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Machine Learning Alpha with Risk Constraints

I have updated the Machine Learning on Quantopian algo to use the new risk model as an optimizer constraint. I had to change the objective to a factor-weighted portfolio in order to pass the contest constraints. Additional research on what factors to include should help bring the cumulative returns to positive and allow anyone to submit it to the contest.

Clone Algorithm
115
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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: 5a81e2757ada5f4237c8d5a9
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2 responses

And here is the Contest Criteria Check NB for it.

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Are we limited to default commissions and slippage in the contest?