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
Root cause: FastMCP uses inspect module to generate tool schemas from function
signatures. **kwargs prevents FastMCP from determining parameter types, causing
tool registration to fail.
Fix: Keep explicit parameters with defaults (signature=None, today_date=None, etc.)
but document in docstring that they are auto-injected.
This preserves:
- ContextInjector always overrides values (defense-in-depth from v0.3.0-alpha.40)
- FastMCP can generate proper tool schema
- Parameters visible to AI, but with clear documentation they're automatic
Trade-off: AI can still see the parameters, but documentation instructs not to provide them.
Combined with ContextInjector override, AI-provided values are ignored anyway.
Fixes TradeTools service crash on startup.
Prevent AI hallucination of runtime parameters by hiding them from the tool schema.
Architecture:
- Public tool functions (buy/sell) only expose symbol and amount to AI
- Use **kwargs to accept hidden parameters (signature, job_id, today_date, session_id)
- Internal _impl functions contain the actual business logic
- ContextInjector injects parameters into kwargs (invisible to AI)
Benefits:
- AI cannot see or hallucinate signature/job_id/session_id parameters
- Cleaner tool schema focuses on trading-relevant parameters only
- Defense-in-depth: ContextInjector still overrides any provided values
- More maintainable: clear separation of public API vs internal implementation
Example AI sees:
buy(symbol: str, amount: int) -> dict
Actual execution:
buy(symbol="AAPL", amount=10, signature="gpt-5", job_id="...", ...)
Fixes #TBD
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.
Complete rewrite of position management in MCP trade tools:
**Trade Tools (agent_tools/tool_trade.py)**
- Replace file-based position.jsonl reads with SQLite queries
- Add get_current_position_from_db() to query positions and holdings tables
- Rewrite buy() and sell() to write directly to database
- Calculate portfolio value and P&L metrics in tools
- Accept job_id and session_id parameters via ContextInjector
- Return errors with proper context for debugging
- Use deployment-aware database path resolution
**Context Injection (agent/context_injector.py)**
- Add job_id and session_id to constructor
- Inject job_id and session_id into buy/sell tool calls
- Support optional parameters (None in standalone mode)
**BaseAgent (agent/base_agent/base_agent.py)**
- Read JOB_ID from runtime config
- Pass job_id to ContextInjector during initialization
- Enable automatic context injection for API mode
**ModelDayExecutor (api/model_day_executor.py)**
- Add _initialize_starting_position() method
- Create initial position record before agent runs
- Load initial_cash from config
- Update context_injector.session_id after session creation
- Link positions to sessions automatically
**Architecture Changes:**
- Eliminates file-based position tracking entirely
- Single source of truth: SQLite database
- Positions automatically linked to trading sessions
- Concurrent execution safe (no file system conflicts)
- Deployment mode aware (prod vs dev databases)
This completes the migration to database-only position storage.
File-based position.jsonl is no longer used or created.
Fixes context injection errors in concurrent simulations.
Add proper exception handling around get_latest_position() calls in both
buy() and sell() functions. Previously, exceptions were caught but code
continued execution with undefined variables, causing "variable referenced
before assignment" errors.
Now returns error dict with context when position lookup fails.
Related to context injection implementation for concurrent simulations.
Resolves issue where MCP trade tools couldn't access SIGNATURE and TODAY_DATE
during concurrent API simulations, causing "SIGNATURE environment variable is
not set" errors.
Problem:
- MCP services run as separate HTTP processes
- Multiple simulations execute concurrently via ThreadPoolExecutor
- Environment variables from executor process not accessible to MCP services
Solution:
- Add ContextInjector that implements ToolCallInterceptor
- Automatically injects signature and today_date into buy/sell tool calls
- Trade tools accept optional parameters, falling back to config/env
- BaseAgent creates interceptor and updates today_date per session
Changes:
- agent/context_injector.py: New interceptor for context injection
- agent/base_agent/base_agent.py: Create and use ContextInjector
- agent_tools/tool_trade.py: Add optional signature/today_date parameters
Benefits:
- Supports concurrent multi-model simulations
- Maintains backward compatibility with CLI mode
- AI model unaware of injected parameters
Remove redundant log file creation for MCP services since output is
already captured by Docker logs. This simplifies deployment by removing
unnecessary volume mounts and file management.
Changes:
- Remove logs/ directory creation from Dockerfile
- Remove logs/ volume mount from docker-compose.yml
- Update start_mcp_services.py to send output to DEVNULL
- Update documentation to reflect changes (DOCKER.md, docs/DOCKER.md)
- Update .env.example to remove logs/ from volume description
Users can still view MCP service output via 'docker logs' or
'docker-compose logs -f'. Trading session logs in data/agent_data/
remain unchanged.
Root cause: Python adds script directory (not working directory)
to sys.path. Services in /app/agent_tools/ couldn't import from
/app/tools/ because sys.path[0] was /app/agent_tools.
Solution: Set PYTHONPATH=/app when starting services so /app is
always in the Python import path.
Fixes: ModuleNotFoundError: No module named 'tools' in price.log
Run MCP service manager from /app instead of /app/agent_tools
to ensure services can import from tools module.
Changes:
- entrypoint.sh: Run start_mcp_services.py from /app directory
- start_mcp_services.py: Use absolute paths for service scripts
- start_mcp_services.py: Set working directory to /app for services
Fixes ModuleNotFoundError: No module named 'tools' in price.log
