Is there any formula to estimate the minimum number of trades in a backtest for different timeframes so that the results are statistically significant? For example, for 1-min forex data, how many trades should the backtest generate to make any sense? Any pointers to articles will be appreciated.
Generally 30 trades is what people look at "statistically significant" however i like to see hundreds. You can use the standerd error formula to find out how much error there is and determine if you have enough samples. 5% or less standard error is usually good.
For example, 150 trades over the last 5 months would ( for me) have enough trades, but the timespan is too short. 50 trades over the past 3 years has ( again for me) a long enough timespan, but too little trades. One could also ask yourself if so little number of trades are tradable in itself. On the other hand, I don't need something to work for the past 10 years, if this ever exists at all. Last, If I would trade micro size I would risk far more, while starting outright with futures I would be far more careful.
Last you need to take ‘the jump from the diving board’ at some time, you will always have uncertainty. Just start with smallest amount or unit possible, and increase size gradually only and only after succes, while keeping a very sharp eye on developing drawdown to know when to pull the plug.
One of my worst enemies are my own false assumptions
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Be careful about using one minute bars and be sure to take into account the spread. It's better to use actual tick data and capture the actual spread at the time of the trades. Most forex pairs spread change depending on the time of day, news, etc...
Mark Douglas suggests 20 samples as adequate to test the win rate of a trade strategy. You can develop a rolling average spreadsheet that calculates the win rate of the most recent 20 samples (discard 1st result as you include 21st result etc.) thus always providing the most recent market results for the strategy.