Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
76 lines
3.6 KiB
Python
76 lines
3.6 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Brokerages import *
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from QuantConnect.Data import *
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from QuantConnect.Data.Market import *
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from QuantConnect.Orders 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("Purchased Stock: {0}".format(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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for kvp in data.Dividends: # update this to Dividends dictionary
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symbol = kvp.Key
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value = kvp.Value.Distribution
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self.Log("{0} >> DIVIDEND >> {1} - {2} - {3} - {4}".format(self.Time, symbol, value, self.Portfolio.Cash, self.Portfolio["MSFT"].Price))
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for kvp in data.Splits: # update this to Splits dictionary
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symbol = kvp.Key
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value = kvp.Value.SplitFactor
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self.Log("{0} >> SPLIT >> {1} - {2} - {3} - {4}".format(self.Time, symbol, value, self.Portfolio.Cash, self.Portfolio["MSFT"].Quantity))
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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("{0} >> ORDER >> {1}".format(self.Time, order)) |