## ADDED Requirements

### Requirement: Predict proxy endpoint
The system SHALL provide a `POST /api/predict` Next.js API route that proxies requests to the Python inference service at `${INFERENCE_API_URL}/predict`. The route SHALL forward the request body (pair, timeframe, candles array) and return the Python service's response. If the inference service is unreachable, the route SHALL return HTTP 503 with `{ "error": "Inference service unavailable" }`.

#### Scenario: Successful prediction proxy
- **WHEN** POST /api/predict is called with valid candle data and the Python service is running
- **THEN** the route forwards the request to the inference service and returns the prediction response with HTTP 200

#### Scenario: Inference service down
- **WHEN** POST /api/predict is called but the Python inference service is unreachable
- **THEN** the route returns HTTP 503 with `{ "error": "Inference service unavailable" }`

#### Scenario: Inference service error
- **WHEN** the Python inference service returns an error status (4xx or 5xx)
- **THEN** the route forwards the error status and message to the client

### Requirement: Batch predict proxy endpoint
The system SHALL provide a `POST /api/predict/batch` Next.js API route that proxies batch prediction requests to `${INFERENCE_API_URL}/predict/batch`. The route SHALL forward pair, timeframe, start_date, and end_date.

#### Scenario: Successful batch prediction
- **WHEN** POST /api/predict/batch is called with valid parameters
- **THEN** the route forwards to the inference service and returns the batch prediction response

#### Scenario: Timeout on large batch
- **WHEN** the batch prediction takes longer than INFERENCE_BATCH_TIMEOUT
- **THEN** the route returns HTTP 504 with `{ "error": "Batch prediction timed out" }`

### 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: "<active_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

### Requirement: Model info proxy endpoint
The system SHALL provide a `GET /api/model/info` Next.js API route that proxies to `${INFERENCE_API_URL}/model/info`. This endpoint returns model metadata and per-class metrics.

#### Scenario: Successful model info
- **WHEN** GET /api/model/info is called and the inference service is running
- **THEN** the route returns the model metadata JSON

#### Scenario: No model available
- **WHEN** GET /api/model/info is called and the inference service returns 503
- **THEN** the route returns HTTP 503 with `{ "error": "No model available" }`
