Survivorship bias is the silent killer of long-horizon backtests: you test your strategy on the current universe of assets, forgetting that the historical universe also included a long tail of names that have since delisted, gone bankrupt, been acquired, or in crypto, been rugged and frozen.
Equity backtests that use only currently-listed S&P 500 tickers ignore the dozens of stocks that fell out of the index over the years. Crypto backtests that use only currently-trading pairs ignore the hundreds of tokens that have died since 2017.
The fix is a 'point-in-time' dataset: at each historical date, you can only see assets that existed and traded at that date, including names that are now gone. Good data vendors price this in; cheap CSV dumps generally do not.
Wie Noon Barbari Survivorship bias nutzt
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Backtest data sources →Verwandte Begriffe
- Backtesting
Look-ahead bias
Using information in a backtest that would not have been available at the time.
- Backtesting
Backtest
Simulating a trading rule on historical data to estimate how it would have performed.
- Backtesting
Out-of-sample
Data the strategy was not allowed to see during parameter selection.
- Backtesting
Overfitting
Fitting a strategy so closely to past data that it captures noise, not signal.