- 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
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
- 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>
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>
