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fix: inject signature and today_date into trade tool calls for concurrent simulations

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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
This commit is contained in:
Bill Ballou
2025-11-02 20:01:32 -05:00
parent 1bdfefae35
commit 08454ff4c9
3 changed files with 106 additions and 29 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
agent/base_agent/base_agent.py
View File

@@ -29,6 +29,7 @@ from tools.deployment_config import (
log_api_key_warning,
get_deployment_mode
)
from agent.context_injector import ContextInjector
# Load environment variables
load_dotenv()
@@ -124,6 +125,9 @@ class BaseAgent:
self.tools: Optional[List] = None
self.model: Optional[ChatOpenAI] = None
self.agent: Optional[Any] = None
# Context injector for MCP tools
self.context_injector: Optional[ContextInjector] = None
# Data paths
self.data_path = os.path.join(self.base_log_path, self.signature)
@@ -169,16 +173,27 @@ class BaseAgent:
print("⚠️ OpenAI base URL not set, using default")
try:
# Create MCP client
self.client = MultiServerMCPClient(self.mcp_config)
# Create context injector for injecting signature and today_date into tool calls
self.context_injector = ContextInjector(
signature=self.signature,
today_date=self.init_date # Will be updated per trading session
)
# Create MCP client with interceptor
self.client = MultiServerMCPClient(
self.mcp_config,
tool_interceptors=[self.context_injector]
)
# Get tools
self.tools = await self.client.get_tools()
if not self.tools:
raw_tools = await self.client.get_tools()
if not raw_tools:
print("⚠️ Warning: No MCP tools loaded. MCP services may not be running.")
print(f" MCP configuration: {self.mcp_config}")
self.tools = []
else:
print(f"✅ Loaded {len(self.tools)} MCP tools")
print(f"✅ Loaded {len(raw_tools)} MCP tools")
self.tools = raw_tools
except Exception as e:
raise RuntimeError(
f"❌ Failed to initialize MCP client: {e}\n"
@@ -336,6 +351,10 @@ Summary:"""
"""
print(f"📈 Starting trading session: {today_date}")
# Update context injector with current trading date
if self.context_injector:
self.context_injector.today_date = today_date
# Clear conversation history for new trading day
self.clear_conversation_history()

50
agent/context_injector.py Normal file
View File

@@ -0,0 +1,50 @@
"""
Tool interceptor for injecting runtime context into MCP tool calls.
This interceptor automatically injects `signature` and `today_date` parameters
into buy/sell tool calls to support concurrent multi-model simulations.
"""
from typing import Any, Dict
class ContextInjector:
"""
Intercepts tool calls to inject runtime context (signature, today_date).
Usage:
interceptor = ContextInjector(signature="gpt-5", today_date="2025-10-01")
client = MultiServerMCPClient(config, tool_interceptors=[interceptor])
"""
def __init__(self, signature: str, today_date: str):
"""
Initialize context injector.
Args:
signature: Model signature to inject
today_date: Trading date to inject
"""
self.signature = signature
self.today_date = today_date
def __call__(self, tool_name: str, tool_input: Dict[str, Any]) -> Dict[str, Any]:
"""
Intercept tool call and inject context parameters.
Args:
tool_name: Name of the tool being called
tool_input: Original tool input parameters
Returns:
Modified tool input with injected context
"""
# Only inject for trade tools (buy/sell)
if tool_name in ["buy", "sell"]:
# Inject signature and today_date if not already provided
if "signature" not in tool_input:
tool_input["signature"] = self.signature
if "today_date" not in tool_input:
tool_input["today_date"] = self.today_date
return tool_input

56
agent_tools/tool_trade.py
View File

@@ -13,41 +13,45 @@ mcp = FastMCP("TradeTools")
@mcp.tool()
def buy(symbol: str, amount: int) -> Dict[str, Any]:
def buy(symbol: str, amount: int, signature: str = None, today_date: str = None) -> Dict[str, Any]:
"""
Buy stock function
This function simulates stock buying operations, including the following steps:
1. Get current position and operation ID
2. Get stock opening price for the day
3. Validate buy conditions (sufficient cash)
4. Update position (increase stock quantity, decrease cash)
5. Record transaction to position.jsonl file
Args:
symbol: Stock symbol, such as "AAPL", "MSFT", etc.
amount: Buy quantity, must be a positive integer, indicating how many shares to buy
signature: Model signature (optional, will use config/env if not provided)
today_date: Trading date (optional, will use config/env if not provided)
Returns:
Dict[str, Any]:
- Success: Returns new position dictionary (containing stock quantity and cash balance)
- Failure: Returns {"error": error message, ...} dictionary
Raises:
ValueError: Raised when SIGNATURE environment variable is not set
Example:
>>> result = buy("AAPL", 10)
>>> print(result) # {"AAPL": 110, "MSFT": 5, "CASH": 5000.0, ...}
"""
# Step 1: Get environment variables and basic information
# Get signature (model name) from environment variable, used to determine data storage path
signature = get_config_value("SIGNATURE")
# Get signature (model name) from parameter or fallback to config/env
if signature is None:
raise ValueError("SIGNATURE environment variable is not set")
# Get current trading date from environment variable
today_date = get_config_value("TODAY_DATE")
signature = get_config_value("SIGNATURE")
if signature is None:
raise ValueError("SIGNATURE not provided and environment variable is not set")
# Get current trading date from parameter or fallback to config/env
if today_date is None:
today_date = get_config_value("TODAY_DATE")
# Step 2: Get current latest position and operation ID
# get_latest_position returns two values: position dictionary and current maximum operation ID
@@ -104,41 +108,45 @@ def buy(symbol: str, amount: int) -> Dict[str, Any]:
return new_position
@mcp.tool()
def sell(symbol: str, amount: int) -> Dict[str, Any]:
def sell(symbol: str, amount: int, signature: str = None, today_date: str = None) -> Dict[str, Any]:
"""
Sell stock function
This function simulates stock selling operations, including the following steps:
1. Get current position and operation ID
2. Get stock opening price for the day
3. Validate sell conditions (position exists, sufficient quantity)
4. Update position (decrease stock quantity, increase cash)
5. Record transaction to position.jsonl file
Args:
symbol: Stock symbol, such as "AAPL", "MSFT", etc.
amount: Sell quantity, must be a positive integer, indicating how many shares to sell
signature: Model signature (optional, will use config/env if not provided)
today_date: Trading date (optional, will use config/env if not provided)
Returns:
Dict[str, Any]:
- Success: Returns new position dictionary (containing stock quantity and cash balance)
- Failure: Returns {"error": error message, ...} dictionary
Raises:
ValueError: Raised when SIGNATURE environment variable is not set
Example:
>>> result = sell("AAPL", 10)
>>> print(result) # {"AAPL": 90, "MSFT": 5, "CASH": 15000.0, ...}
"""
# Step 1: Get environment variables and basic information
# Get signature (model name) from environment variable, used to determine data storage path
signature = get_config_value("SIGNATURE")
# Get signature (model name) from parameter or fallback to config/env
if signature is None:
raise ValueError("SIGNATURE environment variable is not set")
# Get current trading date from environment variable
today_date = get_config_value("TODAY_DATE")
signature = get_config_value("SIGNATURE")
if signature is None:
raise ValueError("SIGNATURE not provided and environment variable is not set")
# Get current trading date from parameter or fallback to config/env
if today_date is None:
today_date = get_config_value("TODAY_DATE")
# Step 2: Get current latest position and operation ID
# get_latest_position returns two values: position dictionary and current maximum operation ID