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>

services/ml/app/main.py

@@ -22,6 +22,12 @@ import mlflow.xgboost
 
 from app.config import load_config, PipelineConfig
 from app.preprocessing import preprocess_candles, extract_feature_columns
+from app.patterns import (
+    TALIB_PATTERNS,
+    get_available_patterns,
+    validate_pattern_names,
+    detect_patterns,
+)
 
 # Configure logging
 logging.basicConfig(
@@ -741,6 +747,85 @@ async def predict_batch(request: BatchPredictRequest):
         )
 
 
+# ---------------------------------------------------------------------------
+# Pattern Detection Endpoints
+# ---------------------------------------------------------------------------
+
+class PatternInfo(BaseModel):
+    """A single supported CDL pattern."""
+    function_name: str
+    display_name: str
+
+
+class DetectPatternsRequest(BaseModel):
+    """Request model for POST /patterns/detect."""
+    candles: List[CandleData] = Field(..., min_length=1, description="Array of candle data")
+    patterns: List[str] = Field(
+        default=[],
+        description="CDL function names to run. Empty list means run all.",
+    )
+
+
+class SpanAnnotation(BaseModel):
+    """A span annotation returned by pattern detection."""
+    start_time: int
+    end_time: int
+    label: str
+    confidence: float = Field(..., ge=0.0, le=1.0)
+    source: str
+    notes: str
+
+
+class DetectPatternsResponse(BaseModel):
+    """Response model for POST /patterns/detect."""
+    annotations: List[SpanAnnotation]
+    metadata: Dict[str, Any]
+
+
+@app.get("/patterns/available", response_model=List[PatternInfo])
+async def patterns_available():
+    """
+    Return all supported CDL pattern names with display names.
+    """
+    return get_available_patterns()
+
+
+@app.post("/patterns/detect", response_model=DetectPatternsResponse)
+async def patterns_detect(request: DetectPatternsRequest):
+    """
+    Detect TA-Lib CDL patterns on provided candle data.
+
+    - Empty ``patterns`` list runs all available CDL functions.
+    - Invalid pattern names return HTTP 400.
+    """
+    # 1.4 – Validate pattern names
+    if request.patterns:
+        invalid = validate_pattern_names(request.patterns)
+        if invalid:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Invalid pattern name(s): {', '.join(invalid)}. "
+                       f"Use GET /patterns/available to see supported patterns.",
+            )
+
+    candles_data = [c.model_dump() for c in request.candles]
+
+    try:
+        raw_annotations = detect_patterns(candles_data, request.patterns or None)
+    except RuntimeError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=str(exc),
+        )
+
+    annotations = [SpanAnnotation(**ann) for ann in raw_annotations]
+
+    return DetectPatternsResponse(
+        annotations=annotations,
+        metadata={"source": "talib", "count": len(annotations)},
+    )
+
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8001)