At Optuma, a part of what we seek to do is validate and benchmark different analysis to see how they compare in different types of markets. Our goal is to show the usefulness of indicators and to demonstrate whether long-held beliefs about different Technical Analysis tools are based on fact or fiction.
We do this via our Signal Tester module. This module has been engineered to allow us to perform complex quantitative tests across hundreds of securities in seconds. Traditional Backtests show whether or not an idea can be traded but they do not provide a full picture about what price is doing before and after the signal. The Signal Tester allows us to examine every instance of a technical signal across thousands of securities and measure the average performance, and distribution of the average.
Our long-held beliefs will be immediately quantified and we will finally know. We may learn many common indicators don’t work as advertised but we might be able to learn from studying the results of the Quantitative Signal Tester on multiple indicators, which components of indicators are effective. This could lead to new, more accurate indicators.
Volatility Based Support and Resistance
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 occur. 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 more accurately forecast 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.
Buying out-performers is too late
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 will show that to maximize 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 the relative strength performance on the RRG charts. The paper will also explain the basic concepts behind the RRG and give the results from testing in all market conditions.
Dow’s Theory of Confirmation Modernized
Charles Dow was concerned with managing returns in a context of risk. His process required identifying and confirming trends. He was aware that 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 modern version of Confirmation is which demonstrates superior risk-adjusted returns.
Gann Based Market Breadth
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. So how can these two seemingly opposed methods be brought together? That is what I hope to answer in this paper.
Relative Rotation Graphs (RRG) are simply one of the most powerful ways that Relative Strength can be displayed and normalized 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 Capitalization are amazing.