Overall Statistics
Total Trades
570
Average Win
1.43%
Average Loss
-1.30%
Compounding Annual Return
3.502%
Drawdown
28.800%
Expectancy
0.049
Net Profit
13.900%
Sharpe Ratio
0.264
Loss Rate
50%
Win Rate
50%
Profit-Loss Ratio
1.11
Alpha
0.035
Beta
0.057
Annual Standard Deviation
0.148
Annual Variance
0.022
Information Ratio
-0.196
Tracking Error
0.182
Treynor Ratio
0.687
Total Fees
$12363.39
 
#
#   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 numpy as np

class BasicTemplateAlgorithm(QCAlgorithm):

	def Initialize(self):


 		self.uso = self.AddEquity('UCO', Resolution.Minute) #United States Oil Fund (USO) etf, data from 2006-04-10
  
		# Set the cash we'd like to use for our backtest							#Proshares Ultra Bloomber Crude Oil (UCO) etf, data from 2008-11-25
		# This is ignored in live trading 
		self.SetCash(100000)
		self.SetStartDate(2013,11,27) #start one day after launch as we need one day of history #2008,11,26
		self.SetEndDate(2017,9,1) #2013,11,26
		

		#schedule opening function every day 15 minutes before market close
		self.Schedule.On(self.DateRules.EveryDay('UCO'), self.TimeRules.BeforeMarketClose('UCO', 165), Action(self.trade_fn))
		
		
	def trade_fn(self):
		
		
		uso_hist = self.History('UCO', 1, Resolution.Daily)
		
		for slice in uso_hist:
			self.close = slice.Close
			
		y_close = self.close
		
		price = self.Securities['UCO'].Price
		
		dec_change = (price - y_close) / y_close
		
		if dec_change >= 0.035:
			self.SetHoldings('UCO', 1)
			holdings = self.Portfolio['UCO'].Quantity
			if holdings != 0:
				self.MarketOnCloseOrder('UCO', -holdings)
			
		if dec_change <= -0.035:
			self.SetHoldings('UCO', -1)
			holdings = self.Portfolio['UCO'].Quantity
			if holdings != 0:
				self.MarketOnCloseOrder('UCO', -holdings)
			


	def OnData(self, slice):
		pass