backtest-data-preplisted

# backtest-data-prep You hand over a universe definition, a date window, and an output directory. The skill emits a clean OHLCV+volume parquet plus a manifest plus an edge-case log. The downstream consumer is a backtester (your Python loop, QuantConnect, vectorbt, zipline-reloaded, etc.), not a human reader. The skill's value is correctness. Quants spend 80% of their time on data prep, and the most common backtest bugs come from five sources: 1. **Survivorship bias.** Backtesting against today's top-500 silently excludes 2022-2024 failures (FRC, SVB, BBBY, SI). The remaining cohort outperforms the true historical population because the losers are gone. 2. **Look-ahead bias on fundamentals.** Using the latest revised consensus instead of point-in-time. The signal contains forward-looking information that wasn't available at the rebalance. 3. **Corporate action mis-adjustment.** Pre-split prices not adjusted, or adjusted using the wrong factor. 4. **Holiday and half-day handling.** Missing days treated as errors or filled wrong. 5. **Inconsistent calendar alignment.** Calendar dates vs trading dates, US vs global market hours. The skill addresses all five and documents the treatment in the manifest. ## When to invoke - A quant says "I need clean OHLCV for the top 500 from 2022 to today for my mean-reversion backtest" - A researcher needs a survivorship-clean dataset for a paper - A factor-research extension wants a longer window than the in-memor