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feat(ml): implement annotation ingestion with windowed/BIO encoding and TA-Lib patterns

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Marko Djordjevic 2026-02-15 12:28:58 +01:00
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openspec/changes/candle-backend/tasks.md
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@ -25,14 +25,14 @@
## 4. Annotation Ingestion Stage
- [ ] 4.1 Create `services/ml/app/annotation_ingestion.py` — load annotations JSON from `data.annotations_path`, filter by min_confidence
- [ ] 4.2 Implement windowed classification encoding — extract fixed-size windows centered on each annotation span, flatten into single rows, handle boundary padding
- [ ] 4.3 Implement BIO sequence labeling encoding — assign B-{label}/I-{label}/O tags per candle, handle overlapping annotations with multiple tag columns
- [ ] 4.4 Implement TA-Lib CDL* programmatic labeling — run configured CDL functions, convert +100/-100 to label names (bullish_/bearish_ prefix)
- [ ] 4.5 Implement human/programmatic label merge strategies — human_priority, programmatic_priority, both (separate columns)
- [ ] 4.6 Implement context padding — include N candles before/after each annotation span
- [ ] 4.7 Add dataset statistics logging — counts per label, class distribution %, avg span length, human/programmatic agreement rate
- [ ] 4.8 Wire annotation ingestion into `pipeline.py` — read enriched CSV + annotations JSON, run encoding, write labeled CSV to `data.labeled_path`
- [x] 4.1 Create `services/ml/app/annotation_ingestion.py` — load annotations JSON from `data.annotations_path`, filter by min_confidence
- [x] 4.2 Implement windowed classification encoding — extract fixed-size windows centered on each annotation span, flatten into single rows, handle boundary padding
- [x] 4.3 Implement BIO sequence labeling encoding — assign B-{label}/I-{label}/O tags per candle, handle overlapping annotations with multiple tag columns
- [x] 4.4 Implement TA-Lib CDL* programmatic labeling — run configured CDL functions, convert +100/-100 to label names (bullish_/bearish_ prefix)
- [x] 4.5 Implement human/programmatic label merge strategies — human_priority, programmatic_priority, both (separate columns)
- [x] 4.6 Implement context padding — include N candles before/after each annotation span
- [x] 4.7 Add dataset statistics logging — counts per label, class distribution %, avg span length, human/programmatic agreement rate
- [x] 4.8 Wire annotation ingestion into `pipeline.py` — read enriched CSV + annotations JSON, run encoding, write labeled CSV to `data.labeled_path`
## 5. Training Stage