Parameter Optimisation & Backtesting – Part 1

When developing a strategy that uses a technical indicator, such as a moving average, it is often difficult to decide on what period to use with the indicator. Should it look at the last 10, 20, or n days? The simple solution is just to iterate over many different parameters and look for the parameter combination that optimises a performance metric perhaps the Sharpe Ratio or minimises the Max Drawdown or some combination of both of them.

This method must be approached in a sensible manner, essentially we are fitting the strategy to the data and must be careful not to over fit and capture spurious profits. It is likely that any parameter that is trained will vary over time, the strategy must be able to deal with changes to this parameter.

For example say we are tuning the variable A, and we find that the optimum solution is A=n in the training data (it is very profitable with high sharpe ratio). How well does the strategy perform when we set A=n+1, or A=n-1 do we still get good results? If you don’t it’s an indicator that maybe you have over fitted.

For more discussion on this see

Part two will contain a script to perform an optimisation on a simple moving average strategy.




2 thoughts on “Parameter Optimisation & Backtesting – Part 1

  1. Pingback: Parameter Optimisation & Backtesting – Part 2 | Gekko Quant – Quantitative Trading

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