Both scanners were implemented by research PRs but never added to default_config.
Without entries they cannot be disabled or tuned from config and are invisible
to the settings UI. Also updated /research-strategy to make config entry mandatory.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two bugs causing zero recommendations:
1. risk_metrics.py was untracked — importing it raised ModuleNotFoundError which
was caught by the outer try/except in filter.py, silently dropping all 32
candidates that reached the fundamental risk check stage.
2. Minervini scanner at max_tickers=200 took >5 min to download 200 tickers x 1y
of OHLCV data. ThreadPoolExecutor.cancel() cannot kill a running thread, so the
download kept running as a zombie thread for 20 more minutes after the pipeline
completed, holding the Python process alive until the 30-min workflow timeout
killed the entire job.
Reducing to 50 tickers brings the download to ~75s, well under the 300s global
scanner timeout.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
yf.download(592 tickers, period=1y) takes 20+ minutes in CI, causing
the 30-minute job timeout to trigger. Add max_tickers=200 (configurable)
to limit the batch download to the first N tickers from the file. The
concurrent scanner pool already has a 5-min global timeout, but the hung
download thread monopolises network connections and starves the filter stage.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
minervini.py existed but was never committed. Without the file on the
remote, the __init__.py import added in the previous fix causes an
ImportError in CI.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add GitHub Actions workflow for daily discovery (8:30 AM ET, weekdays)
- Add headless run_daily_discovery.py script for scheduling
- Expand options_flow scanner to use tickers.txt with parallel execution
- Add recommendation history section to Performance page with filters and charts
- Fix strategy name normalization (momentum/Momentum/Momentum-Hype → momentum)
- Fix strategy metrics to count all recs, not just evaluated ones
- Add error handling to Streamlit page rendering
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Major additions:
- ML win probability scanner: scans ticker universe using trained
LightGBM/TabPFN model, surfaces candidates with P(WIN) above threshold
- 30-feature engineering pipeline (20 base + 10 interaction features)
computed from OHLCV data via stockstats + pandas
- Triple-barrier labeling for training data generation
- Dataset builder and training script with calibration analysis
- Discovery enrichment: confluence scoring, short interest extraction,
earnings estimates, options signal normalization, quant pre-score
- Configurable prompt logging (log_prompts_console flag)
- Enhanced ranker investment thesis (4-6 sentence reasoning)
- Typed DiscoveryConfig dataclass for all discovery settings
- Console price charts for visual ticker analysis
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Created nested "filters" section for all filter-stage settings
(min_average_volume, same-day movers, recent movers, etc.)
- Created nested "enrichment" section for batch news settings
- Updated CandidateFilter to read from new nested structure
- Added backward compatibility fallback for old flat config
- Improved config organization and clarity
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Replace FinnHub with Alpha Vantage API in README documentation
- Implement comprehensive Alpha Vantage modules:
- Stock data (daily OHLCV with date filtering)
- Technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, ATR)
- Fundamental data (overview, balance sheet, cashflow, income statement)
- News and sentiment data with insider transactions
- Update news analyst tools to use ticker-based news search
- Integrate Alpha Vantage vendor methods into interface routing
- Maintain backward compatibility with existing vendor system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
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