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> |
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| .. | ||
| dataflows | ||
| utils | ||
| __init__.py | ||
| conftest.py | ||
| quick_ticker_test.py | ||
| test_concurrent_scanners.py | ||
| test_config.py | ||
| test_discovery_refactor.py | ||
| test_sec_13f_refactor.py | ||
| verify_refactor.py | ||