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Better System Trader

If you’re looking for inspiration, motivation and practical advice on improving your trading results, Better System Trader delivers every week. Each episode brings you an expert trader who shares their own story, along with the steps, both good and bad, that they’ve taken on their path to success. With a focus on actionable insights, the tips and tricks used by the experts contain loads of value, providing you with insanely practical tips and tools you can start using TODAY. Improve your trading with Better System Trader.
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Jun 12, 2016

Today we’re covering a topic which can really be a concern for traders of all levels, from beginner to pro, and that is the topic of strategy evaluation.

  • Have you ever found that real-life performance does not match expected results?
  • Or perhaps you have a strategy that is stuck in a drawdown and wondering if it’s actually broken?

I’m sure we’ve all heard of data mining bias, over-optimization and curve fitting and the impacts this can have on our trading accounts.

We may be even using techniques such as Out Of Sample testing, Walk Forward Analysis, Monte Carlo analysis and a number of other measures to identify or reduce the impact of these issues, but do these approaches actually work? Are there limitations or dangers with these techniques? Are there better ways?

In this episode we talk to someone who evaluates trading systems for a living, plus his research into system evaluation techniques has won awards. The guest is Dave Walton.

Dave was the winner of the Wagner award in 2014 for a paper titled ‘Know your system – turning data mining bias to benefit through System Parameter Permutation’.

In our chat today we talk about the technique in his paper and how it can be applied to trading strategy evaluation. We also discuss some of the assumptions and limitations of the approach, and he shares with us some valuable insights he’s made since publishing the paper which have resulted in an updated approach he now considers a better alternative, so make sure you listen out for that.

Topics discussed

  • How the typical approaches to system development can introduce datamining bias without you knowing
  • The types of systems that can increase the chance of data mining bias and what to look for
  • How the method of splitting your out of sample data could be causing you to throw away good strategies
  • Out of sample, walk forward analysis and Monte Carlo - do they actually reduce data mining bias?
  • The problems with using Monte Carlo analysis to assess strategy performance and why it doesn’t protect from overfitting
  • System Parameter Permutation - how to use it, why use the median, parameter range selection and new insights since the SPP paper was published
  • How System Parameter Randomization solves some of the issues of System Parameter Permutation
  • Stochastic modelling and how it can be used to determine if a rule is adding value to your strategy
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