Article
4 Dec 2025
The Dangers of Overfitting in Algorithmic Trading
Overfitting is one of the most common problems in algorithmic trading. It occurs when a strategy is tuned too closely to historical data, capturing noise rather than genuine market behaviour. The result is often impressive backtest performance followed by disappointing live results.
How Overfitting Happens
Overfitting usually comes from excessive optimisation.
Common causes include:
too many parameters
extremely precise input values
chasing smooth equity curves
removing all historical drawdowns
Each additional parameter increases fragility.
Warning Signs of Overfitting
Typical red flags include:
strong results in one period and failure outside it
sensitivity to small parameter changes
performance collapsing in forward testing
If minor changes break the strategy, it is not robust.
Why Overfitting Feels Convincing
Overfitted strategies often:
look engineered
appear statistically impressive
produce clean optimisation reports
This creates false confidence and encourages premature deployment.
Reducing Overfitting Risk
Practical ways to reduce risk include:
testing across multiple market conditions
limiting the number of parameters
favouring simple, explainable logic
accepting imperfect results
Markets reward robustness, not precision.
Summary
Overfitting is often a psychological mistake rather than a technical one. In algorithmic trading, imperfection is usually a sign of realism.