Gerardo Salazar
a4d49c05ca
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Adds ETF(...) to UniverseDefinitions
* Adds ETF constituents universe framework regression algorithm
for C#/Python
* Address review: adds test cases for ticker/Symbol ETF universe additions
* Fixes bug where null Market would result in null dereference exception
* Address review: add missing Index tests
* Address review: don't hardcode market when creating constituent universe
* Uses Brokerage Model's default markets collection to determine
the market for the given security type
* Address review: restore QC500 and DollarVolume.Top(...)
* Restores algorithms related to both helper universe
definition methods
* Address review: remove copy to output directory for python algos
* Add example algorithms for ETF constituent universes using custom RSI alpha model
* Address review: adjust algorithm to use cache + algo RSI & clean up code
* Address review: make ETF Constituent RSI Alpha Model algo a regression test
* Address review: increase trade count and remove single trade logic
124 lines
5.1 KiB
Python
124 lines
5.1 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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import typing
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from AlgorithmImports import *
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from datetime import timedelta
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### <summary>
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### Example algorithm demonstrating the usage of the RSI indicator
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### in combination with ETF constituents data to replicate the weighting
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### of the ETF's assets in our own account.
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### </summary>
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class ETFConstituentUniverseRSIAlphaModelAlgorithm(QCAlgorithm):
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### <summary>
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### Initialize 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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### </summary>
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def Initialize(self):
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self.SetStartDate(2020, 12, 1)
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self.SetEndDate(2021, 1, 31)
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self.SetCash(100000)
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self.SetAlpha(ConstituentWeightedRsiAlphaModel())
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self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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spy = self.AddEquity("SPY", Resolution.Hour).Symbol
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# We load hourly data for ETF constituents in this algorithm
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self.UniverseSettings.Resolution = Resolution.Hour
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self.Settings.MinimumOrderMarginPortfolioPercentage = 0.01
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self.AddUniverse(self.Universe.ETF(spy, self.UniverseSettings, self.FilterETFConstituents))
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### <summary>
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### Filters ETF constituents
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### </summary>
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### <param name="constituents">ETF constituents</param>
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### <returns>ETF constituent Symbols that we want to include in the algorithm</returns>
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def FilterETFConstituents(self, constituents):
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return [i.Symbol for i in constituents if i.Weight is not None and i.Weight >= 0.001]
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### <summary>
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### Alpha model making use of the RSI indicator and ETF constituent weighting to determine
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### which assets we should invest in and the direction of investment
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### </summary>
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class ConstituentWeightedRsiAlphaModel(AlphaModel):
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def __init__(self, maxTrades=None):
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self.rsiSymbolData = {}
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def Update(self, algorithm: QCAlgorithm, data: Slice):
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algoConstituents = []
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for barSymbol in data.Bars.Keys:
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if not algorithm.Securities[barSymbol].Cache.HasData(ETFConstituentData):
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continue
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constituentData = algorithm.Securities[barSymbol].Cache.GetData[ETFConstituentData]()
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algoConstituents.append(constituentData)
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if len(algoConstituents) == 0 or len(data.Bars) == 0:
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# Don't do anything if we have no data we can work with
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return []
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constituents = {i.Symbol:i for i in algoConstituents}
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for bar in data.Bars.Values:
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if bar.Symbol not in constituents:
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# Dealing with a manually added equity, which in this case is SPY
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continue
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if bar.Symbol not in self.rsiSymbolData:
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# First time we're initializing the RSI.
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# It won't be ready now, but it will be
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# after 7 data points
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constituent = constituents[bar.Symbol]
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self.rsiSymbolData[bar.Symbol] = SymbolData(bar.Symbol, algorithm, constituent, 7)
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allReady = all([sd.rsi.IsReady for sd in self.rsiSymbolData.values()])
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if not allReady:
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# We're still warming up the RSI indicators.
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return []
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insights = []
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for symbol, symbolData in self.rsiSymbolData.items():
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averageLoss = symbolData.rsi.AverageLoss.Current.Value
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averageGain = symbolData.rsi.AverageGain.Current.Value
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# If we've lost more than gained, then we think it's going to go down more
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direction = InsightDirection.Down if averageLoss > averageGain else InsightDirection.Up
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# Set the weight of the insight as the weight of the ETF's
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# holding. The InsightWeightingPortfolioConstructionModel
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# will rebalance our portfolio to have the same percentage
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# of holdings in our algorithm that the ETF has.
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insights.append(Insight.Price(
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symbol,
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timedelta(days=1),
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direction,
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float(averageLoss if direction == InsightDirection.Down else averageGain),
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weight=float(symbolData.constituent.Weight)
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))
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return insights
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class SymbolData:
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def __init__(self, symbol, algorithm, constituent, period):
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self.Symbol = symbol
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self.constituent = constituent
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self.rsi = algorithm.RSI(symbol, period, MovingAverageType.Exponential, Resolution.Hour)
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