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Evaluate strategy stability across market regimes
Report predeclared historical windows separately so aggregate results do not hide failures.
Goal
Goal: Report predeclared historical windows separately so aggregate results do not hide failures.
Steps
- Predefine high/low-volatility or bull/bear ranges before examining results. [backtest]
- Backtest each
--timerangewith the same strategy, fees, wallet, and data. [backtest] - Compare drawdown, profit, trade counts, and failure modes; retain aggregate and segmented reports. [backtest]
How to verify
Each window has adequate coverage and reports sample counts.
Troubleshooting
Mark uncertainty or widen sparse windows; this analysis is not a forecast.
Permissions
Prerequisites
- Python 3.11+, Freqtrade installed, a pinned strategy, and a local user-data directory.
Permissions
- Use public market data and local history; treat every trading decision as research and never connect a live account.
Limitations
Historical regime boundaries are subjective and past performance may not recur.
Pinned upstream source
- Repository
- freqtrade/freqtrade
- File path
- docs/backtesting.md
- Pinned commit
82e4c9604c51523f7e05c5a297167cf2b13a2129- Source checked
- 2026-10-14 · Reviewed against the pinned upstream source
Repository metrics
These values describe the upstream repository, not popularity or likes for an individual skill or playbook.
- Stars
- Not collected
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No metrics snapshot is available yet.
License: GPLv3
Main topic
Build a reproducible crypto research workflow