Merge pull request #10 from ApexAILabs/feature/multi-asset-allocation

Feature/multi-asset-allocation

Author: Mehranmzn

src/tradegraph_financial_advisor/agents/financial_agent.py

@@ -62,7 +62,7 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
                     else:
                         market_data = await self._get_equity_market_data(symbol)
                     symbol_data["market_data"] = (
-                        market_data.dict() if market_data else None
+                        market_data.model_dump() if market_data else None
                     )
 
                 if include_financials:
@@ -73,7 +73,7 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
                             symbol, market_data
                         )
                         symbol_data["financials"] = (
-                            financials.dict() if financials else None
+                            financials.model_dump() if financials else None
                         )
 
                 if include_technical:
@@ -82,7 +82,7 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
                     else:
                         technical = await self._get_equity_technical_indicators(symbol)
                     symbol_data["technical_indicators"] = (
-                        technical.dict() if technical else None
+                        technical.model_dump() if technical else None
                     )
 
                 results[symbol] = symbol_data

src/tradegraph_financial_advisor/agents/news_agent.py

@@ -54,7 +54,7 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         analyzed_articles = await self._analyze_articles(all_articles, symbols)
 
         return {
-            "articles": [article.dict() for article in analyzed_articles],
+            "articles": [article.model_dump() for article in analyzed_articles],
             "total_count": len(analyzed_articles),
             "sources": settings.news_sources,
             "analysis_timestamp": datetime.now().isoformat(),

src/tradegraph_financial_advisor/agents/recommendation_engine.py

@@ -62,11 +62,13 @@ async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         )
 
         return {
-            "individual_recommendations": [rec.dict() for rec in recommendations],
+            "individual_recommendations": [rec.model_dump() for rec in recommendations],
             "portfolio_recommendation": (
-                portfolio_recommendation.dict() if portfolio_recommendation else None
+                portfolio_recommendation.model_dump()
+                if portfolio_recommendation
+                else None
             ),
-            "alerts": [alert.dict() for alert in alerts],
+            "alerts": [alert.model_dump() for alert in alerts],
             "generation_timestamp": datetime.now().isoformat(),
         }
 

src/tradegraph_financial_advisor/config/settings.py

@@ -3,20 +3,24 @@
 
 from dotenv import load_dotenv
 from pydantic import Field
-from pydantic_settings import BaseSettings
+from pydantic_settings import BaseSettings, SettingsConfigDict
 
 load_dotenv()
 
 
 class Settings(BaseSettings):
-    openai_api_key: str = Field("", env="OPENAI_API_KEY")
-    finnhub_api_key: str = Field("", env="FINNHUB_API_KEY")
-    alpha_vantage_api_key: Optional[str] = Field(None, env="ALPHA_VANTAGE_API_KEY")
-    financial_data_api_key: Optional[str] = Field(None, env="FINANCIAL_DATA_API_KEY")
+    model_config = SettingsConfigDict(
+        env_file=".env", case_sensitive=False, extra="ignore"
+    )
+
+    openai_api_key: str = Field(default="")
+    finnhub_api_key: str = Field(default="")
+    alpha_vantage_api_key: Optional[str] = Field(default=None)
+    financial_data_api_key: Optional[str] = Field(default=None)
 
-    log_level: str = Field("INFO", env="LOG_LEVEL")
-    max_concurrent_agents: int = Field(5, env="MAX_CONCURRENT_AGENTS")
-    analysis_timeout_seconds: int = Field(30, env="ANALYSIS_TIMEOUT_SECONDS")
+    log_level: str = Field(default="INFO")
+    max_concurrent_agents: int = Field(default=5)
+    analysis_timeout_seconds: int = Field(default=30)
 
     news_sources: List[str] = Field(
         default_factory=lambda: [
@@ -25,8 +29,7 @@ class Settings(BaseSettings):
             "yahoo-finance",
             "marketwatch",
             "cnbc",
-        ],
-        env="NEWS_SOURCES",
+        ]
     )
     analysis_depth: str = Field("detailed", env="ANALYSIS_DEPTH")
     default_portfolio_size: float = Field(100000.0, env="DEFAULT_PORTFOLIO_SIZE")

src/tradegraph_financial_advisor/main.py

@@ -194,11 +194,21 @@ async def quick_analysis(
                         workflow_results, "sentiment_analysis", {}
                     )
 
+                portfolio_recommendation_payload = (
+                    portfolio_recommendation.dict()
+                    if hasattr(portfolio_recommendation, "dict")
+                    else portfolio_recommendation
+                )
+
                 return {
                     "analysis_type": "basic",
                     "symbols": symbols,
                     "recommendations": recommendations,
-                    "portfolio_recommendation": portfolio_recommendation,
+                    "portfolio_recommendation": (
+                        portfolio_recommendation_payload
+                        if portfolio_recommendation_payload
+                        else None
+                    ),
                     "sentiment_analysis": sentiment_analysis,
                     "analysis_timestamp": datetime.now().isoformat(),
                 }

tests/unit/test_models.py

@@ -312,15 +312,15 @@ def test_model_serialization(self):
         )
 
         # Test dict conversion
-        rec_dict = recommendation.dict()
+        rec_dict = recommendation.model_dump()
         assert rec_dict["symbol"] == "AAPL"
         assert rec_dict["recommendation"] == "buy"
         assert rec_dict["risk_level"] == "medium"
 
         # Test JSON serialization
         import json
 
-        json_str = recommendation.json()
+        json_str = recommendation.model_dump_json()
         parsed = json.loads(json_str)
         assert parsed["symbol"] == "AAPL"