What is crypto backtesting?

Crypto backtesting tests a trading strategy against historical crypto market data. It estimates how the rules would have behaved under past prices, funding, liquidity, fees, and execution assumptions before the trader risks live capital.

A useful backtest needs the same inputs the live strategy would have had at the time: prices, funding, volumes, order book or slippage assumptions, exchange rules, universe membership, fees, and timestamps that prevent future data from leaking into earlier decisions.

The minimum dataset depends on the strategy, but crypto backtests usually need more than candles. Perp strategies often require funding histories, mark or index references, fee schedules, liquidation or maintenance margin assumptions, and liquidity snapshots or conservative slippage models.

If the live strategy would have seen a value only after the trade decision, the backtest should not use that value to form the earlier signal.

Backtesting can reveal whether a strategy idea ever had a plausible edge, when it tended to work, how painful the drawdowns were, and whether the implementation would have turned over too quickly to trade.

A backtest can look good because of overfitting, missing costs, survivorship bias, unrealistic fills, future data leakage, or a market regime that no longer exists. The backtest is evidence, not proof.

Alphora treats a backtest as one artifact in a larger validation stack. The useful question is not only whether the return was high, but whether the rule is reproducible, explainable, robust to assumptions, and suitable for paper tracking before any later trading decision.