|25 October 2013||London, UK||Apex City of London Hotel >>|
This two day backtesting workshop is designed for traders and investment managers who are looking to test a trading strategy. The course will provide delegates with a fundamental and practical understanding of issues in backtesting, optimization, and risk control, and delegates will use relevant software (MATLAB) throughout the workshop. No prior knowledge of MATLAB is required. (Note: Students will be able to apply the principles learnt during the workshop, regardless of which software they choose to use thereafter).
Overview of backtesting
+ 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
+ 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
+ Survey of syntax
+ Advantage of array processing
+ Exercises: Building utility functions useful for backtesting
+ Using toolboxes
Backtesting a single-instrument strategy
+ Exercise: A Bollinger-band strategy for E-mini S&P500 futures (ES) as a prototype mean-reversion strategy
+ The equity curve
+ Excess returns and the importance of the Sharpe ratio
+ Tail risks and maximum drawdown and drawdown duration
+ The importance of transaction cost estimates
Choosing a historical database
+ Criteria for choosing a good historical database
+ Equities data: split/dividend adjustments, survivorship bias
+ Futures data: contructing continuous contracts, settlement vs closing prices
+ Issues with synchronicity of data
+ Issues with intraday/tick data
Backtesting a portfolio strategy
+ 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 transaction costs on strategies
+ Strategy refinement: How small changes can make big differences in performance
Detection and elimination of backtesting pitfalls and bias
+ 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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