Backtesting
Definition
The process of running a defined trading strategy against historical price data to evaluate how it would have performed before risking real capital. A well-constructed backtest gives you a statistical baseline — win rate, profit factor, max drawdown — for a strategy's historical edge.
Example
“"I backtested the opening range breakout strategy on 3 years of data before trading it live — knowing it had a 58% win rate and a 2.1 profit factor gave me confidence to stick with it through the losing streaks."”
Detailed Explanation
Backtesting is how disciplined traders develop conviction in a strategy before putting real money on the line. The logic is straightforward: if you can define your entry and exit rules precisely enough to test them on historical data, you can estimate the statistical properties of your edge — or discover that you have no edge at all. Many strategies that "feel right" are quickly exposed as coin flips or net losers when backtested rigorously.
The most common mistake in backtesting is curve fitting, also called overfitting. If you keep adjusting your rules until the backtest looks great, you're not discovering an edge — you're discovering the shape of past data. A backtested strategy needs to work across multiple time periods, multiple market conditions, and ideally across similar instruments, not just the specific period you optimized it for. Out-of-sample testing (holding back a period of data the strategy has never seen) is the standard check.
There are also practical limitations even in honest backtests. Slippage and fill quality are hard to model accurately — a backtest may assume you got filled at the exact entry price, while live trading often means worse fills, especially in fast markets. Commissions, borrow costs for short strategies, and liquidity constraints (can you actually get into 10,000 shares of a thinly traded stock at the tested price?) all affect real-world results relative to the model.
Despite its limitations, backtesting remains an essential tool. It separates strategies with genuine historical edge from strategies based on recency bias or wishful thinking. It also helps you understand your strategy's worst historical drawdowns — knowing that your system once lost 15 trades in a row (even while being profitable long-term) helps you stay disciplined through losing streaks in live trading rather than abandoning a sound approach at the worst possible moment.