Update app.py

app.py

@@ -7,11 +7,29 @@ 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")
 
+def calculate_performance_metrics(data, initial_capital):
+ cagr = (data['Portfolio Value'].iloc[-1] / initial_capital) ** (1 / ((data.index[-1] - data.index[0]).days / 365.25)) - 1
+ sharpe_ratio = data['Portfolio Value'].pct_change().mean() / data['Portfolio Value'].pct_change().std() * np.sqrt(252)
+ return cagr, sharpe_ratio
+
+def plot_signals(data, ticker, short_window, long_window):
+ 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')
+ ax.set_title(f"{ticker} Price and Trading Signals")
+ ax.set_xlabel("Date")
+ ax.set_ylabel("Price")
+ ax.legend()
+ st.pyplot(fig)
+
 # User inputs for strategy parameters
 st.title("Algorithmic Trading Strategy Backtesting")
+st.markdown("This app allows you to backtest an algorithmic trading strategy using historical stock data.")
 
 ticker = st.text_input("Enter the ticker symbol", "AAPL")
 data = load_data(ticker)
@@ -24,11 +42,8 @@ long_window = st.number_input("Long moving average window", 1, 200, 50)
 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()
-
-# Drop NaN values
 data.dropna(inplace=True)
 
 # Generate Trading Signals
@@ -36,22 +51,15 @@ 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())
-
 # 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()
 
 # 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)
-
+cagr, sharpe_ratio = calculate_performance_metrics(data, initial_capital)
 st.write(f"CAGR: {cagr:.2%}")
 st.write(f"Sharpe Ratio: {sharpe_ratio:.2f}")
 
-# Data Visualization
 # Plot strategy performance
 fig, ax = plt.subplots(figsize=(10, 5))
 ax.plot(data.index, data['Portfolio Value'], label='Portfolio Value')
@@ -61,15 +69,32 @@ ax.set_ylabel("Portfolio Value")
 ax.legend()
 st.pyplot(fig)
 
-# 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')
-ax.set_title(f"{ticker} Price and Trading Signals")
-ax.set_xlabel("Date")
-ax.set_ylabel("Price")
-ax.legend()
-st.pyplot(fig)
+# Plot trading signals
+plot_signals(data, ticker, short_window, long_window)
+
+# Advanced Performance Metrics
+st.subheader("Advanced Performance Metrics")
+# Maximum Drawdown Calculation
+rolling_max = data['Portfolio Value'].cummax()
+daily_drawdown = data['Portfolio Value'] / rolling_max - 1.0
+max_drawdown = daily_drawdown.cummin().iloc[-1]
+st.write(f"Maximum Drawdown: {max_drawdown:.2%}")
+
+# Trade Statistics
+st.subheader("Trade Statistics")
+num_trades = data['Position'].value_counts().sum()
+num_winning_trades = data['Position'][data['Position'] == 1].count()
+num_losing_trades = data['Position'][data['Position'] == -1].count()
+win_rate = num_winning_trades / num_trades
+loss_rate = num_losing_trades / num_trades
+st.write(f"Total Trades: {num_trades}")
+st.write(f"Winning Trades: {num_winning_trades} ({win_rate:.2%})")
+st.write(f"Losing Trades: {num_losing_trades} ({loss_rate:.2%})")
+
+# Add option to upload data
+st.subheader("Upload Your Own Data")
+uploaded_file = st.file_uploader("Choose a file", type="csv")
+if uploaded_file is not None:
+ user_data = pd.read_csv(uploaded_file)
+ st.write("Uploaded data:")
+ st.write(user_data.head())