feat: Use Polygon.io API for fetching current stock prices

src/utils/data_utils.py

@@ -1,10 +1,10 @@
 import os
 import pandas as pd
-import yfinance as yf
 from datetime import datetime, timedelta
 from trading.position_calculator import PositionCalculator
 from utils.common_utils import get_user_input, get_stock_data, get_qualified_stocks
 from typing import Optional
+import requests
 
 def get_float_input(prompt: str) -> Optional[float]:
     return get_user_input(prompt, float)
@@ -13,79 +13,56 @@ def get_current_prices(tickers: list) -> dict:
     """Get current prices for multiple tickers using yfinance"""
     if not tickers:
         return {}
-        
+
+    polygon_api_key = os.environ.get("POLYGON_API_KEY")
+    if not polygon_api_key:
+        print("Error: POLYGON_API_KEY environment variable not set.")
+        return {}
+
     prices = {}
     try:
-        # Create a space-separated string of tickers
+        for ticker in tickers:
-        ticker_str = " ".join(tickers)
+            url = f"https://api.polygon.io/v2/last/trade/{ticker}?apiKey={polygon_api_key}"
-        # Get data for all tickers at once with more reliable settings
+            response = requests.get(url)
-        data = yf.download(
+
-            ticker_str, 
+            if response.status_code == 200:
-            period="1d",  # Just today's data
+                data = response.json()
-            interval="1d",  # Daily interval 
-            group_by='ticker',
-            progress=False,
-            prepost=True  # Include pre/post market prices
-        )
-        
-        # Handle single ticker case
-        if len(tickers) == 1:
-            if isinstance(data, pd.DataFrame) and 'Close' in data.columns:
-                prices[tickers[0]] = float(data['Close'].iloc[-1])
-            else:
-                # Try alternative method
                 try:
-                    stock = yf.Ticker(tickers[0])
+                    prices[ticker] = data['results']['price']
-                    price = stock.info.get('regularMarketPrice', 0.0)
+                except KeyError:
-                    prices[tickers[0]] = float(price)
-                except:
-                    prices[tickers[0]] = 0.0
-        else:
-            # Handle multiple tickers
-            for ticker in tickers:
-                try:
-                    if isinstance(data, pd.DataFrame) and (ticker, 'Close') in data.columns:
-                        prices[ticker] = float(data[ticker]['Close'].iloc[-1])
-                    else:
-                        # Try alternative method
-                        stock = yf.Ticker(ticker)
-                        price = stock.info.get('regularMarketPrice', 0.0)
-                        prices[ticker] = float(price)
-                except:
                     prices[ticker] = 0.0
-                    
+            else:
-        # Verify we have prices for all tickers
+                print(f"Error fetching price for {ticker}: {response.status_code} - {response.text}")
+                prices[ticker] = 0.0
+
+        # Verify that we have prices for all tickers
         for ticker in tickers:
             if ticker not in prices or prices[ticker] == 0:
-                try:
+                print(f"Could not fetch price for {ticker}")
-                    stock = yf.Ticker(ticker)
+                prices[ticker] = 0.0
-                    price = stock.info.get('regularMarketPrice', 0.0)
+
-                    if price:
-                        prices[ticker] = float(price)
-                except:
-                    if ticker not in prices:
-                        prices[ticker] = 0.0
-                        
     except Exception as e:
-        print(f"Error in batch price fetch: {e}")
+        print(f"Error fetching prices: {e}")
-        # Fall back to individual ticker fetching
+        # If there's an error, fall back to setting price to 0.0
         for ticker in tickers:
+            if ticker not in prices:
+                prices[ticker] = 0.0
             try:
                 stock = yf.Ticker(ticker)
                 price = stock.info.get('regularMarketPrice', 0.0)
                 prices[ticker] = float(price)
             except:
                 prices[ticker] = 0.0
-    
+
     return prices
 
 def validate_signal_date(signal_date: datetime) -> datetime:
     """
     Validate and adjust signal date if needed
-    
+
     Args:
         signal_date (datetime): Signal date to validate
-        
+
     Returns:
         datetime: Valid signal date (not in future)
     """
@@ -94,10 +71,10 @@ def validate_signal_date(signal_date: datetime) -> datetime:
         return current_date
     return signal_date
 
-def print_signal(signal_data: dict, signal_type: str = "🔍") -> None:
+def print_signal(signal_ dict, signal_type: str = "🔍") -> None:
     """
     Print standardized signal output
-    
+
     Args:
         signal_data (dict): Dictionary containing signal information
         signal_type (str): Emoji indicator for signal type (default: 🔍)
@@ -117,7 +94,7 @@ def print_signal(signal_data: dict, signal_type: str = "🔍") -> None:
 def save_signals_to_csv(signals: list, scanner_name: str) -> None:
     """
     Save signals to CSV file with standardized format and naming
-    
+
     Args:
         signals (list): List of signal dictionaries
         scanner_name (str): Name of the scanner for file naming
@@ -125,14 +102,12 @@ def save_signals_to_csv(signals: list, scanner_name: str) -> None:
     if not signals:
         print("\nNo signals found")
         return
-        
+
     output_dir = 'reports'
     os.makedirs(output_dir, exist_ok=True)
     output_date = datetime.now().strftime("%Y%m%d_%H%M")
     output_file = f'{output_dir}/{scanner_name}_{output_date}.csv'
-    
+
     df_signals = pd.DataFrame(signals)
     df_signals.to_csv(output_file, index=False)
     print(f"\nSaved {len(signals)} signals to {output_file}")
-
-