Critical fixes:
1. Fixed api/database.py import - use get_db_path() instead of non-existent get_database_path()
2. Fixed state management - use database queries instead of reading from position.jsonl file
3. Fixed action counting - track during trading loop execution instead of retroactively from conversation history
4. Completed integration test to verify P&L calculation works correctly
Changes:
- agent/base_agent/base_agent.py:
* Updated _get_current_portfolio_state() to query database via get_current_position_from_db()
* Added today_date and job_id parameters to method signature
* Count trade actions during trading loop instead of post-processing conversation history
* Removed obsolete action counting logic
- api/database.py:
* Fixed import to use get_db_path() from deployment_config
* Pass correct default database path "data/trading.db"
- tests/integration/test_agent_pnl_integration.py:
* Added proper mocks for dev mode and MCP client
* Mocked get_current_position_from_db to return test data
* Added comprehensive assertions to verify trading_day record fields
* Test now actually validates P&L calculation integration
Test results:
- All unit tests passing (252 passed)
- All P&L integration tests passing (8 passed)
- No regressions detected
This implements Task 5 from the daily P&L results API refactor plan, bringing
together P&L calculation and reasoning summary into the BaseAgent trading session.
Changes:
- Add DailyPnLCalculator and ReasoningSummarizer to BaseAgent.__init__
- Modify run_trading_session() to:
* Calculate P&L at start of day using current market prices
* Create trading_day record with P&L metrics
* Generate reasoning summary after trading using AI model
* Save final holdings to database
* Update trading_day with completion data (cash, portfolio value, summary, actions)
- Add helper methods:
* _get_current_prices() - Get market prices for P&L calculation
* _get_current_portfolio_state() - Read current state from position.jsonl
* _calculate_portfolio_value() - Calculate total portfolio value
Integration test verifies:
- P&L calculation components exist and are importable
- DailyPnLCalculator correctly calculates zero P&L on first day
- ReasoningSummarizer can be instantiated with AI model
This maintains backward compatibility with position.jsonl while adding
comprehensive database tracking for the new results API.
Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
