5 Basic Quant Strategies

 

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

 

What makes a good equity quant strategy?    Intuition.

If you can’t explain why it works, it doesn doesn’’t

work. If younot can’t backtest it, it doesn’t work (note the inverse does necessarily hold).

   Reproducibility.

   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/$$$)

 

5 Basic Quant Strategies Mean Me an Re Reve vers rsio ion n – What goes up… up… (special case: Pairs Trade) Trade)

1.

2. Mo Mome ment ntum um – The trend is your friend. 3.

Val alua uati tion on – Buy low, sell high.

4. Se Sent ntim imen entt – Buy the rumor, sell the news. 5.

Seas Se ason onal alit ity y – Sell in May and go away.

Out of scope for today’s talk:   Acronym soup (e.g. ML, OLMAR, PCA, ICA, ICA, OLS, etc.)  Portfolio construction, risk optimization, etc. 

 Asset clases

 

Pairs Trading   Intuition:

Find two assets linked to a single underlying ‘value’ and exploit transient mispricing between them.

  Reproducibility:   Data:

The phenomenon is well documented1,2.

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, strategy, so you are forgoing any upward drift in the longer term.

1. Pairs Tradin Trading, g, Vidyamurth Vidyamurthy y 2004 2. Quantitative Trading, Chan 2009

 

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

 

Pairs Trading: EWA/EWC A/EWC Pair  Pair  Trading: EW

6/06 – 6/06  – 6/12 Huapu Pan (NYC Algo Tradin Huapu rading g meetup memb member) er) Posted 12/19/13 12/19/13 “Ernie Chan’s EWA/EWC Pair Trading” https://www.quantopian.com/posts/ernie-chans-ewa-slash-ew https://www .quantopian.com/posts/ernie-chans-ewa-slash-ewc-pair-trading c-pair-trading

 

Momentum Trading    Intuition:

Comes in many flavors (stock level, sector level, asset class level) but comes back to the behavioral bias of ‘herding’.

   Reproducibility:    Data:

The phenomenon is well documented 1.

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 Titman, Returns to Buying Winners and Selling Losers: Implications for Stock Jegadeesh and Titman, Market Efficiency. Journal of Finance March 1993 2. Faber, A Quantitative Quantitative Approach to Tactical Tactical Asset Allocation. Journal of Wealth Management 2013

 

Momentum Trading Simple rules based approach  Rank 1 > N stocks (sectors) by : [r 20 – r 200] 

Buy top K stocks (sectors) where absolute momentum (20 vs. 200 day MA) > some threshold.



Else, hold cash.

 

Trading – Me Momentum Trading – Meb b Fa Fabe berr RS RS Stra Strate tegy gy

Backtest Backte st range range:: 11/04 11/04 –  – 2/13 John Chia Posted Feb 2013 “Mebane Faber Relative Strength Strategy with MA Rule” https://www https://ww w.quantopian.com/ .quantopian.com/posts/mebaneposts/mebane-faber-relativ faber-relative-strength-strategy e-strength-strategy-with-ma-rule -with-ma-rule

 

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 acc ordingly..

  Reproducibility:

The phenomenon is well documented.

   Data:

Requires good coverage (breadth and depth) of normalized corporate fundamental data.

   Capacity:

trade.

Small cap stocks can be riskier, and higher friction to

Common pitfalls: 

Some cheap stocks are cheap for a reason. “Catch a falling knife” adage.

 

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 bott om (lowest P/E) decile Market neutral: buy the bottom bott om 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.

 

Valuation: Screen on corporate fundamentals

Backtest rang Backtest range e 11/25/ 11/25/2009 2009 –  – 10/10/2013 Sam Lunt (11/4/2013) (11/4/2013) “Using Fetcher with Quandl Quandl”” https://www.quantopian.com/posts/using-the-fetcher-with-quandl

 

Sentiment: Short sellers   Intuition:

Follow the (short) money. 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 positions .

the Short Squeeze! Crowded short trades can lead to a squeeze as short sellers rush to close

 

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 T Trade rade Share volum volume e

The number of days of ‘average’ trading it would take to unwind the existing short positions.

 

Sentiment: Short sellers – sellers – Rank on Days to Cover 

Backtest Backt est range range:: 3/15/12 3/15/12 –  – 3/15/13 Fawce Faw ce (Apri (Aprill 2013) “Ranking and Trading on Days to Cover” https://www https://ww w.quantopian.com/ .quantopian.com/posts/ranking-a posts/ranking-and-trading-on-da nd-trading-on-days-to-cover  ys-to-cover 

 

Seasonality 

Intuition: Sometimes (calendar driven fund flows



Reproducibility: There’s healthy debate on this



Data: end of day pricing and a calendar.



Capacity: Depends on the instruments.

e.g. month end). one.

 Overfittingpitfalls: / data mining mining is rampant rampant in this type of analysis. analysis. Common

 

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.

 

Seasonality: Sell in May

Backtest Back test rang range: e: 10/1/09 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 https://www .quantopian.com/posts/time-to-sell-in-may-and-go-away

 

Which of these strategies are most popular among the ‘retail’ or individual quants using Quantopian?  Mean

Reversion

 Momentum  Valuation  Sentiment  Seasonality  Other 

 

25 Top Shared d Algorithms of All Time Time Top Share Combo Rank

Post Title

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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

17 18 19 20 21 22 23 24 25

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 algorithm - please revi review ew New Feature: Fetcher!

TOTALS:

Replies

Views

Clones

4 6 5 13 53 34 11 21 27

10062 11940 8800 8228 7621 7496 7815 7443 7507

402 199 455 213 94 129 299 194 108

576

311,029

13,355

64 64 57 28 12 10 22 31 18 17 15 4 18 9 27 6

31913 26039 15117 10222 18348 20400 11104 7760 8363 10821 10387 24906 9212 9539 8192 11794

809 697 839 700 2882 972 617 697 560 327 328 379 318 606 261 270

 

25 Top Shared d Algorithms of All Time Time Top Share Combo Rank

Post Title

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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

17 18 19 20 21 22 23 24 25

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 algorithm - please revi review ew New Feature: Fetcher!

Replies

Views

Clones

4 6 5 13 53 34 11 21 27

10062 11940 8800 8228 7621 7496 7815 7443 7507

402 199 455 213 94 129 299 194 108

64 64 57 28 12 10 22 31 18 17 15 4 18 9 27 6

31913 26039 15117 10222 18348 20400 11104 7760 8363 10821 10387 24906 9212 9539 8192 11794

809 697 839 700 2882 972 617 697 560 327 328 379 318 606 261 270

 

25 Top Top Share Shared d Algorithms of All Time 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??

 

Thank You. Questions?