sync: migrate delta specs to main openspec/specs
- Added 5 new capabilities: feature-engineering, annotation-ingestion, ml-training, ml-inference, prediction-ui - Updated 2 existing capabilities: backend-api, span-annotation - All specs synced from openspec/changes/candle-backend/specs/
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Marko Djordjevic
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7 changed files with 525 additions and 252 deletions
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## ADDED Requirements
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### Requirement: Upload endpoint
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The system SHALL provide a `POST /api/upload` endpoint that accepts a CSV file via multipart form data. The endpoint SHALL create a new chart (named from the uploaded filename without extension), parse the CSV using papaparse, validate the format, and insert all candle records into the `candles` table with the new chart's `chart_id` within a single database transaction. On success, the endpoint SHALL return a JSON response with the chart `id`, chart `name`, and the count of inserted records. On failure, it SHALL return an appropriate error status and message without creating a chart.
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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 upload
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- **WHEN** a valid CSV file named "BTC-1H.csv" is sent to POST /api/upload
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- **THEN** endpoint creates a chart named "BTC-1H", inserts candles with that chart_id, and returns `{ "success": true, "count": <number>, "chart": { "id": <number>, "name": "BTC-1H" } }` with HTTP 200
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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: Invalid CSV upload
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- **WHEN** a CSV with missing or invalid headers is sent to POST /api/upload
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- **THEN** endpoint returns `{ "error": "<description>" }` with HTTP 400 and does not create a chart
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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: No file provided
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- **WHEN** POST /api/upload is called without a file
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- **THEN** endpoint returns `{ "error": "No file provided" }` with HTTP 400
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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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#### Scenario: Duplicate filename
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- **WHEN** a CSV named "BTC-1H.csv" is uploaded and a chart named "BTC-1H" already exists
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- **THEN** endpoint creates a chart named "BTC-1H-2" (or next available suffix) and inserts candles under that chart
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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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### Requirement: Get annotations endpoint
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The system SHALL provide a `GET /api/annotations` endpoint that accepts an optional `chartId` query parameter. When `chartId` is provided, the endpoint SHALL return only annotations belonging to that chart. When `chartId` is omitted, the endpoint SHALL return annotations for the most recently created chart. Each annotation object SHALL include: `id`, `chart_id`, `timestamp`, `label_type`, `geometry` (parsed from JSON string or null), and `created_at`.
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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: Fetch annotations for specific chart
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- **WHEN** GET /api/annotations?chartId=3 is called
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- **THEN** endpoint returns a JSON array of annotations where chart_id equals 3, with HTTP 200
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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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#### Scenario: Fetch annotations without chartId
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- **WHEN** GET /api/annotations is called without a chartId parameter
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- **THEN** endpoint returns annotations for the most recently created chart with HTTP 200
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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: No annotations exist for chart
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- **WHEN** GET /api/annotations?chartId=3 is called and no annotations exist for chart 3
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- **THEN** endpoint returns an empty JSON array `[]` with HTTP 200
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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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### Requirement: Create annotation endpoint
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The system SHALL provide a `POST /api/annotations` endpoint that accepts a JSON body with fields: `timestamp` (required, integer), `label_type` (required, string), `chart_id` (required, integer), and `geometry` (optional, object). The endpoint SHALL validate the input, verify the chart exists, serialize geometry to JSON string if present, and insert the record into the `annotations` table. On success, it SHALL return the created annotation object with its assigned `id`.
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#### Scenario: Create a marker annotation
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- **WHEN** POST /api/annotations is called with `{ "timestamp": 1700000000, "label_type": "break_up", "chart_id": 3 }`
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- **THEN** endpoint saves the annotation with chart_id 3 and returns the created object with `id` and HTTP 201
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#### Scenario: Create a line annotation
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- **WHEN** POST /api/annotations is called with `{ "timestamp": 1700000000, "label_type": "line", "chart_id": 3, "geometry": { "startTime": 1700000000, "startPrice": 1.05, "endTime": 1700100000, "endPrice": 1.06 } }`
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- **THEN** endpoint saves the annotation with chart_id 3 and serialized geometry JSON, returns the created object with HTTP 201
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#### Scenario: Invalid annotation data
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- **WHEN** POST /api/annotations is called with missing required fields (timestamp, label_type, or chart_id)
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- **THEN** endpoint returns `{ "error": "<description>" }` with HTTP 400
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#### Scenario: Annotation for non-existent chart
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- **WHEN** POST /api/annotations is called with a chart_id that does not exist
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- **THEN** endpoint returns `{ "error": "Chart not found" }` with HTTP 404
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### Requirement: Delete annotation endpoint
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The system SHALL provide a `DELETE /api/annotations/[id]` endpoint that removes an annotation by its ID. On success, it SHALL return HTTP 200. If the annotation does not exist, it SHALL return HTTP 404.
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#### Scenario: Delete existing annotation
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- **WHEN** DELETE /api/annotations/5 is called and annotation with id 5 exists
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- **THEN** endpoint removes the annotation and returns HTTP 200
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#### Scenario: Delete non-existent annotation
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- **WHEN** DELETE /api/annotations/999 is called and no annotation with that id exists
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- **THEN** endpoint returns HTTP 404
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### Requirement: Export annotations endpoint
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The system SHALL provide a `GET /api/export` endpoint that accepts an optional `chartId` query parameter. When `chartId` is provided, the endpoint SHALL export only annotations for that chart. When `chartId` is omitted, the endpoint SHALL export annotations for the most recently created chart. The CSV SHALL have columns: `timestamp`, `label_type`, `price`. The response SHALL set `Content-Type: text/csv` and `Content-Disposition: attachment; filename="annotations.csv"` headers.
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#### Scenario: Export for specific chart
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- **WHEN** GET /api/export?chartId=3 is called and annotations exist for chart 3
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- **THEN** endpoint returns a CSV file download with only annotations belonging to chart 3
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#### Scenario: Export without chartId
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- **WHEN** GET /api/export is called without a chartId parameter
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- **THEN** endpoint exports annotations for the most recently created chart
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### Requirement: Get candles endpoint
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The system SHALL provide a `GET /api/candles` endpoint that accepts an optional `chartId` query parameter. When `chartId` is provided, the endpoint SHALL return only candles belonging to that chart. When `chartId` is omitted, the endpoint SHALL return candles for the most recently created chart. Results SHALL be ordered by time ascending. Each object SHALL include: `time`, `open`, `high`, `low`, `close`.
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#### Scenario: Fetch candles for specific chart
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- **WHEN** GET /api/candles?chartId=3 is called
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- **THEN** endpoint returns a JSON array of candle objects where chart_id equals 3, ordered by time ascending with HTTP 200
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#### Scenario: Fetch candles without chartId
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- **WHEN** GET /api/candles is called without a chartId parameter
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- **THEN** endpoint returns candles for the most recently created chart with HTTP 200
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#### Scenario: No candles exist for chart
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- **WHEN** GET /api/candles?chartId=3 is called and no candles exist for chart 3
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- **THEN** endpoint returns an empty JSON array `[]` with HTTP 200
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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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