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2016-7-30 NYC Workshop

For workshop use only, please. Submit your factor tearsheet and backtest tearsheet as notebook attachments on replies.

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6 responses

Volatility isn't all that predictive apparently.

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https://www.quantopian.com/posts/2016-7-30-nyc-workshop#

no factor tear sheet it didnt work

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Workflow Checklist for Factor Modeling

  • Come up with hypothesis for whether a factor might be predictive of
    returns. Obtain and clean data for that factor. Run single-factor
    analysis using alphalens and determine whether it’s a viable factor.
  • Once viability has been determined, check if factor is correlated
    with a list of known factors and factors already present in your
    model. If your new factor can be completely described as a weighted
    sum of existing factors, then it’s not that interesting. If your
    factor has new information, then add it to your set of existing
    factors.
  • Develop a model for weighting each factor in your final
    ranking scheme. Weightings could be equal, volatility weighted, or
    based on something more sophisticated like a adaboost or other signal
    boosting/expert weighting algorithms.
  • Put your new and improved
    multi-factor model in an algorithm along with the weighting scheme if
    it’s more than a fixed or equal weighting.
  • Use portfolio construction
    techniques to ensure that the trades produced by your multi-factor
    ranking scheme do not contain excess risk. Things to consider
    include: CVaR, sector exposure, risk factor exposure. Generally you
    will want to create a list of longs and shorts based on your
    multi-factor ranking scheme, and then set the weights based on
    portfolio optimization techniques as described above to ensure you
    don’t have excess exposure to any given factor.
  • Put the algorithm up
    to trade on paper for a little while. A higher frequency rebalancing
    algorithm will need less time to accumulate the same confidence in
    future performance as an algorithm that rarely trades. On the other
    hand, be careful not to paper trade it until the alpha you originally
    found is old news and exhausted.
  • Do constant risk monitoring of the
    algorithm to determine if alpha is decaying or risk exposures and
    returns are outside of forecasted limits.
  • Put algorithm to trade
    live.
  • Continue risk monitoring.

Delaney,

Thank you for the great workshop today.

Attached is a backtest I finally got to work after getting the Long/Short algo to filter only on the Basic Materials Sector. The factor research I quickly did during the workshop showed that 'Retained Earnings to Assets' and 'Dividend Growth' had some promise in picking longs and shorts in Basic Materials over the 2014 calendar year. It's a very small portfolio of stocks in this sample portfolio, but I thought I would share with the class anyway. It was good to see how factor research matched expected portfolio results in a backtest.

Thanks again.

Hugh

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Factor Tear Sheet Attached...

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Backtest results...

Clone Algorithm
1
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Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
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: 579d3dfa17f86b0ff491174f
There was a runtime error.