mirror of
https://github.com/Xe138/AI-Trader.git
synced 2026-04-01 17:17:24 -04:00
Bill
277714f664
Add debug logging to diagnose negative cash position issue where trades calculate from initial $10,000 instead of accumulating. Issue: After 11 trades, final cash shows -$8,768.68. Each trade appears to calculate from $10,000 starting position instead of previous trade's ending position. Hypothesis: ContextInjector._current_position not updating after trades, possibly due to MCP result type mismatch in isinstance(result, dict) check. Debug logging added: - agent/context_injector.py: Log MCP result type, content, and whether _current_position updates after each trade - agent_tools/tool_trade.py: Log whether injected position is used vs DB query, and full contents of returned position dict This will help identify: 1. What type is returned by MCP tool (dict vs other) 2. Whether _current_position is None on subsequent trades 3. What keys are present in returned position dicts Related to issue where reasoning summary claims no trades executed despite 4 sell orders being recorded.
106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
"""
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Tool interceptor for injecting runtime context into MCP tool calls.
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This interceptor automatically injects `signature` and `today_date` parameters
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into buy/sell tool calls to support concurrent multi-model simulations.
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It also maintains in-memory position state to track cumulative changes within
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a single trading session, ensuring sell proceeds are immediately available for
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subsequent buy orders.
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"""
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from typing import Any, Callable, Awaitable, Dict, Optional
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class ContextInjector:
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"""
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Intercepts tool calls to inject runtime context (signature, today_date).
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Also maintains cumulative position state during trading session to ensure
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sell proceeds are immediately available for subsequent buys.
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Usage:
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interceptor = ContextInjector(signature="gpt-5", today_date="2025-10-01")
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client = MultiServerMCPClient(config, tool_interceptors=[interceptor])
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"""
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def __init__(self, signature: str, today_date: str, job_id: str = None,
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session_id: int = None, trading_day_id: int = None):
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"""
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Initialize context injector.
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Args:
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signature: Model signature to inject
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today_date: Trading date to inject
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job_id: Job UUID to inject (optional)
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session_id: Trading session ID to inject (optional, DEPRECATED)
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trading_day_id: Trading day ID to inject (optional)
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"""
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self.signature = signature
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self.today_date = today_date
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self.job_id = job_id
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self.session_id = session_id # Deprecated but kept for compatibility
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self.trading_day_id = trading_day_id
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self._current_position: Optional[Dict[str, float]] = None
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def reset_position(self) -> None:
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"""
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Reset position state (call at start of each trading day).
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"""
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self._current_position = None
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async def __call__(
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self,
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request: Any, # MCPToolCallRequest
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handler: Callable[[Any], Awaitable[Any]]
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) -> Any: # MCPToolCallResult
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"""
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Intercept tool call and inject context parameters.
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For buy/sell operations, maintains cumulative position state to ensure
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sell proceeds are immediately available for subsequent buys.
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Args:
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request: Tool call request containing name and arguments
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handler: Async callable to execute the actual tool
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Returns:
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Result from handler after injecting context
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"""
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# Inject context parameters for trade tools
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if request.name in ["buy", "sell"]:
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# ALWAYS inject/override context parameters (don't trust AI-provided values)
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request.args["signature"] = self.signature
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request.args["today_date"] = self.today_date
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if self.job_id:
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request.args["job_id"] = self.job_id
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if self.session_id:
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request.args["session_id"] = self.session_id
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if self.trading_day_id:
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request.args["trading_day_id"] = self.trading_day_id
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# Inject current position if we're tracking it
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if self._current_position is not None:
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request.args["_current_position"] = self._current_position
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# Call the actual tool handler
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result = await handler(request)
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# Update position state after successful trade
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if request.name in ["buy", "sell"]:
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# Debug: Log result type and structure
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print(f"[DEBUG ContextInjector] Trade result type: {type(result)}")
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print(f"[DEBUG ContextInjector] Trade result: {result}")
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print(f"[DEBUG ContextInjector] isinstance(result, dict): {isinstance(result, dict)}")
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# Check if result is a valid position dict (not an error)
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if isinstance(result, dict) and "error" not in result and "CASH" in result:
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# Update our tracked position with the new state
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self._current_position = result.copy()
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print(f"[DEBUG ContextInjector] Updated _current_position: {self._current_position}")
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else:
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print(f"[DEBUG ContextInjector] Did NOT update _current_position - check failed")
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print(f"[DEBUG ContextInjector] _current_position remains: {self._current_position}")
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return result
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