ma_backtesting.py

# Import dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
import datetime as dt

# Set up variables
symbol = 'MSFT'
short_window = 20
long_window = 50
initial_capital = 10000 # Starting cash
num_of_years = 3
start_date = dt.date.today() - dt.timedelta(days=365*num_of_years)
end_date = dt.date.today()

# Download data using yfinance
df = yf.download(symbol, start_date, end_date)

# Add columns to the DataFrame
df['Signal'] = 0
df['Short_MA'] = df['Adj Close'].rolling(window=short_window).mean()
df['Long_MA'] = df['Adj Close'].rolling(window=long_window).mean()

# Determine when to buy and sell
df['Signal'][short_window:] = np.where(df['Short_MA'][short_window:] > df['Long_MA'][short_window:], 1, 0)
df['Positions'] = df['Signal'].diff()

# Calculate daily P&L, total P&L, and position sizes
df['Daily P&L'] = df['Adj Close'].diff() * df['Signal']
df['Total P&L'] = df['Daily P&L'].cumsum()
positions = pd.DataFrame(index=df.index).fillna(0.0)        
positions = 100 * df['Positions']
portfolio = pd.DataFrame(index=df.index)
portfolio['Holdings'] = positions * df['Adj Close']       
portfolio['Cash'] = initial_capital - portfolio['Holdings'].cumsum()
portfolio['Total'] = portfolio['Cash'] + positions.cumsum() * df['Adj Close']
portfolio['Returns'] = portfolio['Total'].pct_change()

# Create the plot
fig = plt.figure(figsize=(14,10))
ax1 = plt.subplot(2, 1, 1)
df[['Short_MA', 'Long_MA']].plot(ax=ax1, lw=2.)
ax1.plot(df['Adj Close'])
ax1.plot(df.loc[df['Positions'] == 1.0].index, df.Short_MA[df['Positions'] == 1.0],'^', markersize=10, color='g', label='Long')
ax1.plot(df.loc[df['Positions'] == -1.0].index,  df.Short_MA[df['Positions'] == -1.0],'v', markersize=10, color='r', label='Short')
ax1.set_title(f'{symbol} Closing Price')
ax1.set_ylabel('Price')
ax1.grid()
ax1.legend()

ax2 = plt.subplot(2, 1, 2)
ax2.plot(portfolio['Total'])
ax2.set_ylabel('Portfolio Value')
ax2.set_xlabel('Date')
ax2.legend()
ax2.grid()
plt.show()