Martin Molinero
1d43dcd601
- Adding `BaseData.AdjustResolution()` that should return a valid resolution for the given data and security type. This allows us to set a limitation which is useful to avoid invalid data requests or unnecessary fill forward situations. The user will be notified through a console message. - Adding unit and regression test - Updating example algorithms custom data resolution - Some performance improvements. Wont change console color if `SelectedOptimization` is defined
77 lines
3.2 KiB
Python
77 lines
3.2 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.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.SEC import *
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from QuantConnect.Data.UniverseSelection import *
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class SECReport8KAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2019, 1, 1)
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self.SetEndDate(2019, 8, 21)
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self.SetCash(100000)
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self.UniverseSettings.Resolution = Resolution.Minute
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self.AddUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelector))
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# Request underlying equity data.
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ibm = self.AddEquity("IBM", Resolution.Minute).Symbol
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# Add news data for the underlying IBM asset
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earningsFiling = self.AddData(SECReport10Q, ibm, Resolution.Daily).Symbol
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# Request 120 days of history with the SECReport10Q IBM custom data Symbol
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history = self.History(SECReport10Q, earningsFiling, 120, Resolution.Daily)
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# Count the number of items we get from our history request
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self.Debug(f"We got {len(history)} items from our history request")
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def CoarseSelector(self, coarse):
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# Add SEC 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(SECReport8K, symbol)
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return symbols
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def OnData(self, data):
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# Store the symbols we want to long in a list
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# so that we can have an equal-weighted portfolio
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longEquitySymbols = []
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# Get all SEC data and loop over it
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for report in data.Get(SECReport8K).Values:
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# Get the length of all contents contained within the report
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reportTextLength = sum([len(i.Text) for i in report.Report.Documents])
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if reportTextLength > 20000:
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longEquitySymbols.append(report.Symbol.Underlying)
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for equitySymbol in longEquitySymbols:
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self.SetHoldings(equitySymbol, 1.0 / len(longEquitySymbols))
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def OnSecuritiesChanged(self, changes):
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for r in changes.RemovedSecurities:
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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(SECReport8K, r.Symbol, Market.USA))
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