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Understanding Bad Data: Risks, Causes, and Mitigation Strategies

Bad data can severely impact automated trading strategies, leading to misguided insights and financial losses. Common issues include failure to adjust for stock splits, drastic data drops or gains, and missing significant chunks of information. It is crucial to perform sanity checks on new data, identifying anomalies such as unusual price changes or NaNs, which may indicate errors in data feeds. Scrubbing data is essential; while it’s easier to remove faulty data, repairing may be an option if multiple data sources are available. Ultimately, good data is vital for uncovering reliable trading strategies.

darshan-gopal
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Understanding Bad Data: Risks, Causes, and Mitigation Strategies

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  1. Bad Data Example • Symbol: DD • 1988

  2. Examples of Bad Data • Failure to adjust for splits. • Orders of magnitude drops, followed by offsetting orders of magnitude climbs. • Database updates missing significant chunks of data/symbols.

  3. Why Bad Data is Bad • Automated strategies may exploit bad data, then fail with real data. • You might think you’ve “discovered” something.

  4. Sanity Checks • Scan new data for ~50% drops or 200% gains (probably a split). Very rare for real data. • NaNs in DOW stocks (probably data feed bad). • Recent adjusted prices less than 0.01 • NaNs > 20 trading days?

  5. Scrubbing • Remove or repair? • Easier, more reliable to remove. • Can only repair if you have multiple sources.

  6. Summary • Good data is important. • You may discover false strategies otherwise.

  7. Next • Overview of homework 3.

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