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.
81 lines
3.9 KiB
Python
81 lines
3.9 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.Orders import *
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### <summary>
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### Demonstrate the usage of the BrokerageModel property to help improve backtesting
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### accuracy through simulation of a specific brokerage's rules around restrictions
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### on submitting orders as well as fee structure.
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### </summary>
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### <meta name="tag" content="trading and orders" />
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### <meta name="tag" content="brokerage models" />
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class BrokerageModelAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetCash(100000) # Set Strategy Cash
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self.SetStartDate(2013,10,7) # Set Start Date
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self.SetEndDate(2013,10,11) # Set End Date
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self.AddEquity("SPY", Resolution.Second)
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# there's two ways to set your brokerage model. The easiest would be to call
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# SetBrokerageModel( BrokerageName ); // BrokerageName is an enum
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# SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage);
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# SetBrokerageModel(BrokerageName.Default);
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# the other way is to call SetBrokerageModel( IBrokerageModel ) with your
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# own custom model. I've defined a simple extension to the default brokerage
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# model to take into account a requirement to maintain 500 cash in the account at all times
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self.SetBrokerageModel(MinimumAccountBalanceBrokerageModel(self,500.00))
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self.last = 1
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def OnData(self, slice):
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# Simple buy and hold template
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if not self.Portfolio.Invested:
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self.SetHoldings("SPY", self.last)
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if self.Portfolio["SPY"].Quantity == 0:
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# each time we fail to purchase we'll decrease our set holdings percentage
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self.Debug(str(self.Time) + " - Failed to purchase stock")
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self.last *= 0.95
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else:
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self.Debug("{} - Purchased Stock @ SetHoldings( {} )".format(self.Time, self.last))
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class MinimumAccountBalanceBrokerageModel(DefaultBrokerageModel):
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'''Custom brokerage model that requires clients to maintain a minimum cash balance'''
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def __init__(self, algorithm, minimumAccountBalance):
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self.algorithm = algorithm
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self.minimumAccountBalance = minimumAccountBalance
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def CanSubmitOrder(self,security, order, message):
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'''Prevent orders which would bring the account below a minimum cash balance'''
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message = None
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# we want to model brokerage requirement of minimumAccountBalance cash value in account
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orderCost = order.GetValue(security)
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cash = self.algorithm.Portfolio.Cash
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cashAfterOrder = cash - orderCost
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if cashAfterOrder < self.minimumAccountBalance:
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# return a message describing why we're not allowing this order
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message = BrokerageMessageEvent(BrokerageMessageType.Warning, "InsufficientRemainingCapital", "Account must maintain a minimum of ${0} USD at all times. Order ID: {1}".format(self.minimumAccountBalance, order.Id))
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self.algorithm.Error(str(message))
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return False
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return True |