Ricardo Andrés Marino Rojas
472f78cc53
Build & Test Lean / build (push) Has been cancelled
Regression Tests / build (push) Has been cancelled
* Remove Quandl from LEAN * Nit changes and CustomLiveDataFeedTests.cs * Resolve conflicts * Remove files related with Quandl * Fix bug * Fix QuantBookHistoryTests.cs * Fix bug * Fix bug * Fix unit tests * Try fix regression tests * Nit changes * Fix bug * Some of the requested changes * The missing changes * Requested changes * Nit changes * Revert "Nit changes" This reverts commit 9800bc5c34f3ac20e30bea7a92dd4a9867213bb5. * Nit changes * Fix bug * Requested changes * Missing file using Quandl to be removed * Nit changes * Not applied nit change * Nit change * Nit change * Add nasdaq-auth-code parameter in config.json * Remove 'quandl-auth-token' from config.json Co-authored-by: Martin-Molinero <martin@quantconnect.com>
64 lines
2.8 KiB
Python
64 lines
2.8 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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from HistoryAlgorithm import *
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### <summary>
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### The algorithm creates new indicator value with the existing indicator method by Indicator Extensions
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### Demonstration of using the external custom data to request the IBM and SPY daily data
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="custom data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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### <meta name="tag" content="plotting indicators" />
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### <meta name="tag" content="charting" />
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class CustomDataIndicatorExtensionsAlgorithm(QCAlgorithm):
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# Initialize the data and resolution you require for your strategy
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def Initialize(self):
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self.SetStartDate(2014,1,1)
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self.SetEndDate(2018,1,1)
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self.SetCash(25000)
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self.ibm = 'IBM'
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self.spy = 'SPY'
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# Define the symbol and "type" of our generic data
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self.AddData(CustomDataEquity, self.ibm, Resolution.Daily)
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self.AddData(CustomDataEquity, self.spy, Resolution.Daily)
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# Set up default Indicators, these are just 'identities' of the closing price
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self.ibm_sma = self.SMA(self.ibm, 1, Resolution.Daily)
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self.spy_sma = self.SMA(self.spy, 1, Resolution.Daily)
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# This will create a new indicator whose value is smaSPY / smaIBM
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self.ratio = IndicatorExtensions.Over(self.spy_sma, self.ibm_sma)
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# Plot indicators each time they update using the PlotIndicator function
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self.PlotIndicator("Ratio", self.ratio)
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self.PlotIndicator("Data", self.ibm_sma, self.spy_sma)
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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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def OnData(self, data):
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# Wait for all indicators to fully initialize
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if not (self.ibm_sma.IsReady and self.spy_sma.IsReady and self.ratio.IsReady): return
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if not self.Portfolio.Invested and self.ratio.Current.Value > 1:
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self.MarketOrder(self.ibm, 100)
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elif self.ratio.Current.Value < 1:
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self.Liquidate()
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