Build reproducible backtests with Python

A reproducible backtest gives the same answer from the same inputs and makes disagreements easier to diagnose.

Make the data contract explicit

Define the columns, timezone, sort order, price convention, missing-value policy, and costs required by the backtest. Validate that contract before a strategy function runs so malformed input fails loudly.

Avoid silently downloading fresh data during a historical comparison. Pin the input file or dataset version for each run.

Control hidden randomness

If a test uses sampling, optimisation, or Monte Carlo, record the random seed and generator. If it depends on current time, external APIs, or a changing symbol list, capture those inputs as part of the run metadata.

Use deterministic sorting and stable identifiers.
Pin dependency versions for important reports.
Save configuration and summary metrics as machine-readable files.
Compare a small fixture dataset in automated tests.

Verify the result at the edges

Test the first available bar, session transitions, empty signals, simultaneous exits, missing prices, and the final open position. Edge cases often explain why two apparently identical backtests disagree.

A reproducible process is more valuable than a one-off fast result because it lets you investigate rather than guess.

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