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CustomSecurityInitializerAlgorithm.py
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94 lines (76 loc) · 3.98 KB
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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 clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Brokerages import *
from QuantConnect.Data import BaseData
from QuantConnect.Data.Market import *
from QuantConnect.Securities import *
### <summary>
### This algorithm shows how to set a custom security initializer.
### A security initializer is run immediately after a new security object
### has been created and can be used to security models and other settings,
### such as data normalization mode
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="securities and portfolio" />
### <meta name="tag" content="trading and orders" />
class CustomSecurityInitializerAlgorithm(QCAlgorithm):
def Initialize(self):
# set our initializer to our custom type
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
func_security_seeder = FuncSecuritySeeder(Func[Security, BaseData](self.custom_seed_function))
self.SetSecurityInitializer(CustomSecurityInitializer(self.BrokerageModel, func_security_seeder, DataNormalizationMode.Raw))
self.SetStartDate(2013,10,1)
self.SetEndDate(2013,11,1)
self.AddEquity("SPY", Resolution.Hour)
def OnData(self, data):
if not self.Portfolio.Invested:
self.SetHoldings("SPY", 1)
def custom_seed_function(self, security):
resolution = Resolution.Hour
df = self.History(security.Symbol, 1, resolution)
if df.empty:
return None
last_bar = df.unstack(level=0).iloc[-1]
date_time = last_bar.name.to_pydatetime()
open = last_bar.open.values[0]
high = last_bar.high.values[0]
low = last_bar.low.values[0]
close = last_bar.close.values[0]
volume = last_bar.volume.values[0]
return TradeBar(date_time, security.Symbol, open, high, low, close, volume, Extensions.ToTimeSpan(resolution))
class CustomSecurityInitializer(BrokerageModelSecurityInitializer):
'''Our custom initializer that will set the data normalization mode.
We sub-class the BrokerageModelSecurityInitializer so we can also
take advantage of the default model/leverage setting behaviors'''
def __init__(self, brokerageModel, securitySeeder, dataNormalizationMode):
'''Initializes a new instance of the CustomSecurityInitializer class with the specified normalization mode
brokerageModel -- The brokerage model used to get fill/fee/slippage/settlement models
securitySeeder -- The security seeder to be used
dataNormalizationMode -- The desired data normalization mode'''
self.base = BrokerageModelSecurityInitializer(brokerageModel, securitySeeder)
self.dataNormalizationMode = dataNormalizationMode
def Initialize(self, security):
'''Initializes the specified security by setting up the models
security -- The security to be initialized
seedSecurity -- True to seed the security, false otherwise'''
# first call the default implementation
self.base.Initialize(security)
# now apply our data normalization mode
security.SetDataNormalizationMode(self.dataNormalizationMode)