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Machine Learning With Multiple Random Forest Models Version 2

This is also based on the forum post with the machine learning example. Algorithms that use a random forest can produce very different results with each backtest, so I modified the algorithm to use four random forests. This method may be able to generate alpha with much less variability. I tried to test it with 10 models but it returned an error, apparently using five at a time is the maximum.

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Backtest from to with initial capital
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 598e649dbf9e7750f7b2b522
There was a runtime error.