8 Commits

Author SHA1 Message Date
e590cdc13b fix: prevent already-completed simulations from re-running
Previously, when re-running a job with some model-days already completed:
- _prepare_data() marked them as "skipped" with error="Already completed"
- But _execute_date() didn't check the skip list before launching executors
- ModelDayExecutor would start, change status to "running", and never complete
- Job would hang with status="running" and pending count > 0

Fixed by:
- _prepare_data() now returns completion_skips: {model: {dates}}
- _execute_date() receives completion_skips and filters out already-completed models
- Skipped model-days are not submitted to ThreadPoolExecutor
- Job completes correctly, skipped model-days remain with status="skipped"

This ensures idempotent job behavior - re-running a job only executes
model-days that haven't completed yet.

Fixes #73
2025-11-03 00:03:57 -05:00
1f41e9d7ca feat: add skip status tracking for job orchestration
Implement skip status tracking to fix jobs hanging when dates are
filtered out. Jobs now properly complete when all model-days reach
terminal states (completed/failed/skipped).

Changes:
- database.py: Add 'skipped' status to job_details CHECK constraint
- job_manager.py: Update completion logic to count skipped as done
- job_manager.py: Add skipped count to progress tracking
- simulation_worker.py: Implement skip tracking with per-model granularity
- simulation_worker.py: Add _filter_completed_dates_with_tracking()
- simulation_worker.py: Add _mark_skipped_dates()
- simulation_worker.py: Update _prepare_data() to use skip tracking
- simulation_worker.py: Improve warning messages to distinguish skip types

Skip reasons:
- "Already completed" - Position data exists from previous job
- "Incomplete price data" - Missing prices (weekends/holidays/future)

The implementation correctly handles multi-model scenarios where different
models have different completion states for the same date.
2025-11-02 09:35:58 -05:00
5e5354e2af feat(worker): integrate data preparation into run() method
Call _prepare_data before executing trades:
- Download missing data if needed
- Filter completed dates
- Store warnings
- Handle empty date scenarios

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-01 23:49:24 -04:00
8c3e08a29b feat(worker): add _prepare_data method
Orchestrate data preparation phase:
- Check missing data
- Download if needed
- Filter completed dates
- Update job status

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-01 23:43:49 -04:00
445183d5bf feat(worker): add _add_job_warnings helper method
Delegate to JobManager.add_job_warnings for storing warnings.

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-01 23:31:34 -04:00
2ab78c8552 feat(worker): add _filter_completed_dates helper method
Implement idempotent behavior by skipping already-completed model-days.

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-01 23:30:09 -04:00
88a3c78e07 feat(worker): add _download_price_data helper method
Handle price data download with rate limit detection and warning generation.

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-01 23:29:00 -04:00
fb9583b374 feat: transform to REST API service with SQLite persistence (v0.3.0)
Major architecture transformation from batch-only to API service with
database persistence for Windmill integration.

## REST API Implementation
- POST /simulate/trigger - Start simulation jobs
- GET /simulate/status/{job_id} - Monitor job progress
- GET /results - Query results with filters (job_id, date, model)
- GET /health - Service health checks

## Database Layer
- SQLite persistence with 6 tables (jobs, job_details, positions,
  holdings, reasoning_logs, tool_usage)
- Foreign key constraints with cascade deletes
- Replaces JSONL file storage

## Backend Components
- JobManager: Job lifecycle management with concurrency control
- RuntimeConfigManager: Thread-safe isolated runtime configs
- ModelDayExecutor: Single model-day execution engine
- SimulationWorker: Date-sequential, model-parallel orchestration

## Testing
- 102 unit and integration tests (85% coverage)
- Database: 98% coverage
- Job manager: 98% coverage
- API endpoints: 81% coverage
- Pydantic models: 100% coverage
- TDD approach throughout

## Docker Deployment
- Dual-mode: API server (persistent) + batch (one-time)
- Health checks with 30s interval
- Volume persistence for database and logs
- Separate entrypoints for each mode

## Validation Tools
- scripts/validate_docker_build.sh - Build validation
- scripts/test_api_endpoints.sh - Complete API testing
- scripts/test_batch_mode.sh - Batch mode validation
- DOCKER_API.md - Deployment guide
- TESTING_GUIDE.md - Testing procedures

## Configuration
- API_PORT environment variable (default: 8080)
- Backwards compatible with existing configs
- FastAPI, uvicorn, pydantic>=2.0 dependencies

Co-Authored-By: AI Assistant <noreply@example.com>
2025-10-31 11:47:10 -04:00