chore: import upstream snapshot with attribution
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wehub-resource-sync
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# 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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#
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# A number of companies publicly trade two different classes of shares
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# in US equity markets. If both assets trade with reasonable volume, then
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# the underlying driving forces of each should be similar or the same. Given
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# this, we can create a relatively dollar-neutral long/short portfolio using
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# the dual share classes. Theoretically, any deviation of this portfolio from
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# its mean-value should be corrected, and so the motivating idea is based on
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# mean-reversion. Using a Simple Moving Average indicator, we can
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# compare the value of this portfolio against its SMA and generate insights
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# to buy the under-valued symbol and sell the over-valued symbol.
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#
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# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
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# sourced so the community and client funds can see an example of an alpha.
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#
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class ShareClassMeanReversionAlpha(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2019, 1, 1) #Set Start Date
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self.set_cash(100000) #Set Strategy Cash
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self.set_warm_up(20)
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## Setup Universe settings and tickers to be used
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tickers = ['VIA','VIAB']
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self.universe_settings.resolution = Resolution.MINUTE
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symbols = [ Symbol.create(ticker, SecurityType.EQUITY, Market.USA) for ticker in tickers]
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self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0))) ## Set $0 fees to mimic High-Frequency Trading
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## Set Manual Universe Selection
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self.set_universe_selection( ManualUniverseSelectionModel(symbols) )
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## Set Custom Alpha Model
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self.set_alpha(ShareClassMeanReversionAlphaModel(tickers = tickers))
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## Set Equal Weighting Portfolio Construction Model
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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## Set Immediate Execution Model
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self.set_execution(ImmediateExecutionModel())
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## Set Null Risk Management Model
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self.set_risk_management(NullRiskManagementModel())
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class ShareClassMeanReversionAlphaModel(AlphaModel):
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''' Initialize helper variables for the algorithm'''
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def __init__(self, *args, **kwargs):
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self.sma = SimpleMovingAverage(10)
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self.position_window = RollingWindow(2)
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self.alpha = None
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self.beta = None
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if 'tickers' not in kwargs:
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raise AssertionError('ShareClassMeanReversionAlphaModel: Missing argument: "tickers"')
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self.tickers = kwargs['tickers']
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self.position_value = None
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self.invested = False
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self.liquidate = 'liquidate'
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self.long_symbol = self.tickers[0]
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self.short_symbol = self.tickers[1]
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self.resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.MINUTE
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self.prediction_interval = Time.multiply(Extensions.to_time_span(self.resolution), 5) ## Arbitrary
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self.insight_magnitude = 0.001
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def update(self, algorithm, data):
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insights = []
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## Check to see if either ticker will return a NoneBar, and skip the data slice if so
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for security in algorithm.securities:
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if self.data_event_occured(data, security.key):
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return insights
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## If Alpha and Beta haven't been calculated yet, then do so
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if (self.alpha is None) or (self.beta is None):
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self.calculate_alpha_beta(algorithm, data)
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algorithm.log('Alpha: ' + str(self.alpha))
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algorithm.log('Beta: ' + str(self.beta))
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## If the SMA isn't fully warmed up, then perform an update
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if not self.sma.is_ready:
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self.update_indicators(data)
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return insights
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## Update indicator and Rolling Window for each data slice passed into Update() method
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self.update_indicators(data)
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## Check to see if the portfolio is invested. If no, then perform value comparisons and emit insights accordingly
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if not self.invested:
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if self.position_value >= self.sma.current.value:
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insights.append(Insight(self.long_symbol, self.prediction_interval, InsightType.PRICE, InsightDirection.DOWN, self.insight_magnitude, None))
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insights.append(Insight(self.short_symbol, self.prediction_interval, InsightType.PRICE, InsightDirection.UP, self.insight_magnitude, None))
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## Reset invested boolean
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self.invested = True
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elif self.position_value < self.sma.current.value:
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insights.append(Insight(self.long_symbol, self.prediction_interval, InsightType.PRICE, InsightDirection.UP, self.insight_magnitude, None))
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insights.append(Insight(self.short_symbol, self.prediction_interval, InsightType.PRICE, InsightDirection.DOWN, self.insight_magnitude, None))
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## Reset invested boolean
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self.invested = True
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## If the portfolio is invested and crossed back over the SMA, then emit flat insights
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elif self.invested and self.crossed_mean():
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## Reset invested boolean
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self.invested = False
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return Insight.group(insights)
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def data_event_occured(self, data, symbol):
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## Helper function to check to see if data slice will contain a symbol
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if data.splits.contains_key(symbol) or \
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data.dividends.contains_key(symbol) or \
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data.delistings.contains_key(symbol) or \
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data.symbol_changed_events.contains_key(symbol):
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return True
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def update_indicators(self, data):
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## Calculate position value and update the SMA indicator and Rolling Window
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self.position_value = (self.alpha * data[self.long_symbol].close) - (self.beta * data[self.short_symbol].close)
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self.sma.update(data[self.long_symbol].end_time, self.position_value)
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self.position_window.add(self.position_value)
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def crossed_mean(self):
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## Check to see if the position value has crossed the SMA and then return a boolean value
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if (self.position_window[0] >= self.sma.current.value) and (self.position_window[1] < self.sma.current.value):
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return True
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elif (self.position_window[0] < self.sma.current.value) and (self.position_window[1] >= self.sma.current.value):
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return True
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else:
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return False
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def calculate_alpha_beta(self, algorithm, data):
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## Calculate Alpha and Beta, the initial number of shares for each security needed to achieve a 50/50 weighting
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self.alpha = algorithm.calculate_order_quantity(self.long_symbol, 0.5)
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self.beta = algorithm.calculate_order_quantity(self.short_symbol, 0.5)
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