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- W2336770010 abstract "When automated trading strategies are developed and evaluated using backtests on historical pricing data, there exists a tendency to overfit to the past. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of out-of-sample performance, we study the prevalence and impact of backtest overfitting. Specifically, we find that commonly reported backtest evaluation metrics like the Sharpe ratio offer little value in predicting out of sample performance (R²" @default.
- W2336770010 created "2016-06-24" @default.
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- W2336770010 date "2016-01-01" @default.
- W2336770010 modified "2023-09-25" @default.
- W2336770010 title "All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms" @default.
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- W2336770010 doi "https://doi.org/10.2139/ssrn.2745220" @default.
- W2336770010 hasPublicationYear "2016" @default.
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