class EMAMomentumUniverse(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 7, 1)
self.SetEndDate(2020, 7, 1)
self.SetCash(10000)
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction)
self.averages = { }
self.SetSecurityInitializer(SetCustomFeeModel)
#self.Securities[selected].FeeModel = ConstantFeeModel(0)
#self.SetExecution(ImmediateExecutionModel())
def SetCustomFeeModel(x):
x.FeeModel = ConstantFeeModel(0)
def CoarseSelectionFunction(self, universe):
selected = []
universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)
universe = [c for c in universe if c.Price > 10][:100]
for coarse in universe:
symbol = coarse.Symbol
if symbol not in self.averages:
# 1. Call history to get an array of 200 days of history data
history = self.History(symbol, 200, Resolution.Daily)
#2. Adjust SelectionData to pass in the history result
self.averages[symbol] = SelectionData(history)
self.averages[symbol].update(self.Time, coarse.AdjustedPrice)
if self.averages[symbol].is_ready() and self.averages[symbol].fast > self.averages[symbol].slow:
selected.append(symbol)
return selected[:10]
def OnSecuritiesChanged(self, changes):
for security in changes.RemovedSecurities:
self.Liquidate(security.Symbol)
for security in changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.10)
class SelectionData():
#3. Update the constructor to accept a history array
def __init__(self, history):
self.slow = ExponentialMovingAverage(200)
self.fast = ExponentialMovingAverage(50)
#4. Loop over the history data and update the indicators
for bar in history.itertuples():
self.fast.Update(bar.Index[1], bar.close)
self.slow.Update(bar.Index[1], bar.close)
def is_ready(self):
return self.slow.IsReady and self.fast.IsReady
def update(self, time, price):
self.fast.Update(time, price)
self.slow.Update(time, price)