Historical Futures Data for Backtesting: A Practical Guide
Learn how to choose futures data, validate its schema and sessions, control adjustments, and build a credible backtest with a concrete example.
Read the guideFutures Data and Backtesting Guides.
Detailed, practical references for choosing futures data, handling contract rolls, building realistic backtests, and checking the result before trusting it.
Tick Data vs OHLCV Bars: Choosing the Right Futures Data
Compare futures tick records and OHLCV bars, understand execution-side fields and aggregation, and choose a resolution that fits your strategy.
Read the guideContinuous Futures Contracts: Rolls, Gaps, and Back-Adjustment
Learn why continuous futures have roll gaps, how volume-based rolls and additive back-adjustment work, and how to validate returns around expiries.
Read the guideUsing Parquet Futures Data in Python: pandas, polars, and DuckDB
Read Parquet futures data efficiently with pandas, polars, and DuckDB, including UTC handling, lazy filters, partitioning, resampling, and reproducible checks.
Read the guideHow to Avoid Look-Ahead Bias in Futures Backtests
Prevent look-ahead bias in futures backtests with timestamp discipline, point-in-time rolls, session alignment, and robust automated leakage tests.
Read the guideFutures Backtesting Costs: Commission, Slippage, Spread, and Position Size
Model futures commissions, spread, slippage, rolls, and position size with contract math, numerical examples, and conservative sensitivity checks.
Read the guideFutures Contract Months, Expiration, and Trading Sessions Explained
Decode futures month symbols, distinguish expiration from last trade, handle Chicago sessions and DST, and follow liquidity across contract rolls.
Read the guideA Rigorous Futures Backtesting Checklist: From Data Audit to Out-of-Sample Test
Audit futures data, model rolls and execution, isolate out-of-sample tests, stress assumptions, and make every backtest reproducible before sign-off.
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