Update app.py

app.py

@@ -4,16 +4,18 @@ import numpy as np
 import matplotlib.pyplot as plt
 import yfinance as yf
 
+# Function to load historical data
 @st.cache_data
 def load_data(ticker):
  # Fetch data from Yahoo Finance
  return yf.download(ticker, start="2000-01-01", end="2023-01-01")
 
+# User inputs for strategy parameters
+st.title("Algorithmic Trading Strategy Backtesting")
+
 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)
@@ -21,6 +23,7 @@ long_window = st.number_input("Long moving average window", 1, 200, 50)
 # Initial Capital
 initial_capital = st.number_input("Initial Capital", 1000, 1000000, 100000)
 
+# Data Preprocessing
 # Calculate moving averages
 data['Short_MA'] = data['Close'].rolling(window=short_window).mean()
 data['Long_MA'] = data['Close'].rolling(window=long_window).mean()
@@ -28,7 +31,7 @@ data['Long_MA'] = data['Close'].rolling(window=long_window).mean()
 # Drop NaN values
 data.dropna(inplace=True)
 
-# Generate signals
+# Generate Trading 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()
@@ -36,6 +39,7 @@ data['Position'] = data['Signal'].diff()
 # Show signals in data
 st.write(data.tail())
 
+# Backtesting Engine
 # 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()
@@ -47,14 +51,15 @@ sharpe_ratio = data['Portfolio Value'].pct_change().mean() / data['Portfolio Val
 st.write(f"CAGR: {cagr:.2%}")
 st.write(f"Sharpe Ratio: {sharpe_ratio:.2f}")
 
+# Data Visualization
 # 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()
+fig, ax = plt.subplots(figsize=(10, 5))
+ax.plot(data.index, data['Portfolio Value'], label='Portfolio Value')
+ax.set_title(f"Backtested Performance of {ticker} Strategy")
+ax.set_xlabel("Date")
+ax.set_ylabel("Portfolio Value")
+ax.legend()
+st.pyplot(fig)
 
 # Highlight buy and sell signals
 fig, ax = plt.subplots(figsize=(10, 5))
@@ -63,9 +68,8 @@ ax.plot(data.index, data['Short_MA'], label=f'Short MA ({short_window})', alpha=
 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()
+ax.set_title(f"{ticker} Price and Trading Signals")
+ax.set_xlabel("Date")
+ax.set_ylabel("Price")
+ax.legend()
 st.pyplot(fig)
-