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
```#
#   QuantConnect Basic Template:
#    Fundamentals to using a QuantConnect algorithm.
#
#    You can view the QCAlgorithm base class on Github:
#    https://github.com/QuantConnect/Lean/tree/master/Algorithm
#

import math
import numpy as np
import pandas as pd
import statistics

from datetime import datetime, timedelta

class BasicTemplateAlgorithm(QCAlgorithm):

def Initialize(self):
# Set the cash we'd like to use for our backtest
# This is ignored in live trading
self.SetCash(10000)

# Start and end dates for the backtest.
# These are ignored in live trading.
self.SetStartDate(2017,1,1)
self.SetEndDate(2017,1,30)

# Add assets you'd like to see

# Define the Schedules
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen(self.spy, -45),
Action(self.EveryDayBeforeMarketOpen))
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen(self.spy, 90),
Action(self.Balance))

def OnData(self, slice):
# Simple buy and hold template
if not self.Portfolio.Invested:
#self.SetHoldings(self.spy, 1)
#self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
#self.Log("Hello World!")
self.Log("SSO Close:%s" %self.Securities['SSO'].Close)
#self.Log("VIX Close:%s" %self.Securities['VIX'].Close)

def EveryDayBeforeMarketOpen(self):
self.Log("EveryDayBeforeMarketOpen")

def Balance(self):
self.Log("Balance")                        ```