## 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" }`

### Requirement: Generic error responses
All Next.js API routes SHALL return generic error messages to clients for 500-level errors. The response body SHALL be `{ "error": "Internal server error" }`. The full error details SHALL be logged server-side via `console.error` with request context.

#### Scenario: Internal error returns generic message
- **WHEN** an API route handler throws an unexpected error
- **THEN** the client receives HTTP 500 with `{ "error": "Internal server error" }` and the full error is logged server-side

#### Scenario: No stack traces in response
- **WHEN** a database query fails in any API route
- **THEN** the response does NOT contain table names, file paths, connection strings, or stack traces

### Requirement: Scoped bulk annotation delete
The `DELETE /api/annotations` endpoint SHALL require a `chartId` query parameter when `all=true` is specified. Unscoped delete-all (without chartId) SHALL be rejected with HTTP 400.

#### Scenario: Scoped bulk delete
- **WHEN** `DELETE /api/annotations?all=true&chartId=5` is called
- **THEN** all annotations for chart 5 are deleted

#### Scenario: Unscoped bulk delete rejected
- **WHEN** `DELETE /api/annotations?all=true` is called without chartId
- **THEN** the route returns HTTP 400 with `{ "error": "chartId is required for bulk delete" }`

### Requirement: Transaction-wrapped chart cascade delete
The `DELETE /api/charts/[id]` route SHALL wrap all related deletions (annotations, span annotations, candles, chart) in a single database transaction using `db.transaction()`.

#### Scenario: Cascade delete in transaction
- **WHEN** `DELETE /api/charts/5` is called
- **THEN** span annotations, annotations, candles, and the chart record for chart 5 are all deleted within a single transaction

#### Scenario: Partial failure rolls back
- **WHEN** the candles deletion fails mid-transaction
- **THEN** all deletions are rolled back and the chart remains intact

### Requirement: Span annotations included in chart cascade delete
The `DELETE /api/charts/[id]` route SHALL delete span annotations for the chart in addition to annotations and candles.

#### Scenario: Span annotations deleted with chart
- **WHEN** `DELETE /api/charts/5` is called
- **THEN** all `span_annotations` rows with `chart_id=5` are deleted before the chart record

### Requirement: parseInt validation
All API routes that parse integer query parameters SHALL use `parseInt(value, 10)` with radix 10 and check for `isNaN()`. Invalid integer parameters SHALL return HTTP 400.

#### Scenario: Valid integer parameter
- **WHEN** `GET /api/candles?chartId=5` is called
- **THEN** chartId is parsed as integer 5

#### Scenario: Invalid integer parameter
- **WHEN** `GET /api/candles?chartId=abc` is called
- **THEN** the route returns HTTP 400 with `{ "error": "Invalid chartId" }`

### Requirement: CSV injection protection on exports
All CSV export routes SHALL prefix cell values starting with `=`, `+`, `-`, or `@` with a single quote (`'`) to prevent spreadsheet formula injection.

#### Scenario: Dangerous cell value escaped
- **WHEN** an annotation note contains `=CMD("calc")`
- **THEN** the exported CSV cell contains `'=CMD("calc")`

#### Scenario: Normal values unchanged
- **WHEN** an annotation note contains `regular text`
- **THEN** the exported CSV cell contains `regular text` (no prefix)

### Requirement: response.ok checks on all fetch calls
All `fetch()` calls in frontend components (`page.tsx`, `CandleChart.tsx`) SHALL check `response.ok` before calling `response.json()`. If `!response.ok`, the code SHALL throw an error or handle the failure explicitly.

#### Scenario: Successful response parsed
- **WHEN** a fetch call returns HTTP 200
- **THEN** `response.json()` is called and the data is used normally

#### Scenario: Error response handled
- **WHEN** a fetch call returns HTTP 500
- **THEN** the code detects `!response.ok` and shows an error message instead of attempting JSON parse
