On Wednesday 19th March I gave a talk at the London Financial Python User Group (LFPUG) on backtesting with Python and pandas. As promised this article contains the code for the talk.
For more information on this specific example please take a look at the article on Moving Average Crossover Strategies.
The code in entirety can be found here:
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from abc import ABCMeta, abstractmethod
from pandas.io.data import DataReader
class Strategy(object):
"""Strategy is an abstract base class providing an interface for
all subsequent (inherited) trading strategies.
The goal of a (derived) Strategy object is to output a list of signals,
which has the form of a time series indexed pandas DataFrame.
In this instance only a single symbol/instrument is supported."""
__metaclass__ = ABCMeta
@abstractmethod
def generate_signals(self):
"""An implementation is required to return the DataFrame of symbols
containing the signals to go long, short or hold (1, -1 or 0)."""
raise NotImplementedError("Should implement generate_signals()!")
class MovingAverageCrossStrategy(Strategy):
"""
Requires:
symbol - A stock symbol on which to form a strategy on.
bars - A DataFrame of bars for the above symbol.
short_window - Lookback period for short moving average.
long_window - Lookback period for long moving average."""
def __init__(self, symbol, bars, short_window=100, long_window=400):
self.symbol = symbol
self.bars = bars
self.short_window = short_window
self.long_window = long_window
def generate_signals(self):
"""Returns the DataFrame of symbols containing the signals
to go long, short or hold (1, -1 or 0)."""
signals = pd.DataFrame(index=self.bars.index)
signals['signal'] = 0.0
# Create the set of short and long simple moving averages over the
# respective periods
signals['short_mavg'] = pd.rolling_mean(bars['Close'], self.short_window, min_periods=1)
signals['long_mavg'] = pd.rolling_mean(bars['Close'], self.long_window, min_periods=1)
# Create a 'signal' (invested or not invested) when the short moving average crosses the long
# moving average, but only for the period greater than the shortest moving average window
signals['signal'][self.short_window:] = np.where(signals['short_mavg'][self.short_window:]
> signals['long_mavg'][self.short_window:], 1.0, 0.0)
# Take the difference of the signals in order to generate actual trading orders
signals['positions'] = signals['signal'].diff()
return signals
class Portfolio(object):
"""An abstract base class representing a portfolio of
positions (including both instruments and cash), determined
on the basis of a set of signals provided by a Strategy."""
__metaclass__ = ABCMeta
@abstractmethod
def generate_positions(self):
"""Provides the logic to determine how the portfolio
positions are allocated on the basis of forecasting
signals and available cash."""
raise NotImplementedError("Should implement generate_positions()!")
@abstractmethod
def backtest_portfolio(self):
"""Provides the logic to generate the trading orders
and subsequent equity curve (i.e. growth of total equity),
as a sum of holdings and cash, and the bar-period returns
associated with this curve based on the 'positions' DataFrame.
Produces a portfolio object that can be examined by
other classes/functions."""
raise NotImplementedError("Should implement backtest_portfolio()!")
class MarketOnClosePortfolio(Portfolio):
"""Encapsulates the notion of a portfolio of positions based
on a set of signals as provided by a Strategy.
Requires:
symbol - A stock symbol which forms the basis of the portfolio.
bars - A DataFrame of bars for a symbol set.
signals - A pandas DataFrame of signals (1, 0, -1) for each symbol.
initial_capital - The amount in cash at the start of the portfolio."""
def __init__(self, symbol, bars, signals, initial_capital=100000.0):
self.symbol = symbol
self.bars = bars
self.signals = signals
self.initial_capital = float(initial_capital)
self.positions = self.generate_positions()
def generate_positions(self):
positions = pd.DataFrame(index=signals.index).fillna(0.0)
positions[self.symbol] = 100*signals['signal'] # This strategy buys 100 shares
return positions
def backtest_portfolio(self):
portfolio = self.positions*self.bars['Adj Close']
pos_diff = self.positions.diff()
portfolio['holdings'] = (self.positions*self.bars['Adj Close']).sum(axis=1)
portfolio['cash'] = self.initial_capital - (pos_diff*self.bars['Adj Close']).sum(axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()
return portfolio
if __name__ == "__main__":
# Obtain daily bars of AMZN from Yahoo Finance for the period
# 1st Jan 2009 to 1st Jan 2014
symbol = 'AMZN'
bars = DataReader(symbol, "yahoo", datetime.datetime(2009,1,1), datetime.datetime(2014,1,1))
# Create a Moving Average Cross Strategy instance
# with short and long moving average windows
mac = MovingAverageCrossStrategy(symbol, bars, short_window=40, long_window=100)
signals = mac.generate_signals()
# Create a portfolio of AMZN, with $100,000 initial capital
portfolio = MarketOnClosePortfolio(symbol, bars, signals, initial_capital=100000.0)
returns = portfolio.backtest_portfolio()
# Plot two charts to assess trades and equity curve
fig = plt.figure()
fig.patch.set_facecolor('white') # Set the outer colour to white
ax1 = fig.add_subplot(211, ylabel='Price in $')
# Plot the AMZN closing price overlaid with the moving averages
bars['Close'].plot(ax=ax1, color='r', lw=2.)
signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
# Plot the "buy" trades against AMZN
ax1.plot(signals.ix[signals.positions == 1.0].index,
signals.short_mavg[signals.positions == 1.0],
'^', markersize=10, color='m')
# Plot the "sell" trades against AMZN
ax1.plot(signals.ix[signals.positions == -1.0].index,
signals.short_mavg[signals.positions == -1.0],
'v', markersize=10, color='k')
# Plot the equity curve in dollars
ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')
returns['total'].plot(ax=ax2, lw=2.)
# Plot the "buy" and "sell" trades against the equity curve
ax2.plot(returns.ix[signals.positions == 1.0].index,
returns.total[signals.positions == 1.0],
'^', markersize=10, color='m')
ax2.plot(returns.ix[signals.positions == -1.0].index,
returns.total[signals.positions == -1.0],
'v', markersize=10, color='k')
# Plot the figure
fig.show()
