Educational content only — not financial advice

Most edges fail validation. That is the process working.

VALIDATEDT3 · 1.5h · Technical Track

Realistic backtesting — costs, fills, funding

Clean data is not enough. Most backtests still overstate performance because they ignore fees, slippage, funding, and realistic fill assumptions. How to model friction correctly for both crypto and equities.

In this module

  1. The core problem
  2. The main sources of friction
  3. Fill assumptions — when does the trade actually happen?
  4. Vectorized vs event-driven backtesting
  5. Practical realistic-backtest checklist
  6. Where FreqEdge and the Quant Agent help
  7. Key takeaways
Practice

Re-run one edge with realistic costs

Open Edge Lab and pick any edge you already have. Run or re-run the backtest with realistic fees, slippage, and funding. Compare the new metrics to the frictionless version. Use the Quant Agent Narrate skill on the result and note what changed.

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Educational content only — not financial advice.

Educational content only — not financial advice.