Michael Handschuh
18a559943e
- Adds log to display the python version the algorithm is using. - Fixes python algorithms that were failing because of small subtleties like leading zeroes. - Updates pythonnet with a version compiled with python 3.6 flags - Changes in DockerfileFoundation: we now use miniconda to manage the python environment. - Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and PyTorch/Pyro (#1385). - Changes readme in Algorithm.Python to show steps to install miniconda
77 lines
3.4 KiB
Python
77 lines
3.4 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 BaseData
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from QuantConnect.Data.Market import *
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from QuantConnect.Securities import *
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### <summary>
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### This algorithm shows how to set a custom security initializer.
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### A security initializer is run immediately after a new security object
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### has been created and can be used to security models and other settings,
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### such as data normalization mode
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="securities and portfolio" />
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### <meta name="tag" content="trading and orders" />
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class CustomSecurityInitializerAlgorithm(QCAlgorithm):
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def Initialize(self):
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# set our initializer to our custom type
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self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
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func_security_seeder = FuncSecuritySeeder(Func[Security, BaseData](self.GetLastKnownPrice))
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self.SetSecurityInitializer(CustomSecurityInitializer(self.BrokerageModel, func_security_seeder, DataNormalizationMode.Raw))
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self.SetStartDate(2013,10,1)
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self.SetEndDate(2013,11,1)
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self.AddEquity("SPY", Resolution.Hour)
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def OnData(self, data):
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if not self.Portfolio.Invested:
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self.SetHoldings("SPY", 1)
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class CustomSecurityInitializer(BrokerageModelSecurityInitializer):
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'''Our custom initializer that will set the data normalization mode.
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We sub-class the BrokerageModelSecurityInitializer so we can also
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take advantage of the default model/leverage setting behaviors'''
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def __init__(self, brokerageModel, securitySeeder, dataNormalizationMode):
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'''Initializes a new instance of the CustomSecurityInitializer class with the specified normalization mode
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brokerageModel -- The brokerage model used to get fill/fee/slippage/settlement models
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securitySeeder -- The security seeder to be used
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dataNormalizationMode -- The desired data normalization mode'''
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self.base = BrokerageModelSecurityInitializer(brokerageModel, securitySeeder)
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self.dataNormalizationMode = dataNormalizationMode
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def Initialize(self, security):
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'''Initializes the specified security by setting up the models
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security -- The security to be initialized
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seedSecurity -- True to seed the security, false otherwise'''
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# first call the default implementation
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self.base.Initialize(security)
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# now apply our data normalization mode
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security.SetDataNormalizationMode(self.dataNormalizationMode) |