feat: add FastAPI pattern detection endpoints (Section 1)
- Extract CDL pattern detection logic into services/ml/app/patterns.py
with TALIB_PATTERNS dict, get_available_patterns(), validate_pattern_names(),
and detect_patterns(candles, pattern_names) functions
- Add GET /patterns/available endpoint returning all 54 supported CDL pattern
names with display names
- Add POST /patterns/detect endpoint accepting {candles, patterns}, running
selected CDL functions, returning span annotations with source "talib"
- Add input validation: reject invalid pattern names with HTTP 400,
treat empty patterns list as "run all"
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Marko Djordjevic
parent
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62
openspec/changes/ml-ui-connection/tasks.md
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62
openspec/changes/ml-ui-connection/tasks.md
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## 1. FastAPI Pattern Detection Endpoints
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- [x] 1.1 Extract pattern detection logic from `generate_talib_annotations.py` into a reusable module (e.g., `app/patterns.py`) with `detect_patterns(candles, pattern_names)` function
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- [x] 1.2 Add `GET /patterns/available` endpoint returning all supported CDL pattern names with display names
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- [x] 1.3 Add `POST /patterns/detect` endpoint accepting `{candles, patterns}`, running selected CDL functions, returning span annotations with source "talib"
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- [x] 1.4 Add input validation: reject invalid pattern names with HTTP 400, handle empty patterns list as "run all"
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## 2. FastAPI Training Endpoints
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- [ ] 2.1 Add `POST /training/start` endpoint that launches training in a background thread, returns `{run_id, status: "running"}`, and rejects concurrent runs with HTTP 409
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- [ ] 2.2 Add `GET /training/runs` endpoint returning training run history from the `training_runs` table, sorted by date descending
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- [ ] 2.3 Add `GET /training/dataset-info` endpoint returning labeled dataset file path, existence, size, and row count
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- [ ] 2.4 Add background training thread management: track active run, update DB status on completion/failure
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## 3. FastAPI Model Loading Endpoint
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- [ ] 3.1 Add `POST /model/load` endpoint accepting `{run_id}`, looking up the training run, loading the model artifact, and replacing the active model in `AppState`
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- [ ] 3.2 Add thread-safe model swap with locking to prevent conflicts with in-flight prediction requests
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## 4. Next.js Proxy Routes
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- [ ] 4.1 Add `GET /api/patterns/available` proxy route
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- [ ] 4.2 Add `POST /api/patterns/detect` proxy route
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- [ ] 4.3 Add `POST /api/training/start` proxy route
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- [ ] 4.4 Add `GET /api/training/runs` proxy route
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- [ ] 4.5 Add `GET /api/training/dataset-info` proxy route
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- [ ] 4.6 Add `POST /api/model/load` proxy route
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- [ ] 4.7 Extend `DELETE /api/span-annotations` to support `source` and `label` query parameters for bulk deletion
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## 5. TA-Lib Pattern UI Panel
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- [ ] 5.1 Create `TalibPatternPanel` component with collapsible section, fetching available patterns from `/api/patterns/available` on mount
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- [ ] 5.2 Add pattern checkboxes grouped by category with "Select All" / "Deselect All" toggle
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- [ ] 5.3 Add "Detect Patterns" button that sends selected patterns + chart candles to `/api/patterns/detect`, shows loading state
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- [ ] 5.4 Save detection results as span annotations via `POST /api/span-annotations` with `source: "talib"` and refresh the annotation list
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- [ ] 5.5 Add detection results summary showing pattern counts grouped by name
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- [ ] 5.6 Add "Clear All TA-Lib" bulk delete button calling `DELETE /api/span-annotations?source=talib&chartId=X`
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- [ ] 5.7 Add per-pattern-type delete in results summary
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## 6. Training UI Panel
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- [ ] 6.1 Create `TrainingPanel` component with collapsible section
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- [ ] 6.2 Add model type dropdown (Random Forest / XGBoost) defaulting to Random Forest
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- [ ] 6.3 Add dataset info display fetching from `/api/training/dataset-info`, showing warning if missing
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- [ ] 6.4 Add "Start Training" button with loading state, disabled when training active or dataset missing
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- [ ] 6.5 Add training status polling (5s interval) while a run is active, showing progress indicator
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- [ ] 6.6 Add training completion/failure handling with success message and metrics display
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- [ ] 6.7 Add training run history list fetching from `/api/training/runs`, showing 5 most recent runs with model type, status, date, metrics
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## 7. Model Selector Integration
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- [ ] 7.1 Create `ModelSelector` dropdown component fetching completed training runs from `/api/training/runs`
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- [ ] 7.2 Integrate `ModelSelector` into `PredictionPanel` above action buttons, showing current model as active
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- [ ] 7.3 Wire model switch: on selection call `POST /api/model/load`, clear prediction cache, refresh model info
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- [ ] 7.4 Handle model load errors: show error toast, keep previous model active
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## 8. Sidebar Layout Integration
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- [ ] 8.1 Add `TalibPatternPanel` to the sidebar in `page.tsx` between SpanAnnotationList and PredictionPanel
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- [ ] 8.2 Add `TrainingPanel` to the sidebar between TalibPatternPanel and PredictionPanel
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- [ ] 8.3 Make TalibPatternPanel, TrainingPanel, and PredictionPanel collapsible (default collapsed for new panels)
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- [ ] 8.4 Wire all new component state and callbacks in `page.tsx`
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@ -22,6 +22,12 @@ import mlflow.xgboost
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from app.config import load_config, PipelineConfig
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from app.preprocessing import preprocess_candles, extract_feature_columns
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from app.patterns import (
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TALIB_PATTERNS,
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get_available_patterns,
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validate_pattern_names,
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detect_patterns,
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)
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# Configure logging
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logging.basicConfig(
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@ -741,6 +747,85 @@ async def predict_batch(request: BatchPredictRequest):
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)
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# ---------------------------------------------------------------------------
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# Pattern Detection Endpoints
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# ---------------------------------------------------------------------------
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class PatternInfo(BaseModel):
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"""A single supported CDL pattern."""
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function_name: str
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display_name: str
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class DetectPatternsRequest(BaseModel):
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"""Request model for POST /patterns/detect."""
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candles: List[CandleData] = Field(..., min_length=1, description="Array of candle data")
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patterns: List[str] = Field(
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default=[],
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description="CDL function names to run. Empty list means run all.",
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)
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class SpanAnnotation(BaseModel):
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"""A span annotation returned by pattern detection."""
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start_time: int
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end_time: int
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label: str
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confidence: float = Field(..., ge=0.0, le=1.0)
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source: str
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notes: str
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class DetectPatternsResponse(BaseModel):
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"""Response model for POST /patterns/detect."""
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annotations: List[SpanAnnotation]
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metadata: Dict[str, Any]
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@app.get("/patterns/available", response_model=List[PatternInfo])
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async def patterns_available():
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"""
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Return all supported CDL pattern names with display names.
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"""
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return get_available_patterns()
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@app.post("/patterns/detect", response_model=DetectPatternsResponse)
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async def patterns_detect(request: DetectPatternsRequest):
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"""
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Detect TA-Lib CDL patterns on provided candle data.
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- Empty ``patterns`` list runs all available CDL functions.
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- Invalid pattern names return HTTP 400.
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"""
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# 1.4 – Validate pattern names
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if request.patterns:
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invalid = validate_pattern_names(request.patterns)
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if invalid:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=f"Invalid pattern name(s): {', '.join(invalid)}. "
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f"Use GET /patterns/available to see supported patterns.",
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)
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candles_data = [c.model_dump() for c in request.candles]
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try:
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raw_annotations = detect_patterns(candles_data, request.patterns or None)
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except RuntimeError as exc:
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raise HTTPException(
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status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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detail=str(exc),
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)
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annotations = [SpanAnnotation(**ann) for ann in raw_annotations]
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return DetectPatternsResponse(
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annotations=annotations,
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metadata={"source": "talib", "count": len(annotations)},
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)
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8001)
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173
services/ml/app/patterns.py
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173
services/ml/app/patterns.py
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"""
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TA-Lib CDL pattern detection module.
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Extracted from generate_talib_annotations.py for use in the FastAPI service.
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Provides detect_patterns() for use in pattern detection endpoints.
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"""
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from typing import List, Dict, Any, Optional
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import logging
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import numpy as np
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try:
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import talib
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except ImportError:
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talib = None
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logger = logging.getLogger(__name__)
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TALIB_PATTERNS: Dict[str, str] = {
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'CDLENGULFING': 'Engulfing',
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'CDLHAMMER': 'Hammer',
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'CDLINVERTEDHAMMER': 'Inverted Hammer',
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'CDLSHOOTINGSTAR': 'Shooting Star',
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'CDLDOJI': 'Doji',
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'CDLDOJISTAR': 'Doji Star',
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'CDLMORNINGSTAR': 'Morning Star',
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'CDLEVENINGSTAR': 'Evening Star',
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'CDLHARAMI': 'Harami',
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'CDLHARAMICROSS': 'Harami Cross',
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'CDLPIERCING': 'Piercing',
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'CDLDARKCLOUDCOVER': 'Dark Cloud Cover',
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'CDLMARUBOZU': 'Marubozu',
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'CDLSPINNINGTOP': 'Spinning Top',
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'CDL3WHITESOLDIERS': 'Three White Soldiers',
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'CDL3BLACKCROWS': 'Three Black Crows',
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'CDLABANDONEDBABY': 'Abandoned Baby',
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'CDLADVANCEBLOCK': 'Advance Block',
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'CDLBELTHOLD': 'Belt Hold',
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'CDLBREAKAWAY': 'Breakaway',
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'CDLCLOSINGMARUBOZU': 'Closing Marubozu',
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'CDLCONCEALBABYSWALL': 'Concealing Baby Swallow',
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'CDLCOUNTERATTACK': 'Counterattack',
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'CDLDRAGONFLYDOJI': 'Dragonfly Doji',
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'CDLGAPSIDESIDEWHITE': 'Up/Down Gap Side-by-Side White Lines',
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'CDLGRAVESTONEDOJI': 'Gravestone Doji',
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'CDLHANGINGMAN': 'Hanging Man',
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'CDLHIGHWAVE': 'High Wave',
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'CDLHIKKAKE': 'Hikkake',
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'CDLHIKKAKEMOD': 'Modified Hikkake',
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'CDLHOMINGPIGEON': 'Homing Pigeon',
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'CDLIDENTICAL3CROWS': 'Identical Three Crows',
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'CDLINNECK': 'In-Neck',
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'CDLKICKING': 'Kicking',
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'CDLKICKINGBYLENGTH': 'Kicking by Length',
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'CDLLADDERBOTTOM': 'Ladder Bottom',
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'CDLLONGLEGGEDDOJI': 'Long-Legged Doji',
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'CDLLONGLINE': 'Long Line',
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'CDLMATCHINGLOW': 'Matching Low',
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'CDLMATHOLD': 'Mat Hold',
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'CDLMORNINGDOJISTAR': 'Morning Doji Star',
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'CDLONNECK': 'On-Neck',
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'CDLRISEFALL3METHODS': 'Rising/Falling Three Methods',
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'CDLSEPARATINGLINES': 'Separating Lines',
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'CDLSHORTLINE': 'Short Line',
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'CDLSTALLEDPATTERN': 'Stalled Pattern',
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'CDLSTICKSANDWICH': 'Stick Sandwich',
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'CDLTAKURI': 'Takuri',
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'CDLTASUKIGAP': 'Tasuki Gap',
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'CDLTHRUSTING': 'Thrusting',
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'CDLTRISTAR': 'Tristar',
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'CDLUNIQUE3RIVER': 'Unique Three River',
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'CDLUPSIDEGAP2CROWS': 'Upside Gap Two Crows',
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'CDLXSIDEGAP3METHODS': 'Upside/Downside Gap Three Methods',
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}
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def get_available_patterns() -> List[Dict[str, str]]:
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"""
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Return all supported CDL pattern names with display names.
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Returns:
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List of dicts with function_name and display_name keys.
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"""
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return [
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{"function_name": fn, "display_name": display}
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for fn, display in TALIB_PATTERNS.items()
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]
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def validate_pattern_names(pattern_names: List[str]) -> List[str]:
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"""
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Validate a list of pattern names against the supported set.
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Args:
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pattern_names: List of CDL function names to validate.
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Returns:
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List of invalid pattern names (empty if all valid).
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"""
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return [name for name in pattern_names if name not in TALIB_PATTERNS]
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def detect_patterns(
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candles: List[Dict[str, Any]],
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pattern_names: Optional[List[str]] = None,
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) -> List[Dict[str, Any]]:
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"""
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Run TA-Lib CDL pattern functions on candle data and return span annotations.
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Args:
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candles: List of candle dicts with keys time, open, high, low, close.
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pattern_names: CDL function names to run. None or empty list means run all.
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Returns:
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List of span annotation dicts with keys:
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start_time, end_time, label, confidence, source, notes.
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"""
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if talib is None:
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raise RuntimeError("TA-Lib is not installed")
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if not candles:
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return []
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if not pattern_names:
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pattern_names = list(TALIB_PATTERNS.keys())
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times = np.array([c["time"] for c in candles], dtype=np.float64)
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open_prices = np.array([c["open"] for c in candles], dtype=np.float64)
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high_prices = np.array([c["high"] for c in candles], dtype=np.float64)
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low_prices = np.array([c["low"] for c in candles], dtype=np.float64)
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close_prices = np.array([c["close"] for c in candles], dtype=np.float64)
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n = len(candles)
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annotations = []
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for pattern_func in pattern_names:
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if not hasattr(talib, pattern_func):
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logger.warning("Unknown TA-Lib function: %s", pattern_func)
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continue
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try:
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func = getattr(talib, pattern_func)
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values = func(open_prices, high_prices, low_prices, close_prices)
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except Exception as exc:
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logger.error("Error running %s: %s", pattern_func, exc)
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continue
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friendly_name = TALIB_PATTERNS.get(pattern_func, pattern_func)
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for idx, value in enumerate(values):
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if value == 0:
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continue
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if value > 0:
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label = f"Bullish {friendly_name}"
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else:
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label = f"Bearish {friendly_name}"
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start_idx = max(0, idx - 1)
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end_idx = min(n - 1, idx + 1)
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annotations.append({
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"start_time": int(times[start_idx]),
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"end_time": int(times[end_idx]),
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"label": label,
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"confidence": abs(int(value)) / 100.0,
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"source": "talib",
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"notes": f"TA-Lib {pattern_func} detection",
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})
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logger.info("Detected %d pattern annotations", len(annotations))
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return annotations
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