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Data Snooping, Technical Trading, Rule Performance, and the Bootstrap By Ryan Sullivan, Allan Timmermann and Halbert White DISCUSSION PAPER 303 October 1998___FINANCIAL MARKETS GROUP AN ESRC RESEARCH CENTRE ___LONDON SCHOOL OF ECONOMICSAny opinions expressed are those of the author and not necessarily those of the Financial Markets Group. ISSN 09568549303DataSnooping, Technical Trading Rule Performance, and the Bootstrap RYAN SULLIVAN 6192747708 (phone) 6192747709 (fax) rmsullivan@ucsd
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Define your trading strategy: Determine the technical indicators and parameters you want to use.
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Collect historical data: Gather relevant market data to test your strategy.
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Split the data: Use a portion of the data for training (developing the strategy) and another for validation (testing the strategy).
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Implement the strategy: Code your trading strategy using the chosen indicators and parameters.
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Backtest the strategy: Run the strategy on the training data to evaluate its performance and make adjustments as needed.
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Avoid bias: Ensure the strategy is not overly fitted to the historical data, which can lead to false expectations.
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Validate the results: Test the strategy on the validation set to see how well it performs on unseen data.
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Refine the approach: Based on the validation results, tweak the indicators or parameters, but be cautious of over-optimization.

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Individual traders seeking to improve their trading strategies.
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Financial educators interested in teaching technical analysis.
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Data snooping technical trading refers to the practice of using past data to develop trading strategies that may not be robust when applied to future data. It often involves excessive optimization and backtesting on historical data, leading to overfitting and potentially misleading results.
Typically, professional traders, investment firms, and hedge funds that employ quantitative trading strategies are required to file disclosures related to data snooping technical trading, especially if they manage external capital or provide investment advice.
Filling out data snooping technical trading filings generally involves providing detailed information about the methodology used for developing trading strategies, the data sets analyzed, backtesting results, and any limitations or biases discovered during the research process.
The purpose of data snooping technical trading is to identify potentially profitable trading strategies by analyzing historical data. However, it also serves to highlight the risks associated with relying solely on past performance without considering market changes.
Information that must be reported includes the criteria and parameters used for strategy development, the data samples analyzed, backtesting results, any statistical measures of performance, and disclosures about the potential risks and limitations of the findings.
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