fix: use 'no_trade' action type for initial position

Changed action_type from 'init' to 'no_trade' in _initialize_starting_position()
to comply with database CHECK constraint that only allows 'buy', 'sell', or 'no_trade'.

Fixes sqlite3.IntegrityError during position initialization.

.env.example

@@ -35,7 +35,7 @@ MAX_SIMULATION_DAYS=30
 AUTO_DOWNLOAD_PRICE_DATA=true
 
 # Data Volume Configuration
-# Base directory for all persistent data (will contain data/, logs/, configs/ subdirectories)
+# Base directory for all persistent data (will contain data/ and configs/ subdirectories)
 # Use relative paths (./volumes) or absolute paths (/home/user/ai-trader-volumes)
 # Defaults to current directory (.) if not set
 VOLUME_PATH=.

DOCKER.md

@@ -154,10 +154,9 @@ docker-compose up
 
 ### Volume Mounts
 
-Docker Compose mounts three volumes for persistent data. By default, these are stored in the project directory:
+Docker Compose mounts two volumes for persistent data. By default, these are stored in the project directory:
 
 - `./data:/app/data` - Price data and trading records
-- `./logs:/app/logs` - MCP service logs
 - `./configs:/app/configs` - Configuration files (allows editing configs without rebuilding)
 
 ### Custom Volume Location
@@ -174,7 +173,6 @@ VOLUME_PATH=./volumes
 
 This will store data in:
 - `/home/user/trading-data/data/`
-- `/home/user/trading-data/logs/`
 - `/home/user/trading-data/configs/`
 
 **Note:** The directory structure is automatically created. You'll need to copy your existing configs:
@@ -190,7 +188,7 @@ To reset all trading data:
 
 ```bash
 docker-compose down
-rm -rf ${VOLUME_PATH:-.}/data/agent_data/* ${VOLUME_PATH:-.}/logs/*
+rm -rf ${VOLUME_PATH:-.}/data/agent_data/*
 docker-compose up
 ```
 
@@ -217,8 +215,7 @@ docker pull ghcr.io/xe138/ai-trader-server:latest
 ```bash
 docker run --env-file .env \
   -v $(pwd)/data:/app/data \
-  -v $(pwd)/logs:/app/logs \
-  -p 8000-8003:8000-8003 \
+  -p 8080:8080 \
   ghcr.io/xe138/ai-trader-server:latest
 ```
 
@@ -234,9 +231,9 @@ docker pull ghcr.io/xe138/ai-trader-server:v1.0.0
 **Symptom:** Container exits immediately or errors about ports
 
 **Solutions:**
-- Check ports 8000-8003 not already in use: `lsof -i :8000-8003`
 - View container logs: `docker-compose logs`
-- Check MCP service logs: `cat logs/math.log`
+- Check if API port 8080 is already in use: `lsof -i :8080`
+- Verify MCP services started by checking Docker logs for service startup messages
 
 ### Missing API Keys
 
@@ -258,12 +255,12 @@ docker pull ghcr.io/xe138/ai-trader-server:v1.0.0
 
 ### Permission Issues
 
-**Symptom:** Cannot write to data or logs directories
+**Symptom:** Cannot write to data directory
 
 **Solutions:**
-- Ensure directories writable: `chmod -R 755 data logs`
+- Ensure data directory is writable: `chmod -R 755 data`
 - Check volume mount permissions
-- May need to create directories first: `mkdir -p data logs`
+- May need to create directory first: `mkdir -p data`
 
 ### Container Keeps Restarting
 
@@ -298,13 +295,12 @@ docker buildx build --platform linux/amd64,linux/arm64 -t ai-trader-server .
 docker stats ai-trader-server
 ```
 
-### Access MCP Services Directly
+### Access API Directly
 
-Services exposed on host:
-- Math: http://localhost:8000
-- Search: http://localhost:8001
-- Trade: http://localhost:8002
-- Price: http://localhost:8003
+API server exposed on host:
+- REST API: http://localhost:8080
+
+MCP services run internally and are not exposed to the host.
 
 ## Development Workflow
 

Dockerfile

@@ -27,7 +27,7 @@ WORKDIR /app
 COPY . .
 
 # Create necessary directories
-RUN mkdir -p data logs data/agent_data
+RUN mkdir -p data data/agent_data
 
 # Make entrypoint executable
 RUN chmod +x entrypoint.sh

agent/base_agent/base_agent.py

@@ -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 +173,32 @@ class BaseAgent:
                 print("⚠️  OpenAI base URL not set, using default")
 
         try:
-            # Create MCP client
-            self.client = MultiServerMCPClient(self.mcp_config)
+            # Get job_id from runtime config if available (API mode)
+            from tools.general_tools import get_config_value
+            job_id = get_config_value("JOB_ID")  # Returns None if not in API mode
+
+            # 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
+                job_id=job_id  # Will be None in standalone mode, populated in API mode
+            )
+
+            # 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 +356,10 @@ 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()
 

agent/context_injector.py

@@ -0,0 +1,66 @@
+"""
+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, Callable, Awaitable
+
+
+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, job_id: str = None, session_id: int = None):
+        """
+        Initialize context injector.
+
+        Args:
+            signature: Model signature to inject
+            today_date: Trading date to inject
+            job_id: Job UUID to inject (optional)
+            session_id: Trading session ID to inject (optional, updated during execution)
+        """
+        self.signature = signature
+        self.today_date = today_date
+        self.job_id = job_id
+        self.session_id = session_id
+
+    async def __call__(
+        self,
+        request: Any,  # MCPToolCallRequest
+        handler: Callable[[Any], Awaitable[Any]]
+    ) -> Any:  # MCPToolCallResult
+        """
+        Intercept tool call and inject context parameters.
+
+        Args:
+            request: Tool call request containing name and arguments
+            handler: Async callable to execute the actual tool
+
+        Returns:
+            Result from handler after injecting context
+        """
+        # Inject context parameters for trade tools
+        if request.name in ["buy", "sell"]:
+            # Add signature and today_date to args if not present
+            if "signature" not in request.args:
+                request.args["signature"] = self.signature
+            if "today_date" not in request.args:
+                request.args["today_date"] = self.today_date
+            if "job_id" not in request.args and self.job_id:
+                request.args["job_id"] = self.job_id
+            if "session_id" not in request.args and self.session_id:
+                request.args["session_id"] = self.session_id
+
+            # Debug logging
+            print(f"[ContextInjector] Tool: {request.name}, Args after injection: {request.args}")
+
+        # Call the actual tool handler
+        return await handler(request)

agent_tools/start_mcp_services.py

@@ -52,10 +52,6 @@ class MCPServiceManager:
             }
         }
 
-        # Create logs directory
-        self.log_dir = Path('logs')
-        self.log_dir.mkdir(exist_ok=True)
-        
         # Set signal handlers
         signal.signal(signal.SIGINT, self.signal_handler)
         signal.signal(signal.SIGTERM, self.signal_handler)
@@ -77,27 +73,23 @@ class MCPServiceManager:
             return False
 
         try:
-            # Start service process
-            log_file = self.log_dir / f"{service_id}.log"
-
             # Set PYTHONPATH to /app so services can import from tools module
             env = os.environ.copy()
             env['PYTHONPATH'] = str(Path.cwd())
 
-            with open(log_file, 'w') as f:
-                process = subprocess.Popen(
-                    [sys.executable, str(script_path)],
-                    stdout=f,
-                    stderr=subprocess.STDOUT,
-                    cwd=Path.cwd(),  # Use current working directory (/app)
-                    env=env  # Pass environment with PYTHONPATH
-                )
-            
+            # Start service process (output goes to Docker logs)
+            process = subprocess.Popen(
+                [sys.executable, str(script_path)],
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                cwd=Path.cwd(),  # Use current working directory (/app)
+                env=env  # Pass environment with PYTHONPATH
+            )
+
             self.services[service_id] = {
                 'process': process,
                 'name': service_name,
-                'port': port,
-                'log_file': log_file
+                'port': port
             }
             
             print(f"✅ {service_name} service started (PID: {process.pid}, Port: {port})")
@@ -167,15 +159,14 @@ class MCPServiceManager:
                 print(f"✅ {service['name']} service running normally")
             else:
                 print(f"❌ {service['name']} service failed to start")
-                print(f"   Please check logs: {service['log_file']}")
+                print(f"   Check Docker logs for details: docker logs ai-trader-server")
     
     def print_service_info(self):
         """Print service information"""
         print("\n📋 Service information:")
         for service_id, service in self.services.items():
             print(f"  - {service['name']}: http://localhost:{service['port']} (PID: {service['process'].pid})")
-        
-        print(f"\n📁 Log files location: {self.log_dir.absolute()}")
+
         print("\n🛑 Press Ctrl+C to stop all services")
     
     def keep_alive(self):

agent_tools/tool_trade.py

@@ -1,197 +1,333 @@
 from fastmcp import FastMCP
 import sys
 import os
-from typing import Dict, List, Optional, Any
+from typing import Dict, List, Optional, Any, Tuple
 # Add project root directory to Python path
 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.insert(0, project_root)
-from tools.price_tools import get_yesterday_date, get_open_prices, get_yesterday_open_and_close_price, get_latest_position, get_yesterday_profit
+from tools.price_tools import get_open_prices
 import json
-from tools.general_tools import get_config_value,write_config_value
+from tools.deployment_config import get_db_path
+from api.database import get_db_connection
+from datetime import datetime
 mcp = FastMCP("TradeTools")
 
 
+def get_current_position_from_db(job_id: str, model: str, date: str) -> Tuple[Dict[str, float], int]:
+    """
+    Query current position from SQLite database.
+
+    Args:
+        job_id: Job UUID
+        model: Model signature
+        date: Trading date (YYYY-MM-DD)
+
+    Returns:
+        Tuple of (position_dict, next_action_id)
+        - position_dict: {symbol: quantity, "CASH": amount}
+        - next_action_id: Next available action_id for this job+model
+
+    Raises:
+        Exception: If database query fails
+    """
+    db_path = get_db_path()
+    conn = get_db_connection(db_path)
+    cursor = conn.cursor()
+
+    try:
+        # Get most recent position on or before this date
+        cursor.execute("""
+            SELECT p.id, p.cash
+            FROM positions p
+            WHERE p.job_id = ? AND p.model = ? AND p.date <= ?
+            ORDER BY p.date DESC, p.action_id DESC
+            LIMIT 1
+        """, (job_id, model, date))
+
+        position_row = cursor.fetchone()
+
+        if not position_row:
+            # No position found - this shouldn't happen if ModelDayExecutor initializes properly
+            raise Exception(f"No position found for job_id={job_id}, model={model}, date={date}")
+
+        position_id = position_row[0]
+        cash = position_row[1]
+
+        # Build position dict starting with CASH
+        position_dict = {"CASH": cash}
+
+        # Get holdings for this position
+        cursor.execute("""
+            SELECT symbol, quantity
+            FROM holdings
+            WHERE position_id = ?
+        """, (position_id,))
+
+        for row in cursor.fetchall():
+            symbol = row[0]
+            quantity = row[1]
+            position_dict[symbol] = quantity
+
+        # Get next action_id
+        cursor.execute("""
+            SELECT COALESCE(MAX(action_id), -1) + 1 as next_action_id
+            FROM positions
+            WHERE job_id = ? AND model = ?
+        """, (job_id, model))
+
+        next_action_id = cursor.fetchone()[0]
+
+        return position_dict, next_action_id
+
+    finally:
+        conn.close()
+
 
 @mcp.tool()
-def buy(symbol: str, amount: int) -> Dict[str, Any]:
+def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
+        job_id: str = None, session_id: int = 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
-    
+    Buy stock function - writes to SQLite database.
+
     Args:
-        symbol: Stock symbol, such as "AAPL", "MSFT", etc.
-        amount: Buy quantity, must be a positive integer, indicating how many shares to buy
-        
+        symbol: Stock symbol (e.g., "AAPL", "MSFT")
+        amount: Number of shares to buy (positive integer)
+        signature: Model signature (injected by ContextInjector)
+        today_date: Trading date YYYY-MM-DD (injected by ContextInjector)
+        job_id: Job UUID (injected by ContextInjector)
+        session_id: Trading session ID (injected by ContextInjector)
+
     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, ...}
+          - Success: {"CASH": amount, symbol: quantity, ...}
+          - Failure: {"error": message, ...}
     """
-    # 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")
-    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")
-    
-    # Step 2: Get current latest position and operation ID
-    # get_latest_position returns two values: position dictionary and current maximum operation ID
-    # This ID is used to ensure each operation has a unique identifier
-    try:
-        current_position, current_action_id = get_latest_position(today_date, signature)
-    except Exception as e:
-        print(e)
-        print(current_position, current_action_id)
-        print(today_date, signature)
-    # Step 3: Get stock opening price for the day
-    # Use get_open_prices function to get the opening price of specified stock for the day
-    # If stock symbol does not exist or price data is missing, KeyError exception will be raised
-    try:
-        this_symbol_price = get_open_prices(today_date, [symbol])[f'{symbol}_price']
-    except KeyError:
-        # Stock symbol does not exist or price data is missing, return error message
-        return {"error": f"Symbol {symbol} not found! This action will not be allowed.", "symbol": symbol, "date": today_date}
+    # Validate required parameters
+    if not job_id:
+        return {"error": "Missing required parameter: job_id"}
+    if not signature:
+        return {"error": "Missing required parameter: signature"}
+    if not today_date:
+        return {"error": "Missing required parameter: today_date"}
 
-    # Step 4: Validate buy conditions
-    # Calculate cash required for purchase: stock price × buy quantity
-    try:
-        cash_left = current_position["CASH"] - this_symbol_price * amount
-    except Exception as e:
-        print(current_position, "CASH", this_symbol_price, amount)
+    db_path = get_db_path()
+    conn = get_db_connection(db_path)
+    cursor = conn.cursor()
 
-    # Check if cash balance is sufficient for purchase
-    if cash_left < 0:
-        # Insufficient cash, return error message
-        return {"error": "Insufficient cash! This action will not be allowed.", "required_cash": this_symbol_price * amount, "cash_available": current_position.get("CASH", 0), "symbol": symbol, "date": today_date}
-    else:
-        # Step 5: Execute buy operation, update position
-        # Create a copy of current position to avoid directly modifying original data
+    try:
+        # Step 1: Get current position
+        current_position, next_action_id = get_current_position_from_db(job_id, signature, today_date)
+
+        # Step 2: Get stock price
+        try:
+            this_symbol_price = get_open_prices(today_date, [symbol])[f'{symbol}_price']
+        except KeyError:
+            return {"error": f"Symbol {symbol} not found on {today_date}", "symbol": symbol, "date": today_date}
+
+        # Step 3: Validate sufficient cash
+        cash_required = this_symbol_price * amount
+        cash_available = current_position.get("CASH", 0)
+        cash_left = cash_available - cash_required
+
+        if cash_left < 0:
+            return {
+                "error": "Insufficient cash",
+                "required_cash": cash_required,
+                "cash_available": cash_available,
+                "symbol": symbol,
+                "date": today_date
+            }
+
+        # Step 4: Calculate new position
         new_position = current_position.copy()
-        
-        # Decrease cash balance
         new_position["CASH"] = cash_left
-        
-        # Increase stock position quantity
-        new_position[symbol] += amount
-        
-        # Step 6: Record transaction to position.jsonl file
-        # Build file path: {project_root}/data/agent_data/{signature}/position/position.jsonl
-        # Use append mode ("a") to write new transaction record
-        # Each operation ID increments by 1, ensuring uniqueness of operation sequence
-        position_file_path = os.path.join(project_root, "data", "agent_data", signature, "position", "position.jsonl")
-        with open(position_file_path, "a") as f:
-            # Write JSON format transaction record, containing date, operation ID, transaction details and updated position
-            print(f"Writing to position.jsonl: {json.dumps({'date': today_date, 'id': current_action_id + 1, 'this_action':{'action':'buy','symbol':symbol,'amount':amount},'positions': new_position})}")
-            f.write(json.dumps({"date": today_date, "id": current_action_id + 1, "this_action":{"action":"buy","symbol":symbol,"amount":amount},"positions": new_position}) + "\n")
-        # Step 7: Return updated position
-        write_config_value("IF_TRADE", True)
-        print("IF_TRADE", get_config_value("IF_TRADE"))
+        new_position[symbol] = new_position.get(symbol, 0) + amount
+
+        # Step 5: Calculate portfolio value and P&L
+        portfolio_value = cash_left
+        for sym, qty in new_position.items():
+            if sym != "CASH":
+                try:
+                    price = get_open_prices(today_date, [sym])[f'{sym}_price']
+                    portfolio_value += qty * price
+                except KeyError:
+                    pass  # Symbol price not available, skip
+
+        # Get previous portfolio value for P&L calculation
+        cursor.execute("""
+            SELECT portfolio_value
+            FROM positions
+            WHERE job_id = ? AND model = ? AND date < ?
+            ORDER BY date DESC, action_id DESC
+            LIMIT 1
+        """, (job_id, signature, today_date))
+
+        row = cursor.fetchone()
+        previous_value = row[0] if row else 10000.0  # Default initial value
+
+        daily_profit = portfolio_value - previous_value
+        daily_return_pct = (daily_profit / previous_value * 100) if previous_value > 0 else 0
+
+        # Step 6: Write to positions table
+        created_at = datetime.utcnow().isoformat() + "Z"
+
+        cursor.execute("""
+            INSERT INTO positions (
+                job_id, date, model, action_id, action_type, symbol,
+                amount, price, cash, portfolio_value, daily_profit,
+                daily_return_pct, session_id, created_at
+            )
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            job_id, today_date, signature, next_action_id, "buy", symbol,
+            amount, this_symbol_price, cash_left, portfolio_value, daily_profit,
+            daily_return_pct, session_id, created_at
+        ))
+
+        position_id = cursor.lastrowid
+
+        # Step 7: Write to holdings table
+        for sym, qty in new_position.items():
+            if sym != "CASH":
+                cursor.execute("""
+                    INSERT INTO holdings (position_id, symbol, quantity)
+                    VALUES (?, ?, ?)
+                """, (position_id, sym, qty))
+
+        conn.commit()
+        print(f"[buy] {signature} bought {amount} shares of {symbol} at ${this_symbol_price}")
         return new_position
 
+    except Exception as e:
+        conn.rollback()
+        return {"error": f"Trade failed: {str(e)}", "symbol": symbol, "date": today_date}
+
+    finally:
+        conn.close()
+
+
 @mcp.tool()
-def sell(symbol: str, amount: int) -> Dict[str, Any]:
+def sell(symbol: str, amount: int, signature: str = None, today_date: str = None,
+         job_id: str = None, session_id: int = 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
-    
+    Sell stock function - writes to SQLite database.
+
     Args:
-        symbol: Stock symbol, such as "AAPL", "MSFT", etc.
-        amount: Sell quantity, must be a positive integer, indicating how many shares to sell
-        
+        symbol: Stock symbol (e.g., "AAPL", "MSFT")
+        amount: Number of shares to sell (positive integer)
+        signature: Model signature (injected by ContextInjector)
+        today_date: Trading date YYYY-MM-DD (injected by ContextInjector)
+        job_id: Job UUID (injected by ContextInjector)
+        session_id: Trading session ID (injected by ContextInjector)
+
     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, ...}
+          - Success: {"CASH": amount, symbol: quantity, ...}
+          - Failure: {"error": message, ...}
     """
-    # 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")
-    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")
-    
-    # Step 2: Get current latest position and operation ID
-    # get_latest_position returns two values: position dictionary and current maximum operation ID
-    # This ID is used to ensure each operation has a unique identifier
-    current_position, current_action_id = get_latest_position(today_date, signature)
-    
-    # Step 3: Get stock opening price for the day
-    # Use get_open_prices function to get the opening price of specified stock for the day
-    # If stock symbol does not exist or price data is missing, KeyError exception will be raised
+    # Validate required parameters
+    if not job_id:
+        return {"error": "Missing required parameter: job_id"}
+    if not signature:
+        return {"error": "Missing required parameter: signature"}
+    if not today_date:
+        return {"error": "Missing required parameter: today_date"}
+
+    db_path = get_db_path()
+    conn = get_db_connection(db_path)
+    cursor = conn.cursor()
+
     try:
-        this_symbol_price = get_open_prices(today_date, [symbol])[f'{symbol}_price']
-    except KeyError:
-        # Stock symbol does not exist or price data is missing, return error message
-        return {"error": f"Symbol {symbol} not found! This action will not be allowed.", "symbol": symbol, "date": today_date}
+        # Step 1: Get current position
+        current_position, next_action_id = get_current_position_from_db(job_id, signature, today_date)
 
-    # Step 4: Validate sell conditions
-    # Check if holding this stock
-    if symbol not in current_position:
-        return {"error": f"No position for {symbol}! This action will not be allowed.", "symbol": symbol, "date": today_date}
+        # Step 2: Validate position exists
+        if symbol not in current_position:
+            return {"error": f"No position for {symbol}", "symbol": symbol, "date": today_date}
 
-    # Check if position quantity is sufficient for selling
-    if current_position[symbol] < amount:
-        return {"error": "Insufficient shares! This action will not be allowed.", "have": current_position.get(symbol, 0), "want_to_sell": amount, "symbol": symbol, "date": today_date}
+        if current_position[symbol] < amount:
+            return {
+                "error": "Insufficient shares",
+                "have": current_position[symbol],
+                "want_to_sell": amount,
+                "symbol": symbol,
+                "date": today_date
+            }
 
-    # Step 5: Execute sell operation, update position
-    # Create a copy of current position to avoid directly modifying original data
-    new_position = current_position.copy()
-    
-    # Decrease stock position quantity
-    new_position[symbol] -= amount
-    
-    # Increase cash balance: sell price × sell quantity
-    # Use get method to ensure CASH field exists, default to 0 if not present
-    new_position["CASH"] = new_position.get("CASH", 0) + this_symbol_price * amount
+        # Step 3: Get stock price
+        try:
+            this_symbol_price = get_open_prices(today_date, [symbol])[f'{symbol}_price']
+        except KeyError:
+            return {"error": f"Symbol {symbol} not found on {today_date}", "symbol": symbol, "date": today_date}
 
-    # Step 6: Record transaction to position.jsonl file
-    # Build file path: {project_root}/data/agent_data/{signature}/position/position.jsonl
-    # Use append mode ("a") to write new transaction record
-    # Each operation ID increments by 1, ensuring uniqueness of operation sequence
-    position_file_path = os.path.join(project_root, "data", "agent_data", signature, "position", "position.jsonl")
-    with open(position_file_path, "a") as f:
-        # Write JSON format transaction record, containing date, operation ID and updated position
-        print(f"Writing to position.jsonl: {json.dumps({'date': today_date, 'id': current_action_id + 1, 'this_action':{'action':'sell','symbol':symbol,'amount':amount},'positions': new_position})}")
-        f.write(json.dumps({"date": today_date, "id": current_action_id + 1, "this_action":{"action":"sell","symbol":symbol,"amount":amount},"positions": new_position}) + "\n")
+        # Step 4: Calculate new position
+        new_position = current_position.copy()
+        new_position[symbol] -= amount
+        new_position["CASH"] = new_position.get("CASH", 0) + (this_symbol_price * amount)
+
+        # Step 5: Calculate portfolio value and P&L
+        portfolio_value = new_position["CASH"]
+        for sym, qty in new_position.items():
+            if sym != "CASH":
+                try:
+                    price = get_open_prices(today_date, [sym])[f'{sym}_price']
+                    portfolio_value += qty * price
+                except KeyError:
+                    pass
+
+        # Get previous portfolio value
+        cursor.execute("""
+            SELECT portfolio_value
+            FROM positions
+            WHERE job_id = ? AND model = ? AND date < ?
+            ORDER BY date DESC, action_id DESC
+            LIMIT 1
+        """, (job_id, signature, today_date))
+
+        row = cursor.fetchone()
+        previous_value = row[0] if row else 10000.0
+
+        daily_profit = portfolio_value - previous_value
+        daily_return_pct = (daily_profit / previous_value * 100) if previous_value > 0 else 0
+
+        # Step 6: Write to positions table
+        created_at = datetime.utcnow().isoformat() + "Z"
+
+        cursor.execute("""
+            INSERT INTO positions (
+                job_id, date, model, action_id, action_type, symbol,
+                amount, price, cash, portfolio_value, daily_profit,
+                daily_return_pct, session_id, created_at
+            )
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            job_id, today_date, signature, next_action_id, "sell", symbol,
+            amount, this_symbol_price, new_position["CASH"], portfolio_value, daily_profit,
+            daily_return_pct, session_id, created_at
+        ))
+
+        position_id = cursor.lastrowid
+
+        # Step 7: Write to holdings table
+        for sym, qty in new_position.items():
+            if sym != "CASH":
+                cursor.execute("""
+                    INSERT INTO holdings (position_id, symbol, quantity)
+                    VALUES (?, ?, ?)
+                """, (position_id, sym, qty))
+
+        conn.commit()
+        print(f"[sell] {signature} sold {amount} shares of {symbol} at ${this_symbol_price}")
+        return new_position
+
+    except Exception as e:
+        conn.rollback()
+        return {"error": f"Trade failed: {str(e)}", "symbol": symbol, "date": today_date}
+
+    finally:
+        conn.close()
 
-    # Step 7: Return updated position
-    write_config_value("IF_TRADE", True)
-    return new_position
 
 if __name__ == "__main__":
-    # new_result = buy("AAPL", 1)
-    # print(new_result)
-    # new_result = sell("AAPL", 1)
-    # print(new_result)
     port = int(os.getenv("TRADE_HTTP_PORT", "8002"))
     mcp.run(transport="streamable-http", port=port)

api/model_day_executor.py

@@ -122,12 +122,20 @@ class ModelDayExecutor:
             session_id = self._create_trading_session(cursor)
             conn.commit()
 
+            # Initialize starting position if this is first day
+            self._initialize_starting_position(cursor, session_id)
+            conn.commit()
+
             # Set environment variable for agent to use isolated config
             os.environ["RUNTIME_ENV_PATH"] = self.runtime_config_path
 
             # Initialize agent
             agent = await self._initialize_agent()
 
+            # Update context injector with session_id
+            if hasattr(agent, 'context_injector') and agent.context_injector:
+                agent.context_injector.session_id = session_id
+
             # Run trading session
             logger.info(f"Running trading session for {self.model_sig} on {self.date}")
             session_result = await agent.run_trading_session(self.date)
@@ -287,6 +295,51 @@ class ModelDayExecutor:
 
         return cursor.lastrowid
 
+    def _initialize_starting_position(self, cursor, session_id: int) -> None:
+        """
+        Initialize starting position if no prior positions exist for this job+model.
+
+        Creates action_id=0 position with initial_cash and zero stock holdings.
+
+        Args:
+            cursor: Database cursor
+            session_id: Trading session ID
+        """
+        # Check if any positions exist for this job+model
+        cursor.execute("""
+            SELECT COUNT(*) FROM positions
+            WHERE job_id = ? AND model = ?
+        """, (self.job_id, self.model_sig))
+
+        if cursor.fetchone()[0] > 0:
+            # Positions already exist, no initialization needed
+            return
+
+        # Load config to get initial_cash
+        import json
+        with open(self.config_path, 'r') as f:
+            config = json.load(f)
+
+        agent_config = config.get("agent_config", {})
+        initial_cash = agent_config.get("initial_cash", 10000.0)
+
+        # Create initial position record
+        from datetime import datetime
+        created_at = datetime.utcnow().isoformat() + "Z"
+
+        cursor.execute("""
+            INSERT INTO positions (
+                job_id, date, model, action_id, action_type,
+                cash, portfolio_value, session_id, created_at
+            )
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            self.job_id, self.date, self.model_sig, 0, "no_trade",
+            initial_cash, initial_cash, session_id, created_at
+        ))
+
+        logger.info(f"Initialized starting position for {self.model_sig} with ${initial_cash}")
+
     async def _store_reasoning_logs(
         self,
         cursor,

docker-compose.yml

@@ -7,7 +7,6 @@ services:
     container_name: ai-trader-server
     volumes:
       - ${VOLUME_PATH:-.}/data:/app/data
-      - ${VOLUME_PATH:-.}/logs:/app/logs
       # User configs mounted to /app/user-configs (default config baked into image)
       - ${VOLUME_PATH:-.}/configs:/app/user-configs
     environment:

docs/DOCKER.md

@@ -112,10 +112,9 @@ docker-compose up
 
 ### Volume Mounts
 
-Docker Compose mounts three volumes for persistent data. By default, these are stored in the project directory:
+Docker Compose mounts two volumes for persistent data. By default, these are stored in the project directory:
 
 - `./data:/app/data` - Price data and trading records
-- `./logs:/app/logs` - MCP service logs
 - `./configs:/app/configs` - Configuration files (allows editing configs without rebuilding)
 
 ### Custom Volume Location
@@ -132,7 +131,6 @@ VOLUME_PATH=./volumes
 
 This will store data in:
 - `/home/user/trading-data/data/`
-- `/home/user/trading-data/logs/`
 - `/home/user/trading-data/configs/`
 
 **Note:** The directory structure is automatically created. You'll need to copy your existing configs:
@@ -148,7 +146,7 @@ To reset all trading data:
 
 ```bash
 docker-compose down
-rm -rf ${VOLUME_PATH:-.}/data/agent_data/* ${VOLUME_PATH:-.}/logs/*
+rm -rf ${VOLUME_PATH:-.}/data/agent_data/*
 docker-compose up
 ```
 
@@ -175,8 +173,7 @@ docker pull ghcr.io/xe138/ai-trader-server:latest
 ```bash
 docker run --env-file .env \
   -v $(pwd)/data:/app/data \
-  -v $(pwd)/logs:/app/logs \
-  -p 8000-8003:8000-8003 \
+  -p 8080:8080 \
   ghcr.io/xe138/ai-trader-server:latest
 ```
 
@@ -192,9 +189,9 @@ docker pull ghcr.io/xe138/ai-trader-server:v1.0.0
 **Symptom:** Container exits immediately or errors about ports
 
 **Solutions:**
-- Check ports 8000-8003 not already in use: `lsof -i :8000-8003`
 - View container logs: `docker-compose logs`
-- Check MCP service logs: `cat logs/math.log`
+- Check if API port 8080 is already in use: `lsof -i :8080`
+- Verify MCP services started by checking Docker logs for service startup messages
 
 ### Missing API Keys
 
@@ -216,12 +213,12 @@ docker pull ghcr.io/xe138/ai-trader-server:v1.0.0
 
 ### Permission Issues
 
-**Symptom:** Cannot write to data or logs directories
+**Symptom:** Cannot write to data directory
 
 **Solutions:**
-- Ensure directories writable: `chmod -R 755 data logs`
+- Ensure data directory is writable: `chmod -R 755 data`
 - Check volume mount permissions
-- May need to create directories first: `mkdir -p data logs`
+- May need to create directory first: `mkdir -p data`
 
 ### Container Keeps Restarting
 
@@ -256,13 +253,12 @@ docker buildx build --platform linux/amd64,linux/arm64 -t ai-trader-server .
 docker stats ai-trader-server
 ```
 
-### Access MCP Services Directly
+### Access API Directly
 
-Services exposed on host:
-- Math: http://localhost:8000
-- Search: http://localhost:8001
-- Trade: http://localhost:8002
-- Price: http://localhost:8003
+API server exposed on host:
+- REST API: http://localhost:8080
+
+MCP services run internally and are not exposed to the host.
 
 ## Development Workflow