mirror of
https://github.com/Xe138/AI-Trader.git
synced 2026-04-01 17:17:24 -04:00
Bill
96f61cf347
Remove debug logging and update CHANGELOG for v0.4.2 release. Fixed critical bug where trades calculated from initial $10,000 capital instead of accumulating, allowing over-spending and negative cash balances. Key changes: - Extract position dict from CallToolResult.structuredContent - Enable MCP service logging for better debugging - Update tests to match production MCP behavior All tests passing. Ready for production release.
105 lines
4.0 KiB
Python
105 lines
4.0 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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# Extract position dict from MCP result
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# MCP tools return CallToolResult objects with structuredContent field
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position_dict = None
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if hasattr(result, 'structuredContent') and result.structuredContent:
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position_dict = result.structuredContent
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elif isinstance(result, dict):
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position_dict = result
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# Check if position dict is valid (not an error) and update state
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if position_dict and "error" not in position_dict and "CASH" in position_dict:
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# Update our tracked position with the new state
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self._current_position = position_dict.copy()
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return result
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