Refactored commited files to follow pylint standards

src/data/cache.py

@@ -12,10 +12,10 @@ def _merge_data(self, existing: list[dict] | None, new_data: list[dict], key_fie
         """Merge existing and new data, avoiding duplicates based on a key field."""
         if not existing:
             return new_data
-        
+
         # Create a set of existing keys for O(1) lookup
         existing_keys = {item[key_field] for item in existing}
-        
+
         # Only add items that don't exist yet
         merged = existing.copy()
         merged.extend([item for item in new_data if item[key_field] not in existing_keys])

src/llm/models.py

@@ -28,15 +28,15 @@ class LLMModel(BaseModel):
     def to_choice_tuple(self) -> Tuple[str, str, str]:
         """Convert to format needed for questionary choices"""
         return (self.display_name, self.model_name, self.provider.value)
-    
+
     def has_json_mode(self) -> bool:
         """Check if the model supports JSON mode"""
         return not self.is_deepseek() and not self.is_gemini()
-    
+
     def is_deepseek(self) -> bool:
         """Check if the model is a DeepSeek model"""
         return self.model_name.startswith("deepseek")
-    
+
     def is_gemini(self) -> bool:
         """Check if the model is a Gemini model"""
         return self.model_name.startswith("gemini")
@@ -118,32 +118,35 @@ def get_model(model_name: str, model_provider: ModelProvider) -> ChatOpenAI | Ch
         api_key = os.getenv("GROQ_API_KEY")
         if not api_key:
             # Print error to console
-            print(f"API Key Error: Please make sure GROQ_API_KEY is set in your .env file.")
+            print("API Key Error: Please make sure GROQ_API_KEY is set in your .env file.")
             raise ValueError("Groq API key not found.  Please make sure GROQ_API_KEY is set in your .env file.")
         return ChatGroq(model=model_name, api_key=api_key)
     elif model_provider == ModelProvider.OPENAI:
         # Get and validate API key
         api_key = os.getenv("OPENAI_API_KEY")
         if not api_key:
             # Print error to console
-            print(f"API Key Error: Please make sure OPENAI_API_KEY is set in your .env file.")
+            print("API Key Error: Please make sure OPENAI_API_KEY is set in your .env file.")
             raise ValueError("OpenAI API key not found.  Please make sure OPENAI_API_KEY is set in your .env file.")
         return ChatOpenAI(model=model_name, api_key=api_key)
     elif model_provider == ModelProvider.ANTHROPIC:
         api_key = os.getenv("ANTHROPIC_API_KEY")
         if not api_key:
-            print(f"API Key Error: Please make sure ANTHROPIC_API_KEY is set in your .env file.")
+            print("API Key Error: Please make sure ANTHROPIC_API_KEY is set in your .env file.")
             raise ValueError("Anthropic API key not found.  Please make sure ANTHROPIC_API_KEY is set in your .env file.")
         return ChatAnthropic(model=model_name, api_key=api_key)
     elif model_provider == ModelProvider.DEEPSEEK:
         api_key = os.getenv("DEEPSEEK_API_KEY")
         if not api_key:
-            print(f"API Key Error: Please make sure DEEPSEEK_API_KEY is set in your .env file.")
+            print("API Key Error: Please make sure DEEPSEEK_API_KEY is set in your .env file.")
             raise ValueError("DeepSeek API key not found.  Please make sure DEEPSEEK_API_KEY is set in your .env file.")
         return ChatDeepSeek(model=model_name, api_key=api_key)
     elif model_provider == ModelProvider.GEMINI:
         api_key = os.getenv("GOOGLE_API_KEY")
         if not api_key:
-            print(f"API Key Error: Please make sure GOOGLE_API_KEY is set in your .env file.")
+            print("API Key Error: Please make sure GOOGLE_API_KEY is set in your .env file.")
             raise ValueError("Google API key not found.  Please make sure GOOGLE_API_KEY is set in your .env file.")
-        return ChatGoogleGenerativeAI(model=model_name, api_key=api_key)
+        return ChatGoogleGenerativeAI(model=model_name, api_key=api_key)
+    else:
+        print("Error: Model provider not found.")
+        return None

src/main.py

@@ -1,10 +1,16 @@
 import sys
+import json
 
+import argparse
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+from tabulate import tabulate
 from dotenv import load_dotenv
 from langchain_core.messages import HumanMessage
 from langgraph.graph import END, StateGraph
 from colorama import Fore, Back, Style, init
 import questionary
+
 from agents.ben_graham import ben_graham_agent
 from agents.bill_ackman import bill_ackman_agent
 from agents.fundamentals import fundamentals_agent
@@ -13,19 +19,16 @@
 from agents.risk_manager import risk_management_agent
 from agents.sentiment import sentiment_agent
 from agents.warren_buffett import warren_buffett_agent
-from graph.state import AgentState
 from agents.valuation import valuation_agent
+
+from graph.state import AgentState
+
 from utils.display import print_trading_output
 from utils.analysts import ANALYST_ORDER, get_analyst_nodes
 from utils.progress import progress
-from llm.models import LLM_ORDER, get_model_info
-
-import argparse
-from datetime import datetime
-from dateutil.relativedelta import relativedelta
-from tabulate import tabulate
 from utils.visualize import save_graph_as_png
-import json
+
+from llm.models import LLM_ORDER, get_model_info
 
 # Load environment variables from .env file
 load_dotenv()
@@ -60,6 +63,7 @@ def run_hedge_fund(
     model_name: str = "gpt-4o",
     model_provider: str = "OpenAI",
 ):
+    """Run the hedge fund trading system."""
     # Start progress tracking
     progress.start()
 
@@ -107,7 +111,7 @@ def start(state: AgentState):
     return state
 
 
-def create_workflow(selected_analysts=None):
+def create_workflow(selected_analysts = None):
     """Create the workflow with selected analysts."""
     workflow = StateGraph(AgentState)
     workflow.add_node("start_node", start)
@@ -154,16 +158,31 @@ def create_workflow(selected_analysts=None):
         default=0.0,
         help="Initial margin requirement. Defaults to 0.0"
     )
-    parser.add_argument("--tickers", type=str, required=True, help="Comma-separated list of stock ticker symbols")
+    parser.add_argument(
+        "--tickers",
+        type=str,
+        required=True,
+        help="Comma-separated list of stock ticker symbols"
+    )
     parser.add_argument(
         "--start-date",
         type=str,
         help="Start date (YYYY-MM-DD). Defaults to 3 months before end date",
     )
-    parser.add_argument("--end-date", type=str, help="End date (YYYY-MM-DD). Defaults to today")
-    parser.add_argument("--show-reasoning", action="store_true", help="Show reasoning from each agent")
     parser.add_argument(
-        "--show-agent-graph", action="store_true", help="Show the agent graph"
+        "--end-date",
+        type=str,
+        help="End date (YYYY-MM-DD). Defaults to today"
+    )
+    parser.add_argument(
+        "--show-reasoning", 
+        action="store_true",
+        help="Show reasoning from each agent"
+    )
+    parser.add_argument(
+        "--show-agent-graph",
+        action="store_true",
+        help="Show the agent graph"
     )
 
     args = parser.parse_args()
@@ -237,13 +256,13 @@ def create_workflow(selected_analysts=None):
         try:
             datetime.strptime(args.start_date, "%Y-%m-%d")
         except ValueError:
-            raise ValueError("Start date must be in YYYY-MM-DD format")
+            raise ValueError("Start date must be in YYYY-MM-DD format") from ValueError
 
     if args.end_date:
         try:
             datetime.strptime(args.end_date, "%Y-%m-%d")
         except ValueError:
-            raise ValueError("End date must be in YYYY-MM-DD format")
+            raise ValueError("End date must be in YYYY-MM-DD format") from ValueError
 
     # Set the start and end dates
     end_date = args.end_date or datetime.now().strftime("%Y-%m-%d")

src/tools/api.py

@@ -211,33 +211,33 @@ def get_company_news(
 
     all_news = []
     current_end_date = end_date
-    
+
     while True:
         url = f"https://api.financialdatasets.ai/news/?ticker={ticker}&end_date={current_end_date}"
         if start_date:
             url += f"&start_date={start_date}"
         url += f"&limit={limit}"
-        
+
         response = requests.get(url, headers=headers)
         if response.status_code != 200:
             raise Exception(f"Error fetching data: {ticker} - {response.status_code} - {response.text}")
-        
+
         data = response.json()
         response_model = CompanyNewsResponse(**data)
         company_news = response_model.news
-        
+
         if not company_news:
             break
-            
+
         all_news.extend(company_news)
-        
+
         # Only continue pagination if we have a start_date and got a full page
         if not start_date or len(company_news) < limit:
             break
-            
+
         # Update end_date to the oldest date from current batch for next iteration
         current_end_date = min(news.date for news in company_news).split('T')[0]
-        
+
         # If we've reached or passed the start_date, we can stop
         if current_end_date <= start_date:
             break
@@ -250,7 +250,6 @@ def get_company_news(
     return all_news
 
 
-
 def get_market_cap(
     ticker: str,
     end_date: str,