Jing Wu
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
from System.Collections.Generic import List
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import decimal as d
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class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
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'''In this algorithm we demonstrate how to define a universe as a combination of use the coarse fundamental data and fine fundamental data'''
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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(2010,01,01) #Set Start Date
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self.SetEndDate(2015,01,03) #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.Price)
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# Filter the values of the dict: we only want up-trending securities
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values = 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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# we need to return only the symbol objects
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list = List[Symbol]()
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for x in values[:self.coarse_count]:
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print x.symbol, x.scale
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list.Add(x.symbol)
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return list
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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, 0.2)
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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(10)
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self.slow = ExponentialMovingAverage(30)
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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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datapoint = IndicatorDataPoint(time, value)
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if self.fast.Update(datapoint) and self.slow.Update(datapoint):
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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) / 2) |