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When a strategy is tuned so tightly to past data that it looks amazing on history but falls apart on new, real-world data.
Why It Matters
Overfitting is the single most common way to fool yourself in algorithmic trading. If you keep tweaking a strategy until it perfectly explains the past, you've often just memorized old noise rather than found a real, repeatable pattern. The tell is a backtest that looks too good — flawless equity curves rarely survive contact with live markets. Testing on data the strategy never saw during design (out-of-sample testing) is the standard defense.
Key Points
- Tuning a model to past noise instead of a real pattern
- Symptom: a backtest that looks too perfect
- Guarded against with out-of-sample and forward testing
Related Terms
Common Questions
When a strategy is tuned so tightly to past data that it looks amazing on history but falls apart on new, real-world data. Overfitting is the single most common way to fool yourself in algorithmic trading. If you keep tweaking a strategy until it perfectly explains the past, you've often just memorized old noise rather than found a real, repeatable pattern.
Overfitting is the single most common way to fool yourself in algorithmic trading. If you keep tweaking a strategy until it perfectly explains the past, you've often just memorized old noise rather than found a real, repeatable pattern. The tell is a backtest that looks too good — flawless equity curves rarely survive contact with live markets. Testing on data the strategy never saw during design (out-of-sample testing) is the standard defense.
Tuning a model to past noise instead of a real pattern
Symptom: a backtest that looks too perfect
Guarded against with out-of-sample and forward testing