Implements compute_7d_return, compute_metrics, load_baseline_metrics,
and make_decision functions with full TDD coverage (11 tests passing).
Co-Authored-By: Claude Sonnet 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>
Tests verify:
- Single vendor stops after first success
- Multi-vendor stops after all primaries (even if they fail)
- Fallback vendors are not attempted when primaries are configured
- Tool-level config overrides category-level config
Tests use pytest with fixtures and mocked vendors, can run without API keys in CI/CD.
Run with: pytest tests/test_multi_vendor_routing.py -v
- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER