The model was trained on 94-candle sliding windows flattened to 2820
features (94 candles x 30 features). Inference was sending raw per-candle
features (27 columns).
Changes:
- Rewrite preprocessing to return (X, window_times) tuple
- Add sliding window creation with correct feature ordering
- Fill missing columns (average, barCount) with 0 for feature parity
- Fill NaN from indicator warmup with 0 instead of dropping rows
- Always compute all indicators (including MFI) for feature parity
- Update predict and batch predict endpoints for new signature
The Pydantic model sets volume=None when absent, creating an all-NaN
column rather than a missing column. Check isna().all() in addition
to column existence.
- Fill volume with 0 when column is absent from candle data
- Skip MFI/OBV/AD/ADOSC indicators when no real volume data available
- Fix pandas FutureWarning for inplace fillna in candle_features
- Remove temporary debug NaN logging
