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    Course Description


    Backtesting is the process of feeding historical data to an automated trading strategy and see how it would have performed. We will study various common backtest performance metrics. Backtest performance can easily be made unrealistic and unpredictive of future returns due to a long list of pitfalls, which will be examined in this course. The choice of a software platform for backtesting is also important, and criteria for this choice will be discussed. Illustrative examples are drawn from a futures strategy and a stock portfolio trading strategy.

    This course is based on a pre-recorded workshop conducted in Adobe Connect by Ernest Chan (www.epchan.com). This course focuses on the various practices and pitfalls of backtesting algorithmic trading strategies. Free MATLAB trial licenses will be arranged for extensive in-class exercises. No prior knowledge of MATLAB is assumed, but some programming experience is necessary. The math requirement assumed is basic college level statistics.

    Instructor


    Dr. Ernest P. Chan is the Managing Member of QTS Capital Management, LLC. His career since 1994 has been focusing on the development of statistical models and advanced computer algorithms to find patterns and trends in large quantities of data. He has applied his expertise in statistical pattern recognition to projects ranging from textual retrieval at IBM Research, mining customer relationship data at Morgan Stanley, and statistical arbitrage trading strategy research at Credit Suisse, Mapleridge Capital Management, and other hedge funds.

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    WATCH FIRST TWO LECTURES

    OVERVIEW OF BACKTESTING

    CHOOSING A BACKTESTING PLATFORM

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    Course Curriculum

    Free Section
    Overview of Backtesting FREE 00:31:00
    What is backtesting and how does it differ from “simulations”? The importance of backtesting: Why is backtesting a necessary step for profitable automated trading? The limitations of backtesting: Why is backtesting not a sufficient step to ensure profitability in automated trading? What we can do to increase the predictive power of our backtest results: the avoidance of pitfalls. How to identify good/bad strategies even before a backtest: a preview of various pitfalls through a series of examples.
    Choosing a backtest platform FREE 00:34:00
    Criteria for choosing a suitable backtest platform. A list of backtesting platforms. Discussion of pros and cons of each platform. Special note: integrated backtesting and automated execution platforms. Why do we choose MATLAB?
    Tutorial to MATLAB FREE 00:24:00
    Survey of syntax. Advantage of array processing. Exercises: building utility functions useful for backtesting. Using toolboxes.
    Backtesting a single-instrument strategy FREE 00:41:00
    Exercise: A Bollinger-band strategy for E-mini S&P500 futures (ES) as a prototype mean-reversion strategy.
    Premium Section
    Performance Measurement and Transaction Costs 00:41:00
    The equity curve. Excess returns and the importance of the Sharpe ratio. Tail risks, maximum drawdown and drawdown duration. The importance of transaction costs estimates.
    Choosing a Historical Database 00:27:00
    Criteria for choosing a good historical database. Equities data: split/dividend adjustments, survivorship bias. Futures data: constructing continuous contracts, settlement vs closing prices. Issues with synchronicity of data. Issues with intraday/tick data.
    Backtesting a Portfolio Strategy 00:26:00
    Exercise: A long-short portfolio strategy of stocks in the S&P 500. Relevance of strategy to 2007 quant funds meltdown. The importance of universe selection: impact of market capitalization, liquidity, and transactions costs on strategies.
    Strategy Refinement 00:22:00
    How small changes can make big differences in performance.
    Detection and Elimination of Backtesting Pitfalls and Bias 00:45:00
    How to detect look-ahead bias? How to avoid look-ahead bias? Data snooping bias: why out-of-sample testing is not a panacea. Parameterless trading. The use of linear models or “averaging-in”: pros and cons. Exercise: linearization of the ES Bollinger band strategy. Impact of noisy data on different types of strategies. Impact of historical or current short-sale constraint. The unavoidable limitation of backtesting: Regime change. What to do when live performance is below expectations?

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