wehub-resource-sync
124 lines
5.7 KiB
Python
124 lines
5.7 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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#
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# Academic research suggests that stock market participants generally place their orders at the market open and close.
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# Intraday trading volume is J-Shaped, where the minimum trading volume of the day is during lunch-break. Stocks become
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# more volatile as order flow is reduced and tend to mean-revert during lunch-break.
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#
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# This alpha aims to capture the mean-reversion effect of ETFs during lunch-break by ranking 20 ETFs
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# on their return between the close of the previous day to 12:00 the day after and predicting mean-reversion
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# in price during lunch-break.
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#
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# Source: Lunina, V. (June 2011). The Intraday Dynamics of Stock Returns and Trading Activity: Evidence from OMXS 30 (Master's Essay, Lund University).
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# Retrieved from http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=1973850&fileOId=1973852
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#
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# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community and client funds can see an example of an alpha.
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#
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class MeanReversionLunchBreakAlpha(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2018, 1, 1)
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self.set_cash(100000)
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# Set zero transaction fees
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self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
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# Use Hourly Data For Simplicity
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self.universe_settings.resolution = Resolution.HOUR
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self.set_universe_selection(CoarseFundamentalUniverseSelectionModel(self.coarse_selection_function))
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# Use MeanReversionLunchBreakAlphaModel to establish insights
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self.set_alpha(MeanReversionLunchBreakAlphaModel())
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# Equally weigh securities in portfolio, based on insights
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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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# Sort the data by daily dollar volume and take the top '20' ETFs
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def coarse_selection_function(self, coarse):
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sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)
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filtered = [ x.symbol for x in sorted_by_dollar_volume if not x.has_fundamental_data ]
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return filtered[:20]
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class MeanReversionLunchBreakAlphaModel(AlphaModel):
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'''Uses the price return between the close of previous day to 12:00 the day after to
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predict mean-reversion of stock price during lunch break and creates direction prediction
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for insights accordingly.'''
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def __init__(self, *args, **kwargs):
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lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
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self.resolution = Resolution.HOUR
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self.prediction_interval = Time.multiply(Extensions.to_time_span(self.resolution), lookback)
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self._symbol_data_by_symbol = dict()
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def update(self, algorithm, data):
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for symbol, symbol_data in self._symbol_data_by_symbol.items():
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if data.bars.contains_key(symbol):
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bar = data.bars.get(symbol)
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symbol_data.update(bar.end_time, bar.close)
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return [] if algorithm.time.hour != 12 else \
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[x.insight for x in self._symbol_data_by_symbol.values()]
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def on_securities_changed(self, algorithm, changes):
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for security in changes.removed_securities:
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self._symbol_data_by_symbol.pop(security.symbol, None)
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# Retrieve price history for all securities in the security universe
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# and update the indicators in the SymbolData object
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symbols = [x.symbol for x in changes.added_securities]
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history = algorithm.history(symbols, 1, self.resolution)
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if history.empty:
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algorithm.debug(f"No data on {algorithm.time}")
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return
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history = history.close.unstack(level = 0)
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for ticker, values in history.items():
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symbol = next((x for x in symbols if str(x) == ticker ), None)
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if symbol in self._symbol_data_by_symbol or symbol is None: continue
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self._symbol_data_by_symbol[symbol] = self.SymbolData(symbol, self.prediction_interval)
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self._symbol_data_by_symbol[symbol].update(values.index[0], values[0])
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class SymbolData:
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def __init__(self, symbol, period):
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self._symbol = symbol
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self.period = period
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# Mean value of returns for magnitude prediction
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self.mean_of_price_change = IndicatorExtensions.sma(RateOfChangePercent(1),3)
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# Price change from close price the previous day
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self.price_change = RateOfChangePercent(3)
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def update(self, time, value):
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return self.mean_of_price_change.update(time, value) and \
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self.price_change.update(time, value)
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@property
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def insight(self):
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direction = InsightDirection.DOWN if self.price_change.current.value > 0 else InsightDirection.UP
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margnitude = abs(self.mean_of_price_change.current.value)
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return Insight.price(self._symbol, self.period, direction, margnitude, None)
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