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Alpha Factor based off StockTwits Trader Mood (Long/Short)

This is an algorithm that was inspired by a study documenting the non-linear evidence of social media on stock prices. In the study, Thársis T.P. Souza finds that social media has an effect on stock prices that cannot be explained away by other fundamental factors. My main takeaway from the study wasn't the researcher's findings, but rather the universe he examined (DJIA). In previous algorithms, I had used a blind factor ranking regardless of message volume. In this algorithm, I constrain my universe to purely securities with the highest levels of message volume and get more consistent and stable results through 2015 than my previous work.

Before cloning the algorithm, please read through these strategy notes:

  • This algorithm is basically a culmination of parts 3~4 of the Trader Mood Research Series: Hypothesis testing and crafting a signal to begin strategy creation.
  • This algorithm provides a simple framework for working with Trader Mood by only looking at the previous day's mood. In order to evaluate that signal independent of other factors, slippage and commissions have been set to zero. The algorithm itself is not geared for practical trading and will perform poorly with slippage & commissions on.
  • It uses the StockTwits Trader Mood dataset by PsychSignal between January of 2014 ~ December 2015. The full sample data feed is available from 10 Jul 2009 - two months delay from today.
  • The algorithm uses a weighting of message volume and bullish/bearish intensity to determine positions. The message volume is used as a filter (the top 500 securities with the highest volume of messages are looked at) and the previous day's bullish/bearish intensity is used as the ranking methodology.
  • Basic liquidity floors are used: ADV > 10,000,000 and only looking at the top 1,000 stocks by liquidity ranking

I'm excited to see what progressions we can make with this, please post back with your own variations if you want suggestions/feedback.

Finally, join us for our recorded webinar as we walk through this series with James Crane-Baker from PsychSignal.

This algorithm is for education - the algorithm is not intended to provide investment advice.

Clone Algorithm
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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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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: 56fecb03e199f10f401e380e
There was a runtime error.
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1 response

For those who missed the webinar, you can view it here: http://bit.ly/1SMv1lP

Also attached is the tearsheet for this algorithm and a few notes:

  • Turnover seems pretty stable (approx 30% per month 1.5% per day)
  • Returns are higher in 2015 than in 2014
  • Overall performance is stable
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