Instead of showing 5 recent days with similar scores (all from the same
2-week window), now picks one example per cycle:
- pre-2016, 2016-17 Bull, 2018-19 Bear, 2020-21 Bull, 2022-23 Bear, 2024+
- Sorted by closest score match, then picks one per cycle
- Shows cycle label in brackets next to each example
- Much more representative of how the score performed across different eras
- Historical data (5693+ points per metric) saved in history.json permanently
- Quick refresh: only updates price + Fear & Greed from APIs (~2 seconds)
- Full refresh: only needed for FIRST-TIME setup or if data is missing
- Daily append: new values added to history.json from cache, not re-scraped
- Startup: uses cached on-chain data if it exists, no unnecessary Playwright launches
- On-chain metrics only update once per day, no reason to re-scrape them
- Quick Refresh button: updates price + Fear & Greed only (~2 seconds)
- Full Refresh button: re-scrapes all on-chain data from LookIntoBitcoin (~2-3 min)
- Background auto-refresh: quick every 15min, full only when on-chain data >6h old
- Cached on-chain data preserved between quick refreshes
- On-chain metrics only update daily anyway, no need to re-scrape every 15min
- 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
- 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>
