AlexCatarino
116 lines
5.2 KiB
Python
116 lines
5.2 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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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 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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from clr import AddReference
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Indicators")
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from QuantConnect import *
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from QuantConnect.Indicators import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from datetime import timedelta
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from enum import Enum
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class PairsTradingAlphaModel:
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'''This alpha model is designed to work against a single, predefined pair.
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This model generates alternating long ratio/short ratio insights emitted as a group'''
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class State(Enum):
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ShortRatio = -1
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FlatRatio = 0
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LongRatio = 1
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def __init__(self, asset1, asset2, threshold = 1):
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''' Initializes a new instance of the PairsTradingAlphaModel class
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Args:
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asset1: The first asset's symbol in the pair
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asset2: The second asset's symbol in the pair
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threshold: The percent [0, 100] deviation of the ratio from the mean before emitting an insight'''
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self.asset1 = asset1
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self.asset2 = asset2
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self.threshold = threshold
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self.state = self.State.FlatRatio
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self.asset1Price = None
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self.asset2Price = None
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self.ratio = None
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self.mean = None
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self.upperThreshold = None;
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self.lowerThreshold = None;
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self.Name = '{}({},{},{})'.format(self.__class__.__name__, asset1, asset2, Extensions.Normalize(threshold))
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def Update(self, algorithm, data):
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''' Updates this alpha model with the latest data from the algorithm.
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This is called each time the algorithm receives data for subscribed securities
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Args:
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algorithm: The algorithm instance
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data: The new data available
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Returns:
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The new insights generated'''
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if self.mean is None or not self.mean.IsReady:
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return []
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# don't re-emit the same direction
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if self.state is not self.State.LongRatio and self.ratio > self.upperThreshold:
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self.state = self.State.LongRatio
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# asset1/asset2 is more than 2 std away from mean, short asset1, long asset2
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shortAsset1 = Insight.Price(self.asset1, timedelta(minutes = 15), InsightDirection.Down)
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longAsset2 = Insight.Price(self.asset2, timedelta(minutes = 15), InsightDirection.Up)
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# creates a group id and set the GroupId property on each insight object
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Insight.Group(shortAsset1, longAsset2)
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return [shortAsset1, longAsset2]
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# don't re-emit the same direction
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if self.state is not self.State.ShortRatio and self.ratio < self.lowerThreshold:
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self.state = self.State.ShortRatio
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# asset1/asset2 is less than 2 std away from mean, long asset1, short asset2
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longAsset1 = Insight.Price(self.asset1, timedelta(minutes = 15), InsightDirection.Up)
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shortAsset2 = Insight.Price(self.asset2, timedelta(minutes = 15), InsightDirection.Down)
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# creates a group id and set the GroupId property on each insight object
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Insight.Group(longAsset1, shortAsset2)
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return [longAsset1, shortAsset2]
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return []
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def OnSecuritiesChanged(self, algorithm, changes):
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'''Event fired each time the we add/remove securities from the data feed.
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Args:
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algorithm: The algorithm instance that experienced the change in securities
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changes: The security additions and removals from the algorithm'''
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for added in changes.AddedSecurities:
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# this model is limitted to looking at a single pair of assets
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if added.Symbol != self.asset1 and added.Symbol != self.asset2:
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continue
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if added.Symbol == self.asset1:
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self.asset1Price = algorithm.Identity(added.Symbol)
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else:
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self.asset2Price = algorithm.Identity(added.Symbol)
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if self.ratio is None:
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# initialize indicators dependent on both assets
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if self.asset1Price is not None and self.asset2Price is not None:
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self.ratio = IndicatorExtensions.Over(self.asset1Price, self.asset2Price)
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self.mean = IndicatorExtensions.Of(ExponentialMovingAverage(500), self.ratio)
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upper = ConstantIndicator[IndicatorDataPoint]("ct", 1 + self.threshold / 100)
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self.upperThreshold = IndicatorExtensions.Times(self.mean, upper)
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lower = ConstantIndicator[IndicatorDataPoint]("ct", 1 - self.threshold / 100)
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self.lowerThreshold = IndicatorExtensions.Times(self.mean, lower) |