BBLC Trade Strategy sprint - completed 2026-09-20 03:13

Live portal: https://630f2651.bblc-trade-strategy.pages.dev
Previous portal: https://c1841f18.bblc-trade-strategy.pages.dev (superseded)

What was built:
- Expanded Python codebase: data_feed.py, features.py, signals.py, risk_manager.py, backtest.py, live_engine.py, report.py, config.yaml, requirements.txt
- Backtest engine with regime classification (ADX/Hurst), ATR sizing, vol-normalized momentum, confirmation blending
- Extended visualizations: equity/drawdown, monthly returns heatmap, rolling Sharpe, regime distribution, trade P&L distribution
- Expanded documentation portal pages: Methodology, Data Pipeline, Signals, Risk, Execution, Analytics, Backtest, Operations, Whitepaper
- Investor one-pager PDF generated: public/bblc-investor-one-pager.pdf
- All source files exposed as downloads from the portal

Synthetic demo performance:
- Total Return +148.8%
- Annualized Return +18.8%
- Volatility 11.2%
- Sharpe 1.67
- Max Drawdown -11.3%

Next steps for production:
1. Replace synthetic data with live OANDA/historical tick feed.
2. Calibrate thresholds and vol windows on out-of-sample data.
3. Add slippage, commission, and market-impact assumptions.
4. Run paper trading for 30 days before allocating capital.