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Copy pathSECReport8KAlgorithm.py
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76 lines (61 loc) · 3.18 KB
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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework.Selection import *
from QuantConnect.Data.Custom.SEC import *
from QuantConnect.Data.UniverseSelection import *
class SECReport8KAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2019, 8, 21)
self.SetCash(100000)
self.UniverseSettings.Resolution = Resolution.Minute
self.AddUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelector))
# Request underlying equity data.
ibm = self.AddEquity("IBM", Resolution.Minute).Symbol
# Add news data for the underlying IBM asset
earningsFiling = self.AddData(SECReport10Q, ibm, Resolution.Daily).Symbol
# Request 120 days of history with the SECReport10Q IBM custom data Symbol
history = self.History(SECReport10Q, earningsFiling, 120, Resolution.Daily)
# Count the number of items we get from our history request
self.Debug(f"We got {len(history)} items from our history request")
def CoarseSelector(self, coarse):
# Add SEC data from the filtered coarse selection
symbols = [i.Symbol for i in coarse if i.HasFundamentalData and i.DollarVolume > 50000000][:10]
for symbol in symbols:
self.AddData(SECReport8K, symbol)
return symbols
def OnData(self, data):
# Store the symbols we want to long in a list
# so that we can have an equal-weighted portfolio
longEquitySymbols = []
# Get all SEC data and loop over it
for report in data.Get(SECReport8K).Values:
# Get the length of all contents contained within the report
reportTextLength = sum([len(i.Text) for i in report.Report.Documents])
if reportTextLength > 20000:
longEquitySymbols.append(report.Symbol.Underlying)
for equitySymbol in longEquitySymbols:
self.SetHoldings(equitySymbol, 1.0 / len(longEquitySymbols))
def OnSecuritiesChanged(self, changes):
for r in changes.RemovedSecurities:
# If removed from the universe, liquidate and remove the custom data from the algorithm
self.Liquidate(r.Symbol)
self.RemoveSecurity(Symbol.CreateBase(SECReport8K, r.Symbol, Market.USA))