chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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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 AlgorithmImports import *
+
+import nltk
+# for details of NLTK, please visit https://www.nltk.org/index.html
+
+class NLTKSentimentTradingAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2018, 1, 1)  # Set Start Date
+        self.set_end_date(2019, 1, 1) # Set End Date
+        self.set_cash(100000)  # Set Strategy Cash
+
+        spy = self.add_equity("SPY", Resolution.MINUTE)
+        self.text = self.get_text() # Get custom text data for creating trading signals
+        self._symbols = [spy.symbol] # This can be extended to multiple symbols
+
+        # for what extra models needed to download, please use code nltk.download()
+        nltk.download('punkt')
+        self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.after_market_open("SPY", 30), self.trade)
+
+    def trade(self):
+        current_time = f'{self.time.year}-{self.time.month}-{self.time.day}'
+        current_text = self.text.loc[current_time][0]
+        words = nltk.word_tokenize(current_text)
+
+        # users should decide their own positive and negative words
+        positive_word = 'Up'
+        negative_word = 'Down'
+
+        for holding in self.portfolio.values():
+            # liquidate if it contains negative words
+            if negative_word in words and holding.invested:
+                self.liquidate(holding.symbol)
+
+            # buy if it contains positive words
+            if positive_word in words and not holding.invested:
+                self.set_holdings(holding.symbol, 1 / len(self._symbols))
+
+    def get_text(self):
+        # import custom data
+        # Note: dl must be 1, or it will not download automatically
+        url = 'https://www.dropbox.com/s/7xgvkypg6uxp6xl/EconomicNews.csv?dl=1'
+        data = self.download(url).split('\n')
+
+        headline = [x.split(',')[1] for x in data][1:]
+        date = [x.split(',')[0] for x in data][1:]
+
+        # create a pd dataframe with 1st col being date and 2nd col being headline (content of the text)
+        df = pd.DataFrame(headline, index = date, columns = ['headline'])
+        return df