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
Probabilistic 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
-0.86
Tracking Error
0.192
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
## https://www.quantconnect.com/forum/discussion/7984/trading-volatility-svxy-uvxy-with-momentum-indicators/p1  --> source
## https://www.quantconnect.com/forum/discussion/7928/cash-vix-term-structure/p1  --> getting 9d vix price

# from QuantConnect.Data.Custom.CBOE import CBOE

class VentralTachyonAtmosphericScrubbers(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2017, 12, 6)
        # self.SetEndDate(2018, 1, 1)
        self.SetCash(500)

        self.vix = self.AddData(CBOE, "VIX").Symbol
        self.vix9d = self.AddData(CBOE, "VIX9D").Symbol
        self.vix3m = self.AddData(CBOE, "VIX3M").Symbol
        self.vix6m = self.AddData(CBOE, "VIX6M").Symbol

            
    def OnData(self, data):
        
        if not data.ContainsKey("VIX3M.CBOE") or not data.ContainsKey("VIX.CBOE"):
            return        

        self.Plot("VIX Data", "VIX", data[self.vix].Close)
        self.Plot("VIX9D Data", "VIX9M", data[self.vix9d].Close)
        self.Plot("VIX3M Data", "VIX3M", data[self.vix3m].Close)
        self.Plot("VIX6M Data", "VIX6M", data[self.vix6m].Close)