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
63 lines
3.2 KiB
Python
63 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 AlgorithmImports import *
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### <summary>
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### Demonstration of payments for cash dividends in backtesting. When data normalization mode is set
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### to "Raw" the dividends are paid as cash directly into your portfolio.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="data event handlers" />
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### <meta name="tag" content="dividend event" />
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class DividendAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(1998,1,1) #Set Start Date
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self.SetEndDate(2006,1,21) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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equity = self.AddEquity("MSFT", Resolution.Daily)
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equity.SetDataNormalizationMode(DataNormalizationMode.Raw)
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# this will use the Tradier Brokerage open order split behavior
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# forward split will modify open order to maintain order value
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# reverse split open orders will be cancelled
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self.SetBrokerageModel(BrokerageName.TradierBrokerage)
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def OnData(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
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bar = data["MSFT"]
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if self.Transactions.OrdersCount == 0:
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self.SetHoldings("MSFT", .5)
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# place some orders that won't fill, when the split comes in they'll get modified to reflect the split
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quantity = self.CalculateOrderQuantity("MSFT", .25)
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self.Debug(f"Purchased Stock: {bar.Price}")
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self.StopMarketOrder("MSFT", -quantity, bar.Low/2)
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self.LimitOrder("MSFT", -quantity, bar.High*2)
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if data.Dividends.ContainsKey("MSFT"):
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dividend = data.Dividends["MSFT"]
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self.Log(f"{self.Time} >> DIVIDEND >> {dividend.Symbol} - {dividend.Distribution} - {self.Portfolio.Cash} - {self.Portfolio['MSFT'].Price}")
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if data.Splits.ContainsKey("MSFT"):
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split = data.Splits["MSFT"]
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self.Log(f"{self.Time} >> SPLIT >> {split.Symbol} - {split.SplitFactor} - {self.Portfolio.Cash} - {self.Portfolio['MSFT'].Price}")
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def OnOrderEvent(self, orderEvent):
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# orders get adjusted based on split events to maintain order value
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order = self.Transactions.GetOrderById(orderEvent.OrderId)
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self.Log(f"{self.Time} >> ORDER >> {order}")
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