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10 WAYS
BACKTESTS LIE
TUCKER BALCH, PH.D.
PROFESSOR, GEORGIA TECH
CO-FOUNDER AND CTO, LUCENA RESEARCH
OR…
Mistakes quant developers make that cause backtests to be
inaccurate.
WHAT I’LL COVER
Introductions
What is a backtest?
How backtests lie:
1.  In sample backtesting
2.  Survivor bias
3.  Assume you can observe the close and trade at the close
4.  Ignoring market impact
5.  Assume you can buy $10M of a $1M company
6.  Data mining fallacy
7.  Stateful strategy luck
8.  Buy at the open
9.  Don’t trust complex models
10.  Don’t forward test
ABOUT THE SPEAKER
•  Professor of Interactive Computing at
Georgia Institute of Technology.
•  Teach courses in Artificial Intelligence and
Finance.
•  Teach MOOCs on Machine Learning for
Trading.
•  Published over 120 research publications
related to Robotics and Machine Learning.
•  Co-founder of Lucena Research.
ABOUT THE SPEAKER
•  Professor of Interactive Computing at
Georgia Institute of Technology.
•  Teach courses in Artificial Intelligence and
Finance.
•  Teach MOOCs on Machine Learning for
Trading.
•  Published over 120 research publications
related to Robotics and Machine Learning.
•  Co-founder of Lucena Research.
New book ->
ABOUT LUCENA
RESEARCH
•  We are a fin-tech company who
employ experts in Computational
Finance, Quantitative Analysis,
and Software Development.
•  We deliver investment decision
support technology at a fraction
of the cost of an in house quant
shop.
•  Python-based infrastructure.
•  Erez Katz, CEO
•  Eric Davidson, VP
http://lucenaresearch.com
LUCENA
RESEARCH!
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BUILDING A MODEL
FROM DATA
BACKTESTING TO
VALIDATE THE MODEL
BACKTESTING TO
VALIDATE THE MODEL
BACKTESTING TO
VALIDATE THE MODEL
BACKTESTING TO
VALIDATE THE MODEL
BACKTESTING TO
VALIDATE THE MODEL
BACKTESTING TO
VALIDATE THE MODEL
BACKTESTING TO
VALIDATE THE MODEL
Roll forward cross validation
Out of sample validation
10 WAYS
BACKTESTS LIE
1. IN SAMPLE
BACKTESTING
Description: Backtesting over the same data you used to
train your model.
1. IN SAMPLE
BACKTESTING
Description: Backtesting over the same data you used to
train your model.
This method is doomed to succeed spectacularly!
1. IN SAMPLE
BACKTESTING
Description: Backtesting over the same data you used to
train your model.
Training
Testing
1. IN SAMPLE
BACKTESTING
How to avoid?
Training
Testing
1. IN SAMPLE
BACKTESTING
How to avoid?
More generally, build safeguards and procedures to prevent
testing over the same data you train over. E.g., Train over 2007,
test over 2008-forward.
2. SURVIVOR BIAS
Description: Selective use of data in a statistical study that
emphasizes examples that are “alive” at the end of the study.
The significance of the bias depends on how important
survival is to the quantity being measured.
2. SURVIVOR BIAS
Example: Company claims: “Our drug reduces the blood pressure
of those who take the drug over time.”
5 year study:
•  Randomly select 500 cardiac patients
•  Administer drug to them
•  Measure their blood pressure monthly
Results:
•  160/110 average first month
•  135/80 average at end of study
Do you believe this is a good drug?
2. SURVIVOR BIAS
Problem: 58 of the patients they started with have died since the
start of the study.
Note: 58 of the members of the S&P 500 in 2008 are now
delisted. Not just out of the S&P 500, but gone as companies.
11.6%
2. SURVIVOR BIAS
2. SURVIVOR BIAS
2. SURVIVOR BIAS
2. SURVIVOR BIAS
Green: Current S&P 500, Purple: Point in time S&P 500
--Lucena Research
2. SURVIVOR BIAS
How to prevent?
•  Use historic index membership.
•  Pair with SBF-free data.
•  Use these indices as your universe for testing.
3. OBSERVING THE
CLOSE
Description: You assume you can observe information
recorded at market close, and trade on it.
Examples:
•  Closing price/volume
•  Technicals based on price/volume
3. OBSERVING THE
CLOSE
Description: You assume you can observe information
recorded at market close, and trade on it.
Examples:
•  Closing price/volume
•  Technicals based on price/volume
This is a specific case of “look ahead bias.” Other examples:
•  Earnings reports
•  News feeds
3. OBSERVING THE
CLOSE
How to prevent?
Ensure that information with timestamp X cannot be acted on until
X+1.
Example: Data marked January 15 cannot be traded until the
open on January 16.
4. IGNORING MARKET
IMPACT
Description: The act of trading affects price. Historical data
does not include your trades and is therefore not an accurate
representation of the price you would get.
4. IGNORING MARKET
IMPACT
4. IGNORING MARKET
IMPACT
4. IGNORING MARKET
IMPACT
4. IGNORING MARKET
IMPACT
Swetha Shivakumar, Georgia Tech
4. IGNORING MARKET
IMPACT
How to prevent?
Include a “slippage” or “market impact” model in your backtests.
5. BUY $10M OF A $1M
COMPANY
Description: Backtest allows a strategy to buy (or short) as
much of a symbol as it wants.
5. BUY $10M OF A $1M
COMPANY
Description: Backtest allows a strategy to buy (or short) as
much of a symbol as it wants.
There often is real alpha in thinly traded stocks.
5. BUY $10M OF A $1M
COMPANY
Description: Backtest allows a strategy to buy (or short) as
much of a symbol as it wants.
There often is real alpha in thinly traded stocks.
This is a specific example of the more general issue of capacity
limitations.
5. BUY $10M OF A $1M
COMPANY
How to avoid?
Ensure the backtester prohibits trading more dollar volume than
actually was available on that day.
Add slippage/market impact models to penalize buying too much.
6. DATA MINING
FALLACY
Description: If you generate and test enough strategies you’ll
eventually find one that “works” in a backtest. The quality of
the strategy cannot be distinguished from random luck.
6. DATA MINING
FALLACY
Description: If you generate and test enough strategies you’ll
eventually find one that “works” in a backtest. The quality of
the strategy cannot be distinguished from random luck.
Example: Look for “skilled” coin flipper among 10,000 candidates.
6. DATA MINING
FALLACY
How to avoid?
You can’t! However you can and should forward test before
committing significant capital.
7 THROUGH 10
7. Stateful strategy luck
8. Buy at the open
9. Trust complex models
10. Don’t forward test
THANK YOU!
www.lucenaresearch.com

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