Overall Statistics Total Trades 4 Average Win 4.53% Average Loss -0.30% Compounding Annual Return 1.662% Drawdown 6.200% Expectancy 7.020 Net Profit 4.214% Sharpe Ratio 0.261 Loss Rate 50% Win Rate 50% Profit-Loss Ratio 15.04 Alpha 0.032 Beta -1.028 Annual Standard Deviation 0.058 Annual Variance 0.003 Information Ratio -0.02 Tracking Error 0.058 Treynor Ratio -0.015 Total Fees \$89.16
```from System.Collections.Generic import List
import numpy as np
from QuantConnect.Data.UniverseSelection import *

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class CANSLIM_ALGO(QCAlgorithm):

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2016,1,4)  #Set Start Date
self.SetEndDate(2018,7,5)    #Set End Date
self.SetCash(100000)           #Set Strategy Cash
self.flag1 = 1
self.flag2 = 0
self.flag3 = 0

self.UniverseSettings.Resolution = Resolution.Daily
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))

# Find more symbols here: http://quantconnect.com/data
self.__numberOfSymbols = 1500
self._changes = None
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):

if self.flag1:
CoarseWithFundamental = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
top = sortedByDollarVolume[:self.__numberOfSymbols]
return [i.Symbol for i in top]
else:
return []

def FineSelectionFunction(self, fine):

if self.flag1:
self.flag1 = 0
self.flag2 = 1

filtered_fine = [x for x in fine if (x.EarningReports.BasicEPS.ThreeMonths != 0) and (x.EarningReports.BasicEPS.SixMonths !=0)
and ((x.EarningReports.BasicEPS.ThreeMonths - x.EarningReports.BasicEPS.SixMonths)/ (x.EarningReports.BasicEPS.SixMonths)) *100  > 25]

filtered_fine = [x for x in filtered_fine if x.OperationRatios.ROE.ThreeMonths > 0.17]
filtered_fine = [x for x in filtered_fine if (x.FinancialStatements.IncomeStatement.TotalRevenue.ThreeMonths !=0) and
(x.FinancialStatements.IncomeStatement.TotalRevenue.SixMonths !=0) and ((x.FinancialStatements.IncomeStatement.TotalRevenue.ThreeMonths -x.FinancialStatements.IncomeStatement.TotalRevenue.SixMonths)/ (x.FinancialStatements.IncomeStatement.TotalRevenue.SixMonths)) >0.20]

self.flag3 = self.flag3 + 1

# take the stock symbols
return [x.Symbol for x in filtered_fine]
else:
return []

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

Arguments:
data: Slice object keyed by symbol containing the stock data
'''
if self.flag3 > 0:
if self.flag2 == 1:
self.flag2 = 0

# if we have no changes, do nothing
if self._changes is None: return

# liquidate removed securities
for security in self._changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
self.Log("Sell security" + str(security.Symbol.Value))

if security.Symbol.Value != "SPY":

self._changes = None

# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes

def Rebalancing(self):
self.flag1 = 1