Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
108 lines
4.3 KiB
Python
108 lines
4.3 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.Indicators")
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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.Data import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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from System.Collections.Generic import List
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import decimal as d
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### <summary>
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### In this algorithm we demonstrate how to perform some technical analysis as
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### part of your coarse fundamental universe selection
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise 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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self.SetStartDate(2010,1,1) #Set Start Date
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self.SetEndDate(2015,1,1) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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self.UniverseSettings.Resolution = Resolution.Daily
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self.UniverseSettings.Leverage = 2
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self.coarse_count = 10
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self.averages = { };
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# this add universe method accepts two parameters:
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# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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self.AddUniverse(self.CoarseSelectionFunction)
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# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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def CoarseSelectionFunction(self, coarse):
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# We are going to use a dictionary to refer the object that will keep the moving averages
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for cf in coarse:
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if cf.Symbol not in self.averages:
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self.averages[cf.Symbol] = SymbolData(cf.Symbol)
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# Updates the SymbolData object with current EOD price
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avg = self.averages[cf.Symbol]
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avg.update(cf.EndTime, cf.AdjustedPrice)
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# Filter the values of the dict: we only want up-trending securities
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values = list(filter(lambda x: x.is_uptrend, self.averages.values()))
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# Sorts the values of the dict: we want those with greater difference between the moving averages
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values.sort(key=lambda x: x.scale, reverse=True)
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for x in values[:self.coarse_count]:
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self.Log('symbol: ' + str(x.symbol.Value) + ' scale: ' + str(x.scale))
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# we need to return only the symbol objects
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return [ x.symbol for x in values[:self.coarse_count] ]
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# this event fires whenever we have changes to our universe
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def OnSecuritiesChanged(self, changes):
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# liquidate removed securities
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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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# we want 20% allocation in each security in our universe
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for security in changes.AddedSecurities:
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self.SetHoldings(security.Symbol, 0.1)
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class SymbolData(object):
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def __init__(self, symbol):
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self.symbol = symbol
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self.tolerance = d.Decimal(1.01)
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self.fast = ExponentialMovingAverage(100)
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self.slow = ExponentialMovingAverage(300)
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self.is_uptrend = False
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self.scale = 0
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def update(self, time, value):
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if self.fast.Update(time, value) and self.slow.Update(time, value):
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fast = self.fast.Current.Value
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slow = self.slow.Current.Value
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self.is_uptrend = fast > slow * self.tolerance
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if self.is_uptrend:
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self.scale = (fast - slow) / ((fast + slow) / d.Decimal(2.0)) |