debug: enhance diagnostic logging to detect args field in tool_calls

Added more detailed logging to identify if DeepSeek responses include
both 'function.arguments' and 'args' fields, or if tool_calls are
objects vs dicts, to understand why parse_tool_call isn't converting
string args to dict as expected.

CHANGELOG.md

@@ -7,6 +7,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Fixed
+- Fixed Pydantic validation errors when using DeepSeek models via OpenRouter
+- Root cause: DeepSeek returns tool_calls in non-standard format with `args` field directly, bypassing LangChain's `parse_tool_call()`
+- Solution: Added `ToolCallArgsParsingWrapper` that normalizes non-standard tool_call format to OpenAI standard before LangChain processing
+- Wrapper converts `{name, args, id}` → `{function: {name, arguments}, id}` format
+- Includes diagnostic logging to identify format inconsistencies across providers
+
 ## [0.4.1] - 2025-11-06
 
 ### Fixed

ROADMAP.md

@@ -4,6 +4,78 @@ This document outlines planned features and improvements for the AI-Trader proje
 
 ## Release Planning
 
+### v0.5.0 - Performance Metrics & Status APIs (Planned)
+
+**Focus:** Enhanced observability and performance tracking
+
+#### Performance Metrics API
+- **Performance Summary Endpoint** - Query model performance over date ranges
+  - `GET /metrics/performance` - Aggregated performance metrics
+    - Query parameters: `model`, `start_date`, `end_date`
+    - Returns comprehensive performance summary:
+      - Total return (dollar amount and percentage)
+      - Number of trades executed (buy + sell)
+      - Win rate (profitable trading days / total trading days)
+      - Average daily P&L (profit and loss)
+      - Best/worst trading day (highest/lowest daily P&L)
+      - Final portfolio value (cash + holdings at market value)
+      - Number of trading days in queried range
+      - Starting vs. ending portfolio comparison
+    - Use cases:
+      - Compare model performance across different time periods
+      - Evaluate strategy effectiveness
+      - Identify top-performing models
+    - Example: `GET /metrics/performance?model=gpt-4&start_date=2025-01-01&end_date=2025-01-31`
+  - Filtering options:
+    - Single model or all models
+    - Custom date ranges
+    - Exclude incomplete trading days
+  - Response format: JSON with clear metric definitions
+
+#### Status & Coverage Endpoint
+- **System Status Summary** - Data availability and simulation progress
+  - `GET /status` - Comprehensive system status
+    - Price data coverage section:
+      - Available symbols (NASDAQ 100 constituents)
+      - Date range of downloaded price data per symbol
+      - Total trading days with complete data
+      - Missing data gaps (symbols without data, date gaps)
+      - Last data refresh timestamp
+    - Model simulation status section:
+      - List of all configured models (enabled/disabled)
+      - Date ranges simulated per model (first and last trading day)
+      - Total trading days completed per model
+      - Most recent simulation date per model
+      - Completion percentage (simulated days / available data days)
+    - System health section:
+      - Database connectivity status
+      - MCP services status (Math, Search, Trade, LocalPrices)
+      - API version and deployment mode
+      - Disk space usage (database size, log size)
+    - Use cases:
+      - Verify data availability before triggering simulations
+      - Identify which models need updates to latest data
+      - Monitor system health and readiness
+      - Plan data downloads for missing date ranges
+    - Example: `GET /status` (no parameters required)
+  - Benefits:
+    - Single endpoint for complete system overview
+    - No need to query multiple endpoints for status
+    - Clear visibility into data gaps
+    - Track simulation progress across models
+
+#### Implementation Details
+- Database queries for efficient metric calculation
+- Caching for frequently accessed metrics (optional)
+- Response time target: <500ms for typical queries
+- Comprehensive error handling for missing data
+
+#### Benefits
+- **Better Observability** - Clear view of system state and model performance
+- **Data-Driven Decisions** - Quantitative metrics for model comparison
+- **Proactive Monitoring** - Identify data gaps before simulations fail
+- **User Experience** - Single endpoint to check "what's available and what's been done"
+
 ### v1.0.0 - Production Stability & Validation (Planned)
 
 **Focus:** Comprehensive testing, documentation, and production readiness
@@ -607,11 +679,13 @@ To propose a new feature:
 
 - **v0.1.0** - Initial release with batch execution
 - **v0.2.0** - Docker deployment support
-- **v0.3.0** - REST API, on-demand downloads, database storage (current)
+- **v0.3.0** - REST API, on-demand downloads, database storage
+- **v0.4.0** - Daily P&L calculation, day-centric results API, reasoning summaries (current)
+- **v0.5.0** - Performance metrics & status APIs (planned)
 - **v1.0.0** - Production stability & validation (planned)
 - **v1.1.0** - API authentication & security (planned)
 - **v1.2.0** - Position history & analytics (planned)
-- **v1.3.0** - Performance metrics & analytics (planned)
+- **v1.3.0** - Advanced performance metrics & analytics (planned)
 - **v1.4.0** - Data management API (planned)
 - **v1.5.0** - Web dashboard UI (planned)
 - **v1.6.0** - Advanced configuration & customization (planned)
@@ -619,4 +693,4 @@ To propose a new feature:
 
 ---
 
-Last updated: 2025-11-01
+Last updated: 2025-11-06

agent/base_agent/base_agent.py

@@ -33,6 +33,7 @@ from tools.deployment_config import (
 from agent.context_injector import ContextInjector
 from agent.pnl_calculator import DailyPnLCalculator
 from agent.reasoning_summarizer import ReasoningSummarizer
+from agent.chat_model_wrapper import ToolCallArgsParsingWrapper
 
 # Load environment variables
 load_dotenv()
@@ -211,14 +212,16 @@ class BaseAgent:
                 self.model = MockChatModel(date="2025-01-01")  # Date will be updated per session
                 print(f"🤖 Using MockChatModel (DEV mode)")
             else:
-                self.model = ChatOpenAI(
+                base_model = ChatOpenAI(
                     model=self.basemodel,
                     base_url=self.openai_base_url,
                     api_key=self.openai_api_key,
                     max_retries=3,
                     timeout=30
                 )
-                print(f"🤖 Using {self.basemodel} (PROD mode)")
+                # Wrap model with diagnostic wrapper
+                self.model = ToolCallArgsParsingWrapper(model=base_model)
+                print(f"🤖 Using {self.basemodel} (PROD mode) with diagnostic wrapper")
         except Exception as e:
             raise RuntimeError(f"❌ Failed to initialize AI model: {e}")
 

agent/chat_model_wrapper.py

@@ -1,24 +1,18 @@
 """
-Chat model wrapper - Passthrough wrapper for ChatOpenAI models.
+Chat model wrapper to fix tool_calls args parsing issues.
 
-Originally created to fix DeepSeek tool_calls arg parsing issues, but investigation
+DeepSeek and other providers return tool_calls.args as JSON strings, which need
-revealed DeepSeek already returns the correct format (arguments as JSON strings).
+to be parsed to dicts before AIMessage construction.
-
-This wrapper is now a simple passthrough that proxies all calls to the underlying model.
-Kept for backward compatibility and potential future use.
 """
 
-from typing import Any
+import json
+from typing import Any, Optional, Dict
+from functools import wraps
 
 
 class ToolCallArgsParsingWrapper:
     """
-    Passthrough wrapper around ChatOpenAI models.
+    Wrapper that adds diagnostic logging and fixes tool_calls args if needed.
-
-    After systematic debugging, determined that DeepSeek returns tool_calls.arguments
-    as JSON strings (correct format), so no parsing/conversion is needed.
-
-    This wrapper simply proxies all calls to the wrapped model.
     """
 
     def __init__(self, model: Any, **kwargs):
@@ -30,6 +24,102 @@ class ToolCallArgsParsingWrapper:
             **kwargs: Additional parameters (ignored, for compatibility)
         """
         self.wrapped_model = model
+        self._patch_model()
+
+    def _patch_model(self):
+        """Monkey-patch the model's _create_chat_result to add diagnostics"""
+        if not hasattr(self.wrapped_model, '_create_chat_result'):
+            # Model doesn't have this method (e.g., MockChatModel), skip patching
+            return
+
+        original_create_chat_result = self.wrapped_model._create_chat_result
+
+        @wraps(original_create_chat_result)
+        def patched_create_chat_result(response: Any, generation_info: Optional[Dict] = None):
+            """Patched version with diagnostic logging and args parsing"""
+            response_dict = response if isinstance(response, dict) else response.model_dump()
+
+            # DIAGNOSTIC: Log response structure for debugging
+            print(f"\n[DIAGNOSTIC] Response structure:")
+            print(f"  Response keys: {list(response_dict.keys())}")
+
+            if 'choices' in response_dict and response_dict['choices']:
+                choice = response_dict['choices'][0]
+                print(f"  Choice keys: {list(choice.keys())}")
+
+                if 'message' in choice:
+                    message = choice['message']
+                    print(f"  Message keys: {list(message.keys())}")
+
+                    # Check for raw tool_calls in message (before parse_tool_call processing)
+                    if 'tool_calls' in message:
+                        tool_calls_value = message['tool_calls']
+                        print(f"  message['tool_calls'] type: {type(tool_calls_value)}")
+
+                        if tool_calls_value:
+                            print(f"  tool_calls count: {len(tool_calls_value)}")
+                            for i, tc in enumerate(tool_calls_value):  # Show ALL
+                                print(f"  tool_calls[{i}] type: {type(tc)}")
+                                print(f"  tool_calls[{i}] keys: {list(tc.keys()) if isinstance(tc, dict) else 'N/A'}")
+                                if isinstance(tc, dict):
+                                    if 'function' in tc:
+                                        print(f"    function keys: {list(tc['function'].keys())}")
+                                        if 'arguments' in tc['function']:
+                                            args = tc['function']['arguments']
+                                            print(f"    function.arguments type: {type(args).__name__}")
+                                            print(f"    function.arguments value: {str(args)[:100]}")
+                                    if 'args' in tc:
+                                        print(f"    ALSO HAS 'args' KEY: type={type(tc['args']).__name__}")
+                                        print(f"    args value: {str(tc['args'])[:100]}")
+
+            # Fix tool_calls: Normalize to OpenAI format if needed
+            if 'choices' in response_dict:
+                for choice in response_dict['choices']:
+                    if 'message' not in choice:
+                        continue
+
+                    message = choice['message']
+
+                    # Fix tool_calls: Ensure standard OpenAI format
+                    if 'tool_calls' in message and message['tool_calls']:
+                        print(f"[DIAGNOSTIC] Processing {len(message['tool_calls'])} tool_calls...")
+                        for idx, tool_call in enumerate(message['tool_calls']):
+                            # Check if this is non-standard format (has 'args' directly)
+                            if 'args' in tool_call and 'function' not in tool_call:
+                                print(f"[DIAGNOSTIC] tool_calls[{idx}] has non-standard format (direct args)")
+                                # Convert to standard OpenAI format
+                                args = tool_call['args']
+                                tool_call['function'] = {
+                                    'name': tool_call.get('name', ''),
+                                    'arguments': args if isinstance(args, str) else json.dumps(args)
+                                }
+                                # Remove non-standard fields
+                                if 'name' in tool_call:
+                                    del tool_call['name']
+                                if 'args' in tool_call:
+                                    del tool_call['args']
+                                print(f"[DIAGNOSTIC] Converted tool_calls[{idx}] to standard OpenAI format")
+
+                    # Fix invalid_tool_calls: dict args -> string
+                    if 'invalid_tool_calls' in message and message['invalid_tool_calls']:
+                        print(f"[DIAGNOSTIC] Checking invalid_tool_calls for dict-to-string conversion...")
+                        for idx, invalid_call in enumerate(message['invalid_tool_calls']):
+                            if 'args' in invalid_call:
+                                args = invalid_call['args']
+                                # Convert dict arguments to JSON string
+                                if isinstance(args, dict):
+                                    try:
+                                        invalid_call['args'] = json.dumps(args)
+                                        print(f"[DIAGNOSTIC] Converted invalid_tool_calls[{idx}].args from dict to string")
+                                    except (TypeError, ValueError) as e:
+                                        print(f"[DIAGNOSTIC] Failed to serialize invalid_tool_calls[{idx}].args: {e}")
+                                        # Keep as-is if serialization fails
+
+            # Call original method with fixed response
+            return original_create_chat_result(response_dict, generation_info)
+
+        # Replace the method
+        self.wrapped_model._create_chat_result = patched_create_chat_result
 
     @property
     def _llm_type(self) -> str: