chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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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 *
+
+### <summary>
+### Demonstration of payments for cash dividends in backtesting. When data normalization mode is set
+### to "Raw" the dividends are paid as cash directly into your portfolio.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="data event handlers" />
+### <meta name="tag" content="dividend event" />
+class DividendAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+        self.set_start_date(1998,1,1)  #Set Start Date
+        self.set_end_date(2006,1,21)    #Set End Date
+        self.set_cash(100000)           #Set Strategy Cash
+        # Find more symbols here: http://quantconnect.com/data
+        equity = self.add_equity("MSFT", Resolution.DAILY)
+        equity.set_data_normalization_mode(DataNormalizationMode.RAW)
+
+        # this will use the Tradier Brokerage open order split behavior
+        # forward split will modify open order to maintain order value
+        # reverse split open orders will be cancelled
+        self.set_brokerage_model(BrokerageName.TRADIER_BROKERAGE)
+
+
+    def on_data(self, data):
+        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
+        bar = data["MSFT"]
+        if self.transactions.orders_count == 0:
+            self.set_holdings("MSFT", .5)
+            # place some orders that won't fill, when the split comes in they'll get modified to reflect the split
+            quantity = self.calculate_order_quantity("MSFT", .25)
+            self.debug(f"Purchased Stock: {bar.price}")
+            self.stop_market_order("MSFT", -quantity, bar.low/2)
+            self.limit_order("MSFT", -quantity, bar.high*2)
+
+        if data.dividends.contains_key("MSFT"):
+            dividend = data.dividends["MSFT"]
+            self.log(f"{self.time} >> DIVIDEND >> {dividend.symbol} - {dividend.distribution} - {self.portfolio.cash} - {self.portfolio['MSFT'].price}")
+
+        if data.splits.contains_key("MSFT"):
+            split = data.splits["MSFT"]
+            self.log(f"{self.time} >> SPLIT >> {split.symbol} - {split.split_factor} - {self.portfolio.cash} - {self.portfolio['MSFT'].price}")
+
+    def on_order_event(self, order_event):
+        # orders get adjusted based on split events to maintain order value
+        order = self.transactions.get_order_by_id(order_event.order_id)
+        self.log(f"{self.time} >> ORDER >> {order}")