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Trading12 min read

Building a Backtesting Engine That Doesn't Lie to You

Most backtesting engines produce results that look great but fall apart in live trading. Here's how I built QuantumTrader's backtesting engine to be honest about performance.

Part ofFintech & Trading Systems->
By Jason TeixeiraMarch 28, 2026
PythonBacktestingTradingQuantitativeRisk Management
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Every quantitative trader has had this experience: backtest shows 200% annual returns. Live trading shows -15%.

The problem is almost never the strategy. It's the backtest. Most backtesting engines lie through optimistic assumptions.

The 5 Lies Most Backtests Tell

Lie 1: Perfect Fills

Most engines assume your order fills at the exact price you see. In reality:

  • Market orders fill at the ask (buying) or bid (selling), not the mid-price
  • Large orders move the market (slippage)
  • During volatility, fills can be 5-10 ticks worse than expected

My engine models this: \

Reader route

article -> proof -> offer

ReadClusterProofScope

cluster

Fintech & Trading Systems

intent

Trading

route

next step

What to do with this

Turn the note into a build path.

If this topic maps to a real business problem, keep reading the cluster, study the academy path, or route the work into a scoped engagement.

Jason Teixeira
Written by
Jason Teixeira
Founder, Sage Ideas Studio ยท Principal Engineer
livebuild 81e8c8e2026-07-28 06:02Z
// solo studio// no analytics resold// every commit human-reviewed