mirror of
https://github.com/wassname/catalyst.git
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Eddie Hebert
132 lines
4.7 KiB
Python
Executable File
132 lines
4.7 KiB
Python
Executable File
#!/usr/bin/python
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#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import matplotlib.pyplot as plt
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import numpy as np
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import statsmodels.api as sm
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from zipline.algorithm import TradingAlgorithm
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from zipline.transforms import batch_transform
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from zipline.utils.factory import load_from_yahoo
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@batch_transform
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def ols_transform(data, sid1, sid2):
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"""Computes regression coefficient (slope and intercept)
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via Ordinary Least Squares between two SIDs.
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"""
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p0 = data.price[sid1]
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p1 = sm.add_constant(data.price[sid2])
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slope, intercept = sm.OLS(p0, p1).fit().params
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return slope, intercept
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class Pairtrade(TradingAlgorithm):
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"""Pairtrading relies on cointegration of two stocks.
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The expectation is that once the two stocks drifted apart
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(i.e. there is spread), they will eventually revert again. Thus,
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if we short the upward drifting stock and long the downward
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drifting stock (in short, we buy the spread) once the spread
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widened we can sell the spread with profit once they converged
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again. A nice property of this algorithm is that we enter the
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market in a neutral position.
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This specific algorithm tries to exploit the cointegration of
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Pepsi and Coca Cola by estimating the correlation between the
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two. Divergence of the spread is evaluated by z-scoring.
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"""
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def initialize(self, window_length=100):
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self.spreads = []
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self.zscores = []
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self.invested = 0
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self.window_length = window_length
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self.ols_transform = ols_transform(refresh_period=self.window_length,
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window_length=self.window_length)
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def handle_data(self, data):
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######################################################
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# 1. Compute regression coefficients between PEP and KO
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params = self.ols_transform.handle_data(data, 'PEP', 'KO')
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if params is None:
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return
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slope, intercept = params
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######################################################
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# 2. Compute spread and zscore
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zscore = self.compute_zscore(data, slope, intercept)
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self.zscores.append(zscore)
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######################################################
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# 3. Place orders
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self.place_orders(data, zscore)
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def compute_zscore(self, data, slope, intercept):
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"""1. Compute the spread given slope and intercept.
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2. zscore the spread.
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"""
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spread = (data['PEP'].price - (slope * data['KO'].price + intercept))
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self.spreads.append(spread)
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spread_wind = self.spreads[-self.window_length:]
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zscore = (spread - np.mean(spread_wind)) / np.std(spread_wind)
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return zscore
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def place_orders(self, data, zscore):
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"""Buy spread if zscore is > 2, sell if zscore < .5.
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"""
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if zscore >= 2.0 and not self.invested:
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self.order('PEP', int(100 / data['PEP'].price))
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self.order('KO', -int(100 / data['KO'].price))
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self.invested = True
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elif zscore <= -2.0 and not self.invested:
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self.order('KO', -int(100 / data['KO'].price))
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self.order('PEP', int(100 / data['PEP'].price))
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self.invested = True
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elif abs(zscore) < .5 and self.invested:
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self.sell_spread()
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self.invested = False
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def sell_spread(self):
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"""
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decrease exposure, regardless of position long/short.
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buy for a short position, sell for a long.
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"""
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ko_amount = self.portfolio.positions['KO'].amount
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self.order('KO', -1 * ko_amount)
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pep_amount = self.portfolio.positions['PEP'].amount
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self.order('PEP', -1 * pep_amount)
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if __name__ == '__main__':
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data = load_from_yahoo(stocks=['PEP', 'KO'], indexes={})
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pairtrade = Pairtrade()
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results = pairtrade.run(data)
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data['spreads'] = np.nan
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data.spreads[pairtrade.window_length:] = pairtrade.spreads
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ax1 = plt.subplot(211)
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data[['PEP', 'KO']].plot(ax=ax1)
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plt.ylabel('price')
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plt.setp(ax1.get_xticklabels(), visible=False)
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ax2 = plt.subplot(212, sharex=ax1)
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data.spreads.plot(ax=ax2, color='r')
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plt.ylabel('spread')
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plt.show()
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