Gary Chan presented at the NYC Algorithmic Trading Meetup. More on the presentation, including a sample Excel file, on our blog http://blog.quantopian.com/gary-chan-on-pairs-trading-presentation-from-nyc-algorithmic-trading-meetup/ You can sign up for future meetups here: www.meetup.com/NYC-Algorithmic-Trading/

1. How To Build Your Own Pairs Trading
Algorithm Trading System
Quantopian Meetup
Gary Chan

2. Introduction
• Started programming at a young age
• Played poker as main source of income for a few
years
• Money is just a way to keep score
• Became interested in stock market
• Read ~70 textbooks, mostly corporate finance,
CFA curriculum
• Currently running an algorithmic trading system
out of my apartment

3. Expectations
• I’m not here to divulge secrets
• Teach a man to fish, not give him a fish
• You will see how easy the pairs trading model is
to understand and build
• Deployable by retail investors
• Seeing is believing
• You will not be able to build your own
tomorrow… but one day… if you want it badly
enough
• It’s a small world, so share ideas

4. Why you can beat the professionals
• Definition of professionals
• Algorithmic does not mean high
frequency
• HFT is not for retail investors
• Strategy capacity
• Find a niche

5. Disclaimers
• A lot of learning, before this came to fruition
• Content here will not be complete due to time
constraints
• Survivorship bias, Look ahead bias
• Model risk, implementation risk, execution
risk, over parameterization
• Random models can have profitable backtests
• I’m not responsible if you lose money

6. Algorithmic Trading is Easy
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What does easy / hard mean?
Info, books, tools, cheaply or freely available
Yahoo, Google, Morningstar, Edgar, Quantopian
Visual Studio Express, MySQL, R
Many textbooks written on the subject
Low capital requirements (30K minimum)
compared to other ventures
• Backtest before you use real money!!
• Higher quality data will cost more money

7. My Current System
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Low frequency
Single computer, 3 years old
Runs on Wifi from my apartment
Started coding Oct 2012
Started forward testing play money in March
2013
• Started forward testing real money June 2013
• Current performance consistent with backtests
• Just started using cloud computing

8. My Cloud

9. My Cloud Part 2

10. Backtesting
• Profitable backtests does not mean good
• You need a valid model
• Live trading WILL underperform backtests

11. How A Good Backtest Looks
• Consistent, high R Squared
• Similar parameters have similar equity curves

12. Key Info From Backtests
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Portfolio of 39 pairs
Average of 12 to 18 pairs with opened positions, $5400 max drawdown
$10k sized legs in backtests, $395 average profit per trade
81.3% winning trades, 4540 trades, 26 day average holding period

13. Pairs Trading Books
• Contains Code
• Complete manual to putting together your own system

14. Types of Investment Strategies
• Mean Reversion
– Buy low, sell high / Sell high, buy low
– Fundamental Analysis
– Statistical Arbitrage
• Momentum
– Sell low, buy lower / Buy high, sell higher
– News
• Technical analysis does not work

15. Necessary Math
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Basic statistics
Linear algebra
Standard deviations, regressions
Types of distributions (normal, logarithmic)
Random walks
Stationary (mean reverting) series
Cointegration

16. Mean Reversion Example #1
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Keep an opened mind
Everything can be measured in money
If you don’t believe me, put it on EBay
Fear can be measured in dollars
Unwarranted fear -> Sell volatility to profit

17. Mean Reversion Example #2
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Stocks move in random walks
Some stocks move together
Spread between the stocks are mean reverting
Buy low, sell high on the spread
Statistical arbitrage means you win most of the
time, not all the time
• Buyouts and bankruptcies result in large losses
• Diversification is a must
• Learn some corporate finance / fundamentals

18. Case Study, GLD and IAU
• Two gold ETF’s
• Pull end of day prices from Yahoo Finance into
csv files
• Do a linear regression
• Calculate the spread
• Graph of spread is mean reverting
• Find #Stdevs to enter / exit trade
• Optimize parameters

19. Next Steps
• Try higher quality data
• You could trade this by hand + excel, but
better to automate the process
• Many systems are built in excel
• Optimize the code
• Backtest until you find a profitable strategy
• Use a brokerage with an API, sample code
• Forward test with play money

20. Next Steps Part 2
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Execution, order, position management
Test with small amounts of real money
Tweak your system
Ramp up trading size if still profitable
Exhaust universe for the strategy
Find new strategies
Never stop learning

21. The End
• Ernest P Chan - Quantitative Trading: How to Build
Your Own Algorithmic Trading Business (Wiley Trading)
• Ganapathy Vidyamurthy - Pairs Trading: Quantitative
Methods and Analysis (Wiley Finance)
• R - http://www.r-project.org/ (like Matlab)
Yahoo, Google, Morningstar, Edgar, Quantopian
• Visual Studio Express, MySQL, R
• garychan7@gmail.com
• https://twitter.com/RITrading - I tweet my trades here