Backtesting That Reflects Live Trading

Backtests are only useful when they model the conditions your bot will face in production. Use them to eliminate fragile strategies early.

1) Model real costs

Include maker/taker fees, funding, spreads, and realistic slippage. Small costs compound fast in high-frequency systems.

2) Test across market regimes

Run the same strategy through trending, ranging, and high-volatility periods. A single bull run is not enough.

3) Use walk-forward validation

Split history into sequential train/validation windows. Refit parameters on the train window and validate on the next.

4) Paper trade before going live

Dry-run with real-time data to measure latency, execution quality, and fill rates.

5) Define go-live criteria

Set minimum Sharpe, drawdown, and trade count thresholds. If a strategy fails, archive it.
Backtesting is a filter, not a guarantee. Treat it as a risk-reduction tool, not a promise of profit.