mirror of
https://github.com/Xe138/AI-Trader.git
synced 2026-04-03 01:47:23 -04:00
Bill
019c84fca8
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.
67 lines
2.4 KiB
Python
67 lines
2.4 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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"""
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from typing import Any, Callable, Awaitable
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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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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, session_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, updated during execution)
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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
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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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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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# Add signature and today_date to args if not present
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if "signature" not in request.args:
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request.args["signature"] = self.signature
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if "today_date" not in request.args:
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request.args["today_date"] = self.today_date
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if "job_id" not in request.args and self.job_id:
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request.args["job_id"] = self.job_id
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if "session_id" not in request.args and self.session_id:
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request.args["session_id"] = self.session_id
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# Debug logging
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print(f"[ContextInjector] Tool: {request.name}, Args after injection: {request.args}")
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# Call the actual tool handler
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return await handler(request)
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