Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import yfinance as yf
+
+@st.cache_data
+def load_data(ticker):
+    # Fetch data from Yahoo Finance
+    return yf.download(ticker, start="2000-01-01", end="2023-01-01")
+
+ticker = st.text_input("Enter the ticker symbol", "AAPL")
+data = load_data(ticker)
+
+st.title("Algorithmic Trading Strategy Backtesting")
+
+# Moving Average Windows
+short_window = st.number_input("Short moving average window", 1, 50, 20)
+long_window = st.number_input("Long moving average window", 1, 200, 50)
+
+# Initial Capital
+initial_capital = st.number_input("Initial Capital", 1000, 1000000, 100000)
+
+# Calculate moving averages
+data['Short_MA'] = data['Close'].rolling(window=short_window).mean()
+data['Long_MA'] = data['Close'].rolling(window=long_window).mean()
+
+# Drop NaN values
+data.dropna(inplace=True)
+
+# Generate signals
+data['Signal'] = 0
+data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)
+data['Position'] = data['Signal'].diff()
+
+# Show signals in data
+st.write(data.tail())
+
+# Simulate portfolio
+data['Portfolio Value'] = initial_capital
+data['Portfolio Value'][short_window:] = initial_capital * (1 + data['Signal'][short_window:].shift(1) * data['Close'].pct_change()[short_window:]).cumprod()
+
+# Performance metrics
+cagr = (data['Portfolio Value'].iloc[-1] / initial_capital) ** (1 / ((data.index[-1] - data.index[short_window]).days / 365.25)) - 1
+sharpe_ratio = data['Portfolio Value'].pct_change().mean() / data['Portfolio Value'].pct_change().std() * np.sqrt(252)
+
+st.write(f"CAGR: {cagr:.2%}")
+st.write(f"Sharpe Ratio: {sharpe_ratio:.2f}")
+
+# Plot strategy performance
+plt.figure(figsize=(10, 5))
+plt.plot(data.index, data['Portfolio Value'], label='Portfolio Value')
+plt.title(f"Backtested Performance of {ticker} Strategy")
+plt.xlabel("Date")
+plt.ylabel("Portfolio Value")
+plt.legend()
+st.pyplot()
+
+# Highlight buy and sell signals
+fig, ax = plt.subplots(figsize=(10, 5))
+ax.plot(data.index, data['Close'], label='Close Price', alpha=0.5)
+ax.plot(data.index, data['Short_MA'], label=f'Short MA ({short_window})', alpha=0.75)
+ax.plot(data.index, data['Long_MA'], label=f'Long MA ({long_window})', alpha=0.75)
+ax.plot(data[data['Position'] == 1].index, data['Short_MA'][data['Position'] == 1], '^', markersize=10, color='g', lw=0, label='Buy Signal')
+ax.plot(data[data['Position'] == -1].index, data['Short_MA'][data['Position'] == -1], 'v', markersize=10, color='r', lw=0, label='Sell Signal')
+plt.title(f"{ticker} Price and Trading Signals")
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.legend()
+st.pyplot(fig)
+