Create app.py

app.py

@@ -0,0 +1,150 @@
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+
+def fetch_stock_data(symbol, start_date, end_date):
+    return yf.download(symbol, start=start_date, end=end_date)
+
+def backtest_mixed_investment(data, start_date, end_date, monthly_investment, stock_allocation, savings_rate):
+    investment_dates = []
+    stock_shares = 0
+    savings_balance = 0
+    total_invested = 0
+    stock_value = []
+    savings_value = []
+
+    stock_investment = monthly_investment * stock_allocation
+    savings_investment = monthly_investment * (1 - stock_allocation)
+    daily_rate = (1 + savings_rate) ** (1/365) - 1
+
+    current_date = pd.to_datetime(start_date)
+    prev_date = current_date
+    while current_date <= pd.to_datetime(end_date):
+        if current_date in data.index:
+            price = data.loc[current_date, 'Adj Close']
+            new_shares = stock_investment / price
+            stock_shares += new_shares
+
+            days_passed = (current_date - prev_date).days
+            savings_balance *= (1 + daily_rate) ** days_passed
+            savings_balance += savings_investment
+
+            total_invested += monthly_investment
+
+            investment_dates.append(current_date)
+            stock_value.append(stock_shares * price)
+            savings_value.append(savings_balance)
+
+            prev_date = current_date
+
+        current_date += pd.DateOffset(months=1)
+
+    stock_value_series = pd.Series(stock_value, index=investment_dates).reindex(data.index, method='ffill')
+    savings_value_series = pd.Series(savings_value, index=investment_dates).reindex(data.index, method='ffill')
+    portfolio_value = stock_value_series + savings_value_series
+
+    return portfolio_value, stock_value_series, savings_value_series, total_invested
+
+def backtest_stock_only(data, start_date, end_date, monthly_investment):
+    investment_dates = []
+    total_shares = 0
+    total_invested = 0
+
+    current_date = pd.to_datetime(start_date)
+    while current_date <= pd.to_datetime(end_date):
+        if current_date in data.index:
+            price = data.loc[current_date, 'Adj Close']
+            shares = monthly_investment / price
+            total_shares += shares
+            total_invested += monthly_investment
+
+            investment_dates.append(current_date)
+
+        current_date += pd.DateOffset(months=1)
+
+    total_shares_series = pd.Series([total_shares] * len(investment_dates), index=investment_dates).reindex(data.index, method='ffill')
+    portfolio_value = data['Adj Close'] * total_shares_series
+
+    return portfolio_value, pd.Series([total_invested] * len(data.index), index=data.index), total_invested
+
+def plot_results(portfolio_value, stock_value, savings_value, total_invested, symbol, start_date, end_date, stock_only=False):
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(portfolio_value.index, portfolio_value, label='Portfolio Value')
+    if not stock_only:
+        ax.plot(stock_value.index, stock_value, label='Stock Investment Value')
+        ax.plot(savings_value.index, savings_value, label='Savings Account Value')
+    ax.plot(portfolio_value.index, total_invested, label='Total Cash Invested', linestyle='--')
+    
+    ax.set_title(f'Monthly Investment Analysis: {symbol} ({start_date} to {end_date})')
+    ax.set_xlabel('Date')
+    ax.set_ylabel('Value ($)')
+    ax.legend()
+    ax.grid(True)
+
+    total_return = (portfolio_value[-1] - total_invested[-1]) / total_invested[-1] * 100
+    
+    ax.annotate(f'Total Return: {total_return:.2f}%', xy=(0.05, 0.95), xycoords='axes fraction', fontsize=10, ha='left', va='top')
+    ax.annotate(f'Final Portfolio Value: ${portfolio_value[-1]:.2f}', xy=(0.05, 0.90), xycoords='axes fraction', fontsize=10, ha='left', va='top')
+    if not stock_only:
+        ax.annotate(f'Final Stock Value: ${stock_value[-1]:.2f}', xy=(0.05, 0.85), xycoords='axes fraction', fontsize=10, ha='left', va='top')
+        ax.annotate(f'Final Savings Value: ${savings_value[-1]:.2f}', xy=(0.05, 0.80), xycoords='axes fraction', fontsize=10, ha='left', va='top')
+    ax.annotate(f'Total Invested: ${total_invested[-1]:.2f}', xy=(0.05, 0.75), xycoords='axes fraction', fontsize=10, ha='left', va='top')
+
+    return fig
+
+def main():
+    st.title("InvestSim: Stock & Savings Portfolio Analyzer")
+    st.write("Simulate and analyze your investment strategy with stocks and high-yield savings accounts.")
+
+    # Sidebar inputs
+    st.sidebar.header('Input Parameters')
+    symbol = st.sidebar.text_input('Stock Symbol', 'AAPL')
+    start_date = st.sidebar.date_input('Start Date', datetime(2000, 1, 1))
+    end_date = st.sidebar.date_input('End Date', datetime.now())
+    monthly_investment = st.sidebar.number_input('Monthly Investment ($)', min_value=1, value=100)
+    investment_type = st.sidebar.radio("Investment Type", ("Stock Only", "Mixed (Stock + Savings)"))
+    
+    if investment_type == "Mixed (Stock + Savings)":
+        stock_allocation = st.sidebar.slider('Stock Allocation (%)', 0, 100, 60) / 100
+        savings_rate = st.sidebar.number_input('HYSA Annual Interest Rate (%)', min_value=0.0, max_value=20.0, value=4.5) / 100
+    else:
+        stock_allocation = 1.0
+        savings_rate = 0.0
+
+    if st.sidebar.button('Run Analysis'):
+        # Fetch stock data
+        data = fetch_stock_data(symbol, start_date, end_date)
+
+        if data.empty:
+            st.error(f"No data available for {symbol}. Please check the stock symbol and date range.")
+            return
+
+        # Run backtest
+        if investment_type == "Mixed (Stock + Savings)":
+            portfolio_value, stock_value, savings_value, total_invested = backtest_mixed_investment(
+                data, start_date, end_date, monthly_investment, stock_allocation, savings_rate
+            )
+        else:
+            portfolio_value, total_invested_series, total_invested = backtest_stock_only(
+                data, start_date, end_date, monthly_investment
+            )
+            stock_value = portfolio_value
+            savings_value = pd.Series([0] * len(portfolio_value), index=portfolio_value.index)
+
+        # Plot results
+        fig = plot_results(portfolio_value, stock_value, savings_value, total_invested_series if investment_type == "Stock Only" else pd.Series([total_invested] * len(portfolio_value), index=portfolio_value.index), symbol, start_date, end_date, stock_only=(investment_type == "Stock Only"))
+        st.pyplot(fig)
+
+        # Display summary statistics
+        st.subheader('Investment Summary')
+        st.write(f"Total Return: {((portfolio_value[-1] - total_invested) / total_invested * 100):.2f}%")
+        st.write(f"Final Portfolio Value: ${portfolio_value[-1]:.2f}")
+        if investment_type == "Mixed (Stock + Savings)":
+            st.write(f"Final Stock Value: ${stock_value[-1]:.2f}")
+            st.write(f"Final Savings Value: ${savings_value[-1]:.2f}")
+        st.write(f"Total Invested: ${total_invested:.2f}")
+
+if __name__ == '__main__':
+    main()