Jared
75 lines
3.1 KiB
Python
75 lines
3.1 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.Algorithm")
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AddReference("QuantConnect.Common")
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from datetime import datetime, timedelta
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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.Algorithm.Framework.Selection import *
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from QuantConnect.Data import *
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from QuantConnect.Data.Custom.PsychSignal import *
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from QuantConnect.Data.UniverseSelection import *
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### <summary>
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### Momentum based strategy that follows bullish rated stocks
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### </summary>
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class PsychSignalSentimentAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2018, 3, 1)
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self.SetEndDate(2018, 10, 1)
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self.SetCash(100000)
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self.AddUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseUniverse))
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self.timeEntered = datetime(1, 1, 1)
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# You can use custom data with a universe of assets.
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def CoarseUniverse(self, coarse):
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if (self.Time - self.timeEntered) <= timedelta(days=10):
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return Universe.Unchanged
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# Ask for the universe like normal and then filter it
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symbols = [i.Symbol for i in coarse if i.HasFundamentalData and i.DollarVolume > 50000000][:20]
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# Add the custom data to the underlying security.
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for symbol in symbols:
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self.AddData(PsychSignalSentiment, symbol)
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return symbols
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def OnData(self, data):
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# Scan our last time traded to prevent churn.
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if (self.Time - self.timeEntered) <= timedelta(days=10):
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return
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# Fetch the PsychSignal data for the active securities and trade on any
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for security in self.ActiveSecurities.Values:
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tweets = security.Data.GetAll(PsychSignalSentiment)
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for sentiment in tweets:
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if sentiment.BullIntensity > 2.0 and sentiment.BullScoredMessages > 3:
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self.SetHoldings(sentiment.Symbol.Underlying, 0.05)
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self.timeEntered = self.Time
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# When adding custom data from a universe we should also remove the data afterwards.
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def OnSecuritiesChanged(self, changes):
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# Make sure to filter out other security removals (i.e. custom data)
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for r in [i for i in changes.RemovedSecurities if i.Symbol.SecurityType == SecurityType.Equity]:
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self.Liquidate(r.Symbol)
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# Remove the custom data from our algorithm and collection
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self.RemoveSecurity(Symbol.CreateBase(PsychSignalSentiment, r.Symbol, Market.USA))
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