AlexCatarino
04a529c758
- Removes `UniverseSettings.DataNormalizationMode` (it will ne addressed in a dedicated issue: https://github.com/QuantConnect/Lean/issues/3082) - Adds examples/tests for Tick resolution, Forex (QuoteBar data) and Custom data. - Tick resolution is not allowed: logs a message - Custom data example/test added in `CustomDataNiftyAlgorithm` - Adds support for ATR and VWAP since they are, respectively, a bar and a trade bar indicator. - Adds consolidators to handle difference between data resolution and indicator resolution.
95 lines
3.9 KiB
Python
95 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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AddReference("QuantConnect.Indicators")
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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.Indicators import *
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from QuantConnect.Data.UniverseSelection import *
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class SmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
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'''Provides an example where WarmUpIndicator method is used to warm up indicators
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after their security is added and before (Universe Selection scenario)'''
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count = 10;
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tolerance = 0.01
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targetPercent = 1 / count
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averages = dict()
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def Initialize(self):
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self.UniverseSettings.Leverage = 2
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2018, 1, 1)
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self.SetEndDate(2019, 1, 1)
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self.SetCash(1000000)
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self.EnableAutomaticIndicatorWarmUp = True
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ibm = self.AddEquity("IBM", Resolution.Tick).Symbol
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ibmSma = self.SMA(ibm, 40)
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self.Log(f"{ibmSma.Name}: {ibmSma.Current.Time} - {ibmSma}. IsReady? {ibmSma.IsReady}")
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spy = self.AddEquity("SPY", Resolution.Hour).Symbol
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spySma = self.SMA(spy, 10) # Data point indicator
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spyAtr = self.ATR(spy, 10,) # Bar indicator
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spyVwap = self.VWAP(spy, 10) # TradeBar indicator
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self.Log(f"SPY - Is ready? SMA: {spySma.IsReady}, ATR: {spyAtr.IsReady}, VWAP: {spyVwap.IsReady}")
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eur = self.AddForex("EURUSD", Resolution.Hour).Symbol
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eurSma = self.SMA(eur, 20, Resolution.Daily)
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eurAtr = self.ATR(eur, 20, MovingAverageType.Simple, Resolution.Daily)
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self.Log(f"EURUSD - Is ready? SMA: {eurSma.IsReady}, ATR: {eurAtr.IsReady}")
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self.AddUniverse(self.CoarseSmaSelector)
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# Since the indicators are ready, we will receive error messages
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# reporting that the algorithm manager is trying to add old information
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self.SetWarmUp(10)
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def CoarseSmaSelector(self, coarse):
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score = dict()
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for cf in coarse:
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if not cf.HasFundamentalData:
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continue
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symbol = cf.Symbol
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price = cf.AdjustedPrice
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# grab the SMA instance for this symbol
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avg = self.averages.setdefault(symbol,
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self.WarmUpIndicator(symbol, SimpleMovingAverage(100), Resolution.Daily))
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# Update returns true when the indicators are ready, so don't accept until they are
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if avg.Update(cf.EndTime, price):
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value = avg.Current.Value
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# only pick symbols who have their price over their 100 day sma
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if value > price * self.tolerance:
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score[symbol] = (value - price) / ((value + price) / 2)
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# prefer symbols with a larger delta by percentage between the two averages
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sortedScore = sorted(score.items(), key=lambda kvp: kvp[1], reverse=True)
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return [x[0] for x in sortedScore[:self.count]]
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def OnSecuritiesChanged(self, changes):
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for security in changes.RemovedSecurities:
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if security.Invested:
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self.Liquidate(security.Symbol)
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for security in changes.AddedSecurities:
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self.SetHoldings(security.Symbol, self.targetPercent) |