Create scripts/migrate-users.ts that:
- Creates a default admin user from DEFAULT_ADMIN_EMAIL/DEFAULT_ADMIN_PASSWORD env vars
- Backfills user_id on all existing rows in charts, annotations, annotation_types,
span_annotations, span_label_types
- Is idempotent (safe to run multiple times)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Rewrite scripts/run-migrations.js for PostgreSQL (was better-sqlite3)
- Rewrite scripts/load-initial-data.js for PostgreSQL (was better-sqlite3)
- Make db connection lazy in src/lib/db/index.ts to avoid build-time errors
when DATABASE_URL is not available in Docker build stage
- 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
- 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 complete workflow for using TA-Lib to bootstrap training data:
- generate_talib_annotations.py: Python script to run TA-Lib CDL* functions
and output span annotations in UI-compatible format
- import_talib_annotations.ts: TypeScript script to import generated
annotations into the UI database with auto-label-type creation
- npm script 'import-annotations' for easy execution
- TALIB_WORKFLOW.md: Comprehensive guide covering the full cycle:
* Generate patterns with TA-Lib
* Import into UI
* Review and edit in browser
* Export and train model
* Compare predictions with TA-Lib detections
* Iterate for improvement
This enables the intended workflow: use TA-Lib for initial annotations,
manually refine them, then train a model that learns from corrections.
- Add span_label_types and span_annotations tables to schema
- Seed default span label types (bull_flag, bear_flag, etc.)
- Implement CRUD API endpoints for span label types
- Implement CRUD API endpoints for span annotations
- Add time swap validation in POST endpoint (start_time <= end_time)
