Overall Statistics Total Trades 63 Average Win 0.03% Average Loss -0.02% Compounding Annual Return 0.004% Drawdown 2.200% Expectancy 0.639 Net Profit 0.001% Sharpe Ratio 0.021 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 1.46 Alpha 0.218 Beta -11.345 Annual Standard Deviation 0.04 Annual Variance 0.002 Information Ratio -0.453 Tracking Error 0.04 Treynor Ratio 0 Total Fees \$63.00
```from clr import AddReference

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Python import PythonQuandl

import math
import pandas as pd
import numpy as np
from decimal import *

class DailyAlgorithm(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.'''

# Context
self.stock = 'SPY'
self.SetStartDate(2018,4,1)  #Set Start Date
self.SetEndDate(2018,7,1)    #Set End Date
self.SetCash(10000)          #Set Strategy Cash

def OnData(self, slice):
self.close_price = self.Securities[self.stock].Price
self.open_price = self.Securities[self.stock].Open
self.high_price = self.Securities[self.stock].High
self.low_price = self.Securities[self.stock].Low

daily_perf = (self.close_price - self.open_price) / self.open_price
if daily_perf < 0.003:
self.MarketOrder(self.stock, 1)
elif daily_perf > 0.003:
self.MarketOrder(self.stock, -1)

self.Plot('Stock Plot', 'close', self.close_price)
self.Plot('Stock Plot', 'open', self.open_price)
self.Plot('Stock Plot', 'high', self.high_price)
self.Plot('Stock Plot', 'low', self.low_price)                        ```