- scrapers/history_collector.py: scrapes full time series from 8 LookIntoBitcoin
charts + Fear & Greed API, stores to data/history.json (~5700 days back to 2010)
- backtesting/engine.py: scores each historical day using same thresholds as live
scoring, computes 30d/90d/180d/1yr forward returns, bracket stats, signal events
- dashboard/server.py: adds /backtest page with dual-axis score vs price chart,
bracket performance table, signal event list, current context box; adds backtest
nav link and historical context box on main dashboard; 4 new API endpoints
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fixed LLM failing silently (401 auth error on every iteration)
- Reset provider to Ollama (working) from broken OpenRouter config
- Added /api/clear endpoint + 'New Run' button to reset history
- LLM failures now logged visibly with error details
- LLM suggestions persisted to iteration data (survive restarts)
- Settings page support via llm_settings.json (multi-provider)
Replace day-trading bot with long-term accumulation signal model.
Predicts optimal BUY times using forward return analysis at 7d/30d/90d
horizons, scoring each candle 0-100. Primary metric is now
cost_basis_improvement_pct (model buy price vs DCA).
- train_and_backtest.py: regression models (XGBoost/LSTM hybrid),
accumulation-focused features (price position, momentum, volatility,
volume, cycle), forward return targets, signal quality backtesting
- orchestrator.py: cost improvement scoring, signal count validation
- analyzer.py: accumulation-focused LLM system prompt
- dashboard: cost improvement display, signal metrics table
- config: new accumulation-focused parameters
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
FastAPI dashboard on port 3088 with live iteration tracking,
Sharpe ratio chart, LLM analysis panel, config editor, and
download links. Orchestrator refactored to support library
usage with run_optimization_loop(), stop_flag, and callbacks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
