app.py

import streamlit as st
import pandas as pd
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):
 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)

# 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)

# Data Preprocessing
data['Short_MA'] = data['Close'].rolling(window=short_window).mean()
data['Long_MA'] = data['Close'].rolling(window=long_window).mean()
data.dropna(inplace=True)

# 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()

# Backtesting Engine
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, sharpe_ratio = calculate_performance_metrics(data, initial_capital)
st.write(f"CAGR: {cagr:.2%}")
st.write(f"Sharpe Ratio: {sharpe_ratio:.2f}")

# Plot strategy performance
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)

# 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())