Overall Statistics Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees \$0.00
```from clr import AddReference

from System import *
from System.Collections.Generic import List
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import *
from math import ceil
import numpy as np
import pandas as pd
import scipy as sp
from scipy import stats

### <summary>
### Demonstration of how to estimate constituents of QC500 index based on the company fundamentals
### The algorithm creates a default tradable and liquid universe containing 500 US equities
### which are chosen at the first trading day of each month.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
### <meta name="tag" content="fine universes" />
class ConstituentsQC500GeneratorAlgorithm(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(2018, 1, 1) #Set Start Date
self.SetEndDate(2018, 1, 10) #Set End Date
self.SetCash(50000) #Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily

# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>

self.num_coarse = 100
self.num_fine = 3
self.dollar_volume = {}

self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), Action(self.Market))

def CoarseSelectionFunction(self, coarse):
#if not self.rebalance: return []
# The stocks must have fundamental data
# The stock must have positive previous-day close price
# The stock must have positive volume on the previous trading day
filtered = [x for x in coarse if x.HasFundamentalData
and x.Volume > 0
and x.Price > 0]
# sort the stocks by dollar volume and take the top 1000
sort_filtered = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)[:self.num_coarse]
for i in sort_filtered:
self.dollar_volume[i.Symbol.Value] = i.DollarVolume

# return the symbol objects our sorted collection
return [x.Symbol for x in sort_filtered]

def FineSelectionFunction(self, fine):
#if not self.rebalance: return []
#self.rebalance = False
# The company's headquarter must in the U.S.
# The stock must be traded on either the NYSE or NASDAQ
# At least half a year since its initial public offering
# The stock's market cap must be greater than 500 million
filtered_fine = [x for x in fine if  (x.CompanyReference.CountryId == "USA")
and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS")
and ((self.Time - x.SecurityReference.IPODate).days > 180)
and x.EarningReports.BasicAverageShares.ThreeMonths * (x.EarningReports.BasicEPS.TwelveMonths*x.ValuationRatios.PERatio) > 5e8]

count = len(filtered_fine)
if count == 0: return []

# select stocks with top dollar volume in every single sector
for i in filtered_fine:
i.DollarVolume = self.dollar_volume[i.Symbol.Value]
percent = float(self.num_fine/count)
group_by_code = {}
top_list = []
for code in ["N", "M", "U", "T", "B", "I"]:
group_by_code[code] = list(filter(lambda x: x.CompanyReference.IndustryTemplateCode == code, filtered_fine))
top = sorted(group_by_code[code], key=lambda x: x.DollarVolume, reverse = True)[:ceil(len(group_by_code[code])*percent)]
top_list.append(top)
joined_list = top_list[0]
for ls in top_list[1:]:
joined_list  += ls
self.symbols = [x.Symbol for x in joined_list][:self.num_fine]
return self.symbols

def OnData(self, data):
pass
'''
for kvp in data.Bars:
symbol = kvp.Key
value = kvp.Value
self.Log("OnData(Slice): {0}: {1}: {2}".format(self.Time, symbol, value.Close))
'''

def Market(self):
for i in self.ActiveSecurities.Values:
self.Log(i.Symbol.Value + " Price:"+str(i.Price))
symbols=[i.Symbol for i in self.ActiveSecurities.Values]
h = self.History(symbols, 10, Resolution.Daily)
if h.empty:
self.Error("EMPTY HISTORY" + str(self.Time))
else:
h = h['close'].unstack(level=0)