Martin Molinero
98 lines
4.5 KiB
Python
98 lines
4.5 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("System")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Algorithm.Framework")
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from QuantConnect.Data.UniverseSelection import *
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from QuantConnect.Indicators import ExponentialMovingAverage
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from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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class EmaCrossUniverseSelectionModel(FundamentalUniverseSelectionModel):
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'''Provides an implementation of FundamentalUniverseSelectionModel that subscribes to
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symbols with the larger delta by percentage between the two exponential moving average'''
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def __init__(self,
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fastPeriod = 100,
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slowPeriod = 300,
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universeCount = 500,
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universeSettings = None,
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securityInitializer = None):
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'''Initializes a new instance of the EmaCrossUniverseSelectionModel class
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Args:
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fastPeriod: Fast EMA period
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slowPeriod: Slow EMA period
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universeCount: Maximum number of members of this universe selection
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universeSettings: The settings used when adding symbols to the algorithm, specify null to use algorthm.UniverseSettings
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securityInitializer: Optional security initializer invoked when creating new securities, specify null to use algorithm.SecurityInitializer'''
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super().__init__(False, universeSettings, securityInitializer)
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self.fastPeriod = fastPeriod
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self.slowPeriod = slowPeriod
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self.universeCount = universeCount
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self.tolerance = 0.01
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# holds our coarse fundamental indicators by symbol
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self.averages = {}
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def SelectCoarse(self, algorithm, coarse):
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'''Defines the coarse fundamental selection function.
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Args:
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algorithm: The algorithm instance
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coarse: The coarse fundamental data used to perform filtering</param>
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Returns:
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An enumerable of symbols passing the filter'''
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filtered = []
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for cf in coarse:
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if cf.Symbol not in self.averages:
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self.averages[cf.Symbol] = self.SelectionData(cf.Symbol, self.fastPeriod, self.slowPeriod)
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# grab th SelectionData instance for this symbol
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avg = self.averages.get(cf.Symbol)
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# Update returns true when the indicators are ready, so don't accept until they are
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# and only pick symbols who have their fastPeriod-day ema over their slowPeriod-day ema
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if avg.Update(cf.EndTime, cf.AdjustedPrice) and avg.Fast > avg.Slow * (1 + self.tolerance):
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filtered.append(avg)
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# prefer symbols with a larger delta by percentage between the two averages
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filtered = sorted(filtered, key=lambda avg: avg.ScaledDelta, reverse = True)
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# we only need to return the symbol and return 'universeCount' symbols
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return [x.Symbol for x in filtered[:self.universeCount]]
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# class used to improve readability of the coarse selection function
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class SelectionData:
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def __init__(self, symbol, fastPeriod, slowPeriod):
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self.Symbol = symbol
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self.FastEma = ExponentialMovingAverage(fastPeriod)
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self.SlowEma = ExponentialMovingAverage(slowPeriod)
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@property
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def Fast(self):
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return float(self.FastEma.Current.Value)
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@property
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def Slow(self):
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return float(self.SlowEma.Current.Value)
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# computes an object score of how much large the fast is than the slow
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@property
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def ScaledDelta(self):
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return (self.Fast - self.Slow) / ((self.Fast + self.Slow) / 2)
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# updates the EMAFast and EMASlow indicators, returning true when they're both ready
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def Update(self, time, value):
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return self.SlowEma.Update(time, value) & self.FastEma.Update(time, value) |