Overall Statistics
Total Trades
400
Average Win
3.22%
Average Loss
-1.04%
Compounding Annual Return
236.071%
Drawdown
19.200%
Expectancy
0.651
Net Profit
286.373%
Sharpe Ratio
5.737
Probabilistic Sharpe Ratio
97.121%
Loss Rate
60%
Win Rate
40%
Profit-Loss Ratio
3.11
Alpha
2.539
Beta
0.838
Annual Standard Deviation
0.482
Annual Variance
0.233
Information Ratio
6.176
Tracking Error
0.404
Treynor Ratio
3.302
Total Fees
$465.00
 
from datetime import datetime, timedelta
import decimal
import pandas as pd

class BasicTemplateAlgorithm(QCAlgorithm):

    def Initialize(self):

        
        self.SetStartDate(2020,1,1)
        #self.SetEndDate(2020,1,1)
        self.SetEndDate(datetime.now())
        self.SetCash(10000)
        self.SetBenchmark("SPY")                # Set SPY as benchmark                                                           
        self.SetTimeZone(TimeZones.NewYork)
        
        self.SetWarmUp(30)
        
        self.sma = {}
        self.symbols = ["TQQQ", "TNA"]
        
        for symbol in self.symbols:
            self.AddEquity(symbol, Resolution.Hour)
            self.Securities[symbol].SetLeverage(1.0)
            self.sma[symbol] = self.SMA(symbol, 20, Resolution.Hour)
           

    def OnData(self,data):
        
        
        if self.IsWarmingUp: return             # Make sure all the data we need is in place
        
        for symbol in self.symbols:
            
            if self.Securities[symbol].Price > self.sma[symbol].Current.Value:
                if self.Portfolio[symbol].Invested == False and self.Securities[symbol].Price > self.sma[symbol].Current.Value:
                    self.SetHoldings(symbol, 0.45)
                    #self.Debug(self.sma[symbol]) #print SMA price
               
        for symbol in self.symbols:
             #Close position if price is below SMA
            if self.Securities[symbol].Price < self.sma[symbol].Current.Value and self.Portfolio[symbol].IsLong:                                 
                self.Liquidate(symbol)