Backtesting

Backtesting is the process of evaluating a trading strategy by running it against historical market data. It simulates how the strategy would have performed in the past, providing statistics like total return, win rate, maximum drawdown, and Sharpe Ratio. It is the essential validation step before deploying any trading system with real money.

How Backtesting Is Used in Trading

A proper backtest requires clean historical OHLCV data and pre-calculated indicator values. The strategy is applied bar by bar through the data, recording every entry, exit, and the resulting profit or loss. Realistic assumptions about spread, slippage, and commission must be included to avoid overly optimistic results.

The biggest backtesting pitfall is overfitting: tuning parameters to perfectly match historical data at the expense of future performance. Techniques like walk-forward analysis, out-of-sample testing, and Monte Carlo simulation help detect overfitting before it costs real money.

Historical OHLCV data from the /v1/ohlc endpoint and indicator values from /v1/indicators provide the data needed for robust backtesting. Using API-sourced data ensures your backtest uses the same data quality your live system will receive.

Access via API