AlexCatarino
80 lines
3.5 KiB
Python
80 lines
3.5 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.Algorithm.Framework")
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from QuantConnect.Algorithm.Framework.Alphas import Insight
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class ConstantAlphaModel:
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''' Provides an implementation of IAlphaModel that always returns the same insight for each security'''
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def __init__(self, type, direction, period, magnitude, confidence):
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'''Initializes a new instance of the ConstantAlphaModel class
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Args:
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type: The type of insight
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direction: The direction of the insight
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period: The period over which the insight with come to fruition
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magnitude: The predicted change in magnitude as a +- percentage
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confidence: The confidence in the insight'''
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self.type = type
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self.direction = direction
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self.period = period
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self.magnitude = magnitude
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self.confidence = confidence
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self.securities = []
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self.insightsTimeBySymbol = {}
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def Update(self, algorithm, data):
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''' Creates a constant insight for each security as specified via the constructor
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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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for security in self.securities:
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if self.ShouldEmitInsight(algorithm.UtcTime, security.Symbol):
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yield Insight(security.Symbol, self.period, self.type, self.direction, self.magnitude, self.confidence)
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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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self.securities.append(added)
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# this will allow the insight to be re-sent when the security re-joins the universe
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for removed in changes.RemovedSecurities:
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if removed in self.securities:
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self.securities.remove(removed)
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if removed.Symbol in self.insightsTimeBySymbol:
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self.insightsTimeBySymbol.pop(removed.Symbol)
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def ShouldEmitInsight(self, utcTime, symbol):
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generatedTimeUtc = self.insightsTimeBySymbol.get(symbol)
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if generatedTimeUtc is not None:
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# we previously emitted a insight for this symbol, check it's period to see
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# if we should emit another insight
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if utcTime - generatedTimeUtc < self.period:
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return False
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# we either haven't emitted a insight for this symbol or the previous
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# insight's period has expired, so emit a new insight now for this symbol
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self.insightsTimeBySymbol[symbol] = utcTime
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return True |