Zipline is an open-source algorithmic trading simulator.
The source can be found at: https://github.com/quantopian/zipline
Some benefits include:
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import pandas as pd
import pandas.io.data
import numpy as np
import pytz
from datetime import datetime
import zipline as zp
from zipline.finance.slippage import FixedSlippage
Load Apple (AAPL) historical prices from yahoo finance.
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start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
data = zp.utils.factory.load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
end=end, adjusted=False)
data.plot()
Define trading algorithm by inheriting from zipline.TradingAlgorithm.
We have to overwrite two methods:
initialize(): Called once before the beginning of the simulationhandle_data(data): Called for every bar. Data contains only information available at this point in time.Some commonly used attributes and methods include:
self.order(stock, amount): Order amount of stock's sharesself.run(data): Start the simulation. data can be a pandas.DataFrame that will be streamed through the algorithm and accessed there. It returns a DataFrame containing performance information of our algorithm.
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class BuyApple(zp.TradingAlgorithm): # inherit from TradingAlgorithm
def handle_data(self, data): # overload handle_data() method
self.order('AAPL', 1) # stock (='AAPL') to order and amount (=1 shares)
Instantiate class and call the .run() method to start the backtest.
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algo = BuyApple()
perf = algo.run(data)
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perf
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perf.portfolio_value.plot()
Broadly, algorithmic trading strategies come in two flavors:
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class DualMovingAverage(zp.TradingAlgorithm):
"""Dual Moving Average Crossover algorithm.
This algorithm buys apple once its short moving average crosses
its long moving average (indicating upwards momentum) and sells
its shares once the averages cross again (indicating downwards
momentum).
"""
def initialize(self, short_window=100, long_window=400):
# Add 2 mavg transforms, one with a long window, one
# with a short window.
self.add_transform(zp.transforms.MovingAverage, 'short_mavg', ['price'],
window_length=short_window)
self.add_transform(zp.transforms.MovingAverage, 'long_mavg', ['price'],
window_length=long_window)
# To keep track of whether we invested in the stock or not
self.invested = False
def handle_data(self, data):
short_mavg = data['AAPL'].short_mavg['price']
long_mavg = data['AAPL'].long_mavg['price']
buy = False
sell = False
if short_mavg > long_mavg and not self.invested:
self.order('AAPL', 100)
self.invested = True
buy = True
elif short_mavg < long_mavg and self.invested:
self.order('AAPL', -100)
self.invested = False
sell = True
self.record(short_mavg=short_mavg,
long_mavg=long_mavg,
buy=buy,
sell=sell)
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dma = DualMovingAverage()
perf = dma.run(data)
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fig = plt.figure()
ax1 = fig.add_subplot(211, ylabel='Price in $')
data['AAPL'].plot(ax=ax1, color='r', lw=2.)
perf[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
ax1.plot(perf.ix[perf.buy].index, perf.short_mavg[perf.buy],
'^', markersize=10, color='m')
ax1.plot(perf.ix[perf.sell].index, perf.short_mavg[perf.sell],
'v', markersize=10, color='k')
ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')
perf.portfolio_value.plot(ax=ax2, lw=2.)
ax2.plot(perf.ix[perf.buy].index, perf.portfolio_value[perf.buy],
'^', markersize=10, color='m')
ax2.plot(perf.ix[perf.sell].index, perf.portfolio_value[perf.sell],
'v', markersize=10, color='k')
plt.legend(loc=0)
plt.gcf().set_size_inches(14, 10)
Load stocks that are correlated (and hopefully cointegrated, which means that a linear combination of them will be mean-reverting).
Coca-Cola (KO) and Pepsi (PEP) is a good example as they are both in the same market segment and are both likely to be affected by the same events (e.g. increase of price of corn syrup).
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start = datetime(1997, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(1998, 6, 1, 0, 0, 0, 0, pytz.utc)
data = zp.utils.factory.load_from_yahoo(stocks=['PEP', 'KO'], indexes={},
start=start, end=end, adjusted=True)
The idea is that while neither stock price is mean-reverting, a linear combination of them will be.
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data[['PEP', 'KO']].plot()
plt.ylabel('price')
plt.figure()
integrated = data.PEP - data.KO
integrated.plot()
plt.ylabel('combined price')
plt.axhline(integrated.mean())
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plt.plot(data.PEP, data.KO, 'x')
plt.xlabel('PEP')
plt.ylabel('KO')
Normally, we'd run a statistical test for cointegration like the Dickey-Fuller test but we'll skip this here.
Now, we can exploit this mean-reversion property of the integrated series as follows: Assume that PEP and KO drift apart, as they do Nov 1997. If we assume that they are mean-reverting we expect this gap to close again as the stocks prices come back together (which they do by Jan 1998). In other words, during this time where the spread is large, we think that PEP is overvalued, while KO is undervalued. We thus short PEP with the expectation that it will go down, and long KO under the expectation that it will go up.
In this particular example it seems we can just subtract them but in reality it might be that the correlation is not 1. In our algorithm we thus estimate the regression between them in order to create a mean-reverting series using scipy.stats.linregress(price1, price2).
However, Zipline is designed in such a way that we ever only have access to the current price. We thus need a window of past events. The batch_transform gives us this functionality. It is a decorator for a function which will, behind the scenes, aggregate the data and pass a pandas Panel (a 3D DataFrame) into our a function.
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# Warning: This algorithm is written to illustrate the core idea of pairtrading.
# In reality, however, there are many more details we have to consider to make
# this actually work.
from zipline.transforms import batch_transform
from scipy import stats
@batch_transform
def regression_transform(data):
"""Computes regression coefficient (slope and intercept)
via Ordinary Least Squares between two SIDs.
"""
# data is now an array containg all past events up until now
pep_price = data.price['PEP']
ko_price = data.price['KO']
slope, intercept, _, _, _ = stats.linregress(pep_price, ko_price)
return intercept, slope
class Pairtrade(zp.TradingAlgorithm):
"""Pairtrading relies on cointegration of two stocks.
The expectation is that once the two stocks drifted apart
(i.e. there is spread), they will eventually revert again. Thus,
if we short the upward drifting stock and long the downward
drifting stock (in short, we buy the spread) once the spread
widened we can sell the spread with profit once they converged
again. A nice property of this algorithm is that we enter the
market in a neutral position.
This specific algorithm tries to exploit the cointegration of
Pepsi and Coca Cola by estimating the correlation between the
two. Divergence of the spread is evaluated by z-scoring.
"""
def initialize(self, window_length=100):
self.set_slippage(FixedSlippage())
self.invested = False
self.regression_transform = regression_transform(refresh_period=500,
window_length=window_length)
def handle_data(self, data):
# 1. Compute regression coefficients between PEP and KO
params = self.regression_transform.handle_data(data)
if params is None:
self.record(buy_spread=False, sell_spread=False)
return
intercept, slope = params
# 2. Compute spread
spread = (slope * data['PEP'].price + intercept) - data['KO'].price
buy_spread = False
sell_spread = False
# 3. Place orders
if spread >= 5.0 and not self.invested:
# Stocks drifted apart, long one, short the other
self.order('KO', 100)
self.order('PEP', -100)
self.invested = True
buy_spread = True
elif spread <= 2. and self.invested:
# The spread is closing again, exit positions.
# jQuery20309022272887534528_1427561821985?
# Profit!
self.order('KO', -100)
self.order('PEP', 100)
self.invested = False
sell_spread = True
# Record spread for later analysis
self.record(spread=spread,
buy_spread=buy_spread,
sell_spread=sell_spread)
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# Run strategy
pairtrade = Pairtrade()
results = pairtrade.run(data)
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ax1 = plt.subplot(311)
data[['PEP', 'KO']].plot(ax=ax1)
plt.ylabel('price')
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = plt.subplot(312, sharex=ax1)
results.spread.plot(ax=ax2, color='k')
ax2.axhline(2, color='k')
ax2.axhline(5, color='k')
plt.ylabel('spread')
plt.setp(ax2.get_xticklabels(), visible=False)
ax3 = plt.subplot(313, sharex=ax1)
results.portfolio_value.plot(ax=ax3, color='k')
plt.ylabel('portfolio value')
# Plot spread enter and exit markers
enter_pos = results.index[results.buy_spread]
exit_pos = results.index[results.sell_spread]
for ax in [ax1, ax2, ax3]:
ax.axvline(enter_pos, c='g')
ax.axvline(exit_pos, c='r')
plt.gcf().set_size_inches(16, 12)
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# Load Preis data
data = pd.read_csv('data/GoogleTrendsData.csv', index_col='Date', parse_dates=True)
data.index = data.index.tz_localize(pytz.UTC)
data.head()
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# window length of 3 as in original publication
delta_t = 3
class GoogleTrends(zp.TradingAlgorithm):
def initialize(self):
# Moving window. Defining maxlen causes the window length
# to remain constant as old events get dropped once full.
self.window = deque(maxlen=delta_t)
# Turns off slippage to simplify simulation.
self.set_slippage(FixedSlippage())
def handle_data(self, data):
window_full = len(self.window) == delta_t
# Extract debt search volume.
# 'price' is the default field used if no further information is supplied.
debt = data['debt']['price']
# Exit any prior positions
if 'djia' in self.portfolio.positions:
self.order('djia', -self.portfolio.positions['djia']['amount'])
# Moving average cross-over logic
if debt > np.mean(self.window) and window_full:
self.order('djia', -100)
elif debt < np.mean(self.window) and window_full:
self.order('djia', 100)
self.window.append(debt)