Overall Statistics
Total Trades
125
Average Win
3.82%
Average Loss
-1.89%
Compounding Annual Return
2.182%
Drawdown
28.400%
Expectancy
0.265
Net Profit
20.678%
Sharpe Ratio
0.234
Probabilistic Sharpe Ratio
0.348%
Loss Rate
58%
Win Rate
42%
Profit-Loss Ratio
2.02
Alpha
-0.006
Beta
0.275
Annual Standard Deviation
0.077
Annual Variance
0.006
Information Ratio
-0.552
Tracking Error
0.125
Treynor Ratio
0.066
Total Fees
$231.92
Estimated Strategy Capacity
$670000000.00
Lowest Capacity Asset
SPY R735QTJ8XC9X
 
from AlgorithmImports import *

class FearAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetCash(100000)
        self.SetStartDate(2014, 1, 1)
        self.SetEndDate(2022, 9, 15)
        
        # Add a benchmark asset to synchronize the algorithm
        self.AddEquity("SPY", Resolution.Daily).Symbol
        
        # Add VIX as our fear index.
        self.AddEquity("VXX", Resolution.Daily).Symbol
        
        # Get the 30-day EMA of the VIX:
        self.ema = self.EMA("VXX", 30)
        
        # Set warm up so we're instantly trading:
        self.SetWarmUp(60)
        
    def OnData(self, slice):
        self.Debug("Running algorithm!!")
        
        # Make sure all the data we need is in place
        if self.IsWarmingUp: return 
        if not slice.ContainsKey("VXX"): return
        if not slice.ContainsKey("SPY"): return
        
        # Make some nice plots 
        self.Plot("FEAR", "VXX", self.Securities["VXX"].Price)
        self.Plot("FEAR", "EMA", self.ema.Current.Value)
        
        # Decide when to invest
        if self.Securities["VXX"].Price < self.ema.Current.Value:
            if self.Portfolio.Invested: return
            self.Debug("Investing in SPY...")
            self.SetHoldings("SPY", 1);
        else:
            self.Liquidate()
            self.Debug("FEAR too great, liquidating...")