AlexCatarino
9e6e2f9b22
In the Coarse Universe Selection of the following algorithms - ContingentClaimsAnalysisDefaultPredictionAlpha - GreenblattMagicFormulaAlpha - PriceGapMeanReversionAlpha - SykesShortMicroCapAlpha Universe.Unchanged is now used when the universe is not changed instead of saving a list of symbol and returning it. Other minor refactoring.
115 lines
5.0 KiB
Python
115 lines
5.0 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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from System import *
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from QuantConnect import *
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from QuantConnect.Data.UniverseSelection import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Portfolio import EqualWeightingPortfolioConstructionModel
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from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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class SykesShortMicroCapAlpha(QCAlgorithm):
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''' Alpha Streams: Benchmark Alpha: Identify "pumped" penny stocks and predict that the price of a "pumped" penny stock reverts to mean
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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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def Initialize(self):
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self.SetStartDate(2018, 1, 1)
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self.SetCash(100000)
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# Set zero transaction fees
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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# select stocks using PennyStockUniverseSelectionModel
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetUniverseSelection(PennyStockUniverseSelectionModel())
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# Use SykesShortMicroCapAlphaModel to establish insights
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self.SetAlpha(SykesShortMicroCapAlphaModel())
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# Equally weigh securities in portfolio, based on insights
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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# Set Immediate Execution Model
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self.SetExecution(ImmediateExecutionModel())
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# Set Null Risk Management Model
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self.SetRiskManagement(NullRiskManagementModel())
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class SykesShortMicroCapAlphaModel(AlphaModel):
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'''Uses ranking of intraday percentage difference between open price and close price to create magnitude and direction prediction for insights'''
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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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resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.Daily
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self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(resolution), lookback)
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self.numberOfStocks = kwargs['numberOfStocks'] if 'numberOfStocks' in kwargs else 10
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def Update(self, algorithm, data):
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insights = []
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symbolsRet = dict()
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for security in algorithm.ActiveSecurities.Values:
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if security.HasData:
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open = security.Open
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if open != 0:
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# Intraday price change for penny stocks
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symbolsRet[security.Symbol] = security.Close / open - 1
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# Rank penny stocks on one day price change and retrieve list of ten "pumped" penny stocks
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pumpedStocks = dict(sorted(symbolsRet.items(),
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key = lambda kv: (-round(kv[1], 6), kv[0]))[:self.numberOfStocks])
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# Emit "down" insight for "pumped" penny stocks
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for symbol, value in pumpedStocks.items():
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insights.append(Insight.Price(symbol, self.predictionInterval, InsightDirection.Down, abs(value), None))
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return insights
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class PennyStockUniverseSelectionModel(FundamentalUniverseSelectionModel):
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'''Defines a universe of penny stocks, as a universe selection model for the framework algorithm:
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The stocks must have fundamental data
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The stock must have positive previous-day close price
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The stock must have volume between $1000000 and $10000 on the previous trading day
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The stock must cost less than $5'''
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def __init__(self):
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super().__init__(False)
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# Number of stocks in Coarse Universe
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self.numberOfSymbolsCoarse = 500
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self.lastMonth = -1
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def SelectCoarse(self, algorithm, coarse):
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if algorithm.Time.month == self.lastMonth:
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return Universe.Unchanged
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self.lastMonth = algorithm.Time.month
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# sort the stocks by dollar volume and take the top 500
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top = sorted([x for x in coarse if x.HasFundamentalData
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and 5 > x.Price > 0
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and 1000000 > x.Volume > 10000],
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key=lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse]
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return [x.Symbol for x in top] |