Welcome to my online repository of research and insights on automated trading system development
In the last volatility filters post we saw that trades from a simple Trend Following system (20-50 MA cross-over) had different expectancy based on the relative level of volatility at trade entry. This suggested that a filter blocking trades most volatile at entry (in the top decile: 90 to 100% of past volatility) would raise [...]
Entries Tagged as 'Backtest'
A Different Application of the Bootstrap
September 28th, 2010 · 3 Comments · Backtest
Tags: bootstrap·volatility
Volatility Filters
September 23rd, 2010 · 6 Comments · Backtest, Trend Following
I have touched on trading regimes before; and looking at volatility-based regime switching was in my research stack since then. Today, I’m looking at a practical example: Trend Following results based on entry vs. past volatility. System Code Concept I developed a simple Trading Blox filter, which calculates the current volatility (via the Average True [...]
Tags: Trading Blox·volatility
Walk-Forward in Trading Blox: Back-Testing Adaptive Trading
September 8th, 2010 · 7 Comments · Backtest, Software
A few months ago, I got quite interested when Trading Blox announced that they introduced a new walk-forward functionality in their latest version. I just got round to upgrading, and giving that walk-forward testing a go. Amongst other things, some of the chart features have been improved – as can be seen in the eye [...]
Tags: Trading Blox·walk-forward
Monte Carlo Permutation: Test your Back-Tests
August 18th, 2010 · 2 Comments · Backtest
The second method to evaluate the statistical significance of a back-test result presented by Aronson (in EBTA) is the Monte Carlo Permutation. This is an extension of the classic Monte Carlo method, applied to rule testing. The concept behind the Monte Carlo Permutation is similar to the Bootstrap method: Generate multiple random outputs based [...]
Tags: aronson·monte-carlo
Bootstrap – Take 2: Data Mining bias, Code and using geometric mean
August 13th, 2010 · 34 Comments · Backtest, Code
In part 1 of this bootstrap post, we looked at how to apply the method to establish the statistical significance of a single trading rule. In Part 2, we’ll look at how to deal with the data mining bias, the impact of geometric vs. arithmetic mean return. The code implementing the bootstrap test is available [...]
The Bootstrap Test: How significant are your back-testing results?
August 11th, 2010 · 26 Comments · Backtest, Books
As mentioned in the Evidence-based Technical Analysis review post, the main value of the book lies in the presentation of the two methods allowing for computing the statistical significance of trading strategy results, despite having a single sample of data: Both methods solve the problem of estimating the degree of random variation in a test [...]
Tags: aronson·bootstrap·data mining
