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
0.892
Tracking Error
0.553
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
#https://www.quantconnect.com/project/8573135#code-tab-main_py

# Sybols
#CURE    DFEN    DPST    DRN    FAS    LABU    NAIL    PILL    RETL    TECL    UTSL
#weights
#1    -0.609022204    0.063329879    -1.402526494    -0.076014761    0.121880258    -0.161395148    -1.880186944    0.36683371    -1.632101489    1.980848122

import numpy as np
import time
import gc

gc.enable()

class SlowBacktest(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(2020, 3, 30)
        self.SetCash(100000) 
        self.AddEquity("SPY", Resolution.Minute)
        self.tickers = ['CURE', 'DFEN', 'DPST', 'DRN', 'FAS', 'LABU', 'NAIL', 'PILL', 'RETL', 'TECL', 'UTSL']
        
        
        #create symbols
        self.symbols = []
        for ticker in self.tickers:
            self.symbols.append(Symbol.Create(ticker, SecurityType.Equity, Market.USA))
            
        self.SetUniverseSelection(ManualUniverseSelectionModel(self.symbols))
        
        
        self.Schedule.On(
            self.DateRules.EveryDay("SPY"),
            self.TimeRules.BeforeMarketClose("SPY", 5),
            self.get_data
            )
        
   

    def OnData(self, data):
        pass

        
    def get_data(self):
        
        #now get latest prices
        T=self.Time
        price_list = []
        for symbol in self.symbols:
            price_list.append(self.Securities[symbol.Value].Price)
            
        
        self.Debug(str(T) +  ' ' + str(price_list))