Martin-Molinero
03f56481d4
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Python research import improvements - Improve start.py for research env - Remove unrequired imports * Centralize algorithm imports * Add regression test GH action * Unit test python import clean up * Join research and main imports * More python import clean up * Fix failing skipped regression algorithm
64 lines
2.8 KiB
Python
64 lines
2.8 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 AlgorithmImports import *
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import nltk
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# for details of NLTK, please visit https://www.nltk.org/index.html
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class NLTKSentimentTradingAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2018, 1, 1) # Set Start Date
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self.SetEndDate(2019, 1, 1) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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spy = self.AddEquity("SPY", Resolution.Minute)
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self.text = self.get_text() # Get custom text data for creating trading signals
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self.symbols = [spy.Symbol] # This can be extended to multiple symbols
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# for what extra models needed to download, please use code nltk.download()
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nltk.download('punkt')
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self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade)
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def Trade(self):
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current_time = f'{self.Time.year}-{self.Time.month}-{self.Time.day}'
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current_text = self.text.loc[current_time][0]
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words = nltk.word_tokenize(current_text)
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# users should decide their own positive and negative words
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positive_word = 'Up'
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negative_word = 'Down'
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for holding in self.Portfolio.Values:
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# liquidate if it contains negative words
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if negative_word in words and holding.Invested:
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self.Liquidate(holding.Symbol)
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# buy if it contains positive words
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if positive_word in words and not holding.Invested:
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self.SetHoldings(holding.Symbol, 1 / len(self.symbols))
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def get_text(self):
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# import custom data
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# Note: dl must be 1, or it will not download automatically
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url = 'https://www.dropbox.com/s/7xgvkypg6uxp6xl/EconomicNews.csv?dl=1'
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data = self.Download(url).split('\n')
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headline = [x.split(',')[1] for x in data][1:]
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date = [x.split(',')[0] for x in data][1:]
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# create a pd dataframe with 1st col being date and 2nd col being headline (content of the text)
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df = pd.DataFrame(headline, index = date, columns = ['headline'])
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return df
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