Overall Statistics Total Trades 31 Average Win 0% Average Loss 0% Compounding Annual Return -46.509% Drawdown 6.400% Expectancy 0 Net Profit -5.337% Sharpe Ratio -4.661 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.837 Beta 18.395 Annual Standard Deviation 0.113 Annual Variance 0.013 Information Ratio -4.814 Tracking Error 0.113 Treynor Ratio -0.029 Total Fees \$103.52
```# https://www.quantconnect.com/forum/discussion/2607/important-universe-selection-in-python-algorithms

from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from System.Collections.Generic import List
import decimal as d

class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetCash(100000)
self.SetStartDate(2017,10,1)
self.SetEndDate(  2017,11,1)
self.UniverseSettings.Resolution = Resolution.Daily
self.UniverseSettings.Leverage   = 2
self.coarse_max = 10
self.averages = {}

def CoarseSelectionFunction(self, coarse):
for cf in coarse:
if cf.Symbol not in self.averages:
self.averages[cf.Symbol] = SymbolData(cf.Symbol)

# Update the SymbolData object with current EOD price
avg = self.averages[cf.Symbol]
avg.update(cf)

prc = 0.0
if self.Securities.ContainsKey(cf.Symbol):
prc  = self.Securities[cf.Symbol].Price
sma1 = self.averages[cf.Symbol].sma1.Current.Value
sma2 = self.averages[cf.Symbol].sma2.Current.Value
sma1 = sma1 if sma1 else 0.0
sma2 = sma2 if sma2 else 0.0
diff = sma1 - sma2
self.Log('{}  prc {}  sma1 {}  sma2 {}   diff {} '.format(cf.Symbol, '%.2f' % prc,
'%.3f' % sma1, '%.3f' % sma2, '%.5f' % diff))

''' TODO
Find out why log is this limited ...
2017-10-11 00:00:00 Z UYE69C59FN8L  prc 42.14  sma1 41.987  sma2 41.752   diff 0.23467
2017-10-11 00:00:00 Z UYE69C59FN8L  prc 42.14  sma1 41.810  sma2 41.829   diff -0.01900
2017-10-12 00:00:00 Z UYE69C59FN8L  prc 41.87  sma1 41.633  sma2 41.783   diff -0.14967
2017-10-12 00:00:00 Z UYE69C59FN8L  prc 41.87  sma1 41.433  sma2 41.745   diff -0.31167
2017-10-13 00:00:00 Z UYE69C59FN8L  prc 41.42  sma1 41.463  sma2 41.747   diff -0.28367
2017-10-13 00:00:00 Z UYE69C59FN8L  prc 41.42  sma1 41.467  sma2 41.741   diff -0.27433
2017-10-14 00:00:00 Z UYE69C59FN8L  prc 41.70  sma1 41.550  sma2 41.680   diff -0.13000
2017-10-14 00:00:00 Z UYE69C59FN8L  prc 41.70  sma1 41.460  sma2 41.634   diff -0.17400
2017-10-17 00:00:00 Z UYE69C59FN8L  prc 41.52  sma1 41.363  sma2 41.534   diff -0.17067
2017-10-17 00:00:00 Z UYE69C59FN8L  prc 41.52  sma1 41.213  sma2 41.446   diff -0.23267
2017-10-17 00:00:00 Z UYE69C59FN8L  prc 41.14  sma1 41.283  sma2 41.423   diff -0.13967
2017-10-17 00:00:00 Z UYE69C59FN8L  prc 41.14  sma1 41.407  sma2 41.413   diff -0.00633
Place diff's in a sortable object
Sort them
Select top or bottom coarse_max
'''

# Filter the values of the dict: wait for indicator to be ready
vals = filter(lambda x: x.is_ready, self.averages.values())

# need to return only the symbol objects
return [ x.symbol for x in vals ]

# Error: 'filter' object is not subscriptable
return [ x.symbol for x in vals[:self.coarse_max] ]

# this event fires whenever have changes to universe
def OnSecuritiesChanged(self, changes):
# want n% allocation in each security in universe
self.SetHoldings(security.Symbol, 0.1)

return
# liquidate removed securities
for security in changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)

class SymbolData(object):
def __init__(self, symbol):
self.symbol = symbol
self.sma1 = SimpleMovingAverage(3)
self.sma2 = SimpleMovingAverage(10)