import numpy as np
from datetime import datetime, timedelta
from System.Collections.Generic import List
from QuantConnect.Data.UniverseSelection import *

class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''

def __init__(self):
# set the flag for rebalance
self.reb = 1
# Number of stocks to pass CoarseSelection process
self.num_coarse = 250
# Number of stocks to long/short
self.num_fine = 20
self.symbols = [] #None
self.first_month = 0

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(2015,10,07) #Set Start Date
self.SetEndDate(2017,12,12) #Set End Date
self.SetCash(1000) #Set Strategy Cash
self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

# Schedule the rebalance function to execute at the begining of each month
self.TimeRules.AfterMarketOpen(self.spy, 5), Action(self.rebalance))

#self.UniverseSettings.Resolution = Resolution.Daily
#self.UniverseSettings.MinimumTimeInUniverse = 0
# Add universes in which to find investments
#Warm up 200 bars for all subscribed data.

#self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Cash)
#self.UniverseSettings.Resolution = Resolution.Daily
#self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
# Schedule the rebalance function to execute at the begining of each month
#self.spy = self.AddEquity('SPY', Resolution.Daily).Symbol
#self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance)) #

def CoarseSelectionFunction(self, coarse):
# if the rebalance flag is not 1, return null list to save time.
if self.reb != 1:
return []

# make universe selection once a month
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData)
and (float(x.Price) > 5)]

sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
top = sortedByDollarVolume[:self.num_coarse]
return [i.Symbol for i in top]

def FineSelectionFunction(self, fine):
# return null list if it's not time to rebalance
if self.reb != 1:
return []

self.reb = 0

# drop stocks which don't have the information we need.
# you can try replacing those factor with your own factors here

filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
and x.ValuationRatios.PriceChange1M
and x.ValuationRatios.BookValuePerShare]

self.Log('remained to select %d'%(len(filtered_fine)))

# rank stocks by three factor.
sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)

stock_dict = {}

# assign a score to each stock, you can also change the rule of scoring here.
for i,ele in enumerate(sortedByfactor1):
rank1 = i
rank2 = sortedByfactor2.index(ele)
rank3 = sortedByfactor3.index(ele)
score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
stock_dict[ele] = score

# sort the stocks by their scores
self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
sorted_symbol = [x[0] for x in self.sorted_stock]

# sotre the top stocks into the long_list and the bottom ones into the short_list = [x for x in sorted_symbol[:self.num_fine]]
self.short = [x for x in sorted_symbol[-self.num_fine:]]

topFine = + self.short
return [i.Symbol for i in topFine]

#Our rebalanced method is straightforward: We first liquidate the stocks that are no longer in the long/short list, and then assign equal weight to the stocks we are going to long or short.
def rebalance(self):
# if this month the stock are not going to be long/short, liquidate it.
long_short_list = + self.short
for i in self.Portfolio.Values:
if (i.Invested) and (i.Symbol not in long_short_list):

# Assign each stock equally. Always hold 10% cash to avoid margin call
for i in

for i in self.short:

self.reb = 1

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

data: Slice object keyed by symbol containing the stock data
if not self.Portfolio.Invested:
self.SetHoldings("SPY", 1)