Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import os
+import streamlit as st
+
+# Define path to the Stocks folder inside the Data folder
+stocks_path = "/root/.cache/kagglehub/datasets/borismarjanovic/price-volume-data-for-all-us-stocks-etfs/versions/3/Data/Stocks"
+
+# Function to analyze stock data
+def analyze_stock(stock_file, desired_return, risk_tolerance):
+    df_stock = pd.read_csv(stock_file, sep=",")  # Adjust separator if needed
+    df_stock['Date'] = pd.to_datetime(df_stock['Date'])
+    df_stock = df_stock.sort_values(by='Date')
+
+    # Calculate daily returns
+    df_stock['Daily Return'] = df_stock['Close'].pct_change()
+
+    # Calculate annualized return and volatility
+    annualized_return = df_stock['Daily Return'].mean() * 252
+    volatility = df_stock['Daily Return'].std() * np.sqrt(252)
+
+    # Return analysis results
+    return stock_file, annualized_return, volatility
+
+# Streamlit app for user inputs and stock analysis
+def main():
+    st.title("Investment Advisory System")
+
+    st.sidebar.header("User Inputs")
+    desired_return = st.sidebar.number_input("Enter your desired annual return (as a decimal, e.g., 0.1 for 10%)", min_value=0.0, value=0.1)
+    risk_tolerance = st.sidebar.number_input("Enter your risk tolerance (standard deviation, e.g., 0.2 for 20%)", min_value=0.0, value=0.2)
+
+    st.write(f"Desired Annual Return: {desired_return * 100}%")
+    st.write(f"Risk Tolerance (Volatility): {risk_tolerance * 100}%")
+
+    st.header("Stock Analysis Results")
+
+    # Create a list to hold matching stocks
+    matching_stocks = []
+
+    # Iterate through all stock files in the Stocks folder
+    for stock_file in os.listdir(stocks_path):
+        stock_file_path = os.path.join(stocks_path, stock_file)
+        if stock_file.endswith('.txt'):  # Process only text files
+            stock_name, annualized_return, volatility = analyze_stock(stock_file_path, desired_return, risk_tolerance)
+            
+            # Display results for each stock
+            st.write(f"### {stock_name}")
+            st.write(f"**Annualized Return**: {annualized_return * 100:.2f}%")
+            st.write(f"**Annualized Volatility**: {volatility * 100:.2f}%")
+            
+            # Check if the stock meets the user's criteria
+            if annualized_return >= desired_return and volatility <= risk_tolerance:
+                matching_stocks.append(stock_name)
+                st.success("This stock matches your criteria!")
+            else:
+                st.warning("This stock does not meet your preferences.")
+    
+    # Display matched stocks at the end
+    if matching_stocks:
+        st.header("Matching Stocks")
+        for stock in matching_stocks:
+            st.write(f"- {stock}")
+    else:
+        st.write("No stocks matched your preferences.")
+
+if __name__ == "__main__":
+    main()