- Fix SpanAnnotationManager price range calculation: use direct candle
filtering by time range instead of dummy Candle objects that fell back
to 0/0 high/low, causing invisible rectangles
- Add handleDeleteAllAnnotations in page.tsx (deletes all span annotations
and regular annotations for current chart)
- Add 'Delete All Annotations' button in sidebar below TA-Lib panel
- TalibPatternPanel: pattern checkboxes, detect button, results summary, clear-all and per-pattern delete
- TrainingPanel: model type selector, dataset info, start training, polling, run history
- ModelSelector: dropdown of completed runs, wired into PredictionPanel for model switching
- page.tsx: integrate all three panels into sidebar, wire callbacks (model load, annotations refresh)
- tasks.md: mark all 39 tasks complete
- Archived change to openspec/changes/archive/2026-02-17-ml-db-consolidation/
- Created new postgres-data-layer spec with PostgreSQL connection, schema definitions, Drizzle migrations, npm deps, and SQLite migration requirements
- Updated docker-deployment spec: Docker Compose now PostgreSQL-based (postgres dependency, ml-data volume, DATABASE_URL); env vars updated (DATABASE_URL added, DATABASE_PATH removed); database persistence updated to PostgreSQL volumes; health check updated to PostgreSQL
- Updated ml-training spec: added database name scenario (candle_annotator) and new direct annotation data access requirement
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Archived change to openspec/changes/archive/2026-02-17-line-rectangle-annotations/
- Updated annotation-tools spec: added rectangle tool mode, TrendLine plugin rendering, line hit testing, line selection handles; updated line drawing and delete requirements; removed SVG overlay rendering
- Created new rectangle-annotation spec with full requirements for rectangle drawing, rendering, hit testing, selection, deletion, and database storage
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Convert numpy.int64 to Python int before passing to SQLAlchemy queries
- Prevents psycopg2.ProgrammingError: can't adapt type 'numpy.int64'
- Applied to get_candles(), get_span_annotations(), and get_point_annotations()
- All ML service database access tests now passing successfully
- Updated .env to use DATABASE_URL instead of DATABASE_PATH
- Tested all API endpoints: health, charts, candles, span annotations
- Confirmed JSONB fields work correctly (geometry, sub_spans, model_prediction)
- All 2,836 rows accessible via API
- Database connection pooling working correctly
- Created scripts/migrate-sqlite-to-postgres.py as alternative to TypeScript version
- Handles all type conversions: timestamps, booleans, and JSONB fields
- Successfully migrated all 2,836 rows from SQLite to PostgreSQL
- Verified data integrity: all 6 tables migrated correctly
- Charts: 1, Candles: 2,592, Annotations: 4, Span annotations: 223
- Replace green hacker theme with professional blue-toned design
- Light theme default, manual toggle only (no system detection)
- Compact w-60 sidebar with collapsible sections
- New CSS tokens: sidebar, chart, candle, annotation colors
- Tools displayed as compact grid buttons
- Color swatches as inline bar
- Chart top bar with keyboard shortcut hints
- Inter + JetBrains Mono font pairing
- All components updated for compact styling
- Tailwind config extended with sidebar/chart tokens
- Add scripts/run-migrations.js to run migrations before data loading
- Fix startup.sh ordering: migrations -> data load -> app start
- Fix migration 0005 missing statement-breakpoint between ALTER TABLE statements
- Add migration 0005 to drizzle journal (was missing)
- Fix load-initial-data.js to check table existence before querying
- Fix load-initial-data.js to create chart record before inserting candles (chart_id NOT NULL constraint)
- Simplify db/index.ts migration error handling (remove overly broad 'already exists' catch)
- Add pre-migration check for inconsistent DB state (tables without migration tracking)
- Add hitTest, setSelected, attached/detached lifecycle methods to TrendLine
- Add preview mode support with dashed lines and reduced opacity
- Draw selection handles on endpoints when selected
- Create RectangleDrawingPrimitive plugin with full ISeriesPrimitive implementation
- Support preview mode, selection, hit testing, and autoscaling for rectangles
- Set z-order to bottom for rectangles to render behind candlesticks
Tasks completed: 1.1-1.4, 2.1-2.7
- Implement disagreement visual highlighting with distinct colors
- Yellow highlight for 'missed_by_human' predictions
- Orange for 'label_mismatch' disagreements
- Warning icon on disagreement markers
- Add click-to-convert prediction feedback
- Click disagreement predictions to create span annotations
- Auto-fill with predicted label and times
- Set source as 'model_confirmed' or 'model_corrected'
- Add dismiss action for false positive predictions
- Alt+Click or Ctrl+Click to dismiss predictions
- Saves negative annotation with label 'O'
- Records original prediction in model_prediction field
- Filter predictions when 'Show only disagreements' is enabled
The model info returned empty labels array because the pkl file has
no metadata dict. Now extracts labels from model.classes_ or
model.model.classes_ as fallback.
The model was trained on 94-candle sliding windows flattened to 2820
features (94 candles x 30 features). Inference was sending raw per-candle
features (27 columns).
Changes:
- Rewrite preprocessing to return (X, window_times) tuple
- Add sliding window creation with correct feature ordering
- Fill missing columns (average, barCount) with 0 for feature parity
- Fill NaN from indicator warmup with 0 instead of dropping rows
- Always compute all indicators (including MFI) for feature parity
- Update predict and batch predict endpoints for new signature
The Pydantic model sets volume=None when absent, creating an all-NaN
column rather than a missing column. Check isna().all() in addition
to column existence.
- Fill volume with 0 when column is absent from candle data
- Skip MFI/OBV/AD/ADOSC indicators when no real volume data available
- Fix pandas FutureWarning for inplace fillna in candle_features
- Remove temporary debug NaN logging
- Add GET handler to /api/charts/[id] route to fetch chart metadata
- Fix batch prediction to use regular /predict endpoint with database candles
- Remove /predict/batch usage (was designed for file-based predictions)
- Make volume field optional in CandleData model (database candles don't have volume)
- Convert timestamps to ISO dates for batch requests
Known issue: TA-Lib indicators failing with 'input array type is not double'
- May need to ensure candle data is float64/double type before processing
- Change pair and timeframe fields from required to optional
- Frontend only sends candles array, not pair/timeframe metadata
- These fields are only used for logging, not prediction logic
- Update logging to handle None values with 'unknown' fallback
- Fixes 422 validation error on /predict endpoint
