Changes:
- Update get_today_init_position_from_db to query trading_days table
- Remove obsolete add_no_trade_record_to_db calls from BaseAgent
- Simplify _handle_trading_result (trading_day record handles both scenarios)
Root Cause:
- Code was still querying old positions table after schema migration
- The add_no_trade_record_to_db function is obsolete in new schema
New Schema Behavior:
- trading_day record created at session start (regardless of trading)
- trading_day record updated at session end with final results
- No separate "no-trade" record needed
Impact:
- Fixes: "no such table: positions" error in get_today_init_position_from_db
- Removes unnecessary database writes for no-trade scenarios
- Simplifies codebase by removing obsolete function calls
Related: tools/price_tools.py:340-364, agent/base_agent/base_agent.py:661-673
- Created comprehensive E2E test in tests/e2e/test_full_simulation_workflow.py
- Tests new trading_days schema with manually populated data
- Verifies database helper methods work correctly
- Tests Results API structure and filtering
- Validates holdings chain across multiple days
- Checks daily P&L calculation and storage
- Verifies reasoning summary/full retrieval
- Fixed database index creation for backward compatibility with old schema
- Added migration script for cleaning old positions table
- Test uses dependency override to ensure API uses correct database
NOTE: Test does not run full simulation since model_day_executor
has not yet been migrated to new schema. Instead directly populates
trading_days table and validates API layer works correctly.
Test verifies Task 9 requirements from implementation plan.
Previously, profit calculations compared portfolio value to the previous
day's final value. This caused trades to appear as losses since buying
stocks decreases cash and increases stock value equally (net zero change).
Now profit calculations compare to the start-of-day portfolio value
(action_id=0 for current date), which accurately reflects gains/losses
from price movements and trading decisions.
Changes:
- agent_tools/tool_trade.py: Fixed profit calc in _buy_impl() and _sell_impl()
- tools/price_tools.py: Fixed profit calc in add_no_trade_record_to_db()
Test: test_profit_calculation_accuracy now passes
The get_db_path() function requires a base_db_path argument
to properly resolve PROD vs DEV database paths. Updated all
calls to pass "data/jobs.db" as the base path.
Changes:
- agent_tools/tool_trade.py: Fix 3 occurrences (lines 33, 113, 236)
- tools/price_tools.py: Fix 2 occurrences in new database functions
- Remove unused get_db_path import from tool_trade.py
This fixes TypeError when running simulations:
get_db_path() missing 1 required positional argument: 'base_db_path'
The get_db_connection() function internally calls get_db_path()
to resolve the correct database path based on DEPLOYMENT_MODE.
This commit migrates the system to database-only position storage,
eliminating file-based position.jsonl dependencies and fixing
ContextInjector initialization timing issues.
Key Changes:
1. ContextInjector Lifecycle Refactor:
- Remove ContextInjector creation from BaseAgent.__init__()
- Add BaseAgent.set_context() method for post-initialization injection
- Update ModelDayExecutor to create ContextInjector with correct trading day date
- Ensures ContextInjector receives actual trading date instead of init_date
- Includes session_id injection for proper database linking
2. Database Position Functions:
- Implement get_today_init_position_from_db() for querying previous positions
- Implement add_no_trade_record_to_db() for no-trade day handling
- Both functions query SQLite directly (positions + holdings tables)
- Handle first trading day case with initial cash return
- Include comprehensive error handling and logging
3. System Integration:
- Update get_agent_system_prompt() to use database queries
- Update _handle_trading_result() to write no-trade records to database
- Remove dependencies on position.jsonl file reading/writing
- Use deployment_config for automatic prod/dev database resolution
Data Flow:
- ModelDayExecutor creates runtime config and trading session
- Agent initialized without context
- ContextInjector created with (signature, date, job_id, session_id)
- Context injected via set_context()
- System prompt queries database for yesterday's position
- Trade tools write directly to database
- No-trade handler creates database records
Fixes:
- ContextInjector no longer receives None values
- No FileNotFoundError for missing position.jsonl files
- Database is single source of truth for position tracking
- Session linking maintained across all position records
Design: docs/plans/2025-02-11-database-position-tracking-design.md
