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Checklist, in Backtesting & Validation

Paper-to-Live Transition Checklist

The gap between paper and live is where most strategies die: real fills cost more, latency is real, and the operational machinery can fail in ways a simulation never models. This checklist governs the transition itself, not the backtest that preceded it.

13 items. Published May 26, 2026. By AI Fin Hub Research.

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Sections

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Section 1

Phase 1: Cost and fill reconciliation

3 items

Use the Execution Simulator: Estimate execution cost in closed form — square-root permanent impact, linear temporary impact, half-spread, and a latency-drift band. See the slippage a naive backtest hides.

Use the Broker API Comparator: Alpaca vs IBKR vs Tradier vs Schwab vs Robinhood — compare auth, rate limits, order types, market data, MCP, and fees before wiring a line of code.

Use the Statistical Arbitrage Capacity Calculator: Maximum strategy AUM from alpha, slippage, fees, daily volume and volatility, and signal half-life. Square-root impact closed-form.

Section 2

Phase 2: Operational readiness

4 items

Section 3

Phase 3: Controlled ramp

3 items

Section 4

Phase 4: Live-versus-sim monitoring

3 items

Tips

Small moves that make the checklist easier to finish

The first live week is a plumbing test, not a profit test. Size it so that being wrong about execution costs you tubing fees, not your capital.
Live-versus-backtest tracking error is the most informative number in the transition. A clean strategy tracks the sim tightly; a divergence is an unmodeled cost or bug demanding a stop.
Reconcile positions against the broker on a timer. A single missed fill that desyncs your state will quietly poison every decision that follows until you notice.

Run the numbers next

  • Calculators Drawdown-Recovery Markov Simulator

    Time to recover from an N% drawdown given monthly Sharpe + skew + kurtosis. Cornish-Fisher Monte Carlo, percentile distribution of recovery months.

Sources

  1. Trading and Exchanges: Market Microstructure for Practitioners , Larry Harris, Oxford University Press (2003)
  2. Advances in Financial Machine Learning , Marcos Lopez de Prado, Wiley (2018)

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