Update app.py

app.py

@@ -2,100 +2,18 @@ import streamlit as st
 import yfinance as yf
 import pandas as pd
 
-# Define the path to your CSV file
-sp500_averages_path = 'sp500_averages.csv'
-
-def load_sp500_averages(filepath):
- # Load the CSV without specifying an index column name
- return pd.read_csv(filepath, header=0, names=['Ratio', 'Average']).set_index('Ratio')
-
-# Fetch financial data for a single stock
-def fetch_stock_data(ticker_symbol):
- ticker = yf.Ticker(ticker_symbol)
- info = ticker.info
- 
- # Calculate Book-to-Market Ratio
- pb_ratio = info.get('priceToBook')
- book_to_market_ratio = 1 / pb_ratio if pb_ratio and pb_ratio > 0 else None
- 
- # Extract relevant financial information, including Book-to-Market Ratio
- financials = {
- 'P/E Ratio': info.get('forwardPE'),
- 'P/B Ratio': pb_ratio,
- 'P/S Ratio': info.get('priceToSalesTrailing12Months'),
- 'Debt to Equity Ratio': info.get('debtToEquity'),
- 'Return on Equity': info.get('returnOnEquity'),
- 'Book-to-Market Ratio': book_to_market_ratio,
- }
- 
- return financials, info
-
-
-# Update the cache decorator
-@st.experimental_memo
+# Correctly using st.cache_data as per Streamlit's new caching mechanism
+@st.cache_data
 def get_sp500_list():
  table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
  return table[0]['Symbol'].tolist()
 
-# Use the updated cache function and define the CSV path
-sp500_list = get_sp500_list()
-sp500_averages = load_sp500_averages(sp500_averages_path)
-
-# Calculate combined scores for stocks in the S&P 500
-scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
-scores_df_sorted = scores_df.sort_values(by='Combined Score', ascending=False)
-
-# Use columns for side-by-side layout
-col1, col2 = st.columns([1, 3])
-
-# First column for the sorted overview
-with col1:
- st.subheader("Stock Overview")
- # Create a DataFrame for the sidebar with color-coded combined scores
- scores_df_sorted['color'] = scores_df_sorted['Combined Score'].apply(
- lambda x: 'green' if x > 0 else 'red' if x < 0 else 'grey')
- for index, row in scores_df_sorted.iterrows():
- color = row['color']
- st.markdown(f"<span style='color: {color};'>{row['Stock']}: {row['Combined Score']}</span>", unsafe_allow_html=True)
-
-# Second column for detailed financial ratios and company information
-with col2:
- st.subheader("Stock Details")
- # Dropdown to select stock for details
- ticker_symbol = st.selectbox('Select a stock for details', options=sp500_list)
- if ticker_symbol:
- with st.spinner(f'Fetching data for {ticker_symbol}...'):
- stock_data, info = fetch_stock_data(ticker_symbol)
- comparison, _ = compare_to_index(stock_data, sp500_averages)
- 
- # Display company name and description
- st.write(f"**{info.get('longName')}**")
- st.write(info.get('longBusinessSummary'))
- 
- # Display financial ratios in a table
- st.table(pd.DataFrame.from_dict(stock_data, orient='index', columns=['Value']))
-
-
-
-import streamlit as st
-import yfinance as yf
-import pandas as pd
-
 # Define the path to your CSV file with S&P 500 averages
 sp500_averages_path = 'sp500_averages.csv'
 
-# Define the function to load S&P 500 averages from a CSV file
-@st.experimental_memo
-def get_sp500_list():
- table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
- return table[0]['Symbol'].tolist()
-
-# Define the function to load S&P 500 averages from a CSV file
-sp500_averages_path = 'sp500_averages.csv'
 def load_sp500_averages(filepath):
  return pd.read_csv(filepath, header=0, names=['Ratio', 'Average']).set_index('Ratio')
 
-# Define the function to fetch financial data for a single stock
 def fetch_stock_data(ticker_symbol):
  ticker = yf.Ticker(ticker_symbol)
  info = ticker.info
@@ -107,30 +25,23 @@ def fetch_stock_data(ticker_symbol):
  'Return on Equity': info.get('returnOnEquity'),
  'Book-to-Market Ratio': 1 / info.get('priceToBook') if info.get('priceToBook') else None
  }
- return financials
+ return financials, info
 
-# Define the function to compare stock ratios to S&P 500 averages
 def compare_to_index(stock_ratios, index_averages):
  comparison = {}
  score = 0
  for ratio, value in stock_ratios.items():
- if pd.notna(value):
+ if ratio in index_averages.index and pd.notna(value):
  average = index_averages.loc[ratio, 'Average']
- if value < average: # For ratios where lower is better
- comparison[ratio] = 'Undervalued'
- score += 1
- elif value > average: # For ratios where higher is not better
- comparison[ratio] = 'Overvalued'
- score -= 1
- else:
- comparison[ratio] = 'Data not available'
+ comparison[ratio] = 'Undervalued' if value < average else 'Overvalued'
+ score += 1 if value < average else -1
  return comparison, score
 
-# Define the function to calculate combined scores for stocks
+# Ensure this function is defined before it's called in the script
 def calculate_combined_scores_for_stocks(stocks, index_averages):
  scores = []
  for ticker_symbol in stocks:
- stock_data = fetch_stock_data(ticker_symbol)
+ stock_data, _ = fetch_stock_data(ticker_symbol)
  comparison, score = compare_to_index(stock_data, index_averages)
  scores.append({'Stock': ticker_symbol, 'Combined Score': score})
  return pd.DataFrame(scores)
@@ -138,7 +49,7 @@ def calculate_combined_scores_for_stocks(stocks, index_averages):
 # User interface in Streamlit
 st.title('S&P 500 Stock Comparison Tool')
 
-# Fetch the current S&P 500 list and load the averages
+# Load the current S&P 500 list and averages
 sp500_list = get_sp500_list()
 sp500_averages = load_sp500_averages(sp500_averages_path)
 
@@ -146,28 +57,24 @@ sp500_averages = load_sp500_averages(sp500_averages_path)
 scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
 scores_df_sorted = scores_df.sort_values(by='Combined Score', ascending=False)
 
-# Use columns for side-by-side layout
+# Layout for displaying overview and details
 col1, col2 = st.columns([1, 3])
 
-# First column for the sorted overview
 with col1:
  st.subheader("Stock Overview")
  st.dataframe(scores_df_sorted.style.applymap(lambda x: 'background-color: green' if x > 0 
- else ('background-color: red' if x < 0 else '')))
+ else 'background-color: red' if x < 0 else 'none'), height=600)
 
-# Second column for detailed financial ratios and company information
 with col2:
  st.subheader("Stock Details")
  ticker_symbol = st.selectbox('Select a stock for details', options=sp500_list)
  if ticker_symbol:
- with st.spinner(f'Fetching data for {ticker_symbol}...'):
- stock_data = fetch_stock_data(ticker_symbol)
- comparison, _ = compare_to_index(stock_data, sp500_averages)
- 
- st.write(f"**{ticker_symbol} - {yf.Ticker(ticker_symbol).info['longName']}**")
- st.write("Financial Ratios compared to S&P 500 averages:")
- for ratio, status in comparison.items():
- st.write(f"{ratio}: {status}")
+ stock_data, info = fetch_stock_data(ticker_symbol)
+ comparison, _ = compare_to_index(stock_data, sp500_averages)
+ st.write(f"**{info['longName']}** ({ticker_symbol})")
+ st.write(info['longBusinessSummary'])
+ for ratio, status in comparison.items():
+ st.write(f"{ratio}: {status}")