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 numpy as np

class BasicTemplateAldgorithm(QCAlgorithm):

    def Initialize(self):
        # Set the cash we'd like to use for our backtest
        # This is ignored in live trading 
        self.SetCash(100)
        
        # Start and end dates for the backtest.
        # These are ignored in live trading.
        self.SetStartDate(2018,1,1)
        self.SetEndDate(2018,7,21)
        
        # Set Brokerage model to load OANDA fee structure.
        self.SetBrokerageModel(BrokerageName.OandaBrokerage)
        
  
        # Add assets you'd like to see
        #self.eurusd = self.AddForex("EURUSD", Resolution.Minute).Symbol
        #self.corn = self.AddCfd("CORNUSD", Resolution.Minute).Symbol
        self.equity = self.AddEquity("MSFT", Resolution.Minute).Symbol

    def OnData(self, slice):
        # Simple buy and hold template
        if not self.Portfolio.Invested:
            self.SetHoldings(self.equity, 1)
            self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))