DIY Quant Strategies on Quantopian

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An introduction to implementing 5 basic quant strategies on Quantopian. Presented to the Bay Area Algorithmic Trading Group and the Bay Area Trading Signals meetup groups at the Hacker Dojo Feb 6th, 2014 by Jess Stauth

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  • *Not a Mutually Exclusive CollectivelyExhaustive list.
  • avg 30 day return = 0.93%
  • select po.title, po.replies_count, po.views_count, al.clone_countfrom posts as po left join backtests as ba on ba.id=po.backtest_idjoin algorithms as al on ba.algorithm_id=al.idorder by views_countdesclimit 31
  • DIY Quant Strategies on Quantopian

    1. 1. DIY Quant strategies: Is it possible to roll your own? Jess Stauth, PhD VP Quant Strategy Bay Area Algorithmic Trading Meetup Hacker Dojo * February 6, 2014
    2. 2. What makes a good equity quant strategy?  Intuition. If you can‟t explain why it works, it doesn‟t work.  Reproducibility. If you can‟t backtest it, it doesn‟t work (note the inverse does not necessarily hold).  Access to data. If you can‟t get the signal (or get it in time) you can‟t trade it. ($$$)  Capacity/Execution You can‟t push a camel through the eye of a needle. (1/$$$)
    3. 3. 5 Basic Quant Strategies 1. Mean Reversion – What goes up… (special case: Pairs Trade) 2. Momentum – The trend is your friend. 3. Valuation – Buy low, sell high. 4. Sentiment – Buy the rumor, sell the news. 5. Seasonality – Sell in May and go away. Out of scope for today‟s talk:  Acronym soup (e.g. ML, OLMAR, PCA, ICA, OLS, etc.)  Portfolio construction, risk optimization, etc.  Asset clases
    4. 4. Pairs Trading  Intuition: Find two assets linked to a single underlying „value‟ and exploit transient mispricing between them.  Reproducibility: The phenomenon is well documented1,2.  Data: For basic strategies all you need is pricing.  Capacity: Can be quite small depending on the instruments. Common pitfalls:  Ignore the intuition requirement at your own peril! Cointegration works great, until it doesn‟t.  Market neutral or „hedged‟ strategy, so you are forgoing any upward drift in the longer term. 1. Pairs Trading, Vidyamurthy 2004 2. Quantitative Trading, Chan 2009
    5. 5. Pairs Trading Simplistic Intuition (cont‟d): If you assume the spread between stock 1 and stock 2 is „stationary‟ and „normally distributed‟, then statistically you should be able to make money by „buying‟ or „selling‟ the spread when it takes on extreme tail values. Zx = (Price Stock1 – Price Stock2)/ Price Stock1
    6. 6. Pairs Trading: EWA/EWC Pair 6/06 – 6/12 Huapu Pan (NYC Algo Trading meetup member) Posted 12/19/13 “Ernie Chan‟s EWA/EWC Pair Trading” https://www.quantopian.com/posts/ernie-chans-ewa-slash-ewc-pair-trading
    7. 7. Momentum Trading  Intuition: Comes in many flavors (stock level, sector level, asset class level) but comes back to the behavioral bias of „herding‟.  Reproducibility: The phenomenon is well documented1.  Data: For basic strategies all you need is pricing.  Capacity: Can be quite small depending on the instruments. Common pitfalls:  The trend is your friend, until it isn‟t. Reversals can be devastating, especially when using leverage. 1. Jegadeesh and Titman, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance March 1993 2. Faber, A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management 2013
    8. 8. Momentum Trading Simple rules based approach  Rank 1 > N stocks (sectors) by : [r20 – r200]  Buy top K stocks (sectors) where absolute momentum (20 vs. 200 day MA) > some threshold.  Else, hold cash.
    9. 9. Momentum Trading – Meb Faber RS Strategy Backtest range: 11/04 – 2/13 John Chia Posted Feb 2013 “Mebane Faber Relative Strength Strategy with MA Rule” https://www.quantopian.com/posts/mebane-faber-relative-strength-strategy-with-ma-rule
    10. 10. Valuation  Intuition: In a nutshell, bargain shopping. Use fundamental ratio analysis to identify stocks trading at a discount (or premium) and buy (or sell) them accordingly.  Reproducibility: The phenomenon is well documented.  Data: Requires good coverage (breadth and depth) of normalized corporate fundamental data.  Capacity: Small cap stocks can be riskier, and higher friction to trade. Common pitfalls:  Some cheap stocks are cheap for a reason. “Catch a falling knife” adage.
    11. 11. Valuation Simple example: use price to earnings ratio as a proxy for „value‟ where low P/E looks „cheap‟ and high P/E looks „expensive‟.  Rank universe 1-100 (or sector universe) on P/E  Long only: buy the bottom (lowest P/E) decile  Market neutral: buy the bottom decile, sell the top decile In practice, a quant model would typically blend a number of backward looking ratios an forward looking estimates along with making sector specific adjustments and other bells, whistles.
    12. 12. Valuation: Screen on corporate fundamentals Backtest range 11/25/2009 – 10/10/2013 Sam Lunt (11/4/2013) “Using Fetcher with Quandl” https://www.quantopian.com/posts/using-the-fetcher-with-quandl
    13. 13. Sentiment: Short sellers  Intuition: Follow the (short) money. Short sellers are the „smart money‟, their trades are $ for $ higher conviction (to balance risk).  Reproducibility: The phenomenon is well documented.  Data: Bi-monthly (delayed) short interest can be scraped from NASDAQ. Borrow rates, real-time daily short interest data aggregated from brokers is available for $$$.  Capacity: Can be quite small depending on the instruments. Common pitfalls:  Beware the Short Squeeze! Crowded short trades can lead to a squeeze as short sellers rush to close positions.
    14. 14. Sentiment: Short sellers  Rank stocks 1 > N on Days To Cover ratio*  Buy top 10%, short bottom 10%  Rebalance periodically *Days to cover = Shares Held Short Avg Daily Trade Share volume The number of days of „average‟ trading it would take to unwind the existing short positions.
    15. 15. Sentiment: Short sellers – Rank on Days to Cover Backtest range: 3/15/12 – 3/15/13 Fawce (April 2013) “Ranking and Trading on Days to Cover” https://www.quantopian.com/posts/ranking-and-trading-on-days-to-cover
    16. 16. Seasonality  Intuition: Sometimes (calendar driven fund flows e.g. month end).  Reproducibility: There‟s healthy debate on this one.  Data: end of day pricing and a calendar.  Capacity: Depends on the instruments. Common pitfalls:  Overfitting / data mining is rampant in this type of analysis.
    17. 17. Seasonality Simplest example is a simple 100% stock/bond annual rotation model.  Buy and hold equities (SPY) October thru April  Buy and hold bonds (BSV) May thru Sept.
    18. 18. Seasonality: Sell in May Backtest range: 10/1/09 – 12/31/12 Jess(May 2013) “Sell in May and go away” https://www.quantopian.com/posts/time-to-sell-in-may-and-go-away
    19. 19. Which of these strategies are most popular among the „retail‟ or individual quants using Quantopian?  Mean Reversion  Momentum  Valuation  Sentiment  Seasonality  Other
    20. 20. 25 Top Shared Algorithms of All Time Combo Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Post Title Replies Google Search Terms predict market movements OLMAR implementation Easy Volatility Investing by Tony Cooper @ Double-Digit Numerics Global Minimum Variance Portfolio discuss the sample algorithm ML - Stochastic Gradient Descent Using Hinge Loss Function Mebane Faber Relative Strength Strategy with MA Rule OLMAR w/ NASDAQ 100 & dollar-volume Bollinger Bands With Trading Brent/WTI Spread Fetcher Example Ernie Chan's Pairs Trade Ranking and Trading on Days to Cover Using the CNN Fear & Greed Index as a trading signal Determining price direction using exponential and log-normal distributions Time to sell in may and go away? Simple Mean Reversion Strategy Neural Network that tests for mean-reversion or momentum trending Using weather as a trading signal Momentum Trade Trading Strategy: Mean-reversion Global market rotation strategy trading earnings surprises with Estimize data Turtle Trading Strategy SPY & SH algorithm - please review New Feature: Fetcher! TOTALS: Views Clones 64 64 57 28 12 10 22 31 18 17 15 4 18 9 27 6 4 6 5 13 53 34 11 21 27 31913 26039 15117 10222 18348 20400 11104 7760 8363 10821 10387 24906 9212 9539 8192 11794 10062 11940 8800 8228 7621 7496 7815 7443 7507 809 697 839 700 2882 972 617 697 560 327 328 379 318 606 261 270 402 199 455 213 94 129 299 194 108 576 311,029 13,355
    21. 21. 25 Top Shared Algorithms of All Time Combo Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Post Title Google Search Terms predict market movements OLMAR implementation Easy Volatility Investing by Tony Cooper @ Double-Digit Numerics Global Minimum Variance Portfolio discuss the sample algorithm ML - Stochastic Gradient Descent Using Hinge Loss Function Mebane Faber Relative Strength Strategy with MA Rule OLMAR w/ NASDAQ 100 & dollar-volume Bollinger Bands With Trading Brent/WTI Spread Fetcher Example Ernie Chan's Pairs Trade Ranking and Trading on Days to Cover Using the CNN Fear & Greed Index as a trading signal Determining price direction using exponential and log-normal distributions Time to sell in may and go away? Simple Mean Reversion Strategy Neural Network that tests for mean-reversion or momentum trending Using weather as a trading signal Momentum Trade Trading Strategy: Mean-reversion Global market rotation strategy trading earnings surprises with Estimize data Turtle Trading Strategy SPY & SH algorithm - please review New Feature: Fetcher! Replies Views Clones 64 64 57 28 12 10 22 31 18 17 15 4 18 9 27 6 4 6 5 13 53 34 11 21 27 31913 26039 15117 10222 18348 20400 11104 7760 8363 10821 10387 24906 9212 9539 8192 11794 10062 11940 8800 8228 7621 7496 7815 7443 7507 809 697 839 700 2882 972 617 697 560 327 328 379 318 606 261 270 402 199 455 213 94 129 299 194 108
    22. 22. 25 Top Shared Algorithms of All Time Categorized Volatility 5% Technical 3% Seasonality 3% Portfolio Risk 6% Momentum 18% Mean Reversion 37% Area ~ page views Sentiment 28% What‟s missing from this picture??
    23. 23. Thank You. Questions?

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