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Implement Benzinga custom data as ToolBox application and BaseData class
1 parent dfbbf0a commit 629de49

14 files changed

Lines changed: 869 additions & 7 deletions

‎Algorithm.CSharp/AltData/BenzingaNewsAlgorithm.cs‎

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/*
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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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*/
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using System.Collections.Generic;
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using System.Linq;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Data;
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using QuantConnect.Data.Custom.Benzinga;
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using QuantConnect.Data.UniverseSelection;
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namespace QuantConnect.Algorithm.CSharp
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{
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public class BenzingaNewsAlgorithm : QCAlgorithm
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{
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// Predefine a dictionary of words with scores to scan for in the description
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// of the news article
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private readonly Dictionary<string, double> _words = new Dictionary<string, double>()
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{
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{"bad", -0.5}, {"good", 0.5},
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{"negative", -0.5}, {"great", 0.5},
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{"growth", 0.5}, {"fail", -0.5},
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{"failed", -0.5}, {"success", 0.5},
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{"nailed", 0.5}, {"beat", 0.5},
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{"missed", -0.5}, {"slipped", -0.5},
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{"outperforming", 0.5}, {"underperforming", -0.5},
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{"outperform", 0.5}, {"underperform", -0.5}
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};
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2018, 10, 12);
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SetEndDate(2018, 11, 25);
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SetCash(100000);
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UniverseSettings.Resolution = Resolution.Daily;
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AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
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}
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public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
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{
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// Add Benzinga news data from the filtered coarse selection
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var symbols = coarse.Where(x => x.HasFundamentalData && x.DollarVolume > 50000000)
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.Select(x => x.Symbol)
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.Take(10);
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foreach (var symbol in symbols)
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{
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AddData<BenzingaNews>(symbol);
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}
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return symbols;
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}
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public override void OnData(Slice data)
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{
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// Get all Benzinga data and loop over it
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foreach (var article in data.Get<BenzingaNews>().Values)
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{
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// Get the list of matching words we have sentiment definitions for
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var intersection = article.Contents.ToLowerInvariant()
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.Split(' ')
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.Intersect(_words.Keys);
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// Get the sentiment score
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var sentimentScore = intersection.Select(x => _words[x]).Sum();
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// Set holdings equal to 1/10th of the sentiment score we get
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SetHoldings(article.Symbol.Underlying, sentimentScore / 10);
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}
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}
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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foreach (var r in changes.RemovedSecurities.Where(x => x.Symbol.SecurityType == SecurityType.Equity))
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{
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// If removed from the universe, liquidate and remove the custom data from the algorithm
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Liquidate(r.Symbol);
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RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(BenzingaNews), r.Symbol, Market.USA));
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}
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}
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}
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}

‎Algorithm.CSharp/QuantConnect.Algorithm.CSharp.csproj‎

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<Compile Include="Alphas\TripleLeveragedETFPairVolatilityDecayAlpha.cs" />
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<Compile Include="Alphas\VixDualThrustAlpha.cs" />
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<Compile Include="AltData\CachedAlternativeDataAlgorithm.cs" />
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<Compile Include="AltData\BenzingaNewsAlgorithm.cs" />
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<Compile Include="AltData\SECReport8KAlgorithm.cs" />
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<Compile Include="AltData\SmartInsiderTransactionAlgorithm.cs" />
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<Compile Include="AltData\USTreasuryYieldCurveRateAlgorithm.cs" />
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<Target Name="AfterBuild">
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</Target>
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-->
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</Project>
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</Project>

‎Algorithm.Python/AltData/BenzingaNewsAlgorithm.py‎

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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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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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.Custom.Benzinga import *
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from QuantConnect.Data.UniverseSelection import *
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class BenzingaNewsAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.words = {
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"bad": -0.5, "good": 0.5,
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"negative": -0.5, "great": 0.5,
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"growth": 0.5, "fail": -0.5,
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"failed": -0.5, "success": 0.5,
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"nailed": 0.5, "beat": 0.5,
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"missed": -0.5, "slipped": -0.5,
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"outperforming": 0.5, "underperforming": -0.5,
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"outperform": 0.5, "underperform": -0.5
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}
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self.SetStartDate(2018, 10, 12)
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self.SetEndDate(2018, 11, 25)
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self.SetCash(100000)
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self.UniverseSettings.Resolution = Resolution.Daily
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self.AddUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelector))
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def CoarseSelector(self, coarse):
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# Add Benzinga news data from the filtered coarse selection
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symbols = [i.Symbol for i in coarse if i.HasFundamentalData and i.DollarVolume > 50000000][:10]
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for symbol in symbols:
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self.AddData(BenzingaNews, symbol)
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return symbols
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def OnData(self, data):
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for article in data.Get(BenzingaNews).Values:
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# Split the article into words in all lowercase
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articleWords = article.Contents.lower().split(" ")
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# Get the list of matching words we have sentiment definitions for
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intersection = set(self.words.keys()).intersection(articleWords)
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# Get the sentiment score
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sentimentScore = sum([self.words[i] for i in intersection])
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# Set holdings equal to 1/10th of the sentiment score we get
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self.SetHoldings(article.Symbol.Underlying, sentimentScore / 10.0)
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def OnSecuritiesChanged(self, changes):
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for r in [i for i in changes.RemovedSecurities if i.Symbol.SecurityType == SecurityType.Equity]:
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# If removed from the universe, liquidate and remove the custom data from the algorithm
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self.Liquidate(r.Symbol)
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self.RemoveSecurity(Symbol.CreateBase(BenzingaNews, r.Symbol, Market.USA))

‎Algorithm.Python/QuantConnect.Algorithm.Python.csproj‎

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<Content Include="Alphas\TripleLeverageETFPairVolatilityDecayAlpha.py" />
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<Content Include="Alphas\VIXDualThrustAlpha.py" />
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<Content Include="AltData\CachedAlternativeDataAlgorithm.py" />
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<Content Include="AltData\BenzingaNewsAlgorithm.py" />
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<Content Include="AltData\SECReport8KAlgorithm.py" />
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<Content Include="AltData\SmartInsiderTransactionAlgorithm.py" />
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<Content Include="AltData\USTreasuryYieldCurveRateAlgorithm.py" />
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<Target Name="AfterBuild">
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</Target>
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-->
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</Project>
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</Project>

‎Common/Data/Custom/Benzinga/BenzingaCategory.cs‎

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/*
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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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*/
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using Newtonsoft.Json;
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namespace QuantConnect.Data.Custom.Benzinga
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{
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/// <summary>
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/// News data powered by Benzinga
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/// </summary>
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public class BenzingaCategory
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{
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/// <summary>
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/// Source/Link associated with the article
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/// </summary>
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[JsonProperty("@domain")]
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public string Resource { get; set; }
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/// <summary>
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/// Description of the <see cref="Resource"/> property
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/// </summary>
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[JsonProperty("#text")]
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public string Description { get; set; }
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}
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}

‎Common/Data/Custom/Benzinga/BenzingaMetadata.cs‎

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/*
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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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*/
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using Newtonsoft.Json;
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namespace QuantConnect.Data.Custom.Benzinga
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{
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/// <summary>
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/// Article metadata
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/// </summary>
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public class BenzingaMetadata
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{
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/// <summary>
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/// Is the article for pro subscribers only
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/// </summary>
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[JsonProperty("@pro")]
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public bool IsPro { get; set; }
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/// <summary>
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/// Is the article a first run
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/// </summary>
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[JsonProperty("@firstrun")]
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public bool FirstRun { get; set; }
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/// <summary>
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/// Type of article
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/// </summary>
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[JsonProperty("#text")]
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public string Kind { get; set; }
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}
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}

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