Crypto glossary
Backtesting
Backtesting is testing a trading strategy on historical price data to estimate how it would have performed before risking real money on it.
A backtest applies a strategy's exact rules for entries, exits, position size and costs to past market data and records each simulated trade. The output includes total return, win rate, maximum drawdown and risk-adjusted measures such as the Sharpe ratio. Charting platforms, trading bot services and programming libraries all offer backtesting, and crypto's long, freely available price histories make it easy to start.
A good backtest includes realistic fees, slippage and funding costs, and uses data the strategy could actually have known at the time. Testing on a separate out-of-sample period, or walking the test forward through time, helps check whether results hold beyond the data used to design the rules.
The main trap is overfitting: tuning parameters until a strategy fits past noise perfectly, which usually fails live. Crypto adds its own problems, such as delisted tokens missing from data sets, which inflates results, and market regimes that change sharply between cycles.
Example
A trader tests a moving-average crossover on five years of BTC daily data and gets a 300% return. After adding fees and slippage and testing on a later year he had not used, the edge almost disappears.