Refactor FinancialDatasetAPI integration and add configuration classes for API setup.

src/agents/market_data.py

@@ -1,74 +1,89 @@
+from datetime import datetime
 
 from langchain_openai.chat_models import ChatOpenAI
 
 from agents.state import AgentState
-from tools.api import search_line_items, get_financial_metrics, get_insider_trades, get_market_cap, get_prices
-
-from datetime import datetime
+from tools.api.financial_dataset import (
+    FinancialDatasetAPI,
+)
 
 llm = ChatOpenAI(model="gpt-4o")
 
+
 def market_data_agent(state: AgentState):
     """Responsible for gathering and preprocessing market data"""
     messages = state["messages"]
     data = state["data"]
 
     # Set default dates
-    end_date = data["end_date"] or datetime.now().strftime('%Y-%m-%d')
+    end_date = data["end_date"] or datetime.now().strftime("%Y-%m-%d")
     if not data["start_date"]:
         # Calculate 3 months before end_date
-        end_date_obj = datetime.strptime(end_date, '%Y-%m-%d')
-        start_date = end_date_obj.replace(month=end_date_obj.month - 3) if end_date_obj.month > 3 else \
-            end_date_obj.replace(year=end_date_obj.year - 1, month=end_date_obj.month + 9)
-        start_date = start_date.strftime('%Y-%m-%d')
+        end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
+        start_date = (
+            end_date_obj.replace(month=end_date_obj.month - 3)
+            if end_date_obj.month > 3
+            else end_date_obj.replace(
+                year=end_date_obj.year - 1, month=end_date_obj.month + 9
+            )
+        )
+        start_date = start_date.strftime("%Y-%m-%d")
     else:
         start_date = data["start_date"]
 
+    financial_api = FinancialDatasetAPI()
+
     # Get the historical price data
-    prices = get_prices(
-        ticker=data["ticker"], 
-        start_date=start_date, 
+    prices = financial_api.get_prices(
+        ticker=data["ticker"],
+        start_date=start_date,
         end_date=end_date,
     )
 
     # Get the financial metrics
-    financial_metrics = get_financial_metrics(
-        ticker=data["ticker"], 
-        report_period=end_date, 
-        period='ttm', 
+    financial_metrics = financial_api.get_financial_metrics(
+        ticker=data["ticker"],
+        report_period=end_date,
+        period="ttm",
         limit=1,
     )
 
     # Get the insider trades
-    insider_trades = get_insider_trades(
-        ticker=data["ticker"], 
+    insider_trades = financial_api.get_insider_trades(
+        ticker=data["ticker"],
         end_date=end_date,
         limit=5,
     )
 
     # Get the market cap
-    market_cap = get_market_cap(
+    market_cap = financial_api.get_market_cap(
         ticker=data["ticker"],
     )
 
     # Get the line_items
-    financial_line_items = search_line_items(
-        ticker=data["ticker"], 
-        line_items=["free_cash_flow", "net_income", "depreciation_and_amortization", "capital_expenditure", "working_capital"],
-        period='ttm',
+    financial_line_items = financial_api.search_line_items(
+        ticker=data["ticker"],
+        line_items=[
+            "free_cash_flow",
+            "net_income",
+            "depreciation_and_amortization",
+            "capital_expenditure",
+            "working_capital",
+        ],
+        period="ttm",
         limit=2,
     )
 
     return {
         "messages": messages,
         "data": {
-            **data, 
-            "prices": prices, 
-            "start_date": start_date, 
+            **data,
+            "prices": prices,
+            "start_date": start_date,
             "end_date": end_date,
             "financial_metrics": financial_metrics,
             "insider_trades": insider_trades,
             "market_cap": market_cap,
             "financial_line_items": financial_line_items,
-        }
-    }
+        },
+    }

src/agents/risk_manager.py

@@ -1,12 +1,12 @@
+import ast
+import json
 import math
 
 from langchain_core.messages import HumanMessage
 
 from agents.state import AgentState, show_agent_reasoning
-from tools.api import prices_to_df
+from tools.api.financial_dataset import FinancialDatasetAPI
 
-import json
-import ast
 
 ##### Risk Management Agent #####
 def risk_management_agent(state: AgentState):
@@ -15,7 +15,7 @@ def risk_management_agent(state: AgentState):
     portfolio = state["data"]["portfolio"]
     data = state["data"]
 
-    prices_df = prices_to_df(data["prices"])
+    prices_df = FinancialDatasetAPI.prices_to_df(data["prices"])
 
     # Fetch messages from other agents
     technical_message = next(msg for msg in state["messages"] if msg.name == "technical_analyst_agent")

src/agents/technicals.py

@@ -9,7 +9,7 @@
 import pandas as pd
 import numpy as np
 
-from tools.api import prices_to_df
+from tools.api.financial_dataset import FinancialDatasetAPI
 
 
 ##### Technical Analyst #####
@@ -25,7 +25,7 @@ def technical_analyst_agent(state: AgentState):
     show_reasoning = state["metadata"]["show_reasoning"]
     data = state["data"]
     prices = data["prices"]
-    prices_df = prices_to_df(prices)
+    prices_df = FinancialDatasetAPI.prices_to_df(prices)
 
     # Calculate indicators
     # 1. MACD (Moving Average Convergence Divergence)

src/backtester.py

@@ -4,7 +4,7 @@
 import pandas as pd
 
 from main import run_hedge_fund
-from tools.api import get_price_data
+from tools.api.financial_dataset import FinancialDatasetAPI
 
 class Backtester:
     def __init__(self, agent, ticker, start_date, end_date, initial_capital):
@@ -70,7 +70,7 @@ def run_backtest(self):
             )
 
             action, quantity = self.parse_action(agent_output)
-            df = get_price_data(self.ticker, lookback_start, current_date_str)
+            df = FinancialDatasetAPI().get_prices(self.ticker, lookback_start, current_date_str)
             current_price = df.iloc[-1]['close']
 
             # Execute the trade with validation

src/tools/api/base.py

@@ -0,0 +1,60 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional
+
+import requests
+from requests.exceptions import RequestException
+
+from .config import BaseAPIConfig
+
+
+class BaseAPIClient(ABC):
+    """Abstract base class for API clients."""
+
+    def __init__(self, config: BaseAPIConfig):
+        self.config = config
+        self.headers = self._get_headers()
+
+    @abstractmethod
+    def _get_headers(self) -> Dict[str, str]:
+        """Return headers required for API requests."""
+        pass
+
+    @abstractmethod
+    def _handle_response(self, response: requests.Response) -> Dict[str, Any]:
+        """Handle API response and return parsed data."""
+        pass
+
+    def _get_base_url(self) -> str:
+        """Implementation of abstract method for base URL."""
+        return self.config.base_url
+
+    def _make_request(
+        self,
+        endpoint: str,
+        method: str = "GET",
+        params: Optional[Dict[str, Any]] = None,
+        json_data: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        """Make HTTP request to the API with error handling."""
+        url = f"{self._get_base_url()}/{endpoint.lstrip('/')}"
+
+        try:
+            response = requests.request(
+                method=method,
+                url=url,
+                headers=self.headers,
+                params=params,
+                json=json_data,
+            )
+            return self._handle_response(response)
+        except RequestException as e:
+            raise Exception(f"API request failed: {str(e)}") from e
+
+    def _get_data_or_raise(
+        self, response_data: Dict[str, Any], key: str, error_message: str
+    ) -> Any:
+        """Extract data from response or raise if not found."""
+        data = response_data.get(key)
+        if not data:
+            raise Exception(error_message)
+        return data