Jack Simonson
eb8f7dbc7f
Stumbled upon minor code changes to improve cleanliness and one that affects function
190 lines
7.7 KiB
Python
190 lines
7.7 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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'''
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The motivating idea for this Alpha Model is that a large price gap (here we use true outliers --
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price gaps that whose absolutely values are greater than 3 * Volatility) is due to rebound
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back to an appropriate price or at least retreat from its brief extreme. Using a Coarse Universe selection
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function, the algorithm selects the top x-companies by Dollar Volume (x can be any number you choose)
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to trade with, and then uses the Standard Deviation of the 100 most-recent closing prices to determine
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which price movements are outliers that warrant emitting insights.
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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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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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from QuantConnect.Data.Market import TradeBar
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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from QuantConnect.Algorithm.Framework.Selection import *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import PortfolioTarget, EqualWeightingPortfolioConstructionModel
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import numpy as np
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from datetime import timedelta, datetime
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class PriceGapMeanReversionAlpha(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2018, 1, 1) #Set Start Date
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self.SetCash(100000) #Set Strategy Cash
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## Initialize variables to be used in controlling frequency of universe selection
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self.week = None
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self.symbols = None
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self.SetWarmUp(100)
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## Manual Universe Selection
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction))
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## Set trading fees to $0
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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## Set custom Alpha Model
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self.SetAlpha(PriceGapMeanReversionAlphaModel())
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## Set equal-weighting Portfolio Construction Model
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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## Set Execution Model
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self.SetExecution(ImmediateExecutionModel())
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## Set Risk Management Model
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self.SetRiskManagement(NullRiskManagementModel())
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def CoarseSelectionFunction(self, coarse):
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## If it isn't a new week, return the same symbols
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current_week = self.Time.isocalendar()[1]
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if current_week == self.week:
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return self.symbols
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self.week = current_week
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## If its a new month, then re-filter stocks by Dollar Volume
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sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
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self.symbols = [ x.Symbol for x in sortedByDollarVolume[:25] ]
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return self.symbols
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class PriceGapMeanReversionAlphaModel:
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def __init__(self, *args, **kwargs):
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''' Initialize variables and dictionary for Symbol Data to support algorithm's function '''
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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.ToTimeSpan(self.resolution), 5) ## Arbitrary
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self.symbolDataBySymbol = {}
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def Update(self, algorithm, data):
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insights = []
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## Loop through all Symbol Data objects
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for symbol, symbolData in self.symbolDataBySymbol.items():
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if symbol not in data.Keys: ## Skip this slice if the data dictionary doesn't contain the symbol
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continue
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security = algorithm.Securities[symbol]
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## Update the symbolData properties
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if not symbolData.Update(data, security): return insights
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## Evaluate whether or not the price jump is expected to rebound up or return down, and emit insights accordingly
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if symbolData.DownTrend:
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insights.append(Insight(symbol, self.prediction_interval, InsightType.Price, InsightDirection.Down, symbolData.PriceJump, None))
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elif symbolData.UpTrend:
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insights.append(Insight(symbol, self.prediction_interval, InsightType.Price, InsightDirection.Up, symbolData.PriceJump, None))
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return insights
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def OnSecuritiesChanged(self, algorithm, changes):
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for security in changes.RemovedSecurities:
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if security.Symbol in self.symbolDataBySymbol.keys():
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self.symbolDataBySymbol.pop(security.Symbol)
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algorithm.Log(f'{security.Symbol.Value} removed from Universe')
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history_request_symbols = [ x.Symbol for x in changes.AddedSecurities ]
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history_df = algorithm.History(history_request_symbols, 100, self.resolution)
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for security in changes.AddedSecurities:
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algorithm.Log(f'{security.Symbol.Value} added to Universe')
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if str(security.Symbol) not in history_df.index.get_level_values(0):
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continue
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history = history_df.loc[str(security.Symbol)]
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## Create and initialize SymbolData objects
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symbolData = SymbolData(algorithm, security)
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self.symbolDataBySymbol[security.Symbol] = symbolData
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for tuple in history.itertuples():
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bar = TradeBar(tuple.Index, security.Symbol, tuple.open, tuple.high, tuple.low, tuple.close, tuple.volume)
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symbolData.Initialize(bar, security)
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class SymbolData:
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def __init__(self, algorithm, security):
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self.symbol = security.Symbol
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self.close = 0
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self.last_price = 0
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self.volatility = algorithm.STD(self.symbol, 100)
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self.price_jump = 0
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def Update(self, data, security):
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## Check for any data events that would return a NoneBar in the Alpha Model Update() method
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if not data.Bars.ContainsKey(self.symbol) or data.Bars[self.symbol].Close == 0:
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return False
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price = data.Bars[self.symbol].Close
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self.last_price = self.close
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self.close = price
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self.price_jump = (self.close / self.last_price) - 1
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return True
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def Initialize(self, data, security):
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self.volatility.Update(data.Time, data.Close)
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price = data.Close
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if self.last_price == 0:
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self.last_price = price
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self.close = price
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else:
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self.last_price = self.close
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self.close = price
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@property
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def PriceJump(self):
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return (self.close / self.last_price) - 1
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@property
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def DownTrend(self):
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return (abs(100*self.price_jump) > 3*self.volatility.Current.Value) and (self.price_jump > 0)
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@property
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def UpTrend(self):
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return (abs(100*self.price_jump) > 3*self.volatility.Current.Value) and (self.price_jump < 0) |