import numpy as np
import pandas as pd

class BasicTemplateAlgorithm(QCAlgorithm):
    def __init__(self):
        self.symbols =  ['MDY','IEV','EEM','ILF','EPP','EDV','SHY']
        self.back_period = 73


    def Initialize(self):
        self.SetCash(1000)
        self.SetStartDate(2018,2,1)
        self.SetEndDate(2018,4,20)
        self.SetWarmUp(TimeSpan.FromDays(30))
        
        for i in range(len(self.symbols)):
            symbol = self.AddEquity(self.symbols[i], Resolution.Daily).Symbol
            self.symbols[i] = symbol

    
# calculate historical return and volatility for each stock
    def get_history(self):
        history = self.History(self.back_period, Resolution.Daily)
        for i in self.symbols:
            bars = map(lambda x: x[i], history)
            i.prices = pd.Series([float(x.Close) for x in bars])
            vol = np.mean(i.prices.rolling(20).std()*np.sqrt(self.back_period/20.0))
            i.volatility = vol/i.prices[0]
            i.ret = (i.prices.iloc[-1] - i.prices.iloc[0])/i.prices.iloc[0]

# normalise the mesures of returns and volatilities
    def normalise(self):
        rets = [x.ret for x in self.symbols]
        vols = [x.volatility for x in self.symbols]

        self.ret_max, self.ret_min = max(rets), min(rets)
# vol_min is actually the max volatility. min means low score on this.
        self.vol_min, self.vol_max = max(vols), min(vols)
        
# select the best one with the highest score.
    def select(self):
        self.get_history()
        self.normalise()
        for i in self.symbols:
            self.Debug(str(type(i.ret)))
            ret = (i.ret - self.ret_min)/(self.ret_max - self.ret_min)
            vol = (i.volatility - self.vol_min)/(self.vol_max - self.vol_min)
            i.score = ret*0.7 + vol*0.3
            
        select = sorted(self.symbols, key = lambda x: x.score, reverse = True)
        return select[0]
    
    
    def OnData(self, slice):
        target = self.select()
        # if self.Portfolio[target.Value].Quantity != 0:
        #     return
        self.Liquidate()
        # self.MarketOrder(target.Value, 1)
        self.SetHoldings(target,1)