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Multi Sector OLMAR Strategy

Adapted from the existing algorithm posted here, I've changed some selections based on my own research. Paper is here. Generates some positive momentum over a 5 year daily backtest. Multiple backtests suggest epsilon values below 1 produce less volatility in returns. I would like to implement hedging using a variability reduction model as described here. Collaboration is welcome

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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: 55c11a28ad66df0c6b161d1c
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2 responses

Thanks for sharing. Yes, it looks like this generally follows the benchmark but does slightly beat it in the 2011 - 2013 range. You can see it's similar to the benchmark because the beta is close to 1. I would be curious to see how it performs with a larger set of stocks/more trades and some hedging, which should bring down its beta.

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Gus, I have tried to modify context.stocks to use a highly liquid selection from DollarVolumeUniverse() instead of a list of handpicked stocks but the result is instability. Backtests can randomly crash when the simplex projection function goes out of bounds and I'm still trying to fix it. I agree that a larger set of stocks should bring down the beta.