using this formula (using 1 month net flows):

NF1=DATAFIELD(FEED=FD, FIELD=NetFlows1Month, LATESTONLY=True);
AUM1=DATAFIELD(FEED=FD, FIELD=AssetsUnderManagement, LATESTONLY=True);
NF1 / AUM1

Over the last month, Consumer Discretionary ($XLY) has gained 5.6% of its assets from inflows, which - combined with a price gain of 18% - led to a 26% gain in Assets under Management (AUM). By contrast, the iShares TIPS Bond ETF ($TIP) lost 5% of their AUM.

AUM Pct

To add the data to an ETF watchlist, click the + to add a column, and in the search box type 'net flows' to select the required field from the Fundamental Field list:

Watchlist

One way to use this data is to by looking at divergences for potential changes in trend.

Here's an example in the Invesco QQQ Trust ($QQQ) which tracks the Nasdaq 100 index. Both the price and monthly flows declined at the beginning of the year, but then starting in February fund inflows started to outpace redemptions six weeks before the price bottomed in mid-March. By that time net flows were positive again, helping to push the price higher:

Invesco QQQ Trust

Clients with US Equity and Fundamental data can click the button below to save and open a workbook containing the SPDR and bond ETFs flows. As always, if you have any questions or need help with anything please contact support.

Taking your custom technical ranking system further

Darren Hawkins — Fri, 15 Jul 2022 07:43:00 GMT

Taking your custom technical ranking system further

(Last time) we looked at ways to create a custom ranking system using simple scripting formulas based on a number of technical conditions, and in this article we'll look to take it a step further by calculating breadth measures on a universe of stocks, and also adjusting the weightings.

As a reminder, here's the formula for the ranking system we created:

// Currently trading above the MA50?
V1 = CLOSE()> MA(BARS=50, CALC=Close);
// The 13EMA is sloping up?
V2 = MA(BARS=13, STYLE=Exponential, CALC=Close) IsUp;
// Is the MA33 is above MA88?
V3 = MA(BARS=33, CALC=Close) > MA(BARS=88, CALC=Close);
// Is the RSI(10) above its 10MA?
V4 = RSI(BARS=10) > MA(RSI(BARS=10), BARS=10);
// Positive returns over last 3 months?
V5 = ROC(Month(PERIODAMOUNT=1), BARS=3) > 0;
// Sum the results to get a ranking value
V1+V2+V3+V4+V5

This will create a daily value for of between 1 and 5 for each stock in a universe, for each day. Enterprise Services clients (or those who have purchased the add-on module) will be able to create a daily Market Breadth measure of the number (or percentage) of companies in a universe with any ranking value, such as the percentage of fives, or a range, such as >=4.

Calculating Breadth

Clients with the custom breadth module are able to build their own breadth measures (for a refresher on Market Breadth click here for Mathew's article on Volatility Swings.) Let's say you want to create a time series of the percentage of stocks in the ASX200 index with a ranking value of 5, ie the strongest companies as defined by the system. To do that we can use the same formula used to colour the '5' bars as described in the previous ranking blog, ie using this as the last line to find those with a value of 5:

V1+V2+V3+V4+V5==5

Using the complete formula in the Market Breadth module (located under the Data menu) we can then create the ticker symbol (in this example MYRANK5), give it a name, define the universe of stocks you wish to calculate the measure on (the ASX Top 200 in this example, but you could run it over any Symbol List or imported list of your own portfolio tickers), Date Range (calculates the percentage every day for the last 5 years), timeframe (daily) and Breadth Action (percentage). Once set up, click Build Breadth to calculate the percentage values with a ranking value of 5 for each day.

Breadth Setup

MYRANK5

In the example above, at the time of writing only 6% of the ASX200 companies had a ranking value of 5, down from 35% at the beginning of the year. When added to the chart of the index (XJO) using the Breadth Data tool, you can see a divergence between the index and the breadth data, indicating a possible change in direction. For example, a year ago the XJO was making higher highs, but the breadth line started to make lower highs at the end of May (ie fewer strong stocks), indicating a weakening of the market internals before the end of the rally in XJO in August.

XJO Breadth

Adjusting the Component Weightings of the System

As well as creating breadth measures, it's also possible to change the weightings of the ranking criteria. Let's say that you consider the shorter-term V2 measure (is the 13EMA sloping up?) to be the most important of your 5 measures, and the V5 measure (positive returns over 3 months?) the least important, with the others remaining equal. Remember that the script formulas will give a value of 1 when a condition is true and 0 if false, so to adjust the weighting we just need to multiply the results by our weighting value. In the above example, we'll increase V2 by 25% and reduce V5 by 25% so the last line of the example would be as follows, remembering to use the parentheses:

V1+(V2*1.25)+V3+V4+(V5*0.75)

Of course, such a change would mean that instead of possible results of 0, 1, 2, 3, 4, or 5 you could now get totals of 0.5, 1.5, 2.5, etc:

1 Day Watchlist

If you have any questions about creating your own ranking system or need help with scripting let us know. We have a free Scripting Forum where you can post questions, or if your requirements are more advanced then we also offer consulting services.

How to create a custom technical ranking system

Darren Hawkins — Wed, 06 Jul 2022 17:50:00 GMT

In your studies you may have come across the ‘weight of evidence’ approach to investing which looks at prices and various indicators to help identify potential trading opportunities. Let's say that as a momentum investor you look for opportunities by studying the price relationship to various moving averages, the positive or negative slope of the moving average, the RSI value, and the rate of change. You could save the indicators to a Page Layout and apply it to hundreds of charts and manually plough through them to visualise those that look good - or bad - whilst jotting the tickers down as you go, but that takes time – not to mention a toll on your eyes!

Another solution would be to use the Optuma scripting language to do all the work for you. By assigning values to certain technical conditions you can create your own technical weight of evidence methodology - or custom ranking system.

Here are five indicators that you may look for to determine whether further analysis should be undertaken:

the price relationship to moving averages, eg is the current price above the 50MA?
the slope of a moving average, eg is the 13EMA sloping up?
the value of two moving averages, eg is the 33MA greater than the 88MA?
RSI values, eg RSI(10) is above its 10MA
positive recent gains, eg rate of change over last 3 months is positive.

NOTE: these have been chosen as an example only and should not be used in your analysis without further study.

By default, when a true/false condition is calculated by Optuma a true result is given a value of one, and zero if false. So if we build a formula containing those five conditions above and sum the results we will be able to easily see those stocks where all the conditions are true (the total will be five), where all are false (0), and everything else in between. Note that we won't know which of the five conditions are true, just the overall number. However, we can adjust the values for each condition so those that you deem more important can have a higher weighting – more on that in a follow up article.

Here are the five true/false conditions based on the list above that can be added to a custom watchlist column. Remember when starting a line with // that the text that follows is ignored, allowing you to add comments or descriptions:

// Currently trading above the MA50?
V1 = CLOSE()> MA(BARS=50, CALC=Close);
// The 13EMA is sloping up?
V2 = MA(BARS=13, STYLE=Exponential, CALC=Close) IsUp;
// Is the MA33 is above MA88?
V3 = MA(BARS=33, CALC=Close) > MA(BARS=88, CALC=Close);
// Is the RSI(10) above its 10MA?
V4 = RSI(BARS=10) > MA(RSI(BARS=10), BARS=10);
// Positive returns over last 3 months?
V5 = ROC(Month(PERIODAMOUNT=1), BARS=3) > 0;
// Sum the results to get a ranking value
V1+V2+V3+V4+V5

The last line calculates the sum of the results to display a value, so that the stocks where all five conditions are true will have a ranking value of five, whilst those scoring zero will obviously have no true results. In the example on the right a custom column has been added to a watchlist of the ASX200 with the above example ranking formula being used. This column can be sorted, grouped, and also displayed with a custom label and colour. As you can see in the snippet of the watchlist, BSL, BWP, and CAR have a ranking of five, whilst BOQ, BXB and CBA are all zeroes.

Custom Rank

Creating a Time Series

Once the column has been added to the watchlist then a time series of the ranking value can then be added to a chart by clicking on the column heading and dragging it on to the chart to display it in a new view (tip: once added right-click on the line and change the Plot Style to Shaded Step).

Rank

Custom Bar Colours

Using the same ranking example you can also assign specific bar colours for each value. For example, those bars that have a ranking value of zero are bright red, ones are maroon, twos are pink, etc.

To do this right-click on any bar and change the colour scheme to Custom and then create a formula for each value with the colour as required.

TIP: if you save the original script and give it a name, it can then be referenced by other formulas using the SCRIPT() function.

All that needs to be changed for each colour is the value, so for red bars denoting a rank of zero use the following, remembering to use the double equals sign (==) to denote equality (single equals signs are used to assign variables).

//Get the original saved script, called MyRank;
SCRIPT(SCRIPTNAME=MyRank)==0

The next colour for those with a ranking of 1 would have ==1, and so on.

Once all the colours have been added the chart would look like this:

Bar Colours

Clients subscribed to ASX or US data can click the buttons below to save and open the workbook examples.

Let us know what you think, and feel free to post your scripts or queries on the client forum.

Now available: US ETF Fundamental Data

Darren Hawkins — Wed, 08 Jun 2022 23:25:00 GMT

We are always looking at ways to improve Optuma, and ahead of the upcoming version 2.1 release (we'll be looking for beta testers very soon!) we've added more ETF data to our US Fundamental database.

This means that clients with access to the US data will now be able to add over 70 new datafields to charts or watchlists. Here's the complete list:

ETF Data

Note: not all data fields are available for all ETFs! For example, Metal Type will only be available for metal-based commodity ETFs.

To add the data to a watchlist select the required field from the Fundamental section (or use the search box to filter):

ETF Data Watchlist

Once added the watchlist column can then be sorted or grouped. The datafield can also be displayed on a chart using the Chart Element tool:

EEM

Please contact support if you have specific data questions.

I'll just add that with the upcoming 2.1 release we will also have the ability to quickly and easily add the members of all global ETFs to your Symbol Lists - more details soon!

Measuring price and time ranges

Darren Hawkins — Thu, 26 May 2022 05:37:00 GMT

As you might expect, we get quite a few "how do I...?" questions here at Optuma Towers and we try to update the KnowledgeBase and create Quick Tip videos to help clients find what they need, but this particular query has come up a few times recently.

The question was along the lines of "what's the best way to count time and price movements on the charts?" Like many things in Optuma, there can be a number of different ways to achieve this, so here are examples of some of the tools available, which can be particularly useful in presentations or reports.

Price Measure

This tool can be used to count price change between two user-defined points (click here for the tool's KnowledgeBase article). The Price Measure can be set to show the percentage change between the two points, the price change, or both. Along with the usual tool options to change the line colours and style, there is also a Repeat property with an optional expansion factor to help identify potential targets.

In this example, the the weekly chart of gold on the left measures the 2016 move of 329 points - or 31.4% - which is repeated twice more. The chart on the right takes the 186.48 point move and expands each repeated level by the Fibonacci factor of 1.382:

Price Measure

NOTE: you will see the repeated levels on the left-hand chart above are a constant 329.04 points apart, but the percentage moves obviously decrease as the values become higher. To keep the percentage moves constant - and therefore increase the points value - set the expansion factor to the same value as the initial percentage move, i.e. 1.314 in the example.

Expansion

Time Measure

This tool can be used to count the distance in time between two user-defined points. The Label option can be set to count the bars (i.e. trading days on a daily chart), calendar days, weeks, etc. In this example the measure between the first two highs in the VIX was 223 trading days, with the major high occurring 448 calendar days after that. As with the Price Measure, the values can be repeated and expanded, which can help identify potential cycles.

Time Measure

Time Price Measure

As the name suggests, this is a combination of the two tools, which measures the time and price between two points. The tool includes a number of labels which can be turned on or off, including the dates, prices, and Range %. This tool has the added flexibility to create your own measure, such as (P1+P2)/2 to show the midpoint of the two selected prices:

Time Price Measure

Time Price Labels

The previous tools all measure between two points, but what if you wanted to measure more? You could apply the tools multiple times, or you could use the Time Price Label tool (TPL). This allows you to apply an anchor label, and then by selecting the Add New Label option under the tool's Actions you can add multiple labels, each one counting from the previous.

**Note:** when applying three or more labels the tool can calculate retracement measures of the previous label range.

In this example on a 15 minute chart of Bitcoin, the 6.4% move up from the anchor point at Label 1 to the high at Label 2 took 159 bars (or 1 day, 15 hours and 45 mins), and then 56 bars to retrace 50% of the range.

Time Price Labels

Gann Day Count

Clients with the Gann tool module can use this tool which counts bars, days, weeks, etc from a user-defined starting point to the latest bar. As new data is added to the chart the count increases, and it's possible to set specific counts to be marked on the chart, such as every 90 calendar days, or 144 trading days as seen in the chart of wheat below. Starting at the low on March 31st 2021, the red lines show every 90 calendar days, with the last blue line showing the second 144 day cycle occurred just a few days ago as at the time of writing we are 291 trading days - or 420 calendar days - from the starting point.

Gann Day Count

Range From Extremes

The Range From Extremes automatically calculates how far the current price is from the highest high or lowest low over a user-defined period. Like the Gann Day Count tool, the values will automatically update as new data is added to the chart. This range can be expressed in either percentage, standard deviations, or Average True Range values.

Here we see the RFE tool for the ASX200 index ($XJO), which shows it has been in a fairly narrow range over the last year: just 5.1% above the one year low and 6.9% below the high. The plot below the chart is the Drawdown tool which calculates each day's low price from the 1 year high.

Range From Extremes

These are just a few examples of some of the tools we have to help with analysis - and don't forget that with the scripting language you can create your own tools, such as the 50% level of the all time high. If there are any other types of tools that you would like to learn about, let us know or post in the client forum.

ASX and S&P500 Sector Workbooks

Darren Hawkins — Thu, 31 Mar 2022 09:00:00 GMT

If you are interested in a top-down analysis of sectors then you may find the attached workbooks for both the Australian and US markets useful. Each tab in the workbook contains the current members of the sector (updated following the recent quarterly rebalancing), and includes examples of watchlists, charts (such as seasonality and relative comparison charts), and indicators (eg drawdown, and pivot labels). They also include a new 6 month high / low scan, with the Australian workbook also including the members of the ASX All Tech Index ($XTX).

The first page is a sector overview, which includes a watchlist of the sector performance along with a weekly Relative Rotation Graph® and weekly candlestick chart (click on any symbol in the watchlist to change the chart).

[Image: ASX Overview]{:class="center"} ASX Overview

If you find a particular layout useful remember that you can save it as a Page Layout and apply it to your own charts (delete the watchlist first if you only want the Page Layout to include the chart).

For more information on Page Layouts click here.

Here's an example of the ASX IT sector, which includes a daily chart with a three bar Gann Swing Overlay, and year-to-date performance against the IT sector index ($XIJ). If you have ASX Fundamental Data on your account you will also see the Market Cap column in the watchlist:

[Image: ASX IT]{:class="center"} ASX IT

Here's a screenshot of the Relative Strength Ratios tab from the ASX workbook. This automatically displays a chart relative to the ASX200 Index $XJO using the Relative Index property. As you scroll down the chart will update without the need to create a custom code or Division Spread chart.

The Show View below the chart shows the performance against the index over the last quarter.

[Image: ASX Relative Strength Ratios]{:class="center"} ASX Relative Strength Ratios

This last example shows a watchlist of the ASX 200 companies with a true/false column for new 6 month highs and another for 6 month lows (right-click on the column headings and select Edit Column to see the underlying script formulas). At time of writing there were 10 new highs and 4 new lows on that particular day. The chart on the right highlights the new 6 month highs and lows with a Show Bar arrow, with the last 6 months highlighted in blue using a custom bar colour scheme:

ASX200 6 Month Highs & Lows

Optuma clients can click the buttons below to save the workbook files, and once saved - usually to your Downloads folder - double-click on the file to open. When prompted to move them from the download location to your default Workbooks folder click yes.

Please contact us if you have any questions.

An easy way to find breakouts

Darren Hawkins — Mon, 14 Mar 2022 01:56:00 GMT

Note: This article first appeared in 2020 but has been updated to reflect subsequent changes that have been made in the PIVOT() function to account for unconfirmed pivots. As such, if you have existing scans or formulas from the previous article they should be amended, as per the examples below. <cite>Darren Hawkins</cite>

This version also includes sample workbooks that clients can download and open in their copy of Optuma.

As you may know, Optuma has several tools to help identify significant turning points, such as the various swing overlays, but one tool I’ve started to use more and more is the Pivot Label tool.

You can read more about the tool here, but basically it labels high and low turns based on the number of bars either side, so the higher the number the more significant the turn. In the example for the S&P500 below, the Pivot Labels on the left have been set to 10, which means that there must be at least 10 bars both before and after the high/low for a label to appear (in this case I used daily bars, but they work the same in any timeframe). Compare that to the 20 bar pivot on the right which will obviously show fewer - but more significant - turns as the interval required either side is higher.

SPX Pivots

Note: you can change the chart (or scan) timeframe to weekly to make the turns more significant.

This article will show you how to create a scan to identify breakouts from previous pivot levels and save those results as a watchlist in a workbook, so that every time the workbook is opened the scan will be automatically executed and the list updated - a great timesaver!

Creating the scan using the Pivot Labels tool

If we know the last pivot value then we can create a scan to alert us when that level has been breached, thus signifying a potential breakout that might be a great trading opportunity. In the scripting language we can use the PIVOT() function to calculate these levels.

In the script editor window add the PIVOT() function and click on the text to select your parameters, eg 15 bar pivots based on highs.

NOTE: You should also ensure that the Ignore Unconfirmed option is ticked to prevent the results changing as new data is added to the chart.

PivotScript

This script on its own will simply return true when the current bar is the highest bar for at least 15 bars. To get the actual value at that point, we need to use the VALUEWHEN() function, ie the value when the pivot condition was true. By default the VALUEWHEN() function calculates the closing price of the trigger day, so we’ll need to nest HIGH() in the parentheses to get the high price on the day of the confirmed pivot. The complete formula will be as follows (remember the lines beginning // are comments):

// Define the pivot criteria
V1=PIVOT(MIN=15, TYPE=High, IGNOREUNCONFIRMED=True);
// Get the high price for the V1 pivot
V2=VALUEWHEN(HIGH(), V1);
V2

In this example of $AAPL, the last 15 bar high pivot was at $176.65, as shown in the watchlist and plotted on the chart using the same formula above in a Show Plot tool.

Pivot High Values

So now we have the previous pivot value we can compare it to the current price, and set a true/false signal if that level has been taken out using CrossesAbove in the formula:

// Define the pivot criteria;
V1=PIVOT(MIN=15, TYPE=High, IGNOREUNCONFIRMED=True);
// Get the high price for the V1 pivot;
V2=VALUEWHEN(HIGH(), V1);
// Define signal
CLOSE() CrossesAbove V2

Here’s an example of an ASX300 scan showing 8 breakouts, including $JBH closing above February's 26 bar high of $54.32 on strong volume:

Scan Results

The Days Since Pivot watchlist column formula tells you how long the pivot level has been in place using the TimeSinceSignal function (ie 26 days for $JBH, and 57 for $FFX):

V1=PIVOT(MIN=15, TYPE=High, IGNOREUNCONFIRMED=True);
TIMESINCESIGNAL(V1)

The watchlist also has a % of 20D Avg Vol column to show the if there was strong volume associated with the breakout. In the case of $JBH the day's volume was 50% higher than average, whereas $DXI had less than half.

VOL() / MA(VOL(), BARS=20, CALC=Close)

Breaking below pivot lows

To scan for breaking support the concept is exactly the same but with slightly different syntax for a pivot low because the price action is going in the opposite direction, ie CrossesBelow:

// Define the pivot criteria
V1=PIVOT(MIN=15, TYPE=Low, IGNOREUNCONFIRMED=True);
// Get the low price when the last V1 pivot occurred
V2=VALUEWHEN(LOW(),V1);
CLOSE() CrossesBelow V2

Save time by opening scan results in a Watchlist or Page Layout

A great timesaver is to open scan results as a watchlist or previously saved Page Layout, and then saving that in a workbook. From the Scan Results window click Export Results > Open Results As:

Open Results As

Because the watchlist is linked to the scan, when you open the workbook tomorrow (once the end-of-day data has been downloaded) the list will automatically update with the new breakouts.

This means you can have a workbook containing a series of page tabs linked to scans, so as you click on the tab the stocks with a true result will populate the watchlist.

Sample Workbooks

Optuma clients with access to Australian or US data can click the buttons below to save and open weekly watchlists showing 10 bar breakouts in true/false columns.

Automated scans and outputs

Enterprise Services clients can set up command line prompts in the Windows Task Scheduler, which will automatically run the scans at a time of your choosing. The results will be automatically exported as either images, a PDF, a .csv list of symbols, or even an email (Microsoft Outlook clients only) using your preferred page layouts. You can watch a video on building command lines here.

Need more help?

If you have any questions or need more help with creating custom scans or formulas please post on the scripting forum. Make sure you search the forum first to see if your question has already been answered, but if you need more advanced help then one-on-one consulting sessions are also available.

Optuma Swing Charts: Finding Patterns

Darren Hawkins — Fri, 11 Feb 2022 11:42:00 GMT

As seen in the previous articles on Optuma Swings (here and here), we can use swing highs and lows (based on percentage, Gann Swings, or Volatility moves) to determine the trend of an index, stock, currency, or commodity. But we can also create script formulas to identify when a change of swing direction is confirmed, and whether the new swing highs or lows are higher or lower than the previous swings. In fact, once we know the swing values we can automatically calculate all sorts of things, such as consecutive higher lows, or swing gaps.

Why is this important? As discussed in the other articles, swing charts make it easy to see trends and strength in the market, so these formulas can help you quickly and easily find particular setups based on changes in trend.

Let’s start off by finding the previous swing values using the SWINGSTART function and offsets.

When looking at swing values it’s important to know the current swing direction.

This means that the same script can return different results if the current direction isn’t taken into consideration. If the current swing is up, then the SwingStart value will be a low, and if the swing is down then it will be a high. Likewise, the offset swing values will be different, so the previous swing start will be a high when the current swing is up, and a low with a downswing.

The image below shows the swing high and low values for ANZ on the left (current swing is up) and AMC on the right (swing down).

SwingStart Values

As you can see, the swing offsets on the left have an even number for the lows (ie S[2], S[4], and S[6]) versus odd numbers on the right (ie S[1], S[3], and S[5]).

Here are the scripts, based on a 3 bar Gann Swing:

Current Swing End

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar, USEINSIDE=True);
SWINGEND(SW1)

Swing Start

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar, USEINSIDE=True);
SWINGSTART(SW1)

Previous Swing Start - S[1]

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar, USEINSIDE=True);
SWINGSTART(SW1)[1]

To get preceding swings start levels increase the offset value in the square brackets, ie [2], [3], etc.

Tip: to copy all the swing data (including dates, price levels, and direction) to Excel open the type of swing chart required (not an overlay) and right-click on the line and select Copy Data to Clipboard from the Actions menu, and then CTRL+V to paste in Excel.

Finding Swing Patterns

So now we know the swing values (and offsets) we can look for relationships between the swings. For example, you can scan for when a stock is in an upswing with at least two consecutive higher swing lows:

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar, USEINSIDE=True);
S1 = SWINGSTART(SW1);
SWINGUP(SW1) and (S1 > S1[2]) and (S1[2] > S1[4])

To find two consecutive lower highs with a swing down:

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar, USEINSIDE=True);
S1 = SWINGSTART(SW1);
SWINGDOWN(SW1) and (S1 < S1[2]) and (S1[2] < S1[4])

In this example of the ASX200 watchlist, SFR (and 8 others) has at least two higher swing lows, and WOW (and 14 others) with at least two lower highs.

Higher Lows & Lower Highs

This next example which shows in a watchlist when the latest bar has changed the 10% swing direction from down to up:

SW1 = PERCENTSWING(PERCENT=10.0);
SW1 TurnsUp

This is useful, but what is more interesting - and potentially shows more strength - is if the new confirmed swing low is higher than than the previous low:

SW1 = PERCENTSWING(PERCENT=10.0);
S1 = SWINGSTART(SW1);
SW1 TurnsUp and (S1 > S1[2])

The third column takes it one step further: not only a higher low but also a new higher high:

SW1 = PERCENTSWING(PERCENT=10.0);
S1 = SWINGSTART(SW1);
E1 = SWINGEND(SW1);
SW1 TurnsUp and (S1 > S1[2]) and (E1 > E1[2])

In the image below there were 21 members of the S&P500 whose 10% swing direction turned positive with the day’s price action, of which 11 formed a higher low, and only UHS formed a higher low and a higher high (albeit by only $0.12 - so if you wanted a percentage tolerance could be included in the formula):

SwingTurns

Swing gaps

Finally, this will find instances when the last swing low is at least 2% higher than the previous swing high (which requires an offset of 3 in an upswing).

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar);
S1 = SWINGSTART(SW1);
SWINGUP(SW1) and (S1 > S1[3]*1.02)

2% Swing Gap down

SW1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar);
S1 = SWINGSTART(SW1);
SWINGDOWN(SW1) and (S1 < S1[3]*0.98)

Swing Gaps

Hopefully these examples will help you with your own swing analysis. Click the buttons to download the workbooks with the watchlist formulas so you can open in your copy of Optuma and adjust them to meet your own requirements.

If you have any questions about building your own formulas - not just on swings but on any other technical condition - please search the Scripting Forum and post queries there (provide as much information as you can - images are especially helpful). Alternatively, paid consultations can be arranged where we can build and test the formulas for you. Contact support for details.

Optuma Swing Charts: Trend vs Direction

Darren Hawkins — Fri, 17 Dec 2021 10:11:00 GMT

This is the second in a series of articles on using Optuma's swing charts and overlays - particularly when it comes to creating scripts for scans or testing.

In the first article we looked at an easy way to identify swing trends using overlay colours and a simple scan formula. In this article we can take it a step further by looking at the swing direction within the current trend, i.e. to see whether the current swing is a dip down in an upward trend, or a bounce in a downward trend.

To scan for when the current swing is down but the overall trend is up we can combine the SWINGDOWN() function for direction, with the SWINGTRENDUP() function (or SWINGUP() and SWINGTRENDDOWN() for the opposite).

Here’s the formula for swings based on 10% price moves:

//Set Swing Criteria;
S1 = PERCENTSWING(PERCENT=10.0);
//Determine Trend and Direction;
SWINGTRENDUP(S1) and SWINGDOWN(S1) and CLOSE()>0

Now you might be wondering why the CLOSE()>0 condition is required to produce the desired results. This is one of the difficult issues regarding swing charts, in that there is not a swing value for every date, only when the swings occur (this is what we call a swing list). By adding CLOSE()>0 - which will always be true - this converts the swing list into a bar-by-bar list, therefore giving a swing value for every day, allowing the scan to work as expected.

SwingList vs BarList

If I’ve just confused you, click here for Mathew Verdouw’s detailed explanation on swing lists versus bar-by-bar lists

Using the above formula in a scan will return stocks that are currently in an uptrend but the latest direction is down, as per this example of CBA:

Swing Trend vs Direction

Linking scans to watchlists

See this video to link the scan results to a watchlist in a workbook. Whenever the workbook is opened the list will update with the latest scan results.

Creating a SwingRank value

Using these functions it is possible to create a ranking value depending on the current swing trend and direction. These are the four scenarios, along with their SwingRank value:

Swing Trend negative and Direction negative: SwingRank = 0 Swing Trend negative and Direction positive = 1 Swing Trend positive and Direction negative = 2 Swing Trend positive and Direction positive = 3

Here’s an example of the Dow 30 stocks in a watchlist, based on a 3 bar Gann swing:

Swing Rank

Apple is showing a current value of 3 as both the trend and direction are up, whereas CVX is a 2 and in a possible ‘buy the dip’ situation, with plenty showing 0s or 1s.

Here’s the formula to calculate the SwingRank (remember to change S1 variable to use a different swing type):

//Set Swing Criteria;
S1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar);
//Determine Trend and Direction;
T1 = SWINGTRENDUP(S1) and CLOSE()>0;
D1 = SWINGUP(S1) and CLOSE()>0;
//Calculate rank values;
IF(T1 == 1 and D1 == 1, 3, IF(T1 == 0 and D1 == 0,0, IF(T1 == 1 and D1 == 0,2,1)))

Clients can click the button on the right to save and open a sample workbook with separate tabs for Australian and US markets with the SwingRank watchlist column (based on daily 3 bar Gann Swings). For other regions or lists clients can click the Linked to List watchlist property and select an alternative Symbol List to use.

Next time we’ll look at comparing previous swing values which can be used to identify consecutive higher highs or lows, or to calculate retracement levels.

Apple is showing a current value of 3 as both the trend and direction are up, whereas CVX is a 2 and in a possible ‘buy the dip’ situation, with plenty showing 0s or 1s.

Here’s the formula to calculate the SwingRank (remember to change S1 variable to use a different swing type):

//Set Swing Criteria;
S1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar);
//Determine Trend and Direction;
T1 = SWINGTRENDUP(S1) and CLOSE()>0;
D1 = SWINGUP(S1) and CLOSE()>0;
//Calculate rank values;
IF(T1 == 1 and D1 == 1, 3, IF(T1 == 0 and D1 == 0,0, IF(T1 == 1 and D1 == 0,2,1)))

Introduction to Optuma Swing Charts - Identifying Trends

Darren Hawkins — Fri, 03 Dec 2021 00:36:00 GMT

This is the first in a series of articles on using Optuma's swing charts and overlays - particularly when it comes to creating scripts for scans or testing.

What is a swing chart?

Let's start off with the basics. A swing chart is a line chart that shows the movement of prices, removing both the time element and price 'noise'.

We have four types of swing charts (I won’t go into all of them in detail here, but you can click the links below to learn more about them).

Gann Swings (based on bar-to-bar price relationships)
Percent Swings
Point ($) Swings
Volatility Swings (based on average true range moves).

Below is an example of a 10% Percent Swing chart for Rio Tinto ($RIO) on the ASX, which clearly shows support and resistance levels and when higher highs and higher lows occur:

RIO Swing

In the example above, each swing turn occurs when there is a 10% percent change in price in the opposite direction (of course, you can change this percentage move to anything you like). The current swing for Rio is upwards (green) off of a lower low, and will continue up until the price moves 10% down from the last swing high (currently $96.01), so price would have to fall to $86.41 for a change in direction.

As mentioned above, the time element is removed from the swing charts (notice the lack of dates on the x-axis) allowing you to just focus on price levels. Labels can be enabled on the swing charts to show the dates, prices, and time counts, but it's also possible to overlay the swings on top of a standard price chart. Here’s Rio with the same 10% swings as above overlaid on the price chart (with the swing values labelled):

RIO % Swing Overlay

TIP: enable Training Mode with the swing overlay on a chart, and set the date back to earlier in the year. As you advance the chart you will see when the swing changes get confirmed.

Swing Trend vs Direction

Whilst the current swing on the Rio chart is up, it’s quite obvious that the overall trend is down, because of the previous lower highs and lows. To make it easier to show this trend on a chart, set the Colour Scheme property of the Swing Overlay tool to Trend and the previous swing levels will be taken into account to determine the colour. Here’s how the chart of Rio looks using the Swing Trend colour scheme:

Overlay Properties

RIO Tinto Daily

Whilst the current swing is pointing up, the overall trend is down and the overlay is coloured red. The Swing Trend will only turn green when the last swing high (currently $104.25) is taken out.

Identifying Swing Trend in a scan

Using swing formulas in scripting can be quite tricky (we’ll look into those in a future article!), so we’ve made it easier to identify trends by creating SWINGTRENDUP and SWINGTRENDDOWN functions.

When combined with your preferred swing function (e.g. PERCENTSWING(), GANNSWING(), etc.) it is easy to identify current trend direction - or even when the trend changes.

Here’s an example using a 3-bar Gann swing which will show a change in trend - i.e. when the SWINGTRENDUP or SWINGTRENDDOWN condition becomes true (remember a true condition gets assigned a value of 1, so the signal occurs when the value changes to 1, not when it equals 1):

S1 = GANNSWING(SWINGCOUNT=3, METHOD=Use Next Bar, USEINSIDE=False);
SWINGTRENDUP(S1) ChangeTo 1 or
SWINGTRENDDOWN(S1) ChangeTo 1

Note: change the function in the S1 variable to use a Percent or Volatility swing instead of a Gann Swing.

If you are not familiar with Gann Swings click below for a detailed video by Mathew Verdouw on their construction:

Custom Bar Colours

The same script above can be used in a custom colour scheme so that you don’t really even need the tool overlaid (although the labels can be quite useful). Once the S1 variable has been defined only SWINGTRENDUP(S1) needs to be used for the positive trend (green), with the default colour set to red (i.e. when the script is false). Here’s a 3 bar Gann swing trend on Twitter (TWTR):

Custom Bar Colours

Even if you are already familiar with swing charts and overlays I hope you found this useful. Next time we’ll get into more advanced swing scripting, but if you have any questions please leave a comment below, and don't forget to post any scripting queries on our Optuma Scripting Forum.

Mind the Gap - and watch your profits soar

Darren Hawkins — Thu, 19 Aug 2021 08:30:00 GMT

Gaps are one of the most important tools we have as Technical Analysts. When a significant gap happens in the opposite direction to the trend, that breakaway is telling us that the trend is most likely changing. When we see a gap in the middle of a move, then it is like someone poured fuel on the fire. The question then is if the trend is sustained, or if the gap gets filled.

Gaps are said to be filled when price quickly runs out of steam and retraces back to fill the prices that were skipped by the gap.

Types of Gaps

Breakaway: usually occur at the start of a trend
Runaway / Measuring: occur in the middle of a strong trend, allowing for price projection
Exhaustion: occur at the end of a trend
Opening: when the open of a bar is outside the range of the previous bar

It's important for us to know when gaps occur and how quickly they get filled. This is where the Optuma 2.0 Gap Finder comes in.

Before, the way to show gaps was by using a simple script in a watchlist or chart using a simple formula:

Gap Up

LOW() > HIGH()[1];

Gap Down

HIGH() < LOW()[1];

Gaps

This is useful, but the Gap Finder tool draws a shaded area on a chart to help visualise the gap zones on the chart.

In this example, two green breakaway gaps last November identified the start of a new trend in CSCO, which filled three previous gaps down (in red), the first of which occurred back in July 2019:

Gap Finder

Like most of our tools, the properties can be adjusted to suit your needs, such as the percentage size of the gap, and whether partially-filled gaps should be included. Also, if the gap gets filled within a certain timeframe (20 days by default) then it will have failed, and be ignored.

Using the Gap Finder in Scripting

The GAP() function in scripting has four output values, allowing you to search for any conditions:

GapAbove - Price of the beginning of the next gap zone above.

GapBelow - Price of the beginning of the next gap below.

GapAboveSize - the width of the zone above (in $).

GapBelowSize - the width of the zone below (in $).

For example, to show how far in percent the current price action is above a gap below - based on a 0.5% gap - you would use this:

G1=GAP(MINSIZE=0.50);
(CLOSE() - G1.GapBelow) / G1.GapBelow * 100

Microsoft is currently 4.1% above the partially-filled recent gap, and moving away:

Scripting

In fact, as this gap occurred in a strong trend it could be a potential runaway gap, giving a price target of $326 as measured from the low to the middle of the gap. Of course, if the gap gets filled in the coming days then it could be an exhaustion gap, so one to keep an eye on!

Runaway Gap

The Gap Finder tool is available in Optuma 2 which is currently in beta testing. Contact Optuma support if you would like access to it now.

Optuma - 25 Years and a look at what's next

Mathew Verdouw — Thu, 08 Jul 2021 17:12:00 GMT

With the imminent release of Optuma 2 - what is most likely the largest single upgrade we have ever done - now is a great opportunity for me to take time for us to outline the major directions we are taking with Optuma.

25 Years

On the 14th of June, we celebrated the 25th anniversary of when I first sat behind my PC to write the software that is now Optuma. I'm thankful for the many thousands of people who have chosen Optuma (or Market Analyst) over the past 25 years. It's been an amazing journey and an honour to assist so many people. I'm blown away that we still have a handful of clients actively using Optuma from 24 years ago!

Mathew 25 Years Ago

There's been a lot of coding over the 25 years, we've started from scratch four times realising that the foundations would not support all that our clients were asking us to do. The last rewrite was Market Analyst 7 (started in 2009, released in 2012). The core still allows us to build in everything we want to do and I do not see another rewrite anytime soon!

Optuma Coin

There's no way we'd be able to provide our products and services for so long if it was not for the professionalism and knowledge of our Optuma Team. A few of them are approaching 20 years in the company! Providing the best development and support has always been a priority for us, and our team excels at doing that! I'm sure that like me, you've been glad that they're around when you have a problem that needs to be solved.

You may not realise that our team has been working successfully from home for many years (while I was transitioning back to Australia). So Covid "work from home" was "business as usual" for us. Nevertheless, the future roadmap items require significant collaboration, and we're looking forward to getting the team back together in an Australian office soon.

Looking over the past 25 years, I also want to thank our army of resellers. Ray Barros was the first educator to approach me back in 1997. I built Barros Swings into the software and all his students signed up. It's the model we still predominantly use as it's educated traders/professionals who need and make the best use of Optuma. Our partners have been our sales team and research sounding board and have contributed a great deal to the success of Optuma.

Optuma 2.0

For the last nine months, we've been working on a major upgrade to Optuma. We have over seven pages of changes, fixes and additions! This includes new tools, charts, more script functions, as well as improving calculation speed, amongst a list of other improvements. A big thank you goes out to those who contribute in the forum (forum.optuma.com). For 25 years our mission has been to build Optuma for our clients, your requests help us prioritise the developments.

One of the biggest improvements is our end of day data overhaul. Over the years we haven’t maintained our data well enough. There were many delisted codes and some of our European data was a mess. This year we have increased our investment in data and have been improving our processing of corporate actions to ensure our data is the best it can be. One of the biggest improvements is for Equity Traders who can now switch a chart from Price Returns to Total Returns. This will also be important for accurate quantitative modelling.

The data project has turned out to be much bigger than I first anticipated but we are determined to keep at this for as long as it takes. We expect it to be ongoing for the rest of 2021 with incremental improvements being released all through the year.

Keep an eye out on your Optuma Home screen for an invitation into the Beta Program.

Major Roadmap

While we continue to improve the software as we go, there are major projects we will be working on over the next few years. Through so many interactions with clients (both private and professional) and also through teaching the CMT curriculum, I've just learned so much and I want to bring as much of that into Optuma as possible. The over-arching theme is that we want Optuma to help you manage your analysis and also find unique opportunities.

The following are not necessarily in order, and everything is subject to change as we continue to adapt to the market. This is all high level and we'll add detail as these projects get closer to release.

Data Developments

We will continue to clean up and improve our data. We're also looking to support more APIs from other real-time providers and may even be able to offer delayed intraday US data soon. Other tasks are:

Expand Fundamental data for more markets.
Overhaul and clean up our handling of Futures data.
Consolidate the way we package data into Asset Classes and Regions.
Improve our ETF handling to make it easier to identify funds by size, class, and theme.
Reclassify our data in the Security Selector so it is easier to find and filter the data we have available.
Overhaul all our Breadth Measures.
Add in Survivorship bias-free data for more symbol lists.

Data Center

Quantitative Overhaul

This is in relation to our Scanning, Breadth, Signal Testing, and Back Testing. While the main focus is to improve the speed and accuracy of the tools, we will also be separating the majority of the engine so that it can run on our data servers. By having scans run on the server, you can have results emailed to you after the market closes. Optuma can also have an alert that tells you there are stocks meeting your criteria. The biggest advantage is that your scans and tests will always have the latest data without you needing to worry if your data is fully up to date.

On the Testing Side, there is so much that we want to build in. The more I was researching for our Quant course, the more I realised that we needed to start from scratch and manage tests in a different way.

I started this project last year, but when I started hand-checking results from the data I realised that I needed to shift to the data overhaul first. Quantitative modelling is only as good as the data it is working from!

Advanced Quantitative Testing Course

Web Charts

This is a project that was put on hold for Optuma 2.0. In the first phase, we will take the charting module and make a simple web-based charting service. Eventually, it will be expanded into a fully-fledged Portfolio Management tool that can take inputs from the Quantitative Models. Our planning for this revealed that again we need better Corporate Action processing, hence the huge focus on data. Our final multi-year goal with this is the eventual replication of most of Optuma's features in a browser but I still see a place for Optuma as a downloaded application.

What always made Optuma stand out was the ability to build unique charts and visualisations of multiple securities and display them in a unique way, e.g. 3D Sector Maps (another chart that’s due for an overhaul). We see this is where Optuma can add great value as we continue to create unique displays for the web.

When Web Charts are linked with a scan on the server, you will be able to review your scan results on any device at any time. You'll also be able to set up your own market dashboards. We also see Web Charts as a way to view Optuma workbooks when you are away from your desktop PC. There are so many opportunities here.

Data Feeds

As I mentioned before, we are looking at bringing on a number of new feeds into Optuma for real-time data. One of the issues we have always had is that we need a reasonable number of people who require a feed to be able to justify the development cost to build it.

With this project, we plan to change the way we interface to data feeds and make an API available so that clients with bespoke feeds can have them written without us needing to make changes to the foundations of Optuma. This will also make it faster for us to add new feeds into Optuma. We are currently exploring a number of professional and retail feeds that will be available as well as broker feeds.

Data Feeds

Astro Overhaul

The optional Astro module is used by many of our clients who are looking for correlations between astronomical events and price. This is a module that has been patched together over 20 years and is in need of a re-write. Just because it works is not enough for us, we are determined to provide our clients with the fastest and most flexible solution to better serve their needs.

Optuma Indices

Of all the things I have learnt over the past 25 years, arguably the most important is how professional analysts look at indexes and benchmarking. This leads to Relative Strength and charts like RRGs. The real power of these come when you have full access to security classification and indexes. Unfortunately, Index providers know how important this data is, so they charge incredibly high fees for it. For the most part, Indexes are a collection of securities based on rules. While there is a subjective component to many of the inclusion decisions, we believe we can bring most of the benefits to our clients through objective rules.

This is another project that has had to wait on the completion of our data overhaul.

Education Portal

We've almost finished building our new Education Portal where you can access our courses on getting started with Optuma, scripting, and also CMT courses for professionals who are taking the CMT exams. It should be live by the end of July.

Education Portal

There is a lot going on behind the scenes here at Optuma! We are also constantly looking at trends to see how we can continue to give our clients the edge in trading. We're also exploring Crypto wallets etc to see how we can help clients take advantage of this asset class.

A big thank you again to those who have chosen to use Optuma! My vision for Optuma is as fresh and dynamic as it was when I started 25 years ago, I'm always looking for ways that we can improve and add value for our clients. I hope you are as excited as I am about what we can achieve in the next 25 years.

All the best,

Mathew Verdouw, CMT, CFTe Founder / CEO

Scripting Guide for Relative Rotation Graphs® - Updated

Darren Hawkins — Fri, 21 May 2021 04:47:00 GMT

In this updated article new scripting examples have been added, along with updated workbooks for Australian, US, Indian, and Brazilian markets.

One of the most powerful features of Optuma is our scripting language, which allows you to create your own tools, add custom watchlist columns, create your own scans and tools, and design strategies with our testing modules based on any technical criteria you can come up with. Click here for a video showing you where the scripting language can be used in the software.

On the plus side, this gives you a blank canvas where you can write formulas using any of the tools and technical indicators, getting as complex as you like. On the negative side, it's a blank canvas... where to start? This is especially true with Relative Rotation Graphs® - Julius de Kempenaer's unique visualisation method of looking at the relative trend of multiple instruments against each other and a benchmark.

If you're not familiar with RRGs, see Mathew's video here which describes the theory and how it can help in your decision making. All clients with an Optuma services subscription have access to the RRG Lite version which allows you to create RRGs of your portfolio or index constituents against a benchmark, but Professional and Enterprise Services clients (or Trader Services clients can upgrade for $20 a month) have the ability to write scripts on Optuma's unique derived indicators.

Optuma's derived indicators

The derived indicators are based on different data points that can be used scanning and in quantitative testing of RRGs - many of which you will only find in Optuma. The following is a list of the derived indicators available in the JDKRS() function and how you can write a script to utililise them.

Ratio

Calculates the Ratio value of the data point (X axis): JDKRS().Ratio

Momentum

Calculates the Momentum value of the data point (Y axis): JDKRS().Momentum

Quadrant

Represents which quadrant the security is currently in:

Angle

Represents the current position of the security on the RRG chart based on the points of the compass:

Angle ROC

The Angle Rate of Change measures the distance of the angle of the current data point of a security, to the previous data point. The greater the value, the larger the distance between the two data points. JDKRS().AngleROC IsUp

Distance

This number represents how far away the security is from the centre of the RRG chart (ie the benchmark). One of the observations is that higher alpha comes from those components that make bigger arcs around the benchmark. JDKRS().Distance > 2 will ignore those closest to the centre.

Heading

This is the angle that the arrow is pointing to, again based on the points of the compass, and is one of the most useful measures:

0 degrees = North (up)
45 degrees = Northeast
90 degrees = East (right)
180 degrees = South (down)
270 degrees = West (left)
JDKRS().Heading > 22.5 and** **JDKRS().Heading < 67.5 will show those heading in a general northeast direction, ie increasing ratio and momentum (see scan result image below).

RRGNE2

the workbooks attached below include a watchlist column called Direction which takes the heading value and splits the 360° of the circle in to equal 45° segments (eg North is defined as being between 337.5° and 22.5°, NorthEast 22.5° - 67.5°, and so on). Conditional formatting of the watchlist column then allows us to label the direction, with NorthEast and SouthWest highlighted:

RRG Direction

Velocity

This is the vector difference - or distance - between the last two data points on the line. For more than 3 days of increasing velocity use the DaysUp function: DU(JDKRS().Velocity) > 3 or to display the 5 day tail length in a watchlist, with the longest having the highest relative momentum:

V1=JDKRS().Velocity;
ACC(V1, RANGE=Look Back Period, BARS=5)

Here are the RRG values displayed as a Watchlist by ticking the Grid View property, with the corresponding RRG below:

US Sectors

Of course, you can create scans with multiple criteria based on any other technical indicator combined with RRGs, eg Heading between 30 and 60 degrees, with a distance > 2 on increasing velocity, but only for stocks where the 50 period moving average is also sloping up:

JDKRS().Heading > 30 and JDKRS().Heading < 60 and
JDKRS().Distance > 2 and JDKRS().Velocity IsUp and
MA(BARS=50, CALC=CLOSE) IsUp

Note: if using the scanning manager the results can be exported as an RRG, and saved as part of a workbook. When that RRG is next opened the scan is run again and the results automatically updated.

Setting the Benchmark

If you don't specify the benchmark to be used the main index of the country of where the stock is listed will be used by default (ie XJO for Australia, SPX for the US). To change it, click on the JDKRS() function in the script editor window and change the Comparison Index to another index, or even the Comparison Style to Annual Rate of Return and specify the value.

RRG Line Properties

Forex RRGs

Another benefit of being on the full version of Relative Rotation Graph module is the ability to create RRGs on foreign exchange. The scripting function for this is JDKFOREX() and includes the same derived indicators as for standard RRGs. The example below shows the major currencies (and gold) relative to the US dollar on a daily basis.

Forex RRGs

More information

For more on the Optuma scripting language see our online video tutorial course.

Clients can also log in to the Scripting Forum to post questions and see lots of examples.

We understand that it can take time to get up to speed with learning to create your own scripts, and we'll help where we can, but we also offer consulting services to help you design and test your strategies. Contact us for details.

Workbook Examples

Click the buttons below to save the workbooks for the ASX, US, Indian, and Brazil markets. Note: you will only see all the charts and watchlist columns if you have the exchange data and full RRG module enabled on your account.

Improve Optuma’s Performance with These Few Easy Tweaks

Matthew Humphreys — Sun, 21 Mar 2021 05:52:00 GMT

We are always working on improving Optuma’s performance with every update we release. Whether it be faster processing, using fewer system resources, or improved memory management, we know the leaner we make Optuma, the harder our clients can push its capabilities. Over the years we’ve seen clients take Optuma to the very extreme!

This process however does have limits, and while we continue to strive for improvements there are a few things you can do to your computer system’s setup to optimise the performance of Optuma.

Not all GPUs are created equal

These days your computer system may have access to two video cards, especially on (but not limited to) laptops. One will be a powerful dedicated video card with its own memory and processing power separate from the CPU, usually produced by AMD or nVidia. The other onboard video card will be part of the CPU, less powerful but also consuming significantly less battery, allowing laptops to run for longer when away from main power.

Windows will select which GPU to use, and it’s not always consistent. Because of this you may find there are times where Optuma runs fast and displays well, then suddenly it will be slow and may not look the same (thinner pixelated lines for example). In extreme cases Optuma may not even load if Windows tries to use the onboard graphics.

For optimal performance Optuma should always run using your dedicated video card (where available). To see how this can be setup in the latest update of Windows 10 check out the following article:

If you find that your system only has an onboard Intel video card, the options for improvement become more limited.

For the last few years Windows updates have been automatically updating Intel Video drivers, however the process is not without problems, and at times a video card driver is installed that is not the best match for your system.

Where possible, I would recommend checking directly with Intel’s own driver update utility for the best driver option for your system’s video card:

Giving Optuma an All-Access Pass

Windows security can be a fickle thing. I’ve seen examples of two identical laptops purchased at the same time, from the same company, setup with identical hardware and software install Optuma and have one run without any issues at all, and the other require elevated permissions (admin mode) before Optuma could open.

While Optuma is setup using Microsoft’s best practice for file locations, there still seems to be times where it’s not enough. Optuma will try to create a backup or download some End of Day data only for it to be blocked from accessing one of its own folders.

In situations where you may find Optuma is slow to download data, connect to a 3rd party real-time provider, or save files, running in ‘Administrator mode’ is one option that can see an improvement.

To see how this can be setup check out the following article:

When the Cure is Worse than the Disease

I understand the need for Anti-Virus programs, there are a lot of viruses and scammers that can cause all sorts of problems for people. That being said, some of the solutions out there seem to cause problems that are just as debilitating as the viruses they are trying to protect against! They can hog the CPU, lock down access to all but the most basic of processes, and the release of each update seems to be a coin toss of whether they will stop something (like Optuma) from working when previously it was fine. While I cannot provide specific endorsements of an antivirus solution for everyone (it is very much a personal choice), I will share a list of programs that have caused a lot of problems in the past, and those that have worked well with Optuma:

Norton’s overall is not too bad, however their heuristics feature (called Bloodhound) has a nasty habit of tagging random Optuma EOD data files as potential viruses and moves them to a quarantine folder. Not only is it alarming for people to see these files flagged as a potential threat, but it also means you could open a workbook and find all the data missing for a symbol you had analysed. Heuristics is a self-discovery method that uses shortcuts to get to a result. What the AV software is doing is looking at the alignment of bits in files and if it sees something it recognises, it flags it as a possible virus. That is only true in executable files. In an Optuma data file it’s a coincidence as nothing is executed in those files.

Where possible I would highly recommend making sure Optuma has been setup as a trusted program in your antivirus settings. While it’s not 100% guaranteed to stop all interference with Optuma’s operations, it should prevent the most common issues (such as the Norton’s item discussed above).

To see how Optuma can be setup as a trusted app for the most common antivirus programs around check out the following page:

Before You Add to Cart

If you are in the market for a new computer system and one of the primary functions will be running Optuma, I would keep the following in mind:

Dedicated Video Card

Almost all Desktops will come with an nVidia or AMD card. Laptops however, especially in the budget category, will have Intel only options. Where possible try to go for a system with a dedicated nVidia card. AMD are ok as well, but if you have the choice between the two, I have found nVidia to have the edge when it comes to driver stability.

Memory

Windows 10 is relatively resource hungry, which means there is not much left for other programs like Optuma. Where possible try to go for a system with 16Gb of RAM (especially if you plan on opening a lot of symbols and scripts at once, or running intensive scans).

Hard Disk

If you have the option between a mechanical hard disk drive and an SSD (Solid State Drive), always go for the SSD. The speed difference between the two can be hard to overstate. For a desktop system to switch from a mechanical hard disk to an SSD can almost feel like a full system upgrade, that is the level of speed difference we are talking about, and it flows on to everything, from the login time to scans, opening workbooks, etc.—all will be faster with an SSD.

None of the items discussed in this article are a silver bullet to perfect performance. If you are running Optuma on a decent system but find performance to be slow or encountering display issues, these steps are the first things to try.

If you have done all the above and still find Optuma is not running like it should you can always contact our support team for further advice: support@optuma.com

Using Optuma on a cloud-based PC

Matthew Humphreys — Thu, 25 Feb 2021 01:11:00 GMT

Are you finding that your PC is having trouble keeping up with Optuma? Do you run a Mac and are you getting frustrated with Windows in Parallels or VM Fusion? Would you like to keep your Optuma installation completely separate and in a secure location which you can access anytime? Would you love to be able to access your Optuma PC from anywhere, anytime? Then you need to consider looking at a virtual PC for Optuma.

Installing Optuma on a virtual Windows PC allows you to access your copy of Optuma from any device, which is a great solution if you are dealing with any of the issues above. If you work in a firm with a strict IT policy then you could be prevented from installing Optuma at all, this solution will allow you to use it via a website instead of installing it locally. Additionally, if you run large scans or backtests your computer will no longer be doing the work: all the processing will be done by the virtual machine, freeing up your computer’s resources for other tasks.

How does it work?

One service we've been using is called Paperspace. The Windows Paperspace computer is still a computer, it’s just that it is virtual. It looks and feels exactly like a standard Windows PC, because it is a standard Windows PC. When you log in you see the Windows desktop and all the icons just like your own computer. As such, you can install any Windows-based software on to the virtual machine, and access it from any computer in the world via their website or app.

Note: we aren’t affiliated with Paperspace in any way - we just think it could be a great solution for many of our clients.

For example, you can log in to Optuma via the Paperspace website on your work computer and update your Optuma charts, and then log in from home later on your Mac. Your work will be just as you left it in the office, because essentially you are working on the same Windows computer, just accessing from a different device and location.

How much does it cost?

There are four versions of virtual machines, depending on your requirements. I’ve been using the Air version for basic charting and scanning and it works really well (when I have my local Optuma open beside my Paperspace version, it's hard to tell the difference). You can pay per hour or per month depending on usage, and you can always upgrade your virtual machine if required. Paperspace hourly Core Virtual Server pricing as at March 2023:

[Image: Paperpace Core Pricing as at March 2023]{:class="center"} Paperpace Core Pricing as at March 2023

You can contact Paperspace to create an account (ask for a Standard GPU Desktop), and once set up you can log in via their website, or install their app. Once logged in, install Optuma by opening a web browser in the virtual machine and log in to your account page to save and run the setup file. Once installed, if you are a new client simply run Optuma and log in with the details provided and you’re in business. Existing clients may want to follow the steps below to move their existing work from their local computer to the VM.

Copying your work to the Virtual Machine

By default, your Optuma work is stored on your computer's C: drive in the Documents/Optuma folder, so there are two ways to copy your files across:

Via USB to the virtual machine using the app, or
Connect both computers to a cloud drive (eg OneDrive, Google Drive, etc).

Once the files have been copied to the virtual machine, log in to Optuma. If the files have been moved correctly then the username and password fields of the login screen will be populated - if they aren’t then check that the files are in the Documents/Optuma folder.

When you log in you will be prompted to download the historical data for the exchanges enabled on your account, and once that has completed all your work should be available just as on your local computer. Also, if you use any realtime data providers that require other software to be installed (eg IQFeed, Interactive Brokers) then you will need to install that as well.

As always, if you have any questions please contact us.

How Professional Technical Analysts Handled the Covid-19 Crash

Mathew Verdouw — Thu, 09 Apr 2020 07:01:00 GMT

For the last few weeks global markets have seen periods of greater than average volatility resulting from several factors including the rise of the Covid-19 virus, stimulus packages in response to the economic impacts of the virus, and an oil war between Russia and Saudi Arabia.

For the first time since 1997 the US Stock Markets triggered a temporary trading halt, occurring twice after falls of over 7%. Most markets have now entered Bear Market territory and we have seen sensational headlines plastered over the media. What can we expect from here? Should we have been able to see this coming? We want to provide a pragmatic and levelled analysis of the current situation. To do this, we asked a few of our institutional clients a series of questions regarding the current trading environment to get their take on the situation.

Note that these responses were received last week and in times like this the market can move very fast. The responses were valid at the time they were written.

Bios Of Contributors

Before we get to the questions, we’ll first give a quick introduction to the three respondents.

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Q. The current pull-back from the new highs set only a month or so ago has taken a lot of people by surprise. Was there a certain point you realised these large moves down were coming and was this through the use of T/A?

Adam Koós

A combination of three indicators first caused me to reallocate a portion of my Moderate, Balanced, and Conservative models into bonds, and then to exit the market altogether:

Price closing below the 12-mo MA (Feb 1st)
12-mo ROC RS calculation using SPY, IJR, ACWI, TLT, and BIL, and
13/34-week EMA x-over (I front-ran the signal on Friday the 13th and sold early since mathematically it was almost impossible for it not to trigger the following week)

David Cox

The Feb 24th gap was ugly and unusual—we started taking risk off and by the 25th that was an unusual ATR move by any measure. Volatility stops mostly triggered and cash was raised en masse.

Julius de Kempenaer

I never estimated such a big move and certainly not as vicious as it came. What I did notice was an improvement underway for "defensive" sectors since mid to late January. Recently I started researching the use of "rolling BETA" to classify sectors as defensive or offensive. In short High BETA (>1) sectors are offensive while low BETA (<1) are considered defensive. In Optuma I scripted the size of the bubbles on an RRG to display the difference of the 12-month rolling BETA from 1. This gives me positive numbers for High- and negative numbers for Low BETA sectors. On the RRG chart these bubbles will either be closed for negative numbers or hollow for positive numbers. In the RRG chart in fig 1 I have applied that to a universe of Equal Weight sector ETFs for the US market.

Note how Low BETA sectors started to rotate towards the leading quadrant at the end of January.

As the move unfolded, these defensive sectors continued further right through the leading quadrant indicating a relative uptrend vs the S&P 500 that continued to get stronger. A few sectors even completed rotations at the right side of the RRG (leading-weakening-leading) indicating a strong relative trend.

When defensive sectors start to lead and rotate at the right-hand side of the RRG, that usually signals weakness for the market as a whole.

One thing that struck me is how Technology became a defensive sector (BETA dropped below 1) on 3/13 as investors flocked into the big tech names looking for safe havens.

Q. Black Swan events like this can be difficult to anticipate, do you have any contingency plans for your trading when an event like this takes place? Are there any immediate actions you take once an event like this begins to unfold to protect your portfolio?

Adam Koós

No. Although I wish I had a way to “speed things up” when a crisis arises. There have been countless crises in my two-decade career that “looked” just like this one on the front end. My strategy has been tested over bull, bear, and flat markets, so the occasional anomaly is one that I have to accept, understanding that TA and trend-following will protect me and my clients regardless. There is no “perfect” trading strategy.

David Cox

Take the stops we see and unusual moves in the market are unusual for a reason—it's better not to ask too many questions, but sell our stocks and adhere to the plan with the idea that we'll see what comes thereafter. Internet consistency has been a concern amidst this growing virus shutdown and a risk, so we always know how many shares we own. We're a phone call away from the trading desk—even if our instruments are blind.

Julius de Kempenaer

Not specifically. Have your stops in place as usual when you are a shorter term trader or have a solid asset allocation process in place which should/would prevent your portfolio from being heavily overweight stocks when risk increases.

This RRG shows ETFS representing a few major (US) Asset classes. It is using a weekly timeframe so it will react a bit slower than daily. In the week ending 3/6, ITOT (core S&P total stock market) had rolled over and started rotating at a negative RRG-Heading while the risk-OFF asset classes rotated in exactly the opposite direction. Needless to say since then this rotation has continued hard in these directions with one exception for VNQ (Real Estate) which turned around and is now heading deeper into the lagging quadrant together with stocks.

*I have left out commodities as it is so detached from the rest of the asset classes (way to the left inside lagging).

Q. Do fundamentals have any impact on your decisions during an event like this? For example, airlines may be out of action for several months, many have furloughed 2/3rds of their staff and grounded their entire international fleet. Does news like this have any bearing on your trading decisions or do you focus entirely on what the T/A is telling you?

Adam Koós

No. I focus on Price, Trend, Momentum, Breadth, and Volume. If I ever use fundamentals, it’s as a compliment to my technical shopping lists when I’m looking for new candidates. Sometimes a tie-breaker, so to speak. But I focus primarily on technicals.

David Cox

With the rising VIX I can see trouble, and by watching the sectors and industry groups, I can see where investors are concerned. Fundamentals don't matter and are too slow to be of use. Technicals are sufficient and optimal for making our decisions.

Julius de Kempenaer

I read it and I use it in the back of my mind basically to "paint a picture", but it is not part of my process.

Q. Continuing from this, are there any specific sectors or industries you plan to avoid for now, or is everything on the table subject to your analysis?

Adam Koós

No. I plan on scanning through all asset classes, sectors, and industry groups when I get back into the market with no discrimination against any one group, whether it be for fundamental reasons or otherwise.

David Cox

Energy has been so brutal, it's an easy avoid. It's been brutal for years and trending down for about 12 years... so easy to not get caught up with big moves that can happen with little notice. I also don't like these industries like airlines that are asking for bailouts to move forward. These are tough industries at the best of times. Cruise lines? I think cruising might be dead as a pastime. Those expensive boats and the debt borrowed to build and buy them will probably never get paid back. Reminds me of the shipping industry in '08.

Julius de Kempenaer

All sectors are on my radar and I can keep an eye on them in one single graph ;)

Q. Are there markets you tend to prefer over others while volatility is greater? For example, does FX or Commodities receive more of your attention for trade opportunities over stocks in times like this?

Adam Koós

When the leading asset class is U.S. stocks, let’s say… then most of my focus will be on sectors, industry groups, and individual stocks within the U.S. market. I’ll glance at commodities, foreign currencies, and run through my inventories for those categories each week, regardless of the fact that I’m not buying them. However, they wouldn’t get as much attention when U.S. stocks are soaring. So in times like these, I’d say yes, I’m paying closer attention to, spending more time analyzing, and adding more commodity and currency positions to my watch lists, looking for set-ups and potential entry points with good risk/reward potential.

David Cox

I'll take high volatility over low any day of the week. Any time the complacent buy and holder struggles, I smile. When "blue-chip" dividend stocks get crushed and financial stocks get pummelled, I keep smiling. Most investors have no plan, but actually buy & hope. My process is the opposite and capable of adapting to all circumstances no matter how historic they might be! Early in the stock sell off, we were long palladium, coffee, and gold because the trends supported it—but with rising volatility and collapsing security pricing, it doesn't take much to push sell and walk away as things develop.

Q. Does an event like this change your analysis styles? For example, do you drop some lagging type indicators in favour for those that are more reactionary to shorter term movements?

Adam Koós

For my more conservative models, no. But for my aggressive model, as well as a small piece of my moderate growth model, I’ll consider shorter-term indicators in a bear market in order to attempt to enter trades that can create shorter-term profits than I usually would. When in a bear market, even using shorter-term trend-following indicators, such as an 8EMA over 20SMA x-over is too slow. Same goes for price closing below/above the 20SMA…it’s just too big of a lag and the risk is too high when you’re swimming against the current.

David Cox

I like moving average slopes and following leadership via simple routes like % from highs and following groups at the sector and then industry level. If we can see utilities or technology broadly holding up (for e.g.), we're interested. Using ATRs tells me if we're dealing with unusual circumstances, and dynamic ATR stops get me out of stocks quickly even though we might have liked the names the day before or last week. No matter. Securities are risky and no attachment is allowed. They can always be bought back.

Julius de Kempenaer

Sticking to the same tools, maybe play around with sensitivity settings for RRG rotations in very fast moving markets to get some extra color but the bottomline approach remains the same.

Q. In a similar theme to the previous question, does an event like this change the type of term you’re looking at with your trades? For example, do you swap from a mid / long term outlook to short term only?

Adam Koós

To piggy-back on my response above, yes, for a portion of my more aggressive models, I do “speed up” my analysis to see if there are places we can gain entry into a swing trade in an environment like this. The setups I’m looking for are based out of more traditional technical analysis methods such as bullish momentum divergences, combined with price action that indicates levels of potential support that offer tight stops and thus good risk/reward opportunities. In this kind of market, risk management is paramount. It doesn’t always make sense to “force” trades just to be invested in something. You can turn out to be a failed hero in many cases, so sometimes the best place to be is simply sitting on the sidelines in cash.

David Cox

Yes, given the magnitude of the selloff, it seems HIGHLY unlikely that we could V-bottom. I was wrong in 2018, but surely after this much damage, it's highly unlikely. So using Fibonacci retracements can give me levels that risk is no longer worth taking on, and help to establish hedging/selling spots for equity we bought down low into the lows. I would say that I think there is significant long-term (1-2 year) upside, but so much ahead with global economics at a halt and the significance of asset price moves, that there is no way I'd invest on that timeframe. I'd rather trade/invest for the short-term and see what the market brings. All my intermediate-term bottom indicators are flashed (except for a high volume follow through as of today Mar 31/20) day, so risk is back on, even for trend following with a staged buy program. That said, given retracement levels aren't far away, I'm prepared to put hedges and/or sell quickly if I see reversals and/or selling on volume.

Q. How much further do you think this down move will go? Is there a turn date/price that you are watching.

Adam Koós

I have no idea. I prefer to let the market dictate its direction, and as a metaphorical “financial surgeon”, my plan is to pay attention to and analyze internals – specifically breadth and momentum – and use my model to determine when it’s time to get back in.

David Cox

No clue. But with lower lows now on charts like the S&P 500— the uptrend definition is gone. The Nasdaq is a stand out and mostly captures my attention (and investment dollars). I have no clue what this economic shutdown means but presume the earnings and economic numbers are going to look like a disaster for some time. Not to mention the corporate credit mess looking like trouble and seeing liquidity freeze up in some of the hedge fund and real estate markets reminds me of 2008. Lots of things are possible. Lower asset prices yet is certainly one of those things!

Julius de Kempenaer

I have absolutely no idea and hence not looking for a specific date or price. With regard to S&P 500 (stock market) I am probably watching the same long-term horizontal support levels as everybody else but that's about it.

Q. Is there any general advice would you give to traders currently trying to navigate their way through the markets at the moment?

Adam Koós

If you ever feel the emotional urge to do something right now at this moment, don’t do anything at all. If you extrapolate the gain you “might’ve had” if you would’ve entered that trade over the next few years, the total affect on your portfolio is nominal, at best, but the losses can compound, both in terms of absolute return (loss of money) as well as the negative emotional effect it can have on your psyche and ability to make the next trade work in your favor.
Cash is an investment—don’t feel like you always have to own (or short) something.
Know your personal timeframe and stick with it. If you’re a short-term swing trader, stick to that timeframe and surround yourself with other, smart, successful people you want to be like (Brian Shannon, in this case). If you’re a longer-term investor, slow down your charts, start with a monthly chart to see the big picture from 30,000 feet, and then move down to weekly charts to perform the majority of your analysis. Don’t worry about the day-to-day, and definitely don’t worry about the intra-day!
I learned this from Andrew Thrasher: “As a portfolio manager, the day you realize you’re always going to be wrong, work and life becomes a whole lot easier. You’ll always going to discover, in retrospect, that you bought too much, too little, too early, or too late.”

David Cox

Have a plan and decide on your timeframe. I can take short-term trades, because I'm comfortable, have a process and know how I'm going to manage the risk. Small losses are essential—NEVER risk too much on a trade, i.e., < 1% of your account or -8% from cost based on a given stock or security. It avoids the blow ups and avoids the challenges associated with trying to deal with bleeding positions or crashing markets, or dividend cuts or credit downgrades, you name it! Invest in the direction of the trend only. If you want your portfolio to rise, ONLY invest in things that are rising and if nothing is rising, so be it—stand aside. No shame in observing. If results aren't working out well, shrink your size, make fewer decisions and paper trade until you can get back to be comfortable. If a particular trade style isn't working—do less of it—always do more of what is working and less of what is not.

Julius de Kempenaer

If you have a process in place, stick with it. These are the times when the work that you did to put the process together pays off. If you didn’t have a process going into this volatile period, it may be better to refrain from trading and spend your time to get a process in order that you can start to operate when markets have, at least a little bit, calmed down.

Q. Is there a favourite chart which you keep going back to during this period?

Adam Koós

Wow… I look at a lot of charts every day, but if I were to pick one that I keep referring back to every day, it’s the chart below. What we’re looking at here is the S&P500 in the upper pane, followed by the following three indicators:

Percentage of stocks on the S&P500 above their 200EMA
Percentage of stocks on the Mid Cap 400 above their 200EMA
Percentage of stocks on the Small Cap 600 above their 200EMA

This is one of the many indicators I use to determine whether or not the market is healthy enough to start deploying cash back into stocks. My time frame is intermediate term in nature, so I’m looking at 3-9 months or so, if I had to pick a “range.” So as I analyze the chart below, if at least two of the three indicators below were to cross above 60%, this would be one piece of evidence (amongst several others I’m watching) that could indicate we’re seeing a a completion of the bottoming process in stock market today.

As you clearly see, as of 4/8/2020, large caps sit at only 17.73%, mid caps reside at the 13.64% level, and only 9.70% of small cap stocks are above their 200EMA. Pretty dismal, so patience is key here.

David Cox

Chart 1: Sure, I like watching ATR's on the market and this S&P 500 chart has both a long ATR%Price w/ 20MAV (defined as weekly ATR(20)/MA(20) in the middle panel and a shorter-term version ATR(5)/MA(50). When these indicators climb above their respective 20EMA—we have to be careful. Note the vertical lines when that lower panel 20EMA slope turns up. It tends to preface equity market trouble. gives you an early warning signal.

Chart 2: I love this S&P 500 vs. VIX model— the black middle series is SPX, the red middle series is VIX. Both the 1/10 price oscillators are respectively above and below. When that lower panel 1/10 VIX goes above that horizontal line ~3 we tend to have an equity pullback in place putting me on buy alert. When it settles, the signal fires (green circles)—this last one set a record for 3 higher highs as volatility surged (last green circle)! It eventually fired an equity signal, but continued to keep me on alert. The VIX can and should do that. Volatile times we've been living in. We're now settling and in fact just went below that lower horizontal line, which tends to represent long-term equity entry points (see the last one in early 2019). I have my lower line at -3.

Julius de Kempenaer

Seriously..? You are asking me what my favorite chart is ;)

No doubt. A Relative Rotation Graph, probably showing US sectors. That's by far the most used universe on an RRG!

We hope that you have been able to glean some helpful information from the responses from these professional fund managers/advisors. A key takeaway from them all is the importance of a plan and then sticking to it.

The Mid-Cycle Slow Down - What Comes Next?

Phillip Anderson — Tue, 24 Mar 2020 00:55:00 GMT

Foreword

{:.smallquote}

I read an opinion piece this week by Phil Anderson from Property Share Market Economics, and it was so good that I asked Phil if we could share it with our readers. I've known Phil for nearly 20 years and watched with absolute fascination as the markets have unfolded exactly as he forecast based on his cycle work. In this post Phil and Akhil Patel share an update of major historical events and why history shows us that we can expect the markets to recover quickly from this. This could be one of the most important posts you will read in this strange period we are in right now so that you are ready to act when the time is right. Note that Phil and Akhil's main focus is the Real Estate cycle which has an up phase, then a mid-cycle slow down, then a final aggressive up phase before a major correction. In this piece Phil makes mention of the mid-cycle and this is what he is referring to. His book explains all of this and the basis of the historical research in much more detail. I hope you enjoy, and find encouragement from this piece. <cite>Mathew Verdouw, CMT, CFTe</cite>

Author: Phillip J Anderson

Dated: 23rd March 2020

Director of https://propertysharemarketeconomics.com/ Author of The Secret Life of Real Estate and Banking

If you think that this Covid-19 virus thing caused the market panic, then you are looking at markets the wrong way.

I'll explain below. I also want to bring you some perspective about current events. If you know your history, it wasn’t that hard to work out the likely scenario for 2020.

Take a deep breath…

Fred Harrison, in his book [Boom Bust](https://shepheard-walwyn.co.uk/product/boom-bust/?mc_cid=64dd590d36&mc_eid=6481b889f9), documented the ebb and flow of UK land prices back to 1600. That’s when, what we’ve just seen, all started.

And I have shown you in my own book, [The Secret Life of Real estate and Banking](https://shepheard-walwyn.co.uk/product/the-secret-life-of-real-estate-banking), that from 1800 in the US, every property cycle has been bigger than the one before it.

Since 1990, and the rise of China and the fall of the Berlin Wall, the cycles have become truly global in nature.

Therefore, the emotional events that are going to drive the cycle also have to be global. Hence the virus. When you understand the cycles, this would not have surprised you.

But let’s put some of that into perspective. Current events are a total emotional over reaction. But it was due. And had to happen.

That’s your key takeaway from this update to you. The events of 2020 were due. They were going to happen.

Remember H1N1? That was the 2009 flu pandemic that lasted January 2009 till August 2010. It came to be known as swine flu.

You remember that, right?

It’s believed that the H1N1 virus first showed itself in humans, in Mexico.

It was in fact the second of two pandemics involving the H1N1 influenza virus. The first one was 90 years prior, the so-called Spanish flu. More on that in a minute.

This 2009 swine flu2009 Swine Fluportedly infected about 20% of the world’s population. That was around 1 billion people infected at the time. The fatality rate was about 3%. source

2009 Swine Flu

Does that jog your memory? Me either.

The U.S. Centers for Disease Control and Prevention (CDC) estimated that about 59 million Americans contracted this H1N1 virus. Of that number, 265,000 were hospitalized (0.4% of the estimated total number of people ill), and 12,000 died. That’s 0.02% of those that were infected.

It seems to me we were likely too worried about the market panic that had already taken place by 2009 to be concerned about H1N1.

Do you actually remember H1N1? Do you remember panicking about it?

The cause of the recent market 'panic' is not the virus. The cause is the prior decade run up in asset markets, the creation of credit, the increase in debt and most importantly, the prior large rise in land values - first half of the 18.6 year cycle only.

It’s also about what people did with the money in the prior run-up. To make this clear what I mean by that, here’s an example for you.

Airline companies are one of the worst affected industries at present, obviously. Bloomberg reported, 17 March, that U.S. airlines spent 96% of free cash flow last decade on buying back their own shares.

Buybacks

Not a single cent was kept on the balance sheet by any airline for that ‘rainy day’ that is sure to arrive. And arrive it did.

American Airlines Group Inc., one of the largest airline owners, actually had negative cumulative free cash flow during the decade while it repurchased more than $12.5 billion of its own shares.

That’s done to make the share price look good, increase payouts and lift management bonuses.

Now they’re all running to government for handouts. And sacking workers.

Why should my taxes go to bail them out?

Because the owners of government simply cannot afford another crisis. Nor can they afford yet another collapse so soon after the last one and a revolt by the working class.

So the government proceeds to bail them all out. More than likely, everything they do will help land values on their merry way.

It’s mid-cycle. And mid cycle events never involve a fall in land value.

If you’re new to markets, if you’re seeing this for the first time, if you don’t know your history, then watch and learn.

And don’t forget to relate apples to apples. Let's look at past mid cycle events.

In 2001 we saw 9/11. Travel lockdown. Your freedom restricted. The mid-cycle slowdown.
In 1981, Paul Volker at the Fed lifted interest rates to unbelievable levels (15%) to kill off inflation. Pundits said the US economy would never recover. The mid-cycle slowdown.
In 1961, a credit squeeze seems to have come out of nowhere. Followed by the Cuban Missile crisis. Everyone said back then the world was going to end. The mid-cycle slowdown.

Note the years. I could cite similar examples back to 1800. It’s detailed in my [book](https://shepheard-walwyn.co.uk/product/the-secret-life-of-real-estate-banking).

Note too: end of cycle events are bigger than mid cycle. And the end-of-cycle events have to start and to involve The United States. Because this is where it all begins.

Remember too, the following.

According to Zero Hedge, almost 60% of Americans have less than $1000 in savings for a rainy day fund or an immediate emergency.

The Fed tells us four in ten Americans can’t cover an unexpected $400 expense (CNN).

Forbes says that 78% of American workers live just from one pay to the next.

And apparently 58% of Americans had less than $1,000 saved, as reported by Yahoo Finance.

So any downturn can easily turn into disaster for a lot of folks.

Buybacks

And what has the Trump administration been doing these last four years? Removing the social safety net, one benefit at a time.

And now bails us all out.

And that’s not to even mention Mr Trump’s massive tax giveaway of 2018. It produced a large uptick in US sales of private jets and Rolls Royce’s, amongst other things, but very little in the way of savings.

One day, we might be all enlightened enough to realize how to tax the Economic Rent properly to give us all a yearly citizens dividend. But that’s another story.

So we see panic. If you’ve followed the US real estate cycle, it’s right on time.

And the real estate cycle repeats. Live, die, repeat.

This brings us to one final thing, just in case you think we can’t end up from here with a massive boom in the second half of the cycle. That’s 2021 to 2026. After this current viral interruption. Here’s some past history that will help you see just how possible –indeed likely– this is. Let’s go back 100 years.

November 1919 saw the official marking of the end of the First World War. It also brought the peak of markets for that decade.

This took place amidst a flu pandemic that affected 500 million people and killed between 40 to 70 million. Much more than 3% of those infected.

This subsequently came to be known as the Spanish flu.

It was termed ‘Spanish’ flu because Spain, having remained neutral during the war, was the only country honestly reporting its effects.

Here’s what the website ‘vaccinestoday.eu’ had to say about it.

“There was nothing particular ‘Spanish’ about the flu: it didn't begin in Spain and, while the country was badly affected, it wasn’t hit any harder than others. (The first wave spread in US military camps in 1917.)

However, Spain remained neutral during the conflict and its papers freely reported the outbreak. Media in France, the United Kingdom, Germany, the United States and elsewhere played down the impact on their own country in a bid to keep up morale.

Newspapers were either directly controlled by national governments or keen to self-censor in the interest of patriotism at a time of war. They all happily reported on events in Spain – leading many to incorrectly presume that the Iberian Peninsula was the epicentre.

In summer of 1918, the virus spread among military units who lived in cramped quarters. And, as the war ended, surviving solders returned home – bringing influenza with them.

After four grueling years of conflict, the immediate post-war period was a time for celebration. Public gatherings presented an ideal opportunity for infectious diseases to find new victims. This most likely prolonged the second wave of the outbreak.

A third wave in the early spring of 1919 took war-weary populations by surprise, claiming millions more lives. Just as with seasonal influenza, the worst hit populations were the very old and the very young.”

1921 – note the year – saw a marked economic downturn. That was both in stock markets and commodity prices.

James Grant, of Grant’s Interest-Rate Observer fame called 1921 a depression. He also labelled it ‘America's last governmentally untreated’ downturn.

Government behaviour – their efforts to prevent markets tanking - is now making the booms ever bigger. So the busts will have to get bigger too.

But back to 1921.

1921

I write this to you because of what happened next…

After a major and significant November 1919 decade market peak,

After a world war that killed millions of men,

After a 1918-1920 flu pandemic that killed even more,

And after what we know as a 1921 mid-cycle slowdown…

… the US then witnessed 1923 to 1929, the greatest bull market of all time.

Rinse, wash, repeat.

There’s no reason why this can’t happen again after the 2021 economic lows.

The next boom will then be deliverable to you.

I hope you found this piece as fascinating, and encouraging as I did. Since 1991, Phil and his team of economic cycle experts have been providing their readers with videos, emails, and newsletters. Always outlining how current events relate to the 18.6 year Real Estate cycle (the most influential economic cycle), and giving their readers clues about what is likely to happen next. Every year they provide a "Flight Plan" of what their readers can expect to come in the following year based on cycles. If this sounds like something you are interested in, you can sign up to their service at https://propertysharemarketeconomics.com/

Recovery Planning

Mathew Verdouw — Thu, 19 Mar 2020 05:32:00 GMT

The last couple of weeks has been tough (unless you’re short S&P futures!). I won’t get into whether this fall is justified or not, and no matter the cause, there are opportunities that only come along once or twice a decade. If you are in the "red", or you’ve been sitting on the sidelines waiting for an opportunity to get back into the market, the time to get back in the market may soon be upon us (some may argue that it’s already here).

Market Is On Sale!

We now have a situation where the market is on sale. It’s like your local department store offering a Black Friday special with 50% off. You need to take advantage of that. You can’t wait for the prices to go back up and then go into the store and ask for last week’s discounted price! Really—don’t do that!

Market Is On Sale

We're Going To Recover

If this is your first crash as an investor, let me assure you this is nothing new. We’re going to recover. In times like this I find it helpful to look at history to give me a guide on what to expect going forward. When we look at previous crashes, we see the market always recovers—yes always! Sometimes reasonably quickly, and sometimes a few years later, but it always recovers and eventually goes on to make new highs.

![We're Going To Recover](/images/recovery-planning-img2.png 'We're Going To Recover'){:class="center"} We're Going To Recover

The only losers in a market crash are those who sell at the bottom and walk away. Unfortunately there will be traders and investors who jumped into the market at the top who will walk away saying “I knew the share market was rigged”. We see it every time there is a crash.

A List Of Previous Crashes

Normally we say we are in a bear market when the market retraces 20% from the highs. For this quick study I have used 30% since it identifies a more aggressive event. In the table below, I list : The date the market crossed the 30% drawdown level. The total drawdown to the lowest closing value. How many weeks from the high to the 30% cross. How many weeks from the 30% cross to the lowest low. How many weeks from the 30% cross until the market got back to the previous highs.

This is all calculated on the Dow Jones Industrials Index since we have the most history for it. The vertical lines are the 30% crosses below — when the DJI broke through the 30% drawdown level. The green line is the 30% drawdown level (a new tool in Optuma from Alan Hull). The chart is on Logarithmic Scale since it allows us to compare the crashes in percentage terms.

Down Jones Industrial Average

Since World War 2 we have had five drawdowns of more than 30%. Five samples is a small number to work from, but we can see every time we have an event like this, the market rebounds. What I am more interested in is which type of crash is this current environment most like?

Of the five crashes shown, only the 1987 event came without a preceding change in trend in the market. Every other crash happened at least a year after the major high in the market while the market was in an obvious bear phase. The quickest crash to hit the 30% level was 1987, and it was also the quickest to recover.

1974 was interesting because it was the result of the first oil crisis. The second oil crisis was in 1979 and while we did not get a 30% drawdown then, it delayed the 1974 recovery. It is also interesting that we are having another fun time with oil 45 years on!

Fastest Crash Ever

Looking at where we are right now, we have just crossed the 30% mark this week. That’s only five weeks from the all-time high, and that makes this the fastest 30% crash in the Dow Jones in the past 120 years. 1929 was the next fastest at nine weeks.

Fastest Crash Ever

Another Depression?

Could this be just like 1929? Could it extend into a major depression? We can never rule it out, but I think it highly unlikely (despite the 90 year cycle from 1929 and 45 year cycle from the oil crisis). Yes this is a major event, and yes it will take years to recover, but with Interest Rates so low economic expansion will be relatively easy and the only place where investors can get yields will be in the stock market once we deal with the fear of the pandemic. Ultimately it could be the hunt for investment yield which will fuel the recovery more than anything else.

With trials already underway on cures and vaccines, this crisis could be over almost as quickly as it started. Since the cause for the panic is the Covid-19 virus, as soon as a solution is available confidence will return. Then there will be a rush on investments which could lead to a massive bull market.

Covid-19 virus

Historical Comparison

A better way to look at all the recoveries from the 5 previous crashes is to chart the relative performance of the DJI from each of the 30% drawdowns. This chart compares them from their 30% cross to just over two years after the cross. The scale at the bottom is the number of weeks since the crash. The y-axis is the percentage return from the cross date.

Two-Year Historical Comparison

You can see that within two years all the previous crashes were followed by some significant gains.

A couple of important take-aways:

The market will recover. The faster it falls, the faster it usually recovers.
The average drop over the past five crashes was 40%. We’re already at 32% (may be more by the time you read this).
You don’t need to rush. Wait for a clear signal that the trend is changing before jumping back in the market. This is what Technical Analysis is for!
Don’t let this opportunity pass you by.

Possible Trend Signals

If I think about the purpose of Technical Analysis I have to go all the way back to Charles Dow in the 1800’s. The reason he created his indexes (or “averages” as he called them) was so he could get a feel for the overall trend of the market. He is one of the first to suggest that investors only trade in the direction of the trend.

Charles Dow

We need to use our analysis to identify when the trend is changing. Indicators we can use range from Trend Lines to Moving Averages to ADX. All can be used as a way of determining when the trend has changed back up.

Whenever I think of trend change signals, I always think of Swings. Gann called these his Trend Detector, and they are great for times like this. Sure you will not get the very bottom of the market, but it’s better to wait for clear signals and avoid the pain of getting in too soon.

Gann’s rules said that the trend changed when a previous swing high or low was taken out (see the colour changes on the chart below).

If you want to know more, we have a two-part webinar on Gann Swings.

2008 - 2 Bar Gann Swing Overlay

1987 - 2 Bar Gann Swing Overlay

No indicator is 100% foolproof. There was a failed change in trend in Jan 2009, but that does not take away from the Swings being a great way of giving us an objective measure of trend direction.

Stay Safe

We don’t know how long this will take to be resolved. Take the time to study history and work out when it is the right time for you to get back in. It’s always a balance of risk and reward, but the numbers help us clear through all the noise and focus on the market. Remember to be skeptical of what you hear in the media, and follow the charts.

Pictured below is a chart in the "Chart Room" at Fidelity Management and Research in Boston. It reminds the staff that media stories always appear the most bearish at the bottom of the market.

“Chart Room” at Fidelity Management and Research

Using Optuma to Test Historical Events

Darren Hawkins — Thu, 27 Feb 2020 10:46:00 GMT

It was quite a week in the markets around the world as fears of the Covid-19 virus rattled markets around the world. Here’s a list of the major markets this week, as of the close on Thursday 27th:

Major Market Indices

In the US the S&P500 index closed down 3% on both Monday and Tuesday of this past week so the question was how many times has this happened in the past, and - perhaps more importantly - what happened next?

Using the Signal Tester (available for Enterprise Services clients, or as an optional add-on module) it was easy to quickly answer this type of question using the daily Change function:

CHANGE(INT_TYPE=Day)<-3 and CHANGE(INT_TYPE=Day)[1]<-3

where the [1] signifies the previous day. Before running the test it’s always a good idea to apply the formula to a Show Bar tool, allowing you to visually confirm that the signals are accurate. When we do that, you can see all the back-to-back -3% days on the chart:

S&P 500 Index

Once verified that the signal is correct, the same formula can be used in a signal test.

Before this week, this particular event has only occurred 9 times since 1950 - with half of those occurring at the end of 2008 (as seen with the darker red vertical line above). This isn’t enough data for a meaningful test, but you can show how each of the 9 events played out over the subsequent year by switching the Main Plot of the signal test display to Components:

Signal Analysis

All signals ended the year in positive territory, ranging from 10% to 46%, but with three signals occurring in October 2008 I’m not sure we can read much in to it.

Let’s look at a similar example, but with one giving perhaps more meaningful results. Instead of back-to-back -3% days, here’s looking at 2 day changes of at least -6% using the 2 day Rate of Change formula ROC(BARS=2)<-6. The results show this has happened only 34 previous times in the last 70 years of the S&P500. The Mean Returns show that the next 100 days after each signal stay fairly flat, and after 116 days the 6% fall has been recovered, and going on to return on average 19% one year after each event.

Rate Of Change Signal Analysis

How does that compare to other markets? This last example is for the ASX200 Index, which also fell 6% this week but over the first three days, not two. The test is the same, but the formula has been tweaked for a 3 day rate of change rather than two (ROC(BARS=3)<-6). Of the 18 previous occasions the index continued to fall, not finding a bottom until 80 days later, and not beginning to really recover until 160 days (about 7 months) later.

Rate Of Change Signal Analysis

However, if you look closely at the blue history slider bar you will see white marks highlighting where each of the signals occurred, and the red arrow is showing a cluster of signals at the end of 2008. If you use the the Show Bar on the XJO chart with the same formula you can see straight away that 10 of the 18 were clustered between September and November 2008:

S&P/ASX 200

For more testing examples see our Twitter feed.

Of course, these tests do not guarantee what’s going to happen, but by creating signal tests combined with Show Bars it helps to quickly visualise what has happened in the past to help in your decision-making process.

Don’t forget our scripting forum if you need help with writing formulas, or if your needs are a bit more complex we would be happy to arrange a consultation.

Who’s most important in the Financial Universe?

Mathew Verdouw — Mon, 09 Dec 2019 00:00:00 GMT

When we first took Optuma into institutions, I confess that I didn’t have a full understanding about the different professional roles that existed. I’ve always known about traders. We’d focused on providing tools to them for years. I thought all professionals were traders, and they just traded bigger amounts.

It was after getting lots of business cards, and having lots of conversations, that I started to see that each role is very different. I needed to understand all the roles so that I knew what we needed to build to empower each individual. As time has gone on, I’ve seen how this relates to Relative Strength studies and what every trader and investor has to know.

This post focuses on the model I observe at the largest firm we work with. A $2.1T (yes, that’s a “T”) company. What do I love about their model? It’s something that even a part-time private trader can use. Trust me, I’ll show you how. But first we need to explain the motley crew.

THE HERO

The Hero

Let’s start right at the centre of the financial universe - the portfolio manager (PM). This is the person who’s taking in all the information and deciding what to add and remove from their portfolio. They’re responsible for those decisions and are answerable to the investors. Those investors are constantly watching their returns and comparing them to what is happening in the benchmarks.

Remember that every portfolio has a prescribed benchmark. There are hundreds of indices that could be used and the most appropriate one is chosen based on the objectives of the portfolio. Obviously the most common are the biggest equity indices. For example, a large cap equity fund in the US will use the S&P 500 as the benchmark. This means the investors are expecting the equity fund to do better than the S&P 500.

THE MINIONS

The Minions

Some PMs will execute their own trades, but most will have traders to complete the execution for them. For these large firms, they can’t just put an order for $1.5B through eTrade. They need to slowly feed the trades in so they don’t tip-off the rest of the market about what they are doing. They often take days to scale into - or out of - a position.

The traders are the ones who take the PM’s orders and are on the phone with brokers making the deals (although most of it is computerised these days). Again, they don’t want to tip-off the brokers, so they will break up orders via multiple brokers. If the broker has a Dark Pool (where they match buyers and sellers off the open market), all the better.

Some traders, depending on experience and how much the PM trusts them, are able to have a certain amount of discretion regarding when trades are executed. For instance, if the trader believes the security will have a short-term pull back, they may delay in buying to take advantage of that.

TO BUY OR SELL?

To Buy or Sell

So far, we’ve focused on the “Buy Side”. A Buy Side firm is one that has created funds (portfolios) and is investing in the market on behalf of investors. Obviously they don’t just buy, they also sell from time to time. The point is that they are holders (buyers) of securities.

The Sell Side is what the institutional brokers are called. So we have traders on both sides. Buy Side traders executing via Sell Side traders.

It is more complicated than I am representing it. On the sell side alone we have Position, Execution and Flow Traders. For now, we’ll just group them all together.

Take no prisoners

Ten years ago, Sell Side trading floors were loud and exciting. Lots of traders trying to match their buyers and sellers of securities before they sent them to the exchange. Today, post Dodd-Frank, it has become a little more subdued (although I still sometimes come across a noisy floor). These guys have the typical “take no prisoners” attitude. They exist to get the deals done.

WHO DOES THE ANALYSIS?

Who Does The Analysis

This is the job of the Analyst. Both Buy Side and Sell Side firms will have analysts on staff, although the balance is different for every firm.

Firms will have a mix of Fundamental Analysts and Technical Analysts. We could include Quantitative Models here too as they are also a form of analysis. On the Buy Side, these analysts are employed to provide their opinions to the PM. Typically they do this through a rating system of Buy, Sell or Hold.

Who Does The Analysis

On the Sell Side, the firms are vying for the order-flow because that’s how they make their money. In fact, many firms will offer a Buy Side trader a better fill than market price if…

they believe they can make more money from the commissions
they are holding the other side of the trade and want to clear it
they want to get a favour from the PM to secure future order-flow.

One of the ways that Sell Side firms garner favour with the Buy Side is to employ their own analysts. These do a similar job as Buy Side analysts, except they communicate their recommendations through reports and chart books. You’ll see them coming down the corridor - they are the ones running around with their arms full of charts and reports.

WHO IS MOST IMPORTANT THEN?

It’s the portfolio manager. If the PMs would stop buying and selling, the rest of the financial universe would wither away. The traders would have no trades to execute, the analysts would have no analysis to sell, and there would be no market for the rest of us to take advantage of.

Who Is Most Important Then?

The whole industry is set up to provide services to the PM and help the PM reach their goals. How does a PM measures success? By outperforming their benchmark on a relative basis. Put another way, a PM wants to load up their portfolio with securities that are outperforming the benchmark so that their portfolio will outperform.

Nearly all the professional analysts whom I have worked with, on the Buy Side and the Sell Side, have a heavy focus on Relative Strength (RS) analysis. Analysts know that RS is what the PM cares about most - so that is what they study. One of the most popular tools we have is the RIC (Relative Index Comparison) because an analyst can switch codes and the tool will use the right benchmark.

Of course there are a lot of other roles and other markets. This was a nice simple way that I used to understand how the biggest funds worked, and what was important to each role.

THAT’S NICE. SO WHAT?

What do you think happens when an industry, that is on the hunt for returns, sees a stock outperforming the S&P 500? The analysts spot it and are telling all the PMs about it. The PMs, who want Alpha, decide to add it to their portfolios and the traders start buying. We all know what new buyers mean. The price is going to go up. I would say that RS is one of the biggest drivers of absolute returns. You just need to be able to get in early.

Even if you are trading your own account, you are doing yourself a disservice if you are not considering Relative Strength as part of your analysis. At Optuma, we’ve made that simple by including Relative Rotation Graphs® in every copy of Optuma. You can see a detailed webinar on RRGs here: https://www.youtube.com/watch?v=FtCsPIyM_OI

TIME FOR MORE HATS!

Time For More Hats

Once I started piecing all of this together, one of the first things that struck me was the advantage that each professional had compared to a small RIA or private trader. The institutional guys get to focus on just one job without the biases of the other functions.

When you are running a smaller RIA or a private account, you are doing all the jobs at once. You are analysing the charts and listening to news. You are thinking about new opportunities within the context of how they fit your portfolio. And when you are ready to make a change to the portfolio, you are the one to try and get the best price and execute.

I know from my own experience that it’s very difficult to be objective when analysing a security that I already have a position in. I’m looking for analysis that’s confirming the position that I have. It’s so important to be able to analyse objectively.

I remember hearing that Ken Gerber, from Lambert Gann, would tell students to have a cap that on one side said “Trader” and on the other “Analyst”, and a mirror on their desk. When they were analysing charts, they would turn the cap so “Analyst” was at the front. When trading, they’d turn it the other way. The aim was to help them remember what they were doing to keep their focus. I would suggest a third setting saying “PM”. Perhaps having three hats would be helpful.

If you can do the following:

analyse with no regard to current positions
treat the analysis as one of many inputs into your portfolio management
execute based on the needs of the portfolio and not because of your analysis

…then you will have achieved a strong disciplined approach to your own portfolio management. Also remember that Relative Strength cannot be ignored. It’s such a huge driver of the market.

Optuma Swing Charts and Scripting

Mathew Verdouw — Fri, 01 Nov 2019 00:52:00 GMT

We often get questions about swing chart scripting and we find that there is a lot of confusion about them. This problem really shows up when mixing swing conditions with bar-by-bar conditions. The market can be in an up swing for days and a swing chart (like a Point & Figure chart) will only post a new entry when the swing changes. But what happens if you are using an RSI value (which posts every day) and a Swing?

The issues comes down to the way the data is handled and if you understand this you will be able to avoid a lot of confusing results. Before we start, if you need a primer on Swing Charts, have a look at this Knowledge Base post and video.

OK, grab a coffee – or stimulant of choice – as this is going to go deep!

On the bar chart we see zones where the swing script is true, but that is often a drawing item that has been joined so it looks nice. The scripting engine is not seeing a value for each date in the zone - just a data point for each swing high and low.

Internally, Optuma sees daily data like this:

Bar List

When you look at the dates you can see that the data is sequential, that is there's an entry for every day – it's easy to work with.

Unfortunately with swings it not quite that simple as there is not a value for each day. The 'swing list' looks like this:

Swing List

When you look at the dates above you will see that there are gaps between the start and end date. You will also see that it is possible to get multiple swings on the one date (usually outside bars). It is these gaps that are a nightmare to work with (not to mention the multiple swings on one day).

On a chart the drawing engine handles this swing data and stretches everything so we get nice lines joining the swing highs and lows, but in the scripting engine we don’t have the same luxury. This is because there may be a reason that we want to work with 'swing lists' instead of 'bar lists'. We need to have the flexibility to be able to do both.

In scripting, when I do SwingEnd(GS1) - where GS1 is my swing variable - my result is a swing list with all the date gaps - as we saw in the table above. But when I do GS1.SwingEnd I get a bar list as that function returns the position of the swing for each day. It calculates where the swing would have been on that day, i.e. on the third day of a 10 day up swing, it will calculate where the swing high was up to on that day, and not the final swing end value.

In system testing I have to be careful which version I am using. The worst example was when I was testing Gann Swing Breadth. In that method I was counting the number of S&P 500 stocks which were in an up swing. The results were amazing! So much that I was sure that it was time to retire and live on an island somewhere. The issue I had was that the historical swings already had their ultimate end price calculated. That was when we created the GS1.SwingEnd function so that in history we would know the price the swing high was on that historical date.

So to clarify, for the purpose of testing Gann swing patterns you would need to use SwingStart() / SwingEnd() functions for searching for patterns. If you are mixing swings with other measures, it is better to use the dot notation.

Now to the script!

The GANNSWING() function can be used to define the swing variables, which can then be used with other functions, such as SWINGSTART, SWINGEND and SWINGUP. One very useful way to see what is being calculated is by using a Show Plot tool. The following chart shows the swing start and end values for each day, using the following script:

//Define the swing;
GS1 = GANNSWING(SWINGCOUNT=2, USEINSIDE=True, METHOD=Use Outside Bar, USECLUSTERS=False);
Plot1 = GS1.SwingStart;
Plot1.Colour = Red;
Plot2 = GS1.SwingEnd;
Plot2.Colour = Green;

SwingStart & SwingEnd

You can see how the green line “grows” with the swing end, but the red swing start stays flat as that value never changes.

Converting a Swing List to a Bar List

The simple hack to convert a swing list to a bar list is to add a bar list condition to the script. In the example below we're comparing the relationship of recent swing start values. As soon as Optuma sees the c6 condition (Close() > 0), the script engine will convert the script list to a bar list. It won't look different on the chart but will in the scanning and testing engine.

//Define the GannSwing variable;
GS1 = GANNSWING(SWINGCOUNT=2, USEINSIDE=True, METHOD=Use Outside Bar, USECLUSTERS=False);
//Has the current swing up been confirmed?;
c1 = SWINGUP(GS1);
//Compare SwingStart values for previous swings;
c2 = SWINGSTART(GS1,1) < SWINGSTART(GS1,3);
c3 = SWINGSTART(GS1) > SWINGSTART(GS1,2);
c4 = SWINGSTART(GS1,3) < SWINGSTART(GS1,5);
c5 = SWINGSTART(GS1,2) < SWINGSTART(GS1,4);
//Convert SwingList to BarList;
c6 = Close() > 0;
//Signal when all conditions are true;
c1 and c2 and c3 and c4 and c5 and c6

NOTE: Regarding offsets, SWINGSTART(GS1,1) is the same as SWINGSTART(GS1)[1]: both will calculate the previous swing’s starting value. Also, the current swing direction is very important! If the swing is currently up then the 1, 3, and 5 offsets are the 3 previous swing highs, and 2, 4, and 6 are the previous swing lows. If the current swing is down then it's the opposite: the odd offset numbers are swing lows and evens are swing highs. I told you it was going to get deep!

Let's explain that script in a bit more detail. Here's an example of the swing values calculated in the watchlist columns, which match the labels on the swing chart overlay. The script is looking for confirmed swing up (variable c1), with a higher bottom (c3 and c5) after two lower tops (c2 and c4). When all those conditions are true then a true signal will be triggered.

SwingStart & SwingEnd

In Summary:

SwingStart(GS1) and SwingEnd(GS1) return SwingLists.
GS1.SwingStart and GS1.SwingEnd return BarLists (or a value for each day)

Both have their uses. For example, GS1.SwingEnd[1] will not give me the end of the last swing, but SwingEnd(GS1)[1] will. Instead, GS1.SwingEnd[1] will give me where the swing end was yesterday.

I do feel that it would have made more sense if the two operations were flipped, but with so many people using scripts we can not make that change without blowing up a lot of people’s work. So we are stuck with it working this way.

Click the button to download a workbook with the scripts used above, and next time will look at other swing script examples, including swing trends.

Identifying Divergences

Darren Hawkins — Thu, 11 Jul 2019 00:28:00 GMT

Optuma’s scripting language allows you to create any number of scans and tests on any number of tools and values. But when we were asked if we could create a scan to identify divergences between price and a momentum indicator, such as the RSI, I wasn’t sure how this could be done. After a bit of noodling with charts and working out the logic (ok, and a bit of help from my smart colleagues in Australian HQ) we came up with a way to mathematically determine when a divergence occurs.

What’s the deal with divergences?

Let's back up a bit: why are we interested in divergences? We’ve looked at them before in terms of an index and market breadth (see Mathew’s article here) and it’s the same situation we are looking for: when the price movement is not confirmed by a momentum indicator - such as the RSI. When this happens could be an indication that a change in trend is imminent. A bullish (or positive) divergence occurs when prices are in a downtrend and making lower lows, but the RSI indicator is making higher lows. Conversely, a bearish/negative divergence is when higher price highs occur with lower RSI highs, indicating momentum may be fading and prices may fall.

Positive and Negative Divergence

We could physically scroll through hundreds of chart and try to identify when this happens, but we thought it would be easier to see if we could create a scripting formula to tell us when these events occur.

So how does it work?

By using Pivot Lables and the Pivot() function we can highlight any high or low of a price chart or indicator, with the higher the pivot value the more significant the pivot is. So in the example of a negative divergence we identify the last two highs of the RSI and take the corresponding price value when they occurred. If prices are making new highs but the RSI is making lower highs then negative divergence has been identified.

In this example, the Show Bar lines identify RSI pivots set to 15 bars, ie the high/low must be the most extreme value for 15 bars before and after:

RSI Pivots

Once we’ve identified when the RSI pivots, we take the corresponding price value to work out if the divergence has occurred. Here’s the complete script for a negative divergence, with a condition that the RSI peak must 4% lower than the previous peak, and the price must be at least 4% higher (this is an arbitrary value - you can adjust this tolerance and the pivot values as required):

// Negative RSI divergence
// Get the RSI value
RSI1 = RSI(BARS=14);
// Calculate 15 pivot RSI high
P1 = PIVOT(RSI1, MIN=15, TYPE=High);
// Get value of the RSI peak
V1 = VALUEWHEN(RSI1, P1 <> 0);
// Is RSI high 4% lower than previous?
Sig1 = V1 < V1[1]*0.96;
// get stock high value at RSI peak
V2 = VALUEWHEN(HIGH(), P1 <> 0);
// Is stock high 4% higher than at previous RSI peak?
Sig2 = V2 > V2[1]*1.04;
// Show when RSI has lower high & price higher low
Sig1 and Sig2

Let me explain what each of these lines are doing (remember lines beginning // are comments and are ignored by the formula):

Line 4: variable RSI1 calculates the 14-period RSI for each day Line 6: variable P1 finds all the 15 day pivot highs for the RSI Line 8: variable V1 gets the RSI value when each pivot occurs (ie when the pivot value is not equal to 0) Line 10: compare the RSI peak value with the previous peak, and if it's more than 4% lower (V1[1]*0.96) then variable Sig1 is true. (To change the tolerance to eg 5% it would be 0.95.) Line 12: variable V2 gets the high price of the stock on each day of the RSI peak Line 14: compare the price high with the high at the previous RSI peak, and if it's more than 4% (V2[1]*1.04) higher then variable Sig2 is true. (To change the tolerance to eg 5% it would be 1.05.) Line 16: a divergence signal is triggered when both the RSI (Sig1) and price (Sig2) conditions are true on the same day

The concept is identical for positive divergences, but with pivot and price lows:

// Positive RSI divergence
// Get the RSI value
RSI1 = RSI(BARS=14);
// Calculate 15 pivot RSI low
P1 = PIVOT(RSI1, MIN=15, TYPE=Low);
// Get value of the RSI low
V1 = VALUEWHEN(RSI1, P1 <> 0);
// Is RSI high 4% higher than previous?
Sig1 = V1 > V1[1]*1.04;
// Get stock low value at RSI low
V2 = VALUEWHEN(LOW(), P1 <> 0);
// Is stock high 4% lower than at previous RSI low?
Sig2 = V2 < V2[1]*0.96;
// Show when RSI has higher low & price lower high
Sig1 and Sig2

Of course, divergences don’t just work with RSI. You could try it with other momentum indicators, such as On Balance Volume, or Money Flow Index : simply replace the RSI function in Line 4 with OBV(), for example.

Once applied to a Show Bar or scan then the divergences can be identified. Click the buttons below to save a workbook for ASX and US data showing the divergences on the charts:

RSI Pivots

Being overbought does not mean sell!

Darren Hawkins — Thu, 07 Mar 2019 17:32:00 GMT

As the S&P500 index recovered from its Christmas 2018 lows I began to see comments about the index being overbought, as measured by the Relative Strength Index oscillator, or RSI (not to be confused with relative strength against an index). Typically, an RSI measurement over 70 is deemed ‘overbought’, and below 30 is ‘oversold’ but because it’s a measure of momentum being overbought should not trigger a sell signal (and likewise oversold should not trigger a buy).

The S&P/ASX 200 index ($XJO) in Australia experienced something similar last month. The 14-day RSI crossed above 70 on February 5th following when it broke above resistance at 5900. But since then the momentum has taken it a further 4% - and three more crosses of RSI above 70:

A&P/ASX 200 Index

So if you had sold on that first overbought signal you would have missed out on that subsequent rally!

Taking it to the stock level

Is there an easy way to identify those stocks whose RSI(14) has crossed above or below a certain level multiple times to identify strong positive or negative momentum?

Of course! The Optuma Scripting Language can be used to calculate custom tools and timecounts, and highlight bars when certain conditions occur.

Below is a chart of MSFT with the RSI(14) overbought level set to 66 and the oversold to 33. Why these levels rather than the standard 70 and 30? No particular reason, but I wanted to see if it worked better than the default settings in showing the strength and weakness earlier, but there’s no reason you can’t keep it at 70 and 30, or try 60 and 40, or 70 and 50.

The red Show Bar arrows indicate where the RSI(14) has crossed above 66 (overbought), and the green ones when it crossed below 33 (oversold):

MSFT with the RSI(14)

Since July 2016 when MSFT’s RSI crossed above 66, it crossed above another 23 times until it finally reached oversold levels in October 2018 - 613 trading days since it was previously oversold. These values are calculated in the panels below the price chart using the following formulas in Show View panels:

Days Since Oversold

V1 = RSI(BARS=14) < 33;
TIMESINCESIGNAL(V1)

Number of times Overbought since last Oversold

//Create 2 Boolean conditions
V1 = RSI(BARS=14) CrossesAbove 66;  
V2 = RSI(BARS=14) CrossesBelow 33;  
//Count the number of times V1 occurred since V2
COUNTMATCHSINCESIGNAL(V1,V2)

The COUNTMATCHSINCESIGNAL() function requires two true/false Boolean scripts, and counts the number of times V1 has occurred since V2

Displaying the statistics in a watchlist

The above is a useful look at individual charts, but we can now use the formulas in a sortable watchlist to filter a universe and quickly find opportunities or see where a particular stock sits compared to others.

Here’s a watchlist of the S&P500 companies (with the same RSI(14) and 33/66 levels as the MSFT chart above) sorted by the number of overbought conditions since the last time it was oversold (the #O/B Since O/S column).

As you can see ORLY is ranked top with 15 occasions in the last 407 trading days since it was last oversold.

Watch List RSI Layout

The first column O/B Since O/S gives a true/false result depending on if the RSI has been in overbought territory more recently than it was oversold. This uses the SWITCH() function as follows:

SWITCH(RSI(BARS=14)>66, RSI(BARS=14)<33)

The last two columns shows the current RSI value and whether it is above the 45-day moving average. So whilst ORLY is top of the list its RSI is now in the 50s and below its long-term moving average, so you may want to keep an eye on the chart to see if this short-term weakness turns into something bigger.

ORLY Overbought Oversold Chart

Negative Momentum

To look at those stocks with negative momentum click on the column you wish to sort by, so in this example COF hasn’t been overbought for 280 days, during which time it has crossed into oversold territory 7 times.

Watch List RSI Layout

Copying the Watchlist Data to Excel

If you wish to carry out further analysis in Excel then simply right-click on the watchlist and select Copy CSV Values to Clipboard from Actions, and then paste in to Excel (note that you may need to click on the Text to Columns option under the Data menu in Excel to format the data).

Also, Enterprise Services clients can send a watchlist dynamically to Excel under the Actions > Send To menu - this is particularly useful when linked to a realtime data provider as the values in Excel will automatically update when the watchlist updates in Optuma.

Example Workbooks

Optuma clients with access to our ASX or US end-of-day data can download an example of the above watchlist and charts. Click the buttons below to save an open the workbook and experiment with the formulas as you wish. Don’t forget to post any questions on the Scripting Forum, or contact us to arrange a consulting session.

How to Set Up and Test a Binary Trade

Darren Hawkins — Tue, 05 Feb 2019 23:00:00 GMT

A binary arbitrage strategy is one where you buy A when condition X is true, and then sell A and buy B when condition X is no longer true.

Let’s take stocks vs bonds, where you want to buy stocks when they are relatively stronger than bonds, and buy bonds when they are relatively stronger than stocks.

This is possible to test in the Optuma back tester, but first we need to determine when one asset is stronger than the other by creating a relative strength ratio chart by creating a Custom Code{:target="_blank"}. Here’s an example where we create a ratio chart of the S&P 500 Index ETF ($SPY) divided by the iShares 20+ Year Treasury Bond ETF ($TLT), and also its 50-period moving average (in blue):

Stocks Relative to Bonds

When the black line is above the moving average then stocks are stronger than bonds, and weaker when below the moving average, so in our backtest we would want to buy SPY when the ratio crosses above the moving average, and then sell SPY and buy TLT when the ratio crosses below, because bonds are now relatively stronger. This is getting there but there is still a bit of whipsaw going on. To smooth the ratio and to prevent choppiness we can use a shorter-term moving average crossover to generate the signal.

Below shows the entry and exit signals when a 10-period moving average in orange crosses the 50-period:

Entries and Exits

So how do we test this in the Backtester? The first thing we need to do is to create a manual Symbol List{:target="_blank"} containing the two ticker symbols used in the test – in this example SPY and TLT, in a list which I’ve called SPY/TLT:

Symbol List

In the backtest setup we select the SPY/TLT symbol list under Back Test on Multiple Codes with the following entry formula. In this formula I am using the ISTICKER() function to select the appropriate symbol for the crossover.

Remember from a logic perspective that we want to buy SPY when the ratio crosses above the average. That condition will be true for both symbols, but the IsTicker() condition means that only the symbol which matches will be the one that is bought:

// Calculate the SPY/TLT ratio
S1 = GETDATA(CODE=SPY:US);
T1 = GETDATA(CODE=TLT:US);
Ratio = S1/T1;
 
//Calculate the moving averages of the ratio
MA10 = MA(Ratio, BARS=10, CALC=Close);
MA50 = MA(Ratio, BARS=50, CALC=Close);
 
//When 10MA crosses above 50MA buy SPY, or buy TLT if crosses below
MA10 CrossesAbove MA50 and ISTICKER(CODE=SPY:US) or
MA10 CrossesBelow MA50 and ISTICKER(CODE=TLT:US)

For the Exit criteria we use the opposite conditions, i.e. sell SPY when it crosses below the average, and sell TLT when it crosses above. Copy the entry script formula and then change the last lines:

//When 10MA crosses below 50MA sell SPY, or sell TLT if crosses above
MA10 CrossesBelow MA50 and ISTICKER(CODE=SPY:US) or
MA10 CrossesAbove MA50 and ISTICKER(CODE=TLT:US)

Because the strategy is binary (i.e. all your capital is either in stocks or bonds) the Equity Percentage should be set to 100%, before executing the test over the timeframe required.

Here’s the results over the last 5 years, starting with the SPY entry on March 6th 2014, showing the 37 trades with annualised returns of 6.8%, and a maximum drawdown of 14%:

Results

But how does that compare with buying and holding stocks over that time? We can add that as a comparison code, and you can see that SPY would have out-performed the strategy over that timeframe (the red line in the equity curve window), with annualised returns of 10.2%, but with increased volatility, and a higher drawdown of 20.5%:

Buy & Hold

So maybe the signals can be tweaked, eg when the moving averages of the ratio chart are sloping up for the SPY entries, or when SPY is trading below its 50-period MA for the SPY exits. The point of this post is not to give you a trading strategy but to show you how you can set up binary strategies like this for yourself.

Another example is to buy Growth stocks when relatively stronger than Value stocks, and sell Growth and buy Value when vice versa. Gold is strongly negatively correlated to the US Dollar Index (DXY) so that could be another example using the ratio chart set up.

Now we have the setup in the Back Tester it’s just a matter of creating the symbol list for the required symbols, and tweaking the entry and exit formulas.

As always, we love to hear feedback so if you have any questions please comment below or contact us via support, and for scripting formula queries don’t forget to search the ‌‌**Scripting Forum**{:target="_blank"} if you get stuck, and post a query if you can’t find the answer.

Optuma Testing Errors

Mathew Verdouw — Thu, 15 Nov 2018 01:00:00 GMT

Quantitative Testing is one of the most important things we can do as Technical Analysts. It allows us to have a degree of confidence about the strategies we put into place.

As a company, building quality testing tools is one of our highest priorities because we see that it causes many people so many problems. Did you know starting with a Back Test is one of the worst things you can do? I didn’t for many years. I had no idea how I was sabotaging my ideas through flawed testing.

Because this is an important issue, we are putting together a course on all of the testing tools in Optuma and how they can be used. I will be teaching on the theory of the testing methods and explaining how you can implement them in Optuma. More on that in a moment.

The other thing that I am doing is thoroughly checking everything. Optuma has been developed mainly by our clients telling us what they want to see. In the early days, our partners would tell us the formulas to use, and we would take their word for it. Unfortunately, some of what we were told was not correct. Because of that, and because I am going to teach on this, I want to be 100% sure of all the calculations that are in the testers.

There are a couple of items which I have already found that Optuma users need to be aware of right now (all of these items will be fixed in the next major update - Optuma 1.4):

Annualized Returns in Back Tester and Signal Tester were using a wrong calculation. Monthly returns were multiplied by 12 rather than being compounded.
Monte-Carlo analysis in the Back Tester was diminishing the results of the sample distribution which gave us erroneous p-values. It made poor strategies look more significant than they were. In fixing this I also took the opportunity to make it even faster!
Back Tester only starts reporting results from the first trade taken rather than the beginning of the test period (I need to know if my strategy takes two years to make a trade!). This is not an error, but it does make it hard to compare strategies.
Short strategies in Back Tester work, but using margin on them produces erroneous results. Do not rely on these. As of writing I still have not addressed this.
Back Tester will not take a Short position if a Long position is open.
Trade Tester - a new version of Signal Testing that we introduced in Optuma 1.3 is inflating results making strategies look sensational. That is because the results at the end of the test period are only considering the trades that had not closed out yet. Those are the handful of trades that obviously were doing great because they had not triggered an exit.

The first image shows the inflated results. That would be nice, but as they say, “if it’s too good to be true…….”

Optuma Trade Tester

The solution was to keep all closed trades in the calculations of the averages. You can see how this is more realistic here.

Optuma Trade Tester

These are the issues that I have found (with some help through great questions from others using these tools). This is a major priority for me and we are getting all of these items fixed asap (in fact most of them are already done and are just waiting to be included in an update).

Testing Course

As I mentioned earlier, I am working on a significant course on testing that will cover all the different ways that we can test, and the theories behind them. As an introduction to testing, I have recorded a presentation that I have done at a number of Technical Analysis societies around the world. The overview session - available now - will give you an idea of how the material is presented. You can read more about that by clicking on the image below. If you are interested in this video course, it is on sale right now while we are in the building phase.

Advanced Quantitative Testing Course

New highs are nothing to be scared of

Darren Hawkins — Thu, 20 Sep 2018 05:37:00 GMT

The S&P500 index ($SPX) in the US set a new all-time closing high today - the first since the end of August. I remember reading comments in the financial (and social) media since those last highs that the market is overbought and due for a pullback, especially as we’re now in the longest bull run in history - although that is hotly debated, and possibly the subject of another post.

Following the new high on August 29th investors did seem nervous. In fact the index proceeded to register lower highs and lows for six consecutive days (the first time since May 2012) - but it’s important to remember that it stayed within 2% of that high of the 29th before recovering. So are those commentators correct? Is there any justification that they should feel that a downturn is imminent just because a new high is recorded - especially given that an even higher high is now in place?

Let’s take a look at a long-term chart of the S&P500 index and highlight every new closing high. The easiest way to see them is by using the Show Bar tool set to display as a line. This tool highlights any true/false event on the chart, in this case using the HIGHESTHIGH() function to show when the close is higher than any previous close in the last 50 years:

CLOSE() >= HIGHESTHIGH(CLOSE(), BACKTYPE=Years, BARS=50)

Custom Show Bar Tool

As you can see, the green vertical lines are usually grouped together indicating that new highs are often followed by more new highs, ie once the market has momentum it tends to keep going. However, occasionally they have occurred just before a correction, such as in 1968, 1972, 1987 and more recently in 2007.

This is all interesting from an academic point of view, but how do we know what to do when we are getting a new high and the media starts calling for a pullback? This is where we have to cut through all the noise and look at the statistics to see what has happened previously. We can do that with Optuma’s Signal Tester.

By using the same script formula as the Show Bar above, the Signal Tester takes an entry every time there is a new all-time high (so at every vertical green line in the image above) and calculates the average return after every signal, plotting the returns over time. Today’s high was the 1,274th new all-time high using data since 1950 (ie not including the historical hypothetical data calculated before the index existed). The Signal Test results tell us that the mean return of the previous 1,273 new highs was 4.9% six months - approximately 132 trading days - after each signal, with a probability of gain of 77%. In other words, you would expect three wins for every loss if you took an entry on every new high and would gain, on average, almost 5% six months later.

Signal Analysis

If you look at the left of the chart above the -22 represents 22 days before each signal (about a month of trading). The steep incline leading up to Day 0 (ie each entry) illustrates the strong momentum going in to each new high.

Of course, this doesn’t mean that the index will be 5% higher in 6 months’ time, but history tells us that new highs are certainly nothing to be scared of.

These Are a Few of My Favourite Things (or Charts)

David Cox — Fri, 24 Aug 2018 01:03:00 GMT

As an avid chart watcher I thought I'd share some charts with you to add some flavour to all that goes on in the world of investing. Your feedback is always welcome, so please leave a comment below!

Exhibit A: Nasdaq, Nasdaq on the Wall

The Nasdaq Composite Index ($COMPX) has been strong. That might be an understatement. That said, this summer, it has slipped a little relatively, as small-caps and some of the defensive and more diverse segments of the U.S. market rally. What do we have below? The Nasdaq is in green and you can see that it last made highs in July. In the lower panel, we have the percentage of those Nasdaq stocks that have a rising 50-day moving average (calculated in Optuma's Market Breadth module with the formula MA(BARS=50, CALC=Close) IsUp). We’d like to see this break back upwards (green circle), which would mean more of the stocks are rallying, which in turn would (and likely will) lead to new highs here in the near-term.

Lesson: Market breadth is so crucial to track, follow and understand. It gives clues to investors.

Nasdaq on the Wall

Exhibit B: 65-Week Moving Average

Not that we need to beat a dead horse again and bring up commodities, but really, how can I not bring them up! With so much economic growth firing out of the U.S., it sure would be nice to see commodity prices moving higher. But the reality is, the Commodity Research Bureau (CRB) index is now trading below the falling 65-week moving average, which I call bearish. I don’t try to read more into than that.

Lesson: You can’t make short-term decisions in the investment markets without having a bigger-picture thesis. This is the big picture.

65-Week Moving Average

Exhibit C: Small Caps

It’s surprising how many headlines and articles that I have stumbled across talking about this U.S. stock market rising on the strength of a few stocks. I just don’t get it, and frankly, I don’t see it. The Russell 2000 index just made new all-time highs yesterday and this is an index of 2,000 companies, most of them smaller. This is not bearish.

Lesson: When small-caps are performing well, the market is taking on risk. Small-caps are performing well, so clearly, the market is still keen on taking risks. Ignore the headlines to the contrary please.

Small Caps

Exhibit D: Consumer Staples Resurgence?

The consumer staples were crushed, both absolutely and relatively in early 2018 and have staged some relative bounce this summer. This a custom symbol showing the relative line of the SPDR Consumer Staples Sector ETF ($XLP) vs. the S&P 500 index, and the rising green line is the 50-day moving average. That said, it’s only just turned up and frankly I don’t see this as something that is anything other than some temporary action as we sift through the summer and the market tries to digest tariff and Trump risk.

Lesson: Always know if the sector you’re buying or selling is a strong or weak one. Relative strength is a very important tool in analyzing stock markets.

Consumer Staples Resurgence

Exhibit E: Utilities in North America

In follow up to Exhibit D, above, the utilities have had a move higher as interest rates have settled down to the lower end of their higher range. That has caused a move higher in utility stocks, but surprisingly, only on the U.S. side of the border. The U.S. utility stocks are shown in green ($XLP) and have rallied nicely since the February 9, 2018 market low (about +10%) while the TSX utility stocks ($ZUT.C) are -1.3%. That is quite the difference!

Lesson: Utility stocks are interest rate sensitive and further increases in interest rates tend to cause prices to depreciate.

Utilities in North America

I hope you enjoyed a few of my favourite things this month. If you would like to be added to my mailing list, please send me an email: david.cox@cibc.ca.

Note: the views of David Cox do not necessarily reflect those of CIBC.

I'm a Survivor

Mathew Verdouw — Thu, 19 Jul 2018 00:46:00 GMT

For the last couple of years I’ve routinely stated how important dealing with survivorship bias is in testing. I hope you agree with me that it’s a critical issue that makes it nearly impossible to run a historical test and then being able to repeat those results in the future. The only problem has been that it has historically been really hard to run tests without survivorship bias. Well, we now have the ability to do this!

First - what is Survivorship Bias?

Survivorship bias or survival bias is the logical error of concentrating on the people or things that made it past some selection process—and overlooking those that did not—typically because of their lack of visibility. This can lead to false conclusions in several different ways.

Wikipedia Definition

Target Zones

The initial outworking of this was in World War II when the Engineers were looking at bullet-riddled planes which had returned from missions. They focused on strengthening the armour in the locations that had the most bullet holes. But, these planes were the ones that made it back, so obviously those areas were not critical to the survival of the plane. Instead, the armour should have been added to places where none of the survivors had been hit in the hope that it meant that more planes would survive the mission.

In our application, when we run a back test on equities, we often say that we want to focus our test on the members of a popular index like the S&P500, FTSE100 or ASX200. We collect together the securities that make up that index and do our test on those over the last ten years. The trouble is that members of the S&P500 today are not the same as the members ten years ago. Lehman Bros ring a bell? How can we run a test without including “LEH” in the list?

Similarly, there are a number of current names in the S&P that were not included ten years ago. E.g. Netflix was added to the S&P500 in 2010, so we should not be considering any signals in Netflix before 2010 when it was not part of the index.

Grading Bias

Both these conditions—ignoring companies that are no longer in the index and including those that had not yet “made it”—leads to survivorship bias which skews our tests positively.

To learn how to create tests using the bias-free data see this KnowledgeBase article.

Now for the results

Ok, so now that that is out of the way, let’s have a look at this in real life. Following is a simple test of a 50-period moving average crossing above a 200-period moving average run over the last ten years. Here is the script:

MA(BARS=50) CrossesAbove MA(BARS=200)

50-period moving average crossing above a 200-period moving average

Remember in this chart the Blue shaded plot is our equity from the test. Obviously not a lot of alpha, but it shows a moderate return over the index (red line). The issue is that we have only used the current 504 stocks in our test. We need to set this up to include all the companies that were ever in the index. Not only that, but we also need to adjust our script so we tell Optuma to only take a signal if the company was in the index when we got our signal.

Historical Comparison Chart

In the properties of the test I need to change the new “Membership” property from “Current” to “Historical”. Note that this will only show for Optuma Symbol Lists where we have set up the survivorship data. At this stage, all we are doing is including all the 700(ish) companies that were in the S&P 500 over the last ten years.

To make sure that we only take a signal when the company was actually in the Index, I need to update my script to add the new “IsMember()” function. Note that we don’t change the exit script since we want to exit regardless of membership in the index (although you could exit on removal from the index if you wanted to).

Here is that script:

MA(BARS=50) CrossesAbove MA(BARS=200) AND IsMember()

Using IsMember Function

Suddenly this does not look so good anymore. Our idea did not “beat” the market. Anyone who has ever tried trading a MA crossover like this knows that it’s a great strategy in theory, but the results are really hard to replicate. Finally, the tests are telling us what we already know.

The positive side of this is for those who are working on Short or Long/Short strategies. Removing survivorship bias gives a “lift” to Short results.

The main point of this is to highlight to you how important survivorship bias is, and to ensure that you don’t ignore it in testing. If you have ever been frustrated by your inability to repeat test results in real-life, then this will help you see why that has happened.

A simple rule of thumb, for when you don’t have access to correct survivorship bias-free data, is to subtract around 3% per annum from your results. That will give you a better idea of what you can expect. Just don’t plan your trading strategy by only looking at the survivors. Make sure you properly consider the securities that didn’t make it.

Considering what didn't make it

Note: at present this will only work for the US S&P500 index back to 2000, and the Australian size indices (eg ASX200, ASX300) back to 2012 - it's not easy to find historical changes to these indices, so please contact us if you can help!

Correlation Cycles

Mathew Verdouw — Fri, 11 May 2018 04:26:00 GMT

In the CMT courses we teach on the correlation between different securities. The reason is that when we are constructing a portfolio, we don’t want to invest in too many securities that have high correlation —unless they all go straight up of course—then we’ll take ‘em all! The reality is we cannot know that in advance and the prudent thing to do is to have diversification in our portfolio to minimize risk. The way we do this is to look for uncorrelated securities. In plain english, we look for securities that will not all fall and rise together.

This is a really clever way to manage risk, but there are some dangers with correlations that I want to explain. One of these I was not even aware of until I started writing this post.

Let’s first take a step back and explain correlation. If you’re a stats geek, you can skip this part.

Here’s the interpretation as plainly as I can state it. "Correlation is a value that describes the linear relationship between two variables". Now let’s break that down.

"Variables" are our datasets. In statistics they can be anything, but in our case they are the two securities we want to explore to see if there is a relationship in how they move. We line up the two datasets and measure the percentage gains for each over a rolling period.
"Linear Relationship" is how we would describe the line if we plotted all the data on a chart. Imagine that for every week we measured the percentage change for the two securities. We then plot that point on a XY plot. If we do this with enough data, we can see if there is a relationship. We can show this in Optuma using a Regression Chart.

We can see in the first image below that SPX and DJI move in almost complete unison because a change in one seems to always have the same proportional change in the other. (NOTE: The slope of the line is "Beta" a beta of 1 would be the same percentage change. "High Beta" are values greater than 1 and means that for a 1% change in SPX, there is a bigger change in that stock).

In the second image, the XJO and DJI still have a positive relationship, but the spread of the points shows us the relationship is not as strong. In the final image we show the relationship between the US$ (DXY) and the DJI. The relationship is slightly negative and there is a wide dispersion around the line.

Weekly Regression Charts

The important thing right now is to understand that Correlation tells us how closely related these two data sets are.

"Value" is a single result that is going to reveal if there is a strong linear relationship between the variables. It ranges from +1 for strongly positive to -1 for strongly negative. A Value of 0 means there is no relationship at all between the securities. Now here’s the part that confuses a lot of people. In correlation, the direction is indicated by the positive or negative, while the magnitude of the value represents the strength of the linear relationship. So a +0.95 value tells us there is a very strong positive correlation. It does not tell us the slope of the line in the Regression Chart (that's Beta), only that the spread of the points around the line is really tight (like the first chart above).

Ok, now that we have dealt with that, let’s look at how we actually present Correlations. Below is a Correlation Chart from Optuma. Every security we add gets posted on the rows and columns. To find the correlation value between two securities, you simply find the intersection point and read the value.

Correlation Grid

I know these are all Indices and you may not be adding these to a portfolio, but they are suitable for the purpose of illustration.

If you were holding HSI and you wanted to diversify, you would search along the HSI row and find the indice with the lowest correlation value. That would be the one that is closest to 0, which in this case would be DAXX. Remember that it is closest to 0, not the lowest number.

There are a number of things that jump out at me when I look at this chart:

TSX (Canada) and XJO (Australia) are highly correlated. That makes sense to me as both their economies are heavily dependant on natural resources. In fact, I have written before how the Aussie Dollar is a good proxy for a commodities trade—although that no longer seems to be the case—more on that soon.
There are two zones of high correlation. The SPX, DJI, RUT & NDY (all US-based indices) are obviously highly correlated. Although there is a small disconnect between the small caps and the large caps highlighted by the lower correlation of RUT (Russell 2000) to the others. The second zone seems to be mainly European (and Japan’s Nikkei). That really got me thinking about whether this grid is showing me a correlation between the indices or if the currency fluctuations were getting involved. Again, this is going to be an area that we need to do more research, but is important to consider.
The fact that this chart is mostly green, and there is no red, tells me that all these indices are all positively correlated. I know there are some negative numbers, but nothing strongly negative that it would be painted red by the software.
These values did not look like the correlations that were presented in the text books that I had previously read. For instance this is a similar grid that was presented in a 2007 text on market correlations by Markos Katsanos.

Market Correlations by Markos Katsanos

That chart is a weekly grid made up from 52-weeks of data. To compare, I need to update my chart to be the same time-frame (see below).

Correlation Grid

There are a few different securities in there but for the pairs that overlap, you can see that the values are nothing alike. This did not make sense to me as we had always talked about correlations as constants. If they were there, they were there always. There are many texts/articles that talk about market correlations as absolutes, and this is telling me that that is not the case.

This led me to look for historical values of correlation. I could open up a Script Chart, add the same securities and set the script to reveal to me the weekly correlation to the SPX. Here is that chart:

Correlation Grid Script Chart

Immediately you can see that the correlation is not consistent, there is an oscillation that is happening. Correlations are in no way constant over time. This is only the last five years, but these oscillations seem to go on for many years. In fact, in 1995 Japan’s Nikkei was at -0.90 correlation. Even the Dow and the SPX deviate slightly. Note: there is a currency component to this that needs to be considered - I'll save that for another post.

The only time that all indices are correlated are in times of major bear markets. Then everything is correlated to 1! You can see the 2007-2009 period near the start of this longer-term chart here.

All Indices Correlated

Bottom line is that correlations are not static, we can clearly see there is a cycling in correlations. We could try some Fourier analysis on the cycles, but I’ll save that for the next post as well!

For now, what are the correlation dangers that we have discovered? These are the four things that you need to be aware of if you are using correlations:

Correlations are not static. All securities cycle between being highly correlated and no correlation. The Aussie dollar spent years being highly correlated to the Thomson Reuters Commodity Index (see below). I wrote a post about that relationship a couple of years ago — but no more. It could be the correlation is a function of the market phase that we are in —it appears that the AUDUSD is in an accumulation phase. It would be interesting to see how correlations change during different market phases, sigh —but that again needs more research.

Cycle Between Being Highly Correlated and No Correlation

Correlations are not Causation. This is an observed relationship between securities but does not make a good concrete trading signal. Please do not take this as a signal to trade from. These types of inputs are great as a component of a "weight of the evidence" approach, but should not be used in isolation.
You must work with the same currency. Otherwise you can’t know if you are observing a correlation between the security or the currency.
Correlations lag. Like many indicators, the Correlation is telling us what has happened, not what is going to happen (comes back to point 2 above).

Correlations are a powerful technique that we can use, but as with everything, we need to be sure that we understand what it is that we are working with and what the limitations are. Nothing is 100% perfect 100% of the time.

Of course adding too many uncorrelated securities to a portfolio can also have the effect of diversifying away all risk and, by extension, the opportunity for gain. Our rewards are compensation for the risk we bear.

Experts agree this one habit is essential to investor success

Matthew Humphreys — Wed, 28 Mar 2018 04:30:00 GMT

Maintaining a trading journal, while not a new concept, has evolved over the years from a pen and pad to detailed portfolio logs of every entry and exit made. It's something I have long believed to be an essential component for investors new to the stock market to use. It allows you to go back to the start of a trade and analyse what you were thinking at that time for both the wins and the losses.

However, while reading a recent post from Jeffery Tie - Aiki Trader between one of his students and Mark Shawzin, I realised the benefits of maintaining a trading journal are not limited to those new to the market. In fact for some seasoned traders, the need to keep a journal can increase over time…

{:.smallquote}

I failed. I've actually failed MANY times when trading, so let me be a little more specific. The last time I completely failed was in 2011/2012. I lost about $300,000 all because of one stupid thing. You see... usually I keep a journal of every single trade. So before I finally pull the trigger, I take a screenshot of the chart, write down my analysis, and figure out the exact entry and risk. And then I enter a pending order. I rarely, if ever, enter a live trade. This takes away a lot of the stress in trading AND it lets the market confirm my entry by moving through it. To lose the $300k all I had to do was stop the journal. I had been keeping one for years and then I got complacent. Everything changed in an instant. All of the sudden I was an emotional, amateur trader, who couldn't make a buck in the markets. At the time I had 29 years of experience and 23 of them was spent trading the accounts of high net-worth individuals on Wall Street. So it was amazing to me that by changing one simple thing made me no better than an internet trading newbie. Since then my account has more than recovered the loss and it is all because I kept a journal. <cite>Mark Shawzin, Australia</cite>

The effect a trading journal could have for an investor who had been around for many years intrigued me. Jeffrey Tie also mentioned in the same post that he also maintains a trading journal. So I contacted him to ask what benefits he found by using a journal…

{:.smallquote}

There a few good reasons why I use the Journal function embedded in Optuma: 1. The major reason is to guard against "selective memory". I have many faults and foibles, one of which is a tendency to remember spectacular winning trades and ignore the losing trades that have used the same strategy. Keeping a trade journal helps me guard against my own weakness, and the journal provides an unbiased record of all my trades, as long as I maintain the discipline to write the journal just before trade execution. 2. Keeping the journal allows me to inculcate discipline into my trading approach, and the discipline I practice with the journal will also enhance the discipline required to execute trades according to my trade plan. 3. Optuma makes it so easy to keep journals. More importantly, these journals will never be erased. 4. One very good feature of the Optuma journal is that if I trade a stock, index or commodity, I can immediately see my previous journals that have recorded what I have done in the past with the same stock/index/commodity. The Optuma journal includes: - the text only record where I write down the conditions the market has met for me to execute the trade - the chart showing the market at the time of trade execution I find that this simplifies the creation and maintenance of my personal trading journal.\_ <cite>Jeffery Tie, Aiki Trader</cite>

Beginning to see a pattern emerging, I wanted to know more. From past conversations I had with Ray Barros (a professional trader of many years, author and educator), I knew he believed keeping a journal was important. I asked if he could sum up why…

{:.smallquote}

The tripod for trading success: Mind x Money x Method = Positive Expectancy Return (Trading Success). Mind covers two aspects: - Consistent Execution, and - Constant and Never Ending Improvement (C.A.N.I.) Keeping Equity and Psych Journals are key tools. The Equity Journal provides the quantitative data (the stats), and the Psych Journal provides the qualitative (the insights). _All successful traders with whom I am acquainted keep and use both tools._ <cite>Ray Barros, BarroMetrics Investments Inc</cite>

With so many market professionals not only advocating their students to maintain a trade journal, but still using a journal themselves, it is clear the Journaling System within Optuma is a powerful feature for anyone wishing to implement this habit of successful investors.

The process of maintaining a trading journal within Optuma is fast, efficient, and easy to maintain. Entries can be as long or as short as you like, and can include snapshots of the chart and indicator setup you are reviewing at the time. Information entered into the Optuma Journal can even be displayed within your Watchlists—perfect for quickly reviewing your current portfolio.

If you’d like to start using the Optuma Journaling system, the best place to begin is by watching the following video.

Note: the next Optuma update (beta version coming soon!) will include the option to display journal icons on the chart so they can be accessed quickly.

Optuma Trading Journal

Market Update - The sky is falling....or is it?

Mathew Verdouw — Thu, 01 Feb 2018 06:12:00 GMT

We're always working on new ideas at Optuma. Most of the ideas are great, and sometimes..... well let's just forget about the chart navigation joystick of Market Analyst 5!

A new chart that we have been working on for the next major release of Optuma (expected Q2) is an Historical Comparison Chart. This one lets us put in multiple securities, or the same security multiple times, and set different start dates for each. We can then compare each of their respective growths from their start date. This is to solve many requests we get for looking at overlays of charts and being able to shift their start dates.

As I was completing a preliminary test of the work (it's still unfinished), I noticed this chart and just had to share it.

Here I put the Dow in two times with start dates on October 20, 1987 and March 9, 2009 (well actually three, but I'm hiding 1932), as they were the lowest point in the market. My intention was to see how the recoveries compared from the two biggest recent crashes. I was astounded to see how closely they are tracking! Even the biggest corrections seem to be happening around the same time.

This surprised me because 1987 was a short sharp brutal correction and 2009 was the end of an 18 month global financial crisis. Yet there are so many similarities in how the markets have rebounded from each low. The take-away is obvious, if they continue to match, then the Dow is only just over half-way through the bull run!

Historical Comparison Chart

The equivalent period of time, to where we are now, in the 1987 line is 1996, that is, 2018 in green lines up with 1996 in the red line. So I went searching to see what the sentiment was back then. Here are some things I noticed:

In 1996 the Internet was being increasingly used and companies were starting to talk about their web presence. Many people still did not have internet access (I remember mailing floppy disks to people who bought the software - and you think updates are a pain now!). Today we could be on the cusp of a similar revolution with Blockchain. Many companies are starting blockchain projects (including us - but more on that another time), a lot of people don't fully understand it, and many people are rushing in and investing in what ever they can find in the sector. While the technology is not as impressive as the Internet, the economic consequences are.
1996 was also a period of low volatility. In fact here is a headline from CNN Money in Aug 2017:

Headline from CNN Money in Aug 2017

So both periods were a Low Vol rising market.

In 2007, there was a paper released called Investor Sentiment in the Stock Market by Malcolm Baker and Jeffrey Wurgler. In it they had the following chart which measured investor sentiment from 1966 to 2002. What is astounding is that in the midst of a rampant bull market in 1996, so many investors were undecided with a slight bias to being negative. Sound familiar?

Investor Sentiment in the Stock Market by Malcolm Baker and Jeffrey Wurgler

Of course a single case like this is not enough evidence to base investment decisions on in isolation, but since our markets are driven by the psychology of millions of individuals. We often see similar patterns happening as fear, greed, conservatism, regret, and all the other biases take hold. If 1987 truly is a good model to follow, then it looks like there is still a long way to go. This is also a good reminder to stick with the trend until we have a confirmation of a change. Sounds obvious but not easy to do when everyone is telling us that the sky is falling.

Advisory System

Relative Rotation Graphs® and Asset Allocation

Julius De Kempenaer — Wed, 13 Dec 2017 14:03:00 GMT

As you have seen in other articles and blogs, Relative Rotation Graphs® (RRGs) are a great tool to determine relative strength of a basket of stocks or currencies against a benchmark, but one question we often get is "can RRGs be used for asset allocation purposes?" The answer is YES!

In this example we have created a weekly RRG with six US asset classes, using the following ETFs:

Equities (SPY - SPDR S&P500 ETF)
Government Bonds (IEF - iShares Barclays 7-10 Year Treasury Bond Fund)
Investment Grade Corporate Bonds (LQD - iShares iBoxx $ Investment Grade Corp Bond Fund)
High Yield Corporate Bonds (HYG - iShares iBoxx $ HY Corp Bond Fund)
Real Estate (VNQ - Vanguard REIT)
Commodities (DJP - iPath Bloomberg Commodity Index TR ETN)

We need to use a balanced benchmark as the comparative index, such as the Vanguard Balanced Index Fund (VBINX from Optuma’s US Funds database) which tracks the performance of a portfolio consisting of 60% US equities, and 40% US bonds (broad market-cap weighted).

Here’s a weekly RRG for the year, with the tail length set to 12 weeks:

What is noticeable straight away is that the US Equities (SPY) has remained on the right side of the chart (i.e. in a relative uptrend against the benchmark) for the entire year. In fact, it re-entered the Leading quadrant from Weakening, and continues to move further away from the benchmark with increasing momentum and strength.

By changing the colour scheme on the weekly chart to RRG Quadrant Bars and setting the benchmark for these RRG Quadrant bars to VBINX, it’s easy to see which quadrant it is in for that week. The Time Price Measure tool tells us it's been 87 weeks since equities were lagging (red bars) or improving (blue), during which time the price has increased by over a quarter.

SPY

Commodities - as represented by the iPath Bloomberg Commodity Index Total Return ETN (DJP) - is the only other sector in the Leading quadrant, but weakness over the last few weeks (particularly in gold) has seen it head south towards Weakening. When looking at the weekly bar chart you see the break of resistance around $24 could not be maintained, and last week saw the rising trendline in place since July get breached - obviously not a positive sign!

DJP

The other asset classes in the chart (bonds and real estate) have all been in relative downtrends vis-à-vis the benchmark since October, with government bonds (IEF) being the weakest as it has moved furthest from the center of the chart. Having said that, there was an up-tick in JdK RS-Momentum this week as it finds support around $105.60, but given the fact that JdK RS-Ratio is still sloping slightly down (ie moving West on the RRG chart) and that the JdK RS-Ratio reading is the lowest in this universe continue to make it an asset class to approach with caution - just like HYG and LQD which also continue to head lower on the RS-Ratio scale.

IEF

HYG

Real Estate (VNQ) has been moving sideways all year, jumping between Lagging and Improving as seen in the bar colours, but never managing to break the strong resistance at $86. A downward break of that horizontal support level will very likely cause an acceleration down in both the price and relative graphs.

VNQ

In summary, clearly equities is the asset class that needs to be overweight in a balanced portfolio until we see evidence to the contrary, such as a reversal on the RRG or a series of lower highs and lower lows.

For more information on using RRGs in Optuma see Mathew Verdouw’s webinar recording here.

Bitcoin - The perfect technical market

Mathew Verdouw — Fri, 08 Dec 2017 04:03:00 GMT

**Disclosure:** This is an educational article meant to assist readers in looking at cryptocurrencies in a different way. Full disclosure: I do own Bitcoin...finally. <cite>Mathew Verdouw, CMT, CFTe</cite>

I am the first to admit that I ignored Bitcoin - and other cryto-currencies - for too long. The first time I really paid any attention was back in October 2017 when Bitcoin just broke $5,000. A few of us were having coffee at the IFTA conference in Milan when the topic came up. The price has more than tripled in the six weeks since. While this was happening, I've been sitting on the sidelines learning as much as I can about Bitcoin, the blockchain, and also ICOs (Initial Coin Offer) as a way to fund developments.

XBTUSD Spot

It’s a hot topic right now. It seems that I am hearing more and more people talk about it where ever I go. Interestingly, there are two questions that seem to pop-up every time Bitcoin is mentioned: is it a bubble? and what are the fundamentals? Let's deal with the fundamental question first.

Every tradable security on earth, at least those that I can think of, has an underlying asset. Shares have companies, index futures have a group of shares, commodities are obvious, bonds are based on the credit-worthiness of the companies and governments they were issued by, and even currencies have the GDP and trade balances of the countries they represent.

For all of these there are fundamental factors that we can look at to say XYZ is overvalued or undervalued. Welcome to CFA-101. It’s finding securities that represent growth opportunities be being undervalued. Please don't be one of those technicians who say "technicals is the only way" - you're deluding yourself. Trillions of dollars are invested on a fundamental basis. It works for huge investments with long hold times, but I digress - more on that debate another time.

Even in the tulip bulb mania of the 1600s, there was a physical item. With Bitcoin, there is nothing, nada, nix. There is no fundamental basis for the price of the security. So what does this mean? Basically there is no way to say that Bitcoin is overvalued or undervalued. It just is what it is. This means the only way that we can analyse Bitcoin is by using technical analysis. And all the technicians say “whoo hoo”!

Many of you who have read my writings know that I can talk about quantitative statistical strategies and models through to esoteric Gann methods. I smile when I hear people so entrenched in one area or the other and ridicule the other group. The fact is that we need to know how to use the right tool for the right job. If I need to create an equity-based strategy for a diversified portfolio, I'm going quantitative all the way. But if I have a single security that I am trading, then I am using nothing but Gann and subjective analysis. In that scenario, I have the time to really dig in and get to know that security. Some of the best traders I know have massive wall charts printed on the only security that they trade.They know that chart so well, and get to know the patterns associated with it. I may need to dust off the wall-chart mode printer in the Gann edition of Optuma!

This is a simple chart with a Gann Square tool from the lowest low in Jan this year. It’s a simple tool, but I like how often the solid lines provide support and resistance.

XBTUSD Spot 1 Day Candlestick Chart

Bitcoin

He lists 5 stages from his research:

1. Displacement This is a shock that makes a big change to the way things are. Think of the digital revolution. Some industries are destroyed (bye bye Kodak), and others are created (hello Google). Without a doubt we are seeing a massive displacement in the financial industry right now.

2. Credit Creation The boom is expanded by money creation, i.e. you can use your capital in the new industry as collateral for more money to invest even more. Anyone in 2006 want to get an equity line to buy more houses?

The next question is whether we are in a bubble or not. I have no doubt at all that we are in a bubble - I just don’t know where exactly in bubble we are yet, so we need to examine some tell tale signs. In the CMT level 3 course we teach James Montier's chapter on the Anatomy of a Bubble.

Credit Creation

3. Euphoria This is the stage when the speculators are everywhere and every person you meet is telling you about how much money they made. Think of the Internet Bubble. Companies with no experience were getting tens of millions of dollars for not much more than a good idea.

4. Critical Stage / Financial Distress This is the point where the early adopters decide to cash out. They will never have to work again. Normally this is the founders of a company, but with Bitcoin it’s de-centralized so it’s a bit different. There will have to be a stage where buyers dry up (usually because everyone realizes that the security is overvalued), but with Bitcoin, how do we measure that? Regardless, when this stage happens in other markets, the top is usually put in. Those who are leveraged to their eye-balls will be forced to sell because they cannot meet their obligations without more growth. Their lenders also get nervous and demand part or full repayment. This is Financial Distress!

Financial Distress

5. Revulsion So who was talking about housing or shares as a great investment in 2009? There's a lesson in there if we look at the charts - but that’s for another time. The fact is that the final stage of a bubble is where the majority are lamenting their losses and gnashing their teeth whenever that investment is mentioned.

So, where are we now? I’ve not heard of any credit creation for Bitcoin. There are no banks willing to give you a fiat loan based on the value of your Bitcoin holdings. The CFTC has approved two futures products on Bitcoin. What that will do is the topic for another post, but we will all be watching very closely.

My take (and this may be as a late, but optimistic holder of Bitcoin) is that we have only just seen the start. Ie, we are still in the displacement phase. Unless there is some massive government intervention (I don't know how that can be orchestrated in time), it still seems like there is a long way to go.

As a company, we are creating a number of initiatives:

Getting the best data that we can. We currently have XBTUSD in our Foreign Exchange dataset for Bitcoin, and access to other cryptocurrency data (such as Ethereum and Litecoin) from Quandl (see here for instructions). But we also want to get better cross-rates from a number of exchanges, and we’re working on that too.
Examining ways to accept Bitcoin for our products and services. Reducing credit card fees (yay!)
Looking at an Optuma ICO as an alternative to our planned preference share funding for our new dashboards project. Oh BTW, you can see a sample of our new work at the Relative Rotation Graphs online version. It will be a paid service in time, but it is currently free to access while we are testing it. Login at https://www.relativerotationgraphs.com/partners/optuma login with demo demo as the username and password.

It’s an exciting time as we are witnessing an incredible financial revolution. Blockchain—the core of Bitcoin—has so many applications including as a register of shares. We’ll be able to trade securities without the need for exchanges! Now. that’s a revolution.

Of course Bitcoin is volatile, you will have to use all your money management rules, but it is something that deserves considerable research as it appears to be changing the financial world.

Introducing the DVAN SmartLines Tool Module

Jim Kennedy — Thu, 21 Sep 2017 05:00:00 GMT

The DVAN SmartLines{: target="_blank"} module for Optuma is a suite of market tools that gives an investor access to Divergence Analysis’ 27-year moneyflow methodology. The module is comprised of indicators that create a clear framework in which to analyze markets—including trading signals, trend direction, momentum, support/resistance, and buying/selling cycles. I’m pleased to announce that these tools are now available on Optuma. Contact support@optuma.com to have a free trial enabled on your account, or click here to subscribe{: target="_blank"}.

Let me tell you a bit about how these tools were developed. The year was 1985, and after studying technical analysis for over 10 years, reading everything I could get my hands on, I considered myself adept at this type of analysis. I was a broker working at Alex Brown & Sons Inc., where I continued to see the lag effect from technical analysis techniques, and wanted something better.

I began by writing out in two paragraphs what I wanted to accomplish. In short, I wanted to develop a systematic approach to market entries/exits. But these had to be "leading indicators", as opposed to the 99% of technical indicators of the day that all lagged. They lagged because the inputs were prices—something had to occur by price and it would show up in the indicator afterwards.

I had worked at using raw volume data on two previous occasions, but it was just too volatile to use. I had to develop some math to "calm" this data so it could be used in analysis. Working on my own (mostly in the small hours of the night) I began developing an algorithm that would incorporate raw volume data. My tools were a pencil, a calculator, and many ledger books (the kind accountants used to use). I would work on the algorithm and then apply it by hand, getting data from the Wall Street Journals stacked up in my office at home. Then I would graph the results (yes, on graph paper!) and see how it looked relative to the price graph of the S&P.

Time and time again I would tear up the graph and return to my calculator, working on the algorithm. It wasn’t until I totally eliminated price from the input and the algorithm that I began to see on the graphs what I had been searching for. A leading indicator was emerging that would basically mirror price behavior—except at tops and bottoms. This is where it would diverge from price behavior, and these divergences marked the turning points by prices.

I had succeeded in what I set out to do. The development of this, and several other indicators, using like techniques and methodologies took me from 1985 to 1987. I left my position at Alex Brown & Sons in 1989 and started Divergence Analysis Inc. In the early days we made our reputation by calling tops and bottoms. I learned a lot under fire working with heads of some top hedge funds of the day. I say "under fire" because there was no such thing as a "maybe" or "might" call with those guys—they simply wanted "the call".

I watched virtually every tick in the markets for the next 15 years on the phone with hedge fund partners every day. I also learned that all divergences are not the same. So I had to learn more and put together a systematic way to approach money management. I had a computerized system running in 1989, using Lotus 1-2-3 macros. It wasn’t until 2001 that I developed a web-based (of course the web didn’t evolve until the late 1990s) system, piggybacking on trading platforms. My clients could see everything on their desks for the first time.

In all these 27 years, not one decimal point has been changed from my original algorithms. Every indicator we have is derived from one of these two input streams. The picture below illustrates the way Divergence Analysis’ indicators are leading rather than lagging.

At the top of the graph is the S&P 500 Cash Index, and below it two lines represent input data over the same exact time period. The green line is the input of pure raw volume data, and the blue line is the input of our synthetic volume data.

You can see that both of these lines would be hard to correlate with the actual price data. The pure raw volume data is what we used in the early days to trade and analyze the S&P and other big market indices. The synthetic volume data is what we developed around 2004, so we could use this same methodology (we call moneyflow) to other markets where raw volume data was unavailable. Now we could apply our systematic indicator approach to all markets such as bonds, currencies, individual stocks, options, futures, and commodities.

DVAN SmartLines

Notice the high correlation (patterns) between the raw volume and synthetic volume lines. This proved that our new synthetic volume was a valid representation of raw volume. In short, from 2004 until the present day we built all of our models to be "visually adaptive" for the trader to use, efficiently trading any asset in any time frame.

We are excited to make these tools available to all Optuma users for free for a limited time. When you log in to Optuma you will see a new tools folder called DVAN which contains six indicators, including the DVAN SmartLines.

The DVAN SmartLines

As mentioned above, the DVAN methodology measures underlying moneyflows, rather than price action, to determine a security’s strength and direction. The moneyflow algorithms derive a security’s force from mass and acceleration inputs (F=MA.) DVAN calculates mass with underlying volume and acceleration with velocity and momentum. This proprietary process inputs chaotic market data and translates that data into the DVAN indicators for a clear understanding of the market environment.

DVAN SmartLines

Our aim is to simplify the trading process and create a visual display of the market or individual security—providing clear decision points for trading, analysis, and investment strategy.

The DVAN SmartLines indicator shows color-shaded buy and sell cycles that overlay prices on the chart—equipping the user with a roadmap for direction and force. The changes between buyer dominance and seller dominance can be seen by the shift from red to green price bars, meaning buyers are taking over from sellers, at bottoms, and conversely the shift from green to red at tops. These shifts can easily be seen within the boundaries of the SmartLines buy and sell cycles. When the SmartLines converge, they signal entry into a buy or sell cycle which confirms the shift of dominance between buyers and sellers.

DVAN SmartLines

The major cycles above clearly show the transitions from bull market to bear market in January 2001, and the subsequent bull market beginning in early 2003. The cycles also identify the more recent bear market that began in January 2008, until buyers stepped in, in March 2009. And to further illustrate the leading attributes of the DVAN methodology, notice the red price bars [sellers dominant] at the very tops in 2000, and the 5 months of green price bars [buyers dominant] leading into the major bottom in 2003. Also notice the red price bars again leading into the top in 2007- 2008, and the green price bars leading from the bottom in 2009.

The DVAN methodology helps to mitigate risk by providing visibility and control to the user. Volatile input is translated into decision-useful information, and buy and sell cycles are made visible through the SmartLines. Additionally, the DVAN indicators are not specifically engineered for any one asset and can be applied to virtually any liquid security, over any time frame. For example, here’s a 30 minute currency chart:

DVAN SmartLines

We encourage you to take a look at these tools in Optuma to help you achieve better trading results. If you have any questions about using these tools please contact us at info@divergenceanalysis.com.

Andrews’ Pitchfork: Application and Analytical Methods Using Median Line Analysis

Timothy Brackett — Thu, 13 Jul 2017 05:20:00 GMT

Read Part 1 | A Brief History of the Development of Median Line Analysis – A.K.A. Andrews’ Pitchfork

Read Part 3 | Andrews’ Pitchfork: Advanced Analytical Methods Using Median Line Analysis

In the previous article, A Brief History of the Development of Median Line Analysis, we described the evolution of Andrews' Pitchfork and those who had contributed to the technical tool that we know today. In this second article we’ll walk through the ease at which Andrews' Pitchfork analysis can be used in Optuma, and then proceed to share some examples of how this underutilized tool can be used.

From the onset, you should be advised that the terminology we use is not necessarily the typical labeling used in identifying pivot points (if there is a standard). We chose to label the start of the median line at a reaction high or low as “PO”, or point of origin, and the next swing we label as “P1”, and the next as “P2”. The reason for this labeling method will come to light in the pages that follow.

Applying the Andrews’ pitchfork in Optuma to any price chart is as easy as four clicks. In the example below, we are applying a “low, high, higher low” pitchfork by selecting the tool from the “tools” tab and applying it to the chart with four left-clicks of the mouse. These four clicks are shown in blue with our labeling method in red:

Andrews Pitchfork Points

In this second example we are applying a “high, low, lower high” pitchfork known as an Inverse Pitchfork:

Andrews Inverse Pitchfork

Schiff and Modified Schiff Settings

In addition to the Standard Pitchfork that we applied above, it’s also possible to display Schiff and Schiff Modified settings. You may remember from Part One that Jerome Schiff was a student of Dr. Andrews who developed the Schiff-adjusted pitchfork, and Dr. Andrews took it one step further with Schiff Modified. These adjustments can be made by left-clicking on the pitchfork, then changing the Type in the tool’s Properties box, and selecting the different configurations. The charts below show the calculation and the change in the point of origination of the median line for the three configurations. Note how price in the yellow Schiff Pitchfork better respects the upper and lower parallels or “tines” of the Pitchfork. This configuration would be our analytical choice in this particular example. We will discuss this more, as well as our method and purpose of our “color coding” techniques, as we move forward. But first there are more technical tools imbedded in the Optuma Pitchfork tool to share with you:

Schiff and Modified Schiff Pitchfork

After determining which pitchfork configuration is initially the most appropriate to use in starting a new analysis (Schiff Modified pitchfork on the chart below left), we look to confirm our choice by enabling the Extend Backward option in the Properties box. We can see when this tool is applied (chart below right), that even prior to the time that the pitchfork was drawn, price respected the angle, or “frequency”, of the trend by noting the price points marked with circles. This gives us a greater level of confidence that the Schiff Modified pitchfork represents the correct trend for this particular chart:

Extend Backwards Option

Warning Lines

Warning Lines are parallel lines drawn outside the original pitchfork lines. They are drawn equidistant from the median lines and “tines” of the pitchfork. The number of warning lines, the direction (forwards, backwards, or both), line style (dashed in our example below), width, color, and transparency can all be applied and adjusted by clicking on the Warning Lines option in the Properties box:

Warning Lines

Along with expanding the time frame to weekly, we applied to the above chart two upper and lower warning lines (green dashed lines) to our original example. There is little question now as to the validity of trend or frequency. It’s important to point out that in pitchfork analysis, minor violations on an intra-day or closing basis are not crucial to the spirit of the validity.

As an aside, we choose to color-code the three variations of pitchfork for the purpose of easy identification (red for Standard, yellow for Schiff, and green for Schiff Modified). This can be done by left-clicking on the parallel lines, median line, or swing line (between P1 and P2) of the pitchfork which will bring up an additional dialog box. The color, style, width, and level of transparency of the pitchfork lines can then be changed.

Internal Lines

Along with warning lines, internal lines can also be drawn (another selection in the tool’s Properties box). Choose the percentage of the internal line required. As displayed in the Schiff Adjusted example below, a 50% setting will draw a line halfway between the upper and lower parallel and the median line. Line style, width, color, and transparency can all be adjusted. In our example below we have changed the color to red for clarity purposes:

Internal Lines

Hagopian Lines

Our next example focuses on an additional confirmation tool used in median line analysis. This technique was presented to Dr. Andrews by one of his course members - Mr. Hagopian. This option can also be found in the Properties box, Hagopian Lines. It draws lines connecting PO to P1, and PO to P2 (labelled H1 and H2 in blue below). To paraphrase Dr. Andrews, when prices fail to return to the median line (which studies have claimed to occur 80% of the time), a reversal may be on the horizon. In the chart below, price slowed at the 50% internal line, reversed, and broke above the upper parallel line. The reversal is confirmed by price trading though the upper Hagopian Line (H1), circled below.

Hagopian Lines

Swing Parallel Lines

Another Pitchfork option in Optuma is the Swing Parallel Lines. Selecting these lines in the properties will draw lines across the pitchfork parallel to the base or swing line (in the example P1 to P2). The distance between these swing lines will be the same as the length of the pitchfork handle (PO to PO2, highlighted in orange). These swing parallels, SP1 and SP2, further identify points of support and resistance on the price grid.

Swing Parallel Lines

Sliding Parallels

Sliding Parallels (SP on the example below) are lines drawn parallel to the median line. They can be drawn between the upper and lower parallels or outside the confines of the pitchfork. They always share the same angle, and represent internal and external support and resistance. In the example below, the sliding parallel is drawn by right-click on the pitchfork, then left-hover the arrow over the running man and left-click “copy tool”. Then move the pencil to PO (point of origin) of the sliding parallel. This will draw a new pitchfork of which you can reduce the transparency of the upper and lower parallel and swing line to zero, leaving only the sliding parallel, which in this case we have changed to blue. On close inspection readers will notice that the sliding parallel (SP) is very close to a 50% internal line.

Sliding Parallels

In the third and final article, we will share further pitchfork methods. These include what we have coined as “Combination Pitchforks” and “Dueling Pitchforks”. When combined, these techniques create a support and resistance frequency grid in price and time (one of which we show below). We will also demonstrate pitchforks used in concert with multiple time frame momentum analysis.

Combination Pitchforks

A Brief History of the Development of Median Line Analysis - A.K.A. Andrews’ Pitchfork

Timothy Brackett — Sat, 08 Jul 2017 23:30:00 GMT

Read Part 2 | Andrews’ Pitchfork: Application and Analytical Methods Using Median Line Analysis

Read Part 3 | Andrews’ Pitchfork: Advanced Analytical Methods Using Median Line Analysis

Although the technical methodology known as Median Line Analysis as we know it today is rightly attributed to Dr. Alan Andrews, it should be known that it found its genesis hundreds of years before his time. That said, it was Andrews who brought it forward to the study of stocks, bonds, currencies, and commodities. Andrews’ extensive analysis and studies are the basis of what is now referred to as Andrews’ Pitchfork. It has been elaborated on since his passing by several of his students who studied directly with him, and by a number of learned and talented technicians who have refined and studied it in concert with other indicators. It’s been said his disciples, who were grain traders, were responsible for attaching the moniker Andrews’ Pitchfork to the technique—what we believe is an underutilized technical tool that belongs in every analyst’s toolbox.

In this first article of a series of three, we’ll look at the history and development of the pitchfork, but it may be useful to start with an overview of the tool and see how it is constructed.

What is Andrews’ Pitchfork?

Andrews’ Pitchfork is a trend channel tool consisting of three lines, determined by picking three points on the price chart that mark reaction highs or reaction lows, and is available as a standard tool in Optuma.

In the Gold chart below, we’ve marked the start of the median line at a reaction or swing low at P0 (point of origin). We then mark the next pivot at the high P1, and the next pivot at the higher low at P2, and identify the midpoint of P1 and P2 (click images to enlarge).

Andrews Pitchfork Points

The median line is drawn from the first pivot at P0, through the midpoint of P1 and P2, as shown in the chart below:

Andrews Pitchfork Median Line

A “swing line” is added between the P1 and P2 pivots, and parallel lines are projected from P1 and P2. This creates the pitchfork which represents a price channel:

Andrews Pitchfork Swing Line

Now we understand how the tool is constructed, let’s take a look at its history and development.

Roger Ward Babson

Roger Babson was born in Gloucester, Massachusetts on July 6, 1875. His father, Nathaniel was a dry-goods merchant in the city, and his mother Nellie owned a millinery store. His was a typical childhood of that period, although he relates he was an unruly child who suffered whippings by the hand of his teacher, as did most children who “acted up” during school. The first statistics that Babson compiled even at that early age, were the record of thrashings that various boys and girls received during the school year. His score was forty-seven. This was a dubious start, but a start nonetheless to a renowned career as a statistician, bond trader, businessman, economist, and writer that spanned decades and left a legacy and place of notoriety in the history of financial markets. It was his founding of Babson’s Statistical Organization, and his long-standing relationship with a professor of engineering, George F. Swain, whom he studied under at the Massachusetts Institute of Technology, where Babson received his training as an engineer from 1895-1898. From here the two men’s friendship was forged. It is their philosophical connection that interests us, and their development of the Median Line Analysis.

George Fillmore Swain

Babson was in New York selling his bond statistical analytical research on the day of the 1907 stock market crash, and was taken aback by the large losses accumulated by supposed learned and experienced market professionals. At the time, he had been studying Benner’s Prophecies of Future Ups and Downs in Prices [1] and How Money is Made In Security Investments [2], believing that there must be have been a way to forecast economic and market changes in a more proactive and less reactive manner. With these two books and his own amassed statistical data in hand, he sought out his former professor and friend George Swain. Both concluded that there was the basis in these two books and Babson’s collected data that, when applied properly, could forge a new method of forecasting. It was Professor Swain who introduced and drew a “normal line” through the historical data in the Babson’s Composite Chart of pig iron, corn, and hogs that would normalize the volatile “zig-zagging” index Babson had been developing. He also suggested that Newton’s Law of Action and Reaction may apply to this and other economic indicators, as it does to physics, chemistry, and astronomy. Thus was the origin of the famous Babsoncharts that were integrated into Babson’s Statistical Organization’s publications, and later analytically led to his famous timely prediction of the 1929 crash that was published in New York Magazine.

Business Index

It was Babson’s further devotion to the work of Sir Issac Newton that later induced him to create the Gravity Research Foundation at the suggestion of Thomas Edison. It was at one of Babson’s seminars years later where he illustrated how Newton’s Third Law could be applied to the stock market, when he met Alan Andrews. They became hardened friends, and Babson taught Andrews his action and reaction techniques. Out of respect of Babson’s studies that he shared with Andrews, he later named his median line course the Action-Reaction Course, in acknowledgement of his mentor’s teachings.

Dr Alan Hall Andrews

Although Alan Andrews’ date of birth remains unknown, we do know he passed on in 1985. Little is known of his personal life, wife or children. His father owned a broker/dealer where he traded for clients and his own account, and is said to have made a large amount of money in the Great Depression. Alan’s father sent him to engineering school at Massachusetts Institute of Technology, and then on to Harvard. The story goes that after he graduated, his father challenged him to make one million dollars in one year while working at his father’s brokerage firm. He did not accomplish his father’s task in one year, but in two years had made one million dollars trading commodities. Andrews later became a lecturer in civil engineering at the University of Miami in Florida. After he retired, he returned to his roots and decided to not only manage his own investments, but to teach others. He began publishing a weekly advisory newsletter that he sold by subscription. The newsletter focused on his trading methods, and included recommendations for the coming week. He also created the FFES (Foundation for Economic Stabilization) Case Study Course, applying principles of mathematical probability to the production of profits from prognostication. It detailed various median line methods and other techniques that, including fan lines, he referred to as Horn of Plenty. He sold the course for the tidy sum of $1,500 during the 1960’s and 1970’s, and held seminars and one-on-ones that were attended by New York and Chicago pit traders. Andrews often incorporated his student’s observations and studies into his work, careful to name methods such at Schiff Adjusted Median Line (named after Jerome Schiff, a New York trader who brought it to his attention), and Hagopian Lines named after another one of his students.

In the next article we will see the ease at which Andrews’ Pitchfork (and the Schiff and Hagopian modifications) can be applied in Optuma, along with a few examples showing how it can be used to identify potential turning points.

Andrews Pitchfork in Optuma

[1] George Benner, Benner’s Prophecies of Future Ups and Downs in Prices (Cincinnati: Robert Clarke & Co., 1879). [2] Henry Hall, How Money is Made in Security Investments (New York: The De Vinne Press, 1907).

Andrews’ Pitchfork: Advanced Analytical Methods Using Median Line Analysis

Timothy Brackett — Tue, 20 Jun 2017 22:00:00 GMT

Read Part 1 | A Brief History of the Development of Median Line Analysis – A.K.A. Andrews’ Pitchfork

Read Part 2 | Andrews’ Pitchfork: Application and Analytical Methods Using Median Line Analysis

Price action creates a mathematical grid that, when brought to light, can be used to identify targets, as well as support and resistance levels. The grid becomes visible through independent analysis of three axes: horizontal (price), vertical (time) and diagonal (price and time). The goal of this article is to discuss a few of the techniques found to be invaluable in analyzing the diagonal axis using the Andrews' Pitchfork tool in Optuma. The other individual axes, vertical and horizontal, will not be discussed.

Analyzing the diagonal axis is traditionally accomplished with trend lines. Two points of contact is the minimum requirement to draw a valid trend line. Pitchforks are unique in that they require three points of contact. The importance of this fact cannot be overstated. Three points of contact allows a pitchfork to triangulate the price/time grid unlike any other tool. And, for those technicians who have ventured into the Gann “rabbit hole”, there are no discussions of hidden factors or scaling charts. In fact, Gann students should consider the implications of our method of diagonal discovery (see Combination Pitchforks) and its use as an alternative, mechanical method for “reverse engineering” a chart’s factor.

A few additional points:

Before we continue we would like mention three points. Firstly, median line analysis can be used in all time frames. The monthly chart of the S&P Metals and Mining ETF (XME) and the hourly chart of the Dow Jones Industrial Average (INDU) serve well as examples.

Median Line Analysis

Secondly, minor violations of support on an intra-period basis, as well as on a closing basis, do not reduce the veracity of the indicator (note chart of the NASDAQ 100 E-Mini Future below). Only when price runs past with conviction, or “lingers’” in a state of violation, does it suggest that there has been a technical change of the angle in the price/time grid.

Change of the angle in the price/time grid

Thirdly, and most importantly, in most cases median line/pitchfork analysis is a superior analytical tool when compared to traditional trend line methods, as it gives the chartist far more technical data points to consider during his or her analysis. Reflect on the differences between the weekly charts of the US Aerospace & Defense ETF (ITA) below.

Comparing with traditional trendline

Click here to learn how Schiff and Schiff Modified pitchforks are created.

Combination Pitchforks

The first example of our analytical method of diagonal discovery is by using what we refer to as “Combination Pitchforks”. In the left panel below we have applied a Schiff adjusted pitchfork (labeled “a”) to the daily chart of the US Aerospace & Defense ETF (ITA) using three pivots; the low at POa, to the high at P1a, and higher low at P2a. The panel to the right adds a second Schiff adjusted pitchfork (labeled “b”) utilizing the three pivots that follow; drawn once again from a low at POb, to the high at P1b, and then to the low at P2b. Dashed warning lines are then added outside pitchfork “b”. The reversal at the upper warning line of the second pitchfork at R1 and reversal two and a half weeks later at the upper parallel at R2 indicates we have identified the correct price/time grid. Now observe that both pitchforks, drawn from different pivots, are forming the exact same angle. This is confirmation that we have discovered the dominant frequency within this market’s price/time grid. Going forward, an analyst can continue to utilize this frequency. How so? See below.

Combination Pitchforks

Dueling Pitchforks

Upon confirmation of a market’s dominant frequencies, a technician can take his or her analysis one step further by employing what we refer to as “Dueling Pitchforks”. This technique utilizes two pitchforks (one bearish, one bullish) to form the price/time grid. In the panel below left we have drawn a Schiff adjusted pitchfork on an hourly NASDAQ 100 Index chart from the high at PO, to the low at P1, then to the lower high at P2. We then have added multiple warning lines. Note these warning lines have been respected by price. On the panel to the right we have added a standard pitchfork to the same chart starting from the low at PO, to the high at P1, then to the higher low pivot point P2, and similarly, have added multiple warning lines. These two pitchforks, working from a bear and bull perspective, work to reveal the price/time grid acting as diagonal resistance and support.

Dueling Pitchforks

The chart below of the daily S&P E-Mini Active Contract serves as another example of Dueling Pitchforks, but utilizes three pitchforks to create the price/time grid. The first pitchfork is a bearish Schiff modified drawn from the late February high at PO, down to the April low P1 and up to the lower high at P2. The second originates at the same April swing low at PO and is drawn up to the swing high at P1, and terminates at P2. This is also Schiff modified, and for clarity purposes we have color coded it blue. The third is a standard pitchfork (in red) drawn from the next swing low PO, to a higher high P1 and then down to a higher low P2. This completes the grid.

Recent price action suggests that the price/time grid is beginning to change. This is revealed in the following data points: Price has violated support at the upper warning line, UWL1, and support at the lower parallel LP1 has flipped to resistance. Both of these developments suggest that the “price unit” or leg up since PO may have reached its terminus. What would be the appropriate next step in this analysis given the violation? Add warning lines to the standard pitchfork in red.

Dueling Pitchforks

Confluence and Multiple Time Frame Momentum

The idea of confluence is fundamental to technical analysis. Confluence can be defined as a zone composed of more than one level of support, resistance or targets that work to strengthen a specific point on the price/time grid. An excellent example of median line confluence is found in the monthly chart of the Nikkei225 presented below. Two standard pitchforks labeled a and b have been drawn from consecutive pivots. Observe that price developed a significant swing high at a confluence of resistance composed of both median lines (blue circle).

Confluence and Multiple Time Frame Momentum

How could an analyst have had even more confidence that price would react to this confluence zone? The answer lies in multiple time frame momentum analysis. Examine the next series of charts.

Below left is the previous monthly chart of the Nikkei225 with the addition of a 14-period Relative Strength Index (RSI). Below right is the exact same chart transferred to a weekly timeframe. A vertical line has been drawn on each chart to identify the momentum oscillator signal developing at the point where the median lines cross. Study the monthly chart. The oscillator is producing a tight bearish divergence sell signal at the all-time maximum horizontal displacement level. For those who need a refresher, a bearish divergence occurs when a momentum oscillator creates a lower high as price coincidently makes a higher high. Now examine the weekly chart. Note that weekly momentum is also printing a series of bearish divergences as price tests both median lines. Sell signals developing in multiple time frames as price test median line confluence resistance is a high confidence signal for a reactionary pullback.

Confluence and Multiple Time Frame Momentum

The Devil is in the Details (Real-Time Walkthrough)

The next two examples of pitchfork analysis will be walked through in detail as if the analysis were taking place in real-time. At times the discourse may seem tedious, but for the sake of analytical explanation we ask for our reader’s patience.

Shifts Within the Range of a Single Pitchfork

Below is a daily chart of the SPDR Energy Select Sector ETF (XLE) displaying a Schiff adjusted pitchfork drawn from P0, P1 and P2. A quick look tells us that the XLE is in a downtrend, but a more detailed examination provides insight into the underlying supply/demand dynamics of the market. Our consideration of the progression of price as it tracks lower within the confines of the pitchfork’s warning lines is yet another example where utilizing pitchforks in place of simple trend lines offers a more complete picture.

Schiff adjusted pitchfork

The pivots at point 1 and 2 are confined by the upper and lower parallel lines and help to confirm the Schiff adjusted pitchfork is displaying the dominant frequency. Next, hints of an accelerated decline are revealed with a repeated intraday violation of the lower parallel in the days leading up to pivot 3, which finds support and bounces at the lower warning line. XLE is once again unable to trade above the upper parallel at pivot 4, indicating the downtrend remains in force. Repeated touches of the lower warning line at 5, 6, and 7 reinforce the bottom of the channel and reaffirm the downtrend. Pivot 8 is a subtle clue that the power of the decline is diminishing as prices reach the upper warning line for the first time. At pivot 9, prices hold the lower parallel of the pitchfork at the close and, for several days leading to pivot 10, prices fail to reach the lower parallel line (two more signs of waning selling pressure). XLE closes above the upper parallel for the first time at pivot 11, and once again at pivot 12, it fails to reach the lower parallel line.

In review, pivots 1 through 7 travel between the upper parallel and the lower warning line. Pivots 8 through 12 move between the upper warning line and the lower parallel. This subtle, but hugely important, shift within the pitchfork hints that the multi-month downtrend is likely in the early stages of a reversal. Once again, median line analysis provides the analyst with a level of depth that is not available through traditional trend line methodologies.

Slope of the Median Line

The next example provides another valuable perspective on the additional detail available to pitchfork users. The chart below is a 240-minute chart of the Russell 2000. There are five standard pitchforks marked with additional codification of a through to e. There are a number of technical observations that are offered on close inspection of chart. The first is when prices accelerate through the upper parallel of pitchfork a (noted by the first green circle). A conviction breakout of the pitchfork significantly raises the odds that the three-wave leg lower is complete and the E-Mini is due for a rally.

The next insight comes to light when we apply the second pitchfork at the pivot low at POb. Note that the swing line of the pitchfork b is at the same diagonal angle as the upper parallel of pitchfork a. This was the first confirmation of diagonal discovery of the price/time grid. Moving forward, price fails to reach the upper parallel of the second pitchfork at POc. The swiftness of the reversal, the piercing of the median line and the inability of price to retake the ground above are all clues that a three-wave rally had run its course. Price then begins to creep along the lower parallel of pitchfork c (second green circle) and then begins to violate support. The retest of the lower parallel from below at pivot P2c and subsequent acceleration lower necessitates the drawing of the third pitchfork labeled c. Note pitchfork c follows the same angle, or frequency, as pitchfork a, further confirming that the price/time grid is correct. Prices fail to reach the lower parallel and then reverse higher above the median line and resistance at the upper parallel (third green circle). Also note the breakout of pitchfork c significantly raises the odds of a completed three wave decline. Price then pulls back and finds support at the upper parallel of pitchfork c. We then draw the fourth pitchfork labeled d. Note pitchfork d has a median line with a steeper slope than pitchfork b. This is a subtle, but significant, clue the market is getting stronger. Also, note the two penetrations of the upper parallel further hints that underlying weakness is waning. Upon violation of the lower parallel (fourth green circle), price does not accelerate lower but stabilizes. Pitchfork e is drawn when price begins to move outside the confines of pitchfork d. Note the slope of the median line of pitchfork e is not as steep as pitchforks a and c. This is additional evidence of growing strength within this market.

In review, the slope of multiple pitchforks work to 1) confirm the price/time grid and 2) qualitatively measure the internal strength or weakness within a market.

Slope of the Median Line

Final Thoughts

In conclusion, the Andrews’ Median Line, or pitchfork, is a powerful tool that works to reveal the diagonal axis functioning within all freely traded markets. We argue pitchforks are superior to traditional trend line methodologies and should be a coveted tool within every technician’s tool box. As discussed previously, Combination Pitchforks are implemented to confirm the dominant frequency within a market. Once confirmed, this frequency can be employed in many ways to help forecast future points of support and resistance. One method is to employ Dueling Pitchforks which highlight the price/time grid from multiple directions. Other techniques, such as Median Line confluence, can be used to identify strong points of support or resistance. When used in concert with multiple time frame momentum, the analyst can act with conviction due to non-correlated signals. Finally, the authors believe that the subtle details and shifts within the range of a pitchfork, as well as the slope of median lines reveal underlying supply/demand dynamics better than other price trend discovery tool.

Real (Good) News

Adam D Koós — Wed, 08 Mar 2017 21:06:00 GMT

However “real” the news may be perceived, rarely is news “breaking.” However, in light of all the annoying, repetitive press these days, I thought I’d share some good news that – as a matter of fact – is somewhat ground-breaking.

Something happened this month that hasn’t occurred since November of 2009. A relatively accurate, very-long-term stock market indicator flipped positive after spending the last 23 months on a sell signal.

The indicator in question is called the Price Momentum Oscillator (PMO). It was developed by Carl Swenlin, a legend in the technical analysis and trend following arena. I’ll just say that the tool basically measures momentum over time and provides clues as to whether that momentum is positive or negative.

It can be used over any time frame – short, intermediate, or long-term. The shorter the time frame, the more signals you get, but when looking at a monthly (long-term) time-frame, the signals (positive or negative) are few and far between.

Below is a chart that shows you how infrequent these signals occur. This chart goes back 20 years and you can see there have only been four accurate, positive signals (in 1997, 2003, 2009, and today). The only “false” signal occurred during the quick market correction that took place in 1998, after which a positive signal was triggered, but would have resulted in a losing trade (if this indicator was used all by itself).

S&P 500 Index (Long-term / Monthly)

I’ve drawn blue lines on the chart above for easy viewing of the positive signals. You can also observe the red negative signals, (including the April, 2015 sell signal, which was one of the many reasons we decided to steer clear of stocks as the months marched on throughout 2015-16).

The point of all this is to say that, prior to this month, there have only been four total positive signals in 20 years, three of which provided us with a very strong piece of evidence that suggested the long-term momentum of the market would be up.

Naturally, there is always a chance this could end up being a false signal – and that this “breaking news” could wilt away if the market decides to take a dive over the course of the next several months.

With all that said, since I take a “weight of the evidence” approach to investing, I use many more indicators and tools other than the PMO explained above. For now, the market is short-term overbought (exhausted), but intermediate and long-term trends are looking good. Until that changes, we’ll continue managing our clients’ portfolios in a manner that corresponds with this positive thesis.

Adam D. Koos, CFP® President / Portfolio Manager

Download Optuma Watchlists for US & European Index Constituents

Darren Hawkins — Thu, 09 Feb 2017 15:57:00 GMT

US & European Index Watchlists

If you have a subscription to our Australian end-of-day data then the various size indices are broken out in to separate lists (eg ASX Top 50, Top 100, MidCap50, etc), the constituents of which can be opened as watchlists or as separate charts (open the S&P folder under ASX Shares in the Security Selection window, select the required index and then Open List As...).

With the exception of the S&P500 index for US subscribers, we don’t yet have the same ability for indices from other exchanges, which is why I’ve created the following workbooks for clients with access to our US or European data.

Click the buttons below to save the workbooks to your computer, and then click on the saved files to open in Optuma and you will be prompted to move the file to your default workbook location.

Note: To open the workbooks and see the charts requires the Optuma 64bit version of the software and an active account with a subscription to our US or European data. If you are using the previous version of the software please click here for upgrade instructions.

Download and Customize the Workbooks

US Indices Watchlists – four separate tabs contain watchlists for companies in the Nasdaq 100, S&P400, S&P600 and S&P1500 indices.

Russell 3000 Watchlist – requires US data. As it contains 3000 symbols this list will take longer to load and is not advisable for clients running older systems.

EuroStoxx 600 Watchlist – requires a subscription to our European and Nordic database.

FTSE 350 Watchlist – requires a subscription to our London Stock Exchange database.

Using the lists for scans

With the watchlists open you can add custom columns to show where a condition is true or false, such as is the 50 period moving average sloping up? Or has the RSI crossed above 30? (To learn more about Optuma’s powerful scripting language sign in here to watch a series of introductory videos.)

For scans, back tests and signal tests select the required file from the Workbooks tab under the Codes to Scan option.

Custom Columns

Entry Triggers for Successful Trading

Jeffery Tie — Fri, 13 Jan 2017 06:50:00 GMT

Although this article is focused on Entry Triggers for trade initiation, the successful trader knows that Entry Triggers are just one component of success. In order to have a comprehensive understanding of success in trading, any discussion on the Entry Trigger topic must also address the perspective and context of the underlying market condition.

Perhaps I should start by expressing my thanks to Ray Barros, who taught and guided me at a crucial phase of my growth as a trader. Essentially, Ray's philosophy is to identify the direction and trend of the higher timeframes (he calls this the Perspective). The Perspective timeframes have a strong impact on the direction of the lower timeframe Trader's Trend.

Perspective

If the higher timeframe direction is up, the lower timeframe trend is likely to be up. As long as the analysis suggests that the uptrend is in place and is likely to continue, then the trading Stance is set. In a continuing uptrend, the odds favor a buy-first strategy, and obviously, in a continuing downtrend, the odds will favor a sell-first strategy. In a nutshell, this is a simplified statement on how I determine Perspective, my Trader's timeframe trend, and therefore, my stance.

It is not within the scope of this article to discuss analysis methods to determine whether the prevailing trend is likely to change. Once this can be assessed, then I can initiate a trade against the prevailing trend in the belief the current trend is exhausting, and that a new change in trend is likely to occur. For example, the prevailing trend in the US markets just before the 2007 peak was up. It is a matter of historical record the market rapidly declined after the 2007 peak. Obviously, trading in the direction of the prevailing uptrend at the 2007 peak can result in financial loss. It is also obvious that changing the stance by initiating sell trades against the prevailing trend, resulted in joining the new downtrend early - before the downtrend was established.

In other words, Perspective gives me the context to decide my Stance.

To recap:

In a continuing uptrend, I initiate buy trades. In an exhausted uptrend, I initiate sell trades.
In a continuing downtrend, I initiate sell trades. In an exhausted downtrend, I initiate buy trades.

The Low-risk Trade Entry Zone

Once the Stance is set, the next step is to locate low risk, high probability zones to start the hunting process for the trade entry. Ray has emphasized that in an uptrend it will be less risky, and more rewarding to buy the undervalued correction. In other words, buy at what I assess to be where the correction is likely to end. The next bullish impulse is probably a large and strong move after the resting phase of the correction ends. Aggressive traders can elect to buy the upside breakout - buying high - in the expectation of a strong explosive move to stratospheric prices!

Obviously, if I assess the trend to be bearish, my preference will be to sell at the end of the bear market rally in the expectation the next bearish impulse will be large and prolonged. The aggressive trader can also elect to sell a bearish breakout - selling low - in the belief the future price can be significantly lower, sooner rather than later.

One simple tool I use is the Slow Stochastic. This tool identifies:

overbought or overvalued highs,
oversold or undervalued lows.

Therefore, if I assess the trend to be bullish and likely to continue, I need to see Slow Stochastic in its oversold, undervalued zone.

Obviously, if I assess the trend to be bearish and likely to continue, I need to see Slow Stochastic in its overbought or overvalued zone.

The Trade Trigger

Once the Perspective, Stance and Low Risk Zone are determined, I need to initiate, or trigger the trade entry. The analogy here is I am now ready to draw my sword, and need to correctly time my deployment. I will shortly be sharing ten Trade Triggers I use - but bear with me as I need to address other key components of my trading methodology first.

Money, Risk, and Trade Management

Once the trade is triggered, the next step is to pre-plan the exit strategy. This is a crucial requirement for trading longevity because you can control the exit of a loss and make it relatively small compared to the profit exit - which has to be relatively large as compared to the planned loss. This determination of risk and reward in turn allows for correct position sizing, which is again a key consideration in my survival as a trader. Trade Management is simply the staggered profitable exit of the initiated trade entry. I subscribe to the "Single Entry, Multiple Exit" strategy - my initial trade size must allow for multiple lot size.

It is not within the scope of this article to focus on Money, Risk, and Trade Management, however this cursory discussion is still essential to illustrate my overall trading methodology.

Aiki Trading: Trading in Harmony with the Markets

I have explained my trading philosophy as taught to me by Ray Barros. However, every one of us can’t be a carbon copy of our mentors. I needed a different metaphor to better internalize what I learned from Ray.

I have always had a strong interest in Japanese Martial Arts and have had training in Aikido. The underlying concept in Aikido is that a small force can control and direct a large force; a weaker or smaller trainee can control and direct the motion and momentum of a larger, stronger opponent. This is done by the application of techniques or waza based on the idea of harmonizing the defender's actions with the attacking student's speed and momentum. As the student progresses, his understanding, application, and timing become better. His control of the opponent becomes competent.

In the trading context, all traders, both individual and institutional, are like combatants. The market itself is the opponent we need to win from. The issue here is the capitalization of each trader (individual or institutional) is small compared to the overall market. Think of the Central Banks who are grouped with the large institutions. Remember the Bank of England trying to defend the Pound in 1992? Despite their reputation and size, the market force was against them. Eventually, even the Bank of England capitulated.

What chance does the smaller individual trader have in forcing the market to move just because he is in a trade? The chance is like finding ice in a hot desert. The best approach is to think of harmonizing with the market - like in Aikido. Knowing the Perspective allows me to be with the Force. My entry is when the counter-attack (the correction) is likely to end, and where I assess the main Force is likely to resume. Knowing my capital is relatively very small compared to the market ensures I only engage in high probability low-risk trades, and that I manage drawdown very carefully, and become aggressively adventurous when equity grows positively.

I will now look at the ten specific trade triggers I use.

Ten Trade Triggers

1. The Turtle Soup

The Turtle Soup setup was codified and named by Lawrence Conner and Linda Bradford Raschke in their book Street Smarts; High Probability Short Term Trading Strategies.

Essentially, this is a setup that is backed by the trading statistics of the Turtle Trades. It is beyond the scope of this article to tell the impressive story of how the Turtles as a group were created. Readers who wish to find out more about the Turtle Traders can read Market Wizards by Jack D. Schwager. The Turtle Trades employ a breakout strategy. Looking to buy upside breakouts and sell downside breakouts in the expectation the next impulsive move should accelerate after the congestion or rest phase defined by a sideway trend. This strategy has rewarded the Turtles with significant, consistent and profitable returns over time. But statistically the Turtles have a relatively low hit rate of approximately 30%. This means statistically 70% of breakouts are false, and the market remains in range bound action - moving from the sideway high to sideway low and rotating back and forth from the sideway boundaries. This type of market action has been identified by Wyckoff as the up thrust and the spring. Personally, I like the Turtle Soup moniker, as I can visualize the Turtles becoming Soup when they take a loss!

In my version of the Turtle Soup setup, I will initiate the trade only in the direction of the higher timeframe direction. So in an uptrend of my trader's timeframe, I need the market to enter a sideway range as defined by the Barros Swing, in either my timeframe or one timeframe lower. For myself, my trading timeframe is based on the 18D Barros Swing. Ray defines this as the trend of the monthly timeframe. I can accept either 18D or 5D sideway range to setup the Turtle Soup trade. I then await a downside breakout of the sideway swing action, and validate this zone by checking if the Stochastic is at over-sold zone. This is the setup I need to see. The actual buy-trigger is a bullish candle that closes inside the original sideway zone. This implies the attempted downside break is a false break that is likely to expand the sideway range marginally. Obviously, in a downtrend, I will be hunting for a Turtle Soup setup to initiate a sell trade. The market must be in a downtrend and ranges in either the 18D or 5D Barros Swing, then attempting an upside breakout, which fails by closing inside the sideway range. (See Turtle Soup chart examples in the chart annex)

S&P500

AUDUSD

EURGBP

USDCAD

XADUSG

AAPL

2. The Six Japanese Candlesticks as triggers

Although the Turtle soup setup gives me the highest probability trade entry, it does not occur as often as I would like.

I use these six Japanese Candlestick patterns to trigger my trade entries as standalone triggers. If they occur as part of the Turtle Soup setup, my confidence level in the validity of the signal will rise. These six Japanese candlestick patterns have specific names and have been discussed in many books dealing with Japanese Candlesticks. Readers may wish to consult Steve Nison's excellent book, Beyond Candlesticks: New Japanese Charting Techniques Revealed, published by Wiley in 1994. I will describe these six Japanese Candlestick patterns in three sets of two patterns each because the patterns are the same in its nature. One triggers a buy trade, and the other triggers a sell trade. It will be appropriate for me to define how candlesticks were originally drawn, and what I consider to be bullish candles and bearish candles.

Traditionally, Japanese candlesticks were drawn with black ink on white paper. Bearish candles contained filled in black candle real bodies and were originally called Black Candles. Bullish candles had the body outlined in ink, but were not filled in. They were originally called White Candles.

A bullish candle must have a relatively large white real body, opening near the candle's low and closing near the candle's high. Obviously, a bearish candle must have a relatively large black real body, opening near the candle's high and closing near the candle's low.

In addition, these candles must be of normal size. Normal size is as defined by the ATR (I use the 60 period as my setting for ATR). This is because the distance traveled from the high to the low, or vice versa, represents the strength and intensity of the force creating that particular candle

a. The Hammer

The Hammer candle has a small real body that closes within the upper 33% of the candle's high-low range. It must have a long shadow (from the bottom of the real body to the candle's low). The Hammer candle's range must be at least of normal size. Obviously, if the real small body is closer to the candle's high, clearer is the trigger to buy. If the candlestick's shadow is very long, it would suggest sellers were strongly repelled by the end of that trading session and the market is potentially changing direction from down to up. Using a Martial Arts analogy, sellers push prices 5 paces down, but a strong counter-attack by buyers reclaimed the 5 paces won initially by the selling bears. The Hammer is so named because the Japanese technical analysts of that period consider this action as that of the market Hammering Out A Bottom. (See illustration of Hammers in the chart annex)

Hammers

b. The Shooting Star

The Shooting Star candle has a small real body that closes within the lower 33% of the candle's high-low range. It must have a long shadow from the top of the real body to the candle's high. The Shooting Star candle's range must be at least of normal size Obviously, if the real small body is closer to the candle's low, clearer is the trigger to sell. If the candlestick's shadow is very long, it would suggest buyers were strongly repelled by the end of that trading session and the market is potentially changing direction from up to down. Using a Martial Arts analogy, buyers push prices 5 paces up, but a strong counter-attack by sellers reclaimed the 5 paces won initially by the buying bulls. The Shooting Star is so named because the Japanese technical analysts of that period compare this action to the Shooting Stars seen falling to earth in the night sky. (See illustration of Shooting Stars in the chart annex)

Shooting Stars

c. The Piercing Candle

The Piercing Candle is a two candle pattern. I will describe this pattern based on the end of day chart. Readers can, of course, use and define the candles to suit their trading timeframe. The principles remain the same.

The first candle that forms the Piercing Candle pattern must be a bearish candle and the market closes with negative dark despondency. On the next trade day, a bullish candle of normal size pierces partially into the previous day's bearish body. To me the analogy is clear; a ray of white light is piercing into the black darkness and signals the potential start of an upward move in the market (see illustration of Piercing Candles in the chart annex)

Piercing Candle

d. The Dark Cloud

The Dark Cloud is a two candle pattern. I will describe this pattern based on the end of day chart. Readers can, of course, use and define the candles to suit their trading timeframe. The principles remain the same.

The first candle that forms the Dark Cloud pattern must be a bullish candle and the market closes with positive, white vibrancy. On the next trade day, a bearish candle of normal size partially enters into the previous day's bullish body. To me the analogy is clear; a black dark cloud is partially covering the previous happy white vibrant day and signals the potential start of a stormy period for the market (see illustration of Dark Clouds in the chart annex)

Dark Clouds

e. Bullish Engulfing Candle

The Bullish Engulfing Candle is also a two candle pattern. The first day of this pattern must be a bearish candle. The second trigger day must be a bullish candle that engulfs the previous day's entire high/low range. In traditional Candlestick definition, the white body of the trigger day must engulf the entire previous day's candle. In today's context - especially with 24 hr markets - gaps are rare, so engulfing candles are also rare. I will accept an engulfing body, where the white real body engulfs the previous day's black body, or where the trigger day's range engulfs the previous day's range. (See illustration of Bullish Engulfing Candles in the chart annex)

Bullish Engulfing Candles

f. Bearish Engulfing Candle

The Bearish Engulfing Candle is also a two candle pattern. The first day of this pattern must be a bullish candle. The second trigger day must be a bearish candle that engulfs the previous day's entire high/low range. In traditional Candlestick definition, the black body of the trigger day must engulf the entire previous day's candle. In today's context - especially with 24 hr markets - gaps are rare, so engulfing candles are also rare. I will accept an engulfing body, where the white real body engulfs the previous day's black body, or where the trigger day's range engulfs the previous day's range. (See illustration of Bearish Engulfing Candles in the chart annex)

Bearish Engulfing Candles

3. The Three American Candlestick Triggers

I am quite certain some readers will be searching the internet for American Candlesticks, and are likely to be disappointed by Google directing them to view brass or glass instruments that hold actual candles for lighting purposes.

There is a local Singapore story about how American Candlesticks, as used in Technical Analysis, came to be named. In 1998, Dow Jones Telerate (live data vendors and resellers of the TradeStation charting software) invited Thomas DeMark Junior to Singapore. I was fortunate to have been nominated by my then employer as the company's representative to the Demark one day seminar.

Mr. DeMark Junior showcased a short-term trading methodology. It was based on a comprehensive set of rules. It reminded me of the rules used in Japanese candlestick charting. The group of attendees I was with collectively and cheekily renamed the Demark set of short-term trading triggers as ‘American Candlesticks’ - precisely because these rules were devised by the very American Mr. Thomas DeMark Senior. You may want to read DeMark Indicators, written by Jason Perl. This book is authorized by Thomas Demark Senior, and is published by Wiley Press. In this book these three "American Candlesticks" are discussed in detail.

Here are the three American setups I use:

a. TD Camouflage buy setup

On the trigger day, the market must close lower than the previous trade day (T-1, or Trigger day -1), and T-1 should be a black candle.
However, on Trigger day, the market must close with a white or bullish candle.
The qualification rule to validate the setup is the low of the Trigger day must be lower than the true low of two trade days before Trigger day (T-2). This qualification rule is an attempt to locate a "spring".
The market is potentially camouflaging its true intention. The lower close will be reported as a down day, but the white candle shows buyers in control.

What I am looking for is the zone where I expect the correction to end, so I have the stochastic oscillator in oversold, or undervalued zone. The market must have dipped to reflect the stochastic in oversold zone, and the market is now potentially ready to run up strongly. Therefore TD Camouflage buy setup triggers my trade at (or just before) the end of the day.

TD Camouflage Buy

b. TD Camouflage sell setup

On the trigger day, the market must close higher than the previous trade day (T-1, or Trigger day -1), and T-1 should be a white bullish candle.
However, on Trigger Day, the market must close with a black or bearish candle.
The qualification rule to validate the setup is the high of the Trigger day must be higher than the true high of two trade days before Trigger day (T-2). This qualification rule is an attempt to search for an "up thrust"
The market is potentially camouflaging its true intention. The higher close will be reported as an up day, but the black candle shows sellers in control.

What I am looking for is the zone where I expect the correction to end, so I have the stochastic oscillator in overbought, or overvalued zone. The market must have rallied to reflect the stochastic in overbought zone, and the market is now ready to start its next down impulse. Therefore TD Camouflage sell setup triggers my trade at (or just before) the end of the day.

TD Camouflage Sell

c. TD Open buy setup

On the trigger day, the market must open with a gap below the low of the previous day (T-1).
Place a buy stop at the low of the previous day (T-1).
Hold the trade if the day closes with a white piercing or bullish engulfing candle.

This is an aggressive entry that is placed early, just after the start of the day. If the setup works, the entry is triggered at a superior trade fill, compared to taking the trade at the end of the day.

I'd like to stress I only deploy these trade triggers when I am in a low-risk trade zone, so to use TD Open buy setup, I must be in a stochastic low zone.

TD Open Buy Setup

d. TD Open sell setup

On the trigger day, the market must open with a gap above the high of the previous day (T-1).
Place a sell stop at the high of the previous day (T-1).
Hold the trade if the day closes with a black dark cloud or bearish engulfing candle.

I'd like to stress I only deploy these trade triggers when I am in a low-risk trade zone, so to use TD Open sell setup, I must be in a stochastic high zone.

TD Open Sell Setup

e. TD Trap sell setup

On the trigger day, the market must open within the high/low range of the previous day (T-1).
Place a sell stop at the low of the previous day (T-1).
Hold the trade if the day closes with a black candle.

I'd like to stress I only deploy these trade triggers when I am in a low-risk trade zone, so to use TD Trap sell setup, I must be in a stochastic high zone.

Furthermore, as I am primarily an end of day chartist, I can deploy intraday entries like TD Open and TD Trap only if I am in equity run up mode. If I am doing well, I can be aggressive and adventurous in my trade entries. But if I am in drawdown mode, I will not be initiating these two intraday trade setups.

TD Trap Sell Setup

TD Open and TD Trap

I have reached the end of this journey with you. I can say I have used all the triggers described above. Most of my entries are based on the end of day chart. Trading in US Equities, therefore, requires me to be watching the US equity market one hour before the close. I am usually awake at 4 am Singapore time, in order to scan my watch list and to trigger low-risk high probability trades before the market closes at 5 am Singapore time. By default, I also use the close of New York as the arbitrary close for my FX analysis.

CAVEAT: Note - all trades are supposed to be triggered at the close. I will execute the trade within the last 30 minutes of the trading session as it will be exceedingly difficult to execute trades at the close of the day. Therefore, my actual trade entries will not be exactly at the close, but will be fairly near where the market closes.

Good Luck, and Trade Well.

Optex Bands Part 2

Mathew Verdouw — Sun, 07 Aug 2016 14:03:00 GMT

‌Last week I wrote about the journey I took with the Dynamic Market Profile tool and how it showed some promise as a mean reverting strategy. I performed a number of “back tests” (getting the computer to run the simulation with a model portfolio), but could never get the consistent results I was seeing by observing charts. I found there was a real problem in the way many analysts, including myself, use back testing. The main issue being that traditional back tests combine signal testing and portfolio testing – that’s too many independent variables tested at one time. I’ll write on that in more detail another time. I solved the issue by building a Signal Tester into Optuma, and that led to the Optex Bands breakthrough.

At the start of 2016, Carson Dahlberg, CMT, joined us in our USA office as our Chief Market Strategist. Carson has one of the most disciplined quantitative approaches to the market that I have ever seen. His input has really helped us, and our clients, a lot. As we were preparing for the Optuma release, we were searching for a “killer” indicator to include with Optuma that would really help our clients. There are a number of tools that came out of our search, like “Volatility Swings”, which I’ll write about another time. Trying to come up with new indicators is a lot like panning for gold – it takes a lot of testing before you find something of value. Often you discover specks of hope, and occasionally you find a valuable gem.

Through this process Carson took some time to re-examine my work on Dynamic Market Profile. He found some interesting results when we created a ratio line which measured the distance of price from the Point of Control (POC). If you remember from the last post, the further price gets from the POC, the higher the probability that it will return. So the ratio allows us to measure the excursions from the POC. We also added a volatility measure into the calculation so we would have a consistent scale from one security to the next.

Here is what we found:

NAB Optex Ratio

The ratio is the line on the bottom of the chart. I’ve left the POC on the chart so you can see how the ratio reacts as the price gets further away from the POC. Remember that we also baked in a volatility calculation, so we measure the excursion in units of volatility.

This was exciting! There was a high correlation between the ratio falling to extreme levels and the market making a significant bottom. We played with some fixed levels, similar to the 30/70 commonly used on RSIs, but the ratio did not always fall to the same level. In the end I noticed this was similar to the measurement we were trying to solve with the Dynamic Market Profile, so we added another Dynamic Market Profile to the ratio line. We went on to play with the Standard Deviation ratios to tune it, and it we ended up with the adaptive bands that we could use as quantitative signals.

Here is a look at that same chart with the bands showing:

NAB Optex bands

Note how the bands adapt. When the ratio turns in the bottom zone, it’s a great measure that the market is heavily oversold and primed for a rebound to the POC. With the adaptive bands we had a lot of points that we could test with in the Optuma Signal Tester. There are a few things we noticed with this:

Both the entry and the turning point in the blue zone were significant.
In equities the blue zone showed promise, while the red zone for shorts was not as effective.
This was significantly more effective when the security was in an uptrend. That is, the ratio was showing a short term pull-back within a bigger uptrend.

This became our Optex Bands tool. “Optex” is a diminution of “Optuma Extremes”. Now we needed to get to the job of testing the indicator to see if our observations held across the market.

The first thing we needed to do was define the condition as a script. Here is the one that showed promise:

o1=OPTEX();  
cond1 = o1.Ratio < o1.25Lower;  
cond2 = o1.Ratio TurnsUp;  
cond1 and cond2`

Here we are storing all the results from the Optex function into a variable “o1”. We then set the conditions. The first is that the Ratio line needs to be in our blue zone. The second is that the Ratio line needs to turns up. The final line is telling Optuma which conditions need to be true to show the line on the chart.

I use the “Show Bar” tool set to “Lines” in Optuma to display when these conditions are met.

NAB Optex bands

You can see the that there is a high correlation between the turns in the ratio and some really good times to get into this market. Now I needed to take this and test it over a larger set of securities.

Earlier I was mentioning that we built a new Signal Tester in Optuma. The main point is that I wanted to test the efficacy of a technical signal without the portfolio bias that is usually introduced in traditional back testing. When we observe something that looks good on a chart, we need to test it further to see if it is really as good as we think. In particular, we need to be sure that our strategy will not destroy our portfolio in a bear market.

After lots of testing, we found that the best results were when the market was in an uptrend and the Ratio line entered the blue band. The following image is from the Optuma Signal Tester. We tested from October 2001 to October 2009 on the SP500. We chose that period because the S&P index finished at the same price that it started. That is, it was a “flat” market. The Signal Tester takes every signal that occurs in that period (there were 5,377 of them), and measures the returns of the security 30 days before and 30 days after the signal.

Here are the results of the test. Can you believe that this test on all of the S&P 500 equities takes only 38 seconds?

NAB Optex bands

The green band is the most important. It shows the average performance of the equities before and after the signal. Since this is an indicator showing extreme oversold conditions in the market, it’s not surprising that the green band is falling into Day 0 (the vertical line on the chart). We can see that on average our return one month after this signal is 2.68%. That’s 22.5% annual return – not too shabby!

If you remember from my last post, a lot of information can be hidden inside an average, and we have to dig in to find out what values make up that average. That’s why we have the “Profit Analysis” plot on the chart above. It’s the green distribution plot. What I want to see here is that it’s skewed to the right (making money) and is high and tight. That tells me that a lot of the results are significantly close to the stated average and I can have a lot of confidence in it. If it was low and wide, then I know that my average is not statistically significant at all.

When I look at the stats, it tells me that there is a 63% probability that I am going to get a positive return when I take this signal. What’s more, when I string 10 random signals together (tested 20,000 times in the Monte-Carlo simulation), my probability of being profitable rises to 73%. Look at the blue distribution and the final column of the stats table.

All of Technical Analysis is rooted in probabilities, even if it is by observation. For example, when we see the market find support and resistance at a ratio repetitively, we expect that ratio will be significant in the future. Remember that we call them “indicators” not “certainties”. In the markets nothing is a certainty, but we can put the probabilities in our favour and manage the reality as it unfolds.

More on the Signal Tester another time. It was such an important component in the building of the Optex Bands that I needed to cover the basics here. We discovered that the excursions of the Optex Ratio line into the blue oversold zone was indeed statistically significant.

If you currently use or are trialling Optuma, and have an active Subscription, you can try the Optex Bands tool for yourself. Let us know what you think.

What about FX? Here is a chart of the Euro US Dollar. Notice how in this chart both sets of zones are significant.

NAB Optex bands

The reason for this is that the Optex Bands Ratio measures the distance from the POC. If the price slowly climbs, as it usually does in equities, then the POC is always following and there are no extreme moves to the upside. With equities the moves down tend to be more aggressive (everyone getting out at the same time) leading to the big excursions and the oversold signals. When we look at currencies, they don’t have the same slow buy, rapid sell trait. They are more balanced which leads to signals on both sides.

Whew! There is a lot to take in here. We’re really excited about the Optex Bands and hope you are able to benefit from them. Remember to be very careful how you layer new strategies into your work-flow. We’re a company that is continually experimenting with new ideas and we believe there are many new Technical Analysis techniques waiting to be discovered. Make sure you are subscribed to our blog so you you can stay up to date with the new work we are doing.

Buying Out Performers is Too Late

Mathew Verdouw — Thu, 04 Aug 2016 22:59:00 GMT

In this paper we test the results of buying securities that have been outperforming the market. We are told two rules in finance: “Buy Low and Sell High” and also “Past Performance is not a guarantee of Future Returns”. Yet many advisers and investors will recommend the best-performing securities based on that very assumption. This paper shows that to maximise returns, there has to be a different way to examine when a security should be bought. We do this by using the Relative Rotation Graphs (RRG) to test if absolute returns can be improved by responding to the relative strength performance of each security using RRG charts. The paper also explains the basic concepts behind the RRG and gives the results from testing in all market conditions.

Optex Bands Part 1

Mathew Verdouw — Mon, 01 Aug 2016 01:03:00 GMT

Optex Bands is a new tool we created to measure potential extremes away from the “consensus” price. To explain the how and why of Optex Bands, we have to first take a journey through the evolution of this tool. Along the way, you will learn why they are a powerful measure of overbought and oversold markets.

In 1998 I was at an ATAA presentation in Canberra, listening to Ray Barros present on Market Profile. Ray explained when price gets out to the 3rd Standard Deviation, there is a higher probability for it to return to the point of control. Whenever we look for these opportunities, we call it a “Mean Reversion Strategy”. We’re expecting the price to revert back to the mean.

Whoa! That’s a lot of terminology. Let’s take this one step at a time. The first thing we need to do is explain the concept of Market Profile. It was developed by the late Peter Steidlmayer in 1985 as a way of visualising market structure as the bars develop through the day.

Time to Profile the Market

Take the image below. Steidlmayer divided the bars up into “units” and gave each bar its own letter. This is just like the “normal” bar or candle chart, except that we display each bar using these units.

Profile Construction

He then compressed all the bars and allowed them to stack up, but not overlap. Much like the old game of Tetris (for those of us who remember).

Profile Construction

The result was the following. You can see each bar has been pushed to the left and the bars allowed to be split up. Steidlmayer wanted to know how much time the security spent at a particular price. The rationale was that the price where the volume was highest would act like a magnet, because it was the most agreed upon price. Said another way, if price moved too far away, and no more buyers or sellers were found, price would drift back to find more buyers and sellers.

In this case, you can see row 19 has the most blocks in it. Steidlmayer called this “The Point of Control” - it’s where the volume is the highest.

A note on the use of “volume” in this context. In the Market Profile work we are not talking about actual trade volumes, but rather the volume of units. That is, how many bars on our chart contained that price? In the case of row 19 in the image to the right, it was 11. We’re exploring options for using underlying intraday data to use the actual trade volume in this process. Even though it would be much better, it relies on a lot of data.

Completed Profile

Bring on the Statistics

Steidlmayer noticed that the profile resembled a traditional Gaussian Distribution.

Gaussian Distribution

For those unfamiliar with Statistics, the x-axis contains values that we are observing, and the y-axis is the percentage of time that value occurred.

This is easiest when we think of something we can identify with, like average heights of men and women. There are going to be a lot of people around the middle (average height), some very tall people and some very short. Have a look at the following chart:

Height Distribution

There’s a lot to be seen from this. Men are on average taller than women. The fact that the Frequency (y-axis) is higher for women tells us there is less variation in the heights of women than in men. We also see that the tallest men are around ten inches taller than the tallest women. These plots are very important for describing observations, and that is exactly what Steidlmayer thought. Too often we are caught looking at an average, forgetting to examine all the numbers that went into that average.

In our first distribution above, you’ll notice there are three coloured zones on each side of the average. These are the demarcation points for the First and Second Standard Deviation. Don’t let the terms scare you. Standard Deviation is just the way we quantify the variance around the mean (average). This is best described with an example.

Let’s take two lots of three numbers:

The average for both is 50, but the variance (the measure of how scattered the values are around the middle) is very different for each.

Standard Deviations use squares and square roots. That’s beyond what I want to cover here, but at least you get the idea. It’s a derived measure of variance. When all the calculations are done, we find that 68.2% of all the observations are inside the first Standard Deviation. The next 26.2% cover the second Standard Deviation, with the remainder in the third.

Steidlmayer did this same process with the units in Market Profile. He would start at the Point of Control and count units on each side until he had counted enough units to reach the percentage for the first Standard Deviation. He’d continue on until he had all the Standard Deviations marked.

Here’s an example:

Market Profile on Microsoft

The box shows the candles that are being combined together to make the profile. The red horizontal line is the Point of Control. From that point we are counting the units to calculate the boundaries of the Standard Deviations. In this case we ran out of units on the top side and there is no third Standard Deviation Zone. This tells us that the “Profile” has a bullish skew.

If we project those boundaries forward, you can see how important they are and how the price keeps coming back to the Point of Control (POC).

Market Profile on Microsoft with Zone Lines

This is the part of Ray Barros’ presentation that stuck with me for years and years. The POC was like a magnet. The further price got away from the POC, the higher the probability that it was going to revert back. The trouble is, as time goes by, the influence of the POC gets increasingly weaker. In fact, there’s always a new POC developing that we should be considering.

Ten years later I was working on the Bollinger Bands tool in Optuma (Market Analyst) and had an epiphany. While checking the Standard Deviation calculations, I remembered Market Profile. We should be able to create a Dynamic Market Profile tool that is more adaptive by using a similar band idea. Instead of standard deviations around a moving average, why not around the Point of Control?

So how does it work? Imagine that we take 20 bars and create a Market Profile calculating the POC and all the boundaries of the Standard Deviations. We then move the box by one bar and repeat the calculations. We continue to progress through our data bar by bar, calculating all the major points and joining those values together. Here is a chart of what that looks like:

Dynamic Market Profile

I’ve made the POC line bold so you can see how it adapts to the market. The blue zone tells me the limits of the first Standard Deviation at any time. When price is in the blue zone, it’s within the consensus. Green is the second and the red is the third. Some things to note on the chart (look for the numbered green boxes):

The candles are in the red third Standard Deviation zone. Price cannot stay there. As I look through charts, I see example after example how price gets pushed back to the now-adaptive Point of Control.
Here the POC is resetting and there’s a new consensus about what the accepted price is.
Another example of the price reaching an extreme excursion from the POC. Taking a Long at this point is dangerous and would need a lot of confirmation.

This was a great breakthrough. It was useful for working with Mean Reversion Strategies, but my testing was just not giving the results I was expecting. Using traditional back-testing doesn’t help when we need to benchmark the efficacy of indicators. For that I had to wait another five years, get my CMT designation and learn a lot more about quantitative testing. More on that next time!

For where I was in 2008, the best use I could find was to apply an offset to the tool equal to the box size. I could see some really good opportunities to identify the trend and stay out of congestion at turning points. Here is an example:

Dynamic Market Profile - Offset

Another observation is that the POC line is a great demarcation of trend. When the price is above the POC I can say the trend is up, and when below, the trend is down. Here is a chart with a 260 bar POC. In this one I have left the zones off. I do this with a “Show Plot” tool in Optuma and set the script to:

DMPA(BARS=260).PointOfControl

260 Bar Point of Control

You can see how an up-trending market is consistently above the Point of Control. If I wanted to filter on this I could write a script like this:

Close() > DMPA(BARS=260).PointOfControl

This would give us stocks in an up-trend.

I’ll leave you with this for now. The “Dynamic Market Profile” tool is in Optuma. It’s worth exploring. Next week I’ll explain how we went from this discovery to the creation of Optex Bands and the journey we have taken in piecing together ideas to create a powerful new indicator.

Continue reading at Optex Bands Part 2

Gann-Based Market Breadth

Mathew Verdouw — Sun, 31 Jul 2016 23:04:00 GMT

The subjective works of WD Gann from the early part of the twentieth century are not normally associated with one of the modern pillars of twenty first century Technical Analysis, Market Breadth. After all, the concept of Market Breadth requires an enormous amount of computational power basing calculations on objective data, while Gann drew all his charts by hand and was mostly concerned with geometry, angles, timing, and ratios which could be subjective. How can these two seemingly opposed methods be brought together? The answer will be found in this paper.

Volatility Based Support and Resistance

Kirk Northington — Fri, 29 Jul 2016 22:54:00 GMT

Favorable risk-adjusted returns can be, at times, as difficult to attain as a porcupine in a balloon factory. Forces are constantly at work to ensure that neither of these occurs. What follows will outline an emerging solution for forecasting technical price and volatility value levels. The solution is termed Volatility-Based Support Resistance (VBSR).

You will be able to understand how VBSR enables risk analysts, portfolio managers, and trading execution teams to forecast the best price levels for accumulation. Additionally, light will be shed on identifying points where market-implied volatility can be forecasted to alter its prevailing directional track.

Proper risk governance & oversight mandates that we execute careful processes for selecting opportunities. Steps to include macro market influences, investment mandates, and price/value forecasting involve multiple groups in the decision framework. Processes consume time and cause opportunities to be missed. Time, not unlike confirmation, consumes Alpha.

Dow's Theory of Confirmation Modernized

Carson Dahlberg — Wed, 27 Jul 2016 23:01:00 GMT

Charles Dow was concerned with managing returns in a context of risk. His process required identifying and confirming trends. He was aware the market trends were influenced by economics as well as behavioral biases. Therefore he required that up trends be confirmed in order to be more likely to persist. Dow’s Theory of Confirmation created a framework for making investment decisions. This paper takes Dow’s concept and models a modern version of Confirmation which demonstrates superior risk-adjusted returns.

RRG Weights

Mathew Verdouw — Sun, 23 Feb 2014 23:06:00 GMT

Relative Rotation Graphs (RRG) are one of the most powerful ways that Relative Strength can be displayed and normalised on a single chart. In this paper, we explore if a set of securities on the RRG, which is benchmarked against an index that is made up of only those securities can be balanced on the X and Y axis. The results once we include Market Capitalisation are amazing.