## ADDED Requirements ### Requirement: Pattern detection endpoint The FastAPI service SHALL provide a `POST /patterns/detect` endpoint that accepts candle data and a list of CDL pattern names. The endpoint SHALL run the specified TA-Lib CDL functions on the candle data and return detected patterns as span annotation objects. Each returned annotation SHALL include start_time, end_time, label, confidence, and source ("talib"). #### Scenario: Detect specific patterns - **WHEN** `POST /patterns/detect` is called with `{candles: [...], patterns: ["CDLENGULFING", "CDLHAMMER"]}` - **THEN** the endpoint runs only Engulfing and Hammer detection and returns matching span annotations #### Scenario: Detect all patterns - **WHEN** `POST /patterns/detect` is called with `{candles: [...], patterns: []}` (empty list) - **THEN** the endpoint runs all available CDL pattern functions #### Scenario: No patterns found - **WHEN** detection runs but no patterns match - **THEN** the endpoint returns `{annotations: [], metadata: {count: 0}}` #### Scenario: Invalid pattern name - **WHEN** a pattern name is not a valid TA-Lib CDL function - **THEN** the endpoint returns HTTP 400 with the invalid pattern name in the error message ### Requirement: Available patterns endpoint The FastAPI service SHALL provide a `GET /patterns/available` endpoint that returns the list of all supported CDL pattern names with their friendly display names. #### Scenario: List available patterns - **WHEN** `GET /patterns/available` is called - **THEN** the endpoint returns a list of `{function_name, display_name}` for all supported CDL patterns ### Requirement: Training start endpoint The FastAPI service SHALL provide a `POST /training/start` endpoint that triggers a training run in a background thread. The endpoint SHALL accept `{model_type}` and return immediately with a run_id and status "running". Only one training run SHALL be allowed at a time. #### Scenario: Start training - **WHEN** `POST /training/start` is called with `{model_type: "random_forest"}` - **THEN** the endpoint returns `{run_id, status: "running"}` and training begins in the background #### Scenario: Training already in progress - **WHEN** `POST /training/start` is called while a training run is active - **THEN** the endpoint returns HTTP 409 with `{error: "Training already in progress", run_id: ""}` #### Scenario: Invalid model type - **WHEN** `POST /training/start` is called with an unsupported model type - **THEN** the endpoint returns HTTP 400 with `{error: "Unsupported model type. Available: random_forest, xgboost"}` ### Requirement: Training runs endpoint The FastAPI service SHALL provide a `GET /training/runs` endpoint that returns training run history from the database. Each entry SHALL include run_id, model_type, status, created_at, completed_at, and metrics_summary. Results SHALL be sorted by created_at descending. #### Scenario: List training runs - **WHEN** `GET /training/runs` is called - **THEN** the endpoint returns training run records sorted by date descending #### Scenario: No training runs - **WHEN** no training runs exist in the database - **THEN** the endpoint returns `{runs: []}` ### Requirement: Model load endpoint The FastAPI service SHALL provide a `POST /model/load` endpoint that loads a model by run_id. The endpoint SHALL look up the training run, find the model artifact (MLflow or local), and replace the currently loaded model. The endpoint SHALL return the new model's info. #### Scenario: Load model by run_id - **WHEN** `POST /model/load` is called with `{run_id: "abc123"}` - **THEN** the endpoint loads the model associated with that run, updates the active model, and returns model info #### Scenario: Run not found - **WHEN** `POST /model/load` is called with a non-existent run_id - **THEN** the endpoint returns HTTP 404 with `{error: "Training run not found"}` #### Scenario: Model artifact missing - **WHEN** the training run exists but the model file is missing - **THEN** the endpoint returns HTTP 500 with `{error: "Model artifact not found for run"}` ### Requirement: Dataset info endpoint The FastAPI service SHALL provide a `GET /training/dataset-info` endpoint that returns information about the training dataset: file path, existence status, file size, and last modified date. #### Scenario: Dataset exists - **WHEN** `GET /training/dataset-info` is called and the labeled dataset file exists - **THEN** the endpoint returns `{path, exists: true, size_bytes, last_modified, row_count}` #### Scenario: Dataset missing - **WHEN** `GET /training/dataset-info` is called and the labeled dataset file does not exist - **THEN** the endpoint returns `{path, exists: false}` ### Requirement: Pattern detection proxy The Next.js API SHALL provide a `POST /api/patterns/detect` route that proxies to the FastAPI `/patterns/detect` endpoint. #### Scenario: Proxy pattern detection - **WHEN** `POST /api/patterns/detect` is called - **THEN** the route forwards the request to the FastAPI service and returns the response ### Requirement: Available patterns proxy The Next.js API SHALL provide a `GET /api/patterns/available` route that proxies to the FastAPI `/patterns/available` endpoint. #### Scenario: Proxy available patterns - **WHEN** `GET /api/patterns/available` is called - **THEN** the route forwards to the FastAPI service and returns the pattern list ### Requirement: Training proxy endpoints The Next.js API SHALL provide proxy routes for training operations: `POST /api/training/start`, `GET /api/training/runs`, and `GET /api/training/dataset-info`. #### Scenario: Proxy training start - **WHEN** `POST /api/training/start` is called - **THEN** the route forwards to the FastAPI service and returns the response #### Scenario: Proxy training runs - **WHEN** `GET /api/training/runs` is called - **THEN** the route forwards to the FastAPI service and returns the run list ### Requirement: Model load proxy The Next.js API SHALL provide a `POST /api/model/load` route that proxies to the FastAPI `/model/load` endpoint. #### Scenario: Proxy model load - **WHEN** `POST /api/model/load` is called with a run_id - **THEN** the route forwards to the FastAPI service and returns the response ### Requirement: Bulk delete by source The Next.js API `DELETE /api/span-annotations` endpoint SHALL support a `source` query parameter for bulk deletion. When `source` is provided, all span annotations matching that source (and optionally `label` filter) for the current chart SHALL be deleted. #### Scenario: Bulk delete TA-Lib annotations - **WHEN** `DELETE /api/span-annotations?chartId=1&source=talib` is called - **THEN** all span annotations with `source: "talib"` for chart 1 are deleted #### Scenario: Bulk delete by source and label - **WHEN** `DELETE /api/span-annotations?chartId=1&source=talib&label=Engulfing` is called - **THEN** only TA-Lib annotations containing "Engulfing" in the label for chart 1 are deleted