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First Pass from AI derived trading signals looking for multi sigma events

Using our AI, we applied deep learning to generate signals. First pass results using a naive trading approach

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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: 5967d9a6a1deeb5664209246
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3 responses

Here's the notebook for above model

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Hi Pej, thanks for sharing your work. If you are interested in being considered for an allocation to the strategy, I'd recommend:

  • Screen a large dynamic universe, using the Q1500
  • Reduce the position concentration, I'd suggest a threshold of 5% cap or below to start.
  • Update the strategy to be cross-sectional with low common factor exposure (sector, fama-french, beta)

Then I'd run analyses to find the optimal trading time. The market open window tends to have higher spreads and thus costs, which are not being modeled in the the simulation. I'd examine how does the algo perform at different trading intervals and different days.

Dan goes further into our selection criteria in this latest post: https://www.quantopian.com/posts/getting-an-allocation-june-2017-update. Good luck!

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Hi Pej,

Thank you for sharing your work. Impressive stuff indeed. Did you consider using a larger universe and minimizing the drawback? I will probably use your algorithm as the base for my own AI algorithm.

Cheers,

Pieter