Update app.py

app.py

@@ -2,15 +2,11 @@ import streamlit as st
 import yfinance as yf
 import pandas as pd
 
-# Correctly using st.cache_data as per Streamlit's new caching mechanism
-@st.cache_data
+@st.experimental_singleton
 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 path to your CSV file with S&P 500 averages
-sp500_averages_path = 'sp500_averages.csv'
-
 def load_sp500_averages(filepath):
  return pd.read_csv(filepath, header=0, names=['Ratio', 'Average']).set_index('Ratio')
 
@@ -37,7 +33,6 @@ def compare_to_index(stock_ratios, index_averages):
  score += 1 if value < average else -1
  return comparison, score
 
-# 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:
@@ -46,63 +41,54 @@ def calculate_combined_scores_for_stocks(stocks, index_averages):
  scores.append({'Stock': ticker_symbol, 'Combined Score': score})
  return pd.DataFrame(scores)
 
-# Define the color-coding function for the 'Combined Score' column
 def color_combined_score(value):
- """Colors the combined score cell based on its value."""
  if value > 0:
  color = 'green'
  elif value < 0:
  color = 'red'
  else:
- color = 'none'
+ color = 'lightgrey'
  return f'background-color: {color};'
 
-def filter_incomplete_stocks(df):
- # Assuming that 'N/A' represents missing data, replace it with NaN
- df.replace('N/A', pd.NA, inplace=True)
- # Drop rows with any NaN values in the specified columns
- return df.dropna(subset=['P/E Ratio', 'P/B Ratio', 'P/S Ratio', 'Debt to Equity Ratio', 'Return on Equity', 'Book-to-Market Ratio'])
- 
-# User interface in Streamlit
-st.title('S&P 500 Stock Comparison Tool')
+def filter_incomplete_stocks(df, required_columns):
+ df = df.dropna(subset=required_columns)
+ return df
 
+st.title('S&P 500 Stock Comparison Tool')
 
-# Load the current S&P 500 list and averages
 sp500_list = get_sp500_list()
-sp500_averages = load_sp500_averages(sp500_averages_path)
+sp500_averages = load_sp500_averages('sp500_averages.csv')
 
-# Calculate combined scores for all S&P 500 stocks
 scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
-scores_df_sorted = scores_df.sort_values(by='Combined Score', ascending=False)
+required_columns = ['P/E Ratio', 'P/B Ratio', 'P/S Ratio', 'Debt to Equity Ratio', 'Return on Equity', 'Book-to-Market Ratio']
 
-# Filter out stocks with incomplete data
-scores_df_filtered = filter_incomplete_stocks(scores_df_sorted)
+# Ensure filtering is done correctly with complete data
+scores_df_filtered = filter_incomplete_stocks(scores_df, required_columns)
+scores_df_sorted = scores_df_filtered.sort_values(by='Combined Score', ascending=False)
 
-# Layout for displaying overview and details
-col1, col2 = st.columns([3, 5]) # For example, this will give the first column 3/8 of the width
+col1, col2 = st.columns([3, 5])
 
 with col1:
  st.subheader("Stock Overview")
- # Apply color based on 'Combined Score' value and display the DataFrame
- styled_scores_df = scores_df_filtered.style.applymap(color_combined_score, subset=['Combined Score'])
+ styled_scores_df = scores_df_sorted.style.applymap(color_combined_score, subset=['Combined Score'])
  st.dataframe(styled_scores_df)
 
 with col2:
  st.subheader("Stock Details")
- # Use the filtered DataFrame to create the dropdown for stock selection
- sorted_tickers = scores_df_filtered['Stock'].tolist()
+ sorted_tickers = scores_df_sorted['Stock'].tolist()
  ticker_symbol = st.selectbox('Select a stock for details', options=sorted_tickers)
  if ticker_symbol:
  with st.spinner(f'Fetching data for {ticker_symbol}...'):
- stock_data, _ = fetch_stock_data(ticker_symbol)
+ stock_data, info = fetch_stock_data(ticker_symbol)
  comparison, _ = compare_to_index(stock_data, sp500_averages)
  
- # Display the company name and ticker symbol
- st.write(f"**{ticker_symbol}**")
+ st.write(f"**{info.get('longName', 'N/A')}** ({ticker_symbol})")
+ st.write(info.get('longBusinessSummary', 'N/A'))
  
- # Display each financial ratio, its value, and the S&P 500 average
- for ratio in stock_data.keys():
- value = stock_data[ratio]
+ # Iterate over required_columns instead of required_ratios
+ for ratio in required_columns:
+ value = stock_data.get(ratio, 'N/A')
  average = sp500_averages.loc[ratio, 'Average'] if ratio in sp500_averages.index else 'N/A'
- comparison_result = comparison[ratio] if ratio in comparison else 'N/A'
- st.write(f"{ratio}: {value} (Your Ratio) | {average} (S&P 500 Avg) - {comparison_result}")
+ status = comparison.get(ratio, 'N/A')
+ st.write(f"{ratio}: {value} (Your Ratio) | {average} (S&P 500 Avg) - {status}")
+