wehub-resource-sync
90 lines
4.1 KiB
Python
90 lines
4.1 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 AlgorithmImports import *
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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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'''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 algorithm.UniverseSettings'''
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super().__init__(False, universeSettings)
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self.fast_period = fastPeriod
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self.slow_period = slowPeriod
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self.universe_count = 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 select_coarse(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]) -> list[Symbol]:
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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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fundamental: 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 fundamental:
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if cf.symbol not in self.averages:
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self.averages[cf.symbol] = self.SelectionData(cf.symbol, self.fast_period, self.slow_period)
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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.end_time, cf.adjusted_price) 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.scaled_delta, 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.universe_count]]
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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, fast_period, slow_period):
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self.symbol = symbol
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self.fast_ema = ExponentialMovingAverage(fast_period)
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self.slow_ema = ExponentialMovingAverage(slow_period)
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@property
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def fast(self):
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return float(self.fast_ema.current.value)
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@property
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def slow(self):
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return float(self.slow_ema.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 scaled_delta(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.slow_ema.update(time, value) & self.fast_ema.update(time, value)
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