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
```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(2018,1,1)
# self.SetEndDate(2016,2,1)

for i in range(len(self.symbols)):
self.symbols[i] = symbol

def OnData(self, slice):
pass

# 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(pd.rolling_std(i.prices, 20)*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:
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 rebalance(self):
target = self.select()

if self.Portfolio[target].Quantity != 0:
return

self.Liquidate()
self.SetHoldings(target,1)                        ```