- Bracket table now shows Avg 30d, 90d, 180d, and 1yr columns
- Signal events show all 4 timeframes
- Current context shows all 4 average returns
- Comparable examples show all available timeframes
- Updated backtest screenshot
Dashboard main view, backtest page, and settings screenshots.
README includes tech stack table, project structure, run instructions,
score interpretation, and all metric descriptions.
COMPLETE PIVOT from ML trading optimizer to on-chain metrics monitor.
Architecture:
- Playwright scrapes LookIntoBitcoin Plotly Dash charts for real on-chain data
- 10 proven metrics: Puell Multiple, MVRV Z-Score, Fear & Greed, Reserve Risk,
RHODL Ratio, NUPL, LTH Realized Price, 200W SMA, Hash Ribbons, Drawdown
- Each metric scores 0-10, composite 0-100
- No ML, no black box — every signal transparent and traceable
- Historical backtest validates scoring against actual BTC forward returns
- Recency-weighted analysis accounts for diminishing cycle returns
Full documentation in ARCHITECTURE.md
- 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>
Major upgrade to the ML engine:
- LSTM model type: 2-layer PyTorch LSTM with early stopping, GPU support
- Hybrid mode: LSTM (60%) + XGBoost (40%) with agreement gating
- StandardScaler normalization (critical for LSTM)
- PCA dimensionality reduction (configurable variance retention)
- ATR-based dynamic stop-loss/take-profit adapting to volatility
- Rolling window retraining for more realistic time series validation
- Updated LLM system prompt with docs for all new parameters
- All backward compatible (xgboost/lightgbm/catboost still work)
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>
Multi-machine optimization loop:
- VPS orchestrator coordinates training and LLM analysis
- Windows PC (RTX 4070 Ti) runs XGBoost/LightGBM/CatBoost with GPU
- Mac Mini runs qwen3.5:27b via Ollama for strategy analysis
Includes 60+ technical features, walk-forward validation,
confidence-scaled position sizing, and automated convergence detection.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
