Codex 9d0ca7f04f feat: complete output schema + signal processor integration
- EventFlag: full spec Section 8.4 compliance
  * Fixed duplicate confidence kwarg
  * FlagType mapping updated for core EventType values (hack, whale, listing, etc.)
- VelocityComputer: hype_velocity/pub_velocity now return -100 to +100
- SignalProcessor:
  * Populates contributing_events (fear_driver, greed_driver, velocity_driver)
  * EventFlag generation with all spec fields: detail_factor, base_impact, t_zero, decay_remaining, half_life_minutes, impact_duration_minutes, direction, is_scheduled, triggered_at, sources, details_extracted, flag_type, flags
  * _compute_detail_factor implementation per spec Section 6.1
  * _map_event_to_flag_type covers core EventType values
- ProcessedItem: added raw_text field (required for detail_factor)
- NLP pipeline: passes raw_text when creating ProcessedItem
- ScoringEngine: populates contributing_events at market/industry levels
- All 46 core NLP unit tests pass
- All 19 core crypto semantic tests + 6 calibration scenarios pass
- Full pipeline integration test runs successfully with real e5-large-v2 encoder
2026-09-17 22:12:15 +02:00
Description
Sentiment analysis engine with ONNX FinBERT + LoRA adapters
7.8 MiB
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Python 99.9%
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