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
 
# Find more symbols here: http://quantconnect.com/data
import numpy as np

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class FischerBlack(QCAlgorithm):
    '''Basic template algorithm simply initializes the date range and cash'''

    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.'''

        self.SetStartDate(2018, 1, 1)  #Set Start Date
        self.SetEndDate(2018, 9, 5)   #Set End Date
        self.SetCash(100000)        #Set Strategy Cash
        
        self.etf = self.AddEquity("SPXL", Resolution.Daily)
        self.etf.SetDataNormalizationMode(DataNormalizationMode.SplitAdjusted)
        
        self.vix = self.AddEquity("VIX", Resolution.Daily)
        self.vix.SetDataNormalizationMode(DataNormalizationMode.Adjusted)
        
        self.spy = self.AddEquity("SPY", Resolution.Daily)
        self.spy.SetDataNormalizationMode(DataNormalizationMode.Adjusted)
        
        self.sma50etf = self.SMA("SPXL", 50, Resolution.Daily)
        self.sma20etf = self.SMA("SPXL", 20, Resolution.Daily)
        
        self.sma10vix = self.SMA("VIX", 10, Resolution.Daily)
        self.sma20vix = self.SMA("VIX", 50, Resolution.Daily)
        
        self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
        
    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. 
           Each new data point will be pumped in here.
        Arguments:
            data: Slice object keyed by symbol containing the stock data'''
            
        if (self.sma20vix > self.sma10vix):
            
            if (self.sma20etf > self.sma50etf):
                self.MarketOrder("SPXL", self.CalculateOrderQuantity("SPXL", 1));
            elif (self.sma50etf > self.sma20etf):
                self.MarketOrder("SPXL", -(self.CalculateOrderQuantity("SPXL", 0.25)));
                
        elif (self.sma10vix > self.sma20vix):
                self.LimitOrder("SPY", self.CalculateOrderQuantity("SPY", 0.5), (Securities["SPY"].Price - (Securities["IBM"].Price * 0.1)));
                    
    
        self.Debug( str(self.Portfolio["SPXL"].AveragePrice) )
        self.Debug( str(self.Portfolio["VIX"].AveragePrice) )
        self.Debug( str(self.Portfolio["SPy"].AveragePrice) )