346 lines
20 KiB
Markdown
346 lines
20 KiB
Markdown
## ADDED Requirements
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### Requirement: Predict proxy endpoint
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The system SHALL provide a `POST /api/predict` Next.js API route that proxies requests to the Python inference service at `${INFERENCE_API_URL}/predict`. The route SHALL forward the request body (pair, timeframe, candles array) and return the Python service's response. If the inference service is unreachable, the route SHALL return HTTP 503 with `{ "error": "Inference service unavailable" }`.
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#### Scenario: Successful prediction proxy
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- **WHEN** POST /api/predict is called with valid candle data and the Python service is running
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- **THEN** the route forwards the request to the inference service and returns the prediction response with HTTP 200
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#### Scenario: Inference service down
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- **WHEN** POST /api/predict is called but the Python inference service is unreachable
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- **THEN** the route returns HTTP 503 with `{ "error": "Inference service unavailable" }`
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#### Scenario: Inference service error
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- **WHEN** the Python inference service returns an error status (4xx or 5xx)
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- **THEN** the route forwards the error status and message to the client
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### Requirement: Batch predict proxy endpoint
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The system SHALL provide a `POST /api/predict/batch` Next.js API route that proxies batch prediction requests to `${INFERENCE_API_URL}/predict/batch`. The route SHALL forward pair, timeframe, start_date, and end_date.
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#### Scenario: Successful batch prediction
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- **WHEN** POST /api/predict/batch is called with valid parameters
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- **THEN** the route forwards to the inference service and returns the batch prediction response
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#### Scenario: Timeout on large batch
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- **WHEN** the batch prediction takes longer than INFERENCE_BATCH_TIMEOUT
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- **THEN** the route returns HTTP 504 with `{ "error": "Batch prediction timed out" }`
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### Requirement: Pattern detection endpoint
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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").
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#### Scenario: Detect specific patterns
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- **WHEN** `POST /patterns/detect` is called with `{candles: [...], patterns: ["CDLENGULFING", "CDLHAMMER"]}`
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- **THEN** the endpoint runs only Engulfing and Hammer detection and returns matching span annotations
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#### Scenario: Detect all patterns
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- **WHEN** `POST /patterns/detect` is called with `{candles: [...], patterns: []}` (empty list)
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- **THEN** the endpoint runs all available CDL pattern functions
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#### Scenario: No patterns found
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- **WHEN** detection runs but no patterns match
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- **THEN** the endpoint returns `{annotations: [], metadata: {count: 0}}`
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#### Scenario: Invalid pattern name
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- **WHEN** a pattern name is not a valid TA-Lib CDL function
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- **THEN** the endpoint returns HTTP 400 with the invalid pattern name in the error message
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### Requirement: Available patterns endpoint
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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.
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#### Scenario: List available patterns
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- **WHEN** `GET /patterns/available` is called
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- **THEN** the endpoint returns a list of `{function_name, display_name}` for all supported CDL patterns
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### Requirement: Training start endpoint
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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.
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#### Scenario: Start training
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- **WHEN** `POST /training/start` is called with `{model_type: "random_forest"}`
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- **THEN** the endpoint returns `{run_id, status: "running"}` and training begins in the background
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#### Scenario: Training already in progress
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- **WHEN** `POST /training/start` is called while a training run is active
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- **THEN** the endpoint returns HTTP 409 with `{error: "Training already in progress", run_id: "<active_run_id>"}`
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#### Scenario: Invalid model type
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- **WHEN** `POST /training/start` is called with an unsupported model type
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- **THEN** the endpoint returns HTTP 400 with `{error: "Unsupported model type. Available: random_forest, xgboost"}`
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### Requirement: Training runs endpoint
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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.
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#### Scenario: List training runs
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- **WHEN** `GET /training/runs` is called
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- **THEN** the endpoint returns training run records sorted by date descending
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#### Scenario: No training runs
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- **WHEN** no training runs exist in the database
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- **THEN** the endpoint returns `{runs: []}`
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### Requirement: Model load endpoint
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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.
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#### Scenario: Load model by run_id
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- **WHEN** `POST /model/load` is called with `{run_id: "abc123"}`
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- **THEN** the endpoint loads the model associated with that run, updates the active model, and returns model info
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#### Scenario: Run not found
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- **WHEN** `POST /model/load` is called with a non-existent run_id
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- **THEN** the endpoint returns HTTP 404 with `{error: "Training run not found"}`
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#### Scenario: Model artifact missing
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- **WHEN** the training run exists but the model file is missing
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- **THEN** the endpoint returns HTTP 500 with `{error: "Model artifact not found for run"}`
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### Requirement: Dataset info endpoint
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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.
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#### Scenario: Dataset exists
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- **WHEN** `GET /training/dataset-info` is called and the labeled dataset file exists
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- **THEN** the endpoint returns `{path, exists: true, size_bytes, last_modified, row_count}`
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#### Scenario: Dataset missing
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- **WHEN** `GET /training/dataset-info` is called and the labeled dataset file does not exist
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- **THEN** the endpoint returns `{path, exists: false}`
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### Requirement: Pattern detection proxy
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The Next.js API SHALL provide a `POST /api/patterns/detect` route that proxies to the FastAPI `/patterns/detect` endpoint.
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#### Scenario: Proxy pattern detection
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- **WHEN** `POST /api/patterns/detect` is called
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- **THEN** the route forwards the request to the FastAPI service and returns the response
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### Requirement: Available patterns proxy
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The Next.js API SHALL provide a `GET /api/patterns/available` route that proxies to the FastAPI `/patterns/available` endpoint.
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#### Scenario: Proxy available patterns
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- **WHEN** `GET /api/patterns/available` is called
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- **THEN** the route forwards to the FastAPI service and returns the pattern list
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### Requirement: Training proxy endpoints
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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`.
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#### Scenario: Proxy training start
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- **WHEN** `POST /api/training/start` is called
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- **THEN** the route forwards to the FastAPI service and returns the response
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#### Scenario: Proxy training runs
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- **WHEN** `GET /api/training/runs` is called
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- **THEN** the route forwards to the FastAPI service and returns the run list
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### Requirement: Model load proxy
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The Next.js API SHALL provide a `POST /api/model/load` route that proxies to the FastAPI `/model/load` endpoint.
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#### Scenario: Proxy model load
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- **WHEN** `POST /api/model/load` is called with a run_id
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- **THEN** the route forwards to the FastAPI service and returns the response
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### Requirement: Bulk delete by source
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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.
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#### Scenario: Bulk delete TA-Lib annotations
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- **WHEN** `DELETE /api/span-annotations?chartId=1&source=talib` is called
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- **THEN** all span annotations with `source: "talib"` for chart 1 are deleted
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#### Scenario: Bulk delete by source and label
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- **WHEN** `DELETE /api/span-annotations?chartId=1&source=talib&label=Engulfing` is called
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- **THEN** only TA-Lib annotations containing "Engulfing" in the label for chart 1 are deleted
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### Requirement: Model info proxy endpoint
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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.
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#### Scenario: Successful model info
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- **WHEN** GET /api/model/info is called and the inference service is running
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- **THEN** the route returns the model metadata JSON
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#### Scenario: No model available
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- **WHEN** GET /api/model/info is called and the inference service returns 503
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- **THEN** the route returns HTTP 503 with `{ "error": "No model available" }`
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### Requirement: Generic error responses
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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.
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#### Scenario: Internal error returns generic message
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- **WHEN** an API route handler throws an unexpected error
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- **THEN** the client receives HTTP 500 with `{ "error": "Internal server error" }` and the full error is logged server-side
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#### Scenario: No stack traces in response
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- **WHEN** a database query fails in any API route
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- **THEN** the response does NOT contain table names, file paths, connection strings, or stack traces
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### Requirement: Scoped bulk annotation delete
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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.
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#### Scenario: Scoped bulk delete
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- **WHEN** `DELETE /api/annotations?all=true&chartId=5` is called
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- **THEN** all annotations for chart 5 are deleted
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#### Scenario: Unscoped bulk delete rejected
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- **WHEN** `DELETE /api/annotations?all=true` is called without chartId
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- **THEN** the route returns HTTP 400 with `{ "error": "chartId is required for bulk delete" }`
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### Requirement: Transaction-wrapped chart cascade delete
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The `DELETE /api/charts/[id]` route SHALL wrap all related deletions (annotations, span annotations, candles, chart) in a single database transaction using `db.transaction()`.
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#### Scenario: Cascade delete in transaction
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- **WHEN** `DELETE /api/charts/5` is called
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- **THEN** span annotations, annotations, candles, and the chart record for chart 5 are all deleted within a single transaction
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#### Scenario: Partial failure rolls back
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- **WHEN** the candles deletion fails mid-transaction
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- **THEN** all deletions are rolled back and the chart remains intact
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### Requirement: Span annotations included in chart cascade delete
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The `DELETE /api/charts/[id]` route SHALL delete span annotations for the chart in addition to annotations and candles.
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#### Scenario: Span annotations deleted with chart
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- **WHEN** `DELETE /api/charts/5` is called
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- **THEN** all `span_annotations` rows with `chart_id=5` are deleted before the chart record
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### Requirement: parseInt validation
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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.
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#### Scenario: Valid integer parameter
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- **WHEN** `GET /api/candles?chartId=5` is called
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- **THEN** chartId is parsed as integer 5
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#### Scenario: Invalid integer parameter
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- **WHEN** `GET /api/candles?chartId=abc` is called
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- **THEN** the route returns HTTP 400 with `{ "error": "Invalid chartId" }`
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### Requirement: CSV injection protection on exports
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All CSV export routes SHALL prefix cell values starting with `=`, `+`, `-`, or `@` with a single quote (`'`) to prevent spreadsheet formula injection.
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#### Scenario: Dangerous cell value escaped
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- **WHEN** an annotation note contains `=CMD("calc")`
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- **THEN** the exported CSV cell contains `'=CMD("calc")`
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#### Scenario: Normal values unchanged
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- **WHEN** an annotation note contains `regular text`
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- **THEN** the exported CSV cell contains `regular text` (no prefix)
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### Requirement: response.ok checks on all fetch calls
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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.
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#### Scenario: Successful response parsed
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- **WHEN** a fetch call returns HTTP 200
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- **THEN** `response.json()` is called and the data is used normally
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#### Scenario: Error response handled
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- **WHEN** a fetch call returns HTTP 500
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- **THEN** the code detects `!response.ok` and shows an error message instead of attempting JSON parse
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## MODIFIED Requirements (user-accounts)
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### Requirement: Predict proxy endpoint
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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 require authentication via `getAuthUser()`. The route SHALL forward the request body (pair, timeframe, candles array) and include the `X-User-ID` header with the authenticated user's UUID. If the inference service is unreachable, the route SHALL return HTTP 503 with `{ "error": "Inference service unavailable" }`.
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#### Scenario: Successful prediction proxy
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- **WHEN** an authenticated user calls POST /api/predict with valid candle data and the Python service is running
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- **THEN** the route forwards the request with `X-User-ID` header and returns the prediction response with HTTP 200
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#### Scenario: Unauthenticated prediction request
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- **WHEN** POST /api/predict is called without authentication
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- **THEN** the route returns HTTP 401 with `{ "error": "Unauthorized" }`
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#### Scenario: Inference service down
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- **WHEN** POST /api/predict is called but the Python inference service is unreachable
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- **THEN** the route returns HTTP 503 with `{ "error": "Inference service unavailable" }`
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#### Scenario: Inference service error
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- **WHEN** the Python inference service returns an error status (4xx or 5xx)
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- **THEN** the route forwards the error status and message to the client
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### Requirement: Batch predict proxy endpoint
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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 require authentication and include the `X-User-ID` header.
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#### Scenario: Successful batch prediction
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- **WHEN** an authenticated user calls POST /api/predict/batch with valid parameters
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- **THEN** the route forwards to the inference service with `X-User-ID` header and returns the response
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#### Scenario: Timeout on large batch
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- **WHEN** the batch prediction takes longer than INFERENCE_BATCH_TIMEOUT
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- **THEN** the route returns HTTP 504 with `{ "error": "Batch prediction timed out" }`
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### Requirement: Training proxy endpoints
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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`. All training proxy routes SHALL require authentication and include the `X-User-ID` header.
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#### Scenario: Proxy training start
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- **WHEN** an authenticated user calls POST /api/training/start
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- **THEN** the route forwards to the FastAPI service with `X-User-ID` header
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#### Scenario: Proxy training runs
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- **WHEN** an authenticated user calls GET /api/training/runs
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- **THEN** the route forwards to the FastAPI service with `X-User-ID` header and returns the run list
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### Requirement: Model load proxy
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The Next.js API SHALL provide a `POST /api/model/load` route that proxies to the FastAPI `/model/load` endpoint. The route SHALL require authentication and include the `X-User-ID` header.
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#### Scenario: Proxy model load
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- **WHEN** an authenticated user calls POST /api/model/load with a run_id
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- **THEN** the route forwards to the FastAPI service with `X-User-ID` header
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### Requirement: Model info proxy endpoint
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The system SHALL provide a `GET /api/model/info` Next.js API route that proxies to `${INFERENCE_API_URL}/model/info`. The route SHALL require authentication and include the `X-User-ID` header.
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#### Scenario: Successful model info
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- **WHEN** an authenticated user calls GET /api/model/info
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- **THEN** the route returns the model metadata JSON
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#### Scenario: No model available
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- **WHEN** GET /api/model/info is called and the inference service returns 503
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- **THEN** the route returns HTTP 503 with `{ "error": "No model available" }`
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### Requirement: Pattern detection proxy
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The Next.js API SHALL provide a `POST /api/patterns/detect` route that proxies to the FastAPI `/patterns/detect` endpoint. The route SHALL require authentication and include the `X-User-ID` header.
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#### Scenario: Proxy pattern detection
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- **WHEN** an authenticated user calls POST /api/patterns/detect
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- **THEN** the route forwards the request with `X-User-ID` header and returns the response
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### Requirement: Available patterns proxy
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The Next.js API SHALL provide a `GET /api/patterns/available` route that proxies to the FastAPI `/patterns/available` endpoint. The route SHALL require authentication.
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#### Scenario: Proxy available patterns
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- **WHEN** an authenticated user calls GET /api/patterns/available
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- **THEN** the route forwards to the FastAPI service and returns the pattern list
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### Requirement: Bulk delete by source
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The Next.js API `DELETE /api/span-annotations` endpoint SHALL require authentication and scope deletion by the authenticated user. When `source` is provided, all span annotations matching that source (and optionally `label` filter) for the current chart belonging to the authenticated user SHALL be deleted.
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#### Scenario: Bulk delete TA-Lib annotations
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- **WHEN** an authenticated user calls `DELETE /api/span-annotations?chartId=1&source=talib`
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- **THEN** all span annotations with `source: "talib"` for chart 1 belonging to that user are deleted
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#### Scenario: Bulk delete by source and label
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- **WHEN** an authenticated user calls `DELETE /api/span-annotations?chartId=1&source=talib&label=Engulfing`
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- **THEN** only TA-Lib annotations containing "Engulfing" for chart 1 belonging to that user are deleted
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## ADDED Requirements (user-accounts)
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### Requirement: Auth guard on all data API routes
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All existing data API routes (`/api/upload`, `/api/candles`, `/api/annotations`, `/api/annotation-types`, `/api/charts`, `/api/span-annotations`, `/api/span-label-types`, `/api/export`) SHALL call `getAuthUser()` at the top. If the user is not authenticated, the route SHALL return HTTP 401.
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#### Scenario: Unauthenticated data API access
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- **WHEN** any data API route is called without authentication
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- **THEN** the route returns HTTP 401 with `{ "error": "Unauthorized" }`
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#### Scenario: Authenticated data API access
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- **WHEN** any data API route is called with valid authentication
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- **THEN** the route proceeds with queries scoped to the authenticated user's ID
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### Requirement: User-scoped queries in all data routes
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All Drizzle queries in data API routes SHALL include a `user_id` filter matching the authenticated user. INSERT operations SHALL set `user_id` to the authenticated user's UUID.
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#### Scenario: GET queries filtered by user
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- **WHEN** an authenticated user requests data (charts, annotations, annotation types, etc.)
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- **THEN** the query includes `.where(eq(table.user_id, user.id))` or equivalent join condition
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#### Scenario: INSERT operations set user_id
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- **WHEN** an authenticated user creates new data (upload, create annotation, etc.)
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- **THEN** the inserted row has `user_id` set to the authenticated user's UUID
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#### Scenario: Cross-user data isolation
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- **WHEN** user A requests data
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- **THEN** no data belonging to user B is returned
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