Reads features from Supabase, simulates SMA(3)/SMA(20) crossover signals,
and outputs P&L, drawdown, and trade logs per instrument/granularity.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sanitize NaN/inf values that pandas re-introduces into float columns
after conversion, preventing JSON serialization errors. Fix upsert
response check to detect success via resp.data instead of status_code.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Track granularity (M1/M5) through the pipeline so rows are
distinguishable after upload. Drop helper columns (tr, typical_price,
pv) from the feature DataFrame. Add SQL schema with composite PK on
(time, instrument, granularity).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Create config/system.yaml with full master prompt template
- Create src/config_loader.py to load YAML config and .env
- Update data_engine.build_all_features() to support lists of windows
(e.g. sma_windows: [3, 20] produces sma_3 and sma_20 columns)
- Rewrite get_candles.py to loop over all instruments × granularities
from config instead of hardcoding EUR_USD/M5
- Fix supabase_upload.py indentation bug and wire up config for table name
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Brent Neale