Mechanism
In simulation, you get ideal fills, stable spreads, and no hesitation. In real trading, you face slippage, spread expansion, and emotional interference.
Many strategies are only profitable under the simulated assumptions.
Paper is useful for learning mechanics, not for proving an edge.
Testing reality: backtests hide execution, spreads, and regime clustering. If your edge survives only in clean data, it’s a data story, not a trading edge.
- Test with variable spreads and slippage by session.
- Stress the tails: worst 1% days matter more than average days.
- Prefer rules you can execute consistently over rules that optimize the past.
How it kills accounts
Paper success → false confidence → go live big → execution + emotion crush → panic → abandon.
How it kills accounts:
- Model works on clean history.
- Regime changes and execution friction increases.
- Performance decays slowly, so you rationalize.
- You optimize parameters instead of reducing risk.
- Drawdown becomes the teacher.
Rule that survives
Use paper to learn the workflow.
Prove edge live at tiny size with real costs.
Don’t scale until discipline is proven under stress.
Rule that survives:
- Stress test tails and execution, not averages.
- Prefer robust plateaus over optimized peaks.
- When performance decays, reduce risk before “fixing” the model.
Example archetype
Your backtest and demo look great. Live, the same setup triggers during volatile conditions and fills worse. You conclude the market is ‘rigged’. It’s just real.
Tell: if small parameter tweaks flip your results, your model is fragile.
Deep dive
What paper is good for
Paper is great for learning order types and building a checklist. It’s not proof of profitability.
Related: Costs are a strategy.
Field checklist
- Stress-test the tails. The worst days define survival.
- Use variable spreads and slippage in testing.
- Prefer robust plateaus over optimized peaks.
- Look at drawdown shape, not only profit.
- If a tiny parameter change breaks the model, the model is fragile.