## Different Volatility Measures Effect on Daily MR

Daily swing trading strategies these days are usually inclined towards MR rather than trend FT. While there is a lot of factor that moderate a daily MR strategy, one of particular interest is volatility. Usually, higher volatility is favorable for short-term strategies (think RSI 2). For this bit of research I tried several volatility formulas on several time frames and compared the effect on RSI 2 returns.

Methodology: I Applied RSI 2 strategy to SPY’s returns from 2000 then classified volatility in percentile. I used three different methods to compute volatility figures, the classic standard deviation, the Garman Klass – Yang Zhang and the Yang Zhang method. The following formulas explain how I came up with the figures; I used the volatility function of the TTR package in R:

Garman Klass – Yang Zhang method:

Yang Zhang method:

Where:

*The GK-YZ method allows for opening gaps while the YZ method is independent of both drift and opening gaps.

The table below shows average trade result and winning percentage by percentile for monthly (21 days) and annual (252 days) time frames. Note that these time frames were arbitrarily chosen and a future post will likely expand on this with different time frames.

At first glance, it seems that the added computation complexity does not significantly improve our system accuracy. On the monthly time frame though, the system average trade returns in the lower percentile was higher when mitigated by the YZ indicator. Regardless, the rest of the results were not significant enough for me to pronounce a volatility indicator better suited for a MR system. Traders might want to KISS and stay with the good old classic standard deviation.

QF

## The Importance of Return Distributions

When designing a strategy, I like to observe the probability distribution of the asset I plan to trade. It yields precious information on the behavior of the underlying and can also help identify the market regime in effect for a given period.

Of course eyeballing the probability density curve or the empirical cumulative distribution can work, but from a quantitative trading point of view, it does not really help; we want something more mechanical that we can rely on over and over. The answer is quite simple; simple curve analysis can give us the mechanical capability we are looking for. Data on the mean, median, skew, kurtosis, etc. of a distribution can all be fed as parameters to be analyzed in a trading system.

We don’t have to use this technique uniquely on raw return data, it is often helpful to know how a given indicator or strategy affect the return distribution. A comparison between the raw return distribution and one processed with signals from a strategy can help indicate whether the strategy is traded with the right bias for the current market regime or if a strategy is suddenly diminishing in profitability. Furthermore, for the daily swing traders amongst us, probability densities analysis can help us see if our indicators really help mitigate daily MR.

Finally, distribution analysis can be an extremely valuable tool when developing adaptive strategies. It provides a strategy with instant feedback on its performance and its effect on the return series of the underlying; taking that in consideration can be a good base to build on when designing your own adaptive algorithms (more on that later..).

QF

## Shout-Outs

I have no qualms about accepting a useful idea merely because it wasn’t my own.” ~ Thrawn

In this short post, I want to acknowledge the blogs and people (in no particular order) who motivated me to start the Quantum Financier blog. I am a big fan of their work and of their blog, so a big shout-out to you gentlemen.

Michael Stokes of MarketSci

Max Dama of Max Dama on Automated Trading

Rob Hanna of Quantifiable Edges

and finally the Quantivity blog

QF

## Welcome to the Quantum Financier Blog

This blog will contain the results of my research and the things I am currently working on. For now,  I expect my posting to focus on applications of machine learning, mathematics and quantitative methods to develop automated trading strategies. My ultimate goal is the creation of adaptive investment strategies that “think” and “analyze” the market and evolve accordingly.

When reading my posts, keep in mind that I am only a student and that my goal is to learn as I post, I will make every effort not to make mistakes when posting but it might happen. Regardless, I look forward to receive feedback and comments on the content of this blog and to discuss the ideas posted.

QF