wehub-resource-sync
116 lines
5.4 KiB
Python
116 lines
5.4 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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class EmaCrossAlphaModel(AlphaModel):
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'''Alpha model that uses an EMA cross to create insights'''
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def __init__(self,
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fast_period = 12,
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slow_period = 26,
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resolution = Resolution.DAILY):
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'''Initializes a new instance of the EmaCrossAlphaModel class
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Args:
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fast_period: The fast EMA period
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slow_period: The slow EMA period'''
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self.fast_period = fast_period
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self.slow_period = slow_period
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self.resolution = resolution
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self.prediction_interval = Time.multiply(Extensions.to_time_span(resolution), fast_period)
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self.symbol_data_by_symbol = {}
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self.name = '{}({},{},{})'.format(self.__class__.__name__, fast_period, slow_period, resolution)
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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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insights = []
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for symbol, symbol_data in self.symbol_data_by_symbol.items():
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if symbol_data.fast.is_ready and symbol_data.slow.is_ready:
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if symbol_data.fast_is_over_slow:
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if symbol_data.slow > symbol_data.fast:
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insights.append(Insight.price(symbol_data.symbol, self.prediction_interval, InsightDirection.DOWN))
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elif symbol_data.slow_is_over_fast:
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if symbol_data.fast > symbol_data.slow:
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insights.append(Insight.price(symbol_data.symbol, self.prediction_interval, InsightDirection.UP))
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symbol_data.fast_is_over_slow = symbol_data.fast > symbol_data.slow
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return insights
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def on_securities_changed(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.added_securities:
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symbol_data = self.symbol_data_by_symbol.get(added.symbol)
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if symbol_data is None:
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symbol_data = SymbolData(added, self.fast_period, self.slow_period, algorithm, self.resolution)
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self.symbol_data_by_symbol[added.symbol] = symbol_data
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else:
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# a security that was already initialized was re-added, reset the indicators
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symbol_data.fast.reset()
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symbol_data.slow.reset()
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for removed in changes.removed_securities:
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data = self.symbol_data_by_symbol.pop(removed.symbol, None)
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if data is not None:
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# clean up our consolidators
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data.remove_consolidators()
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class SymbolData:
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'''Contains data specific to a symbol required by this model'''
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def __init__(self, security, fast_period, slow_period, algorithm, resolution):
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self.security = security
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self.symbol = security.symbol
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self.algorithm = algorithm
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self.fast_consolidator = algorithm.resolve_consolidator(security.symbol, resolution)
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self.slow_consolidator = algorithm.resolve_consolidator(security.symbol, resolution)
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algorithm.subscription_manager.add_consolidator(security.symbol, self.fast_consolidator)
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algorithm.subscription_manager.add_consolidator(security.symbol, self.slow_consolidator)
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# create fast/slow EMAs
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self.fast = ExponentialMovingAverage(security.symbol, fast_period, ExponentialMovingAverage.smoothing_factor_default(fast_period))
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self.slow = ExponentialMovingAverage(security.symbol, slow_period, ExponentialMovingAverage.smoothing_factor_default(slow_period))
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algorithm.register_indicator(security.symbol, self.fast, self.fast_consolidator);
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algorithm.register_indicator(security.symbol, self.slow, self.slow_consolidator);
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algorithm.warm_up_indicator(security.symbol, self.fast, resolution);
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algorithm.warm_up_indicator(security.symbol, self.slow, resolution);
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# True if the fast is above the slow, otherwise false.
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# This is used to prevent emitting the same signal repeatedly
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self.fast_is_over_slow = False
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def remove_consolidators(self):
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self.algorithm.subscription_manager.remove_consolidator(self.security.symbol, self.fast_consolidator)
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self.algorithm.subscription_manager.remove_consolidator(self.security.symbol, self.slow_consolidator)
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@property
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def slow_is_over_fast(self):
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return not self.fast_is_over_slow
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