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 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
import numpy as np import decimal as d ### <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 BasicTemplateAlgorithm(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(2020,11,27) #Set Start Date self.SetEndDate(2020,11,28) #Set End Date self.SetCash(1000) #Set Strategy Cash self.SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash) # Find more symbols here: http://quantconnect.com/data symbol = self.AddCrypto("BTCUSD", Resolution.Minute) #self.AddCrypto("ETHUSD", Resolution.Minute) #self.AddCrypto("BTCEUR", Resolution.Minute) #symbol = self.AddCrypto("LTCUSD", Resolution.Minute).Symbol # create two moving averages # self.fast = self.EMA(symbol, 30, Resolution.Minute) # self.slow = self.EMA(symbol, 60, Resolution.Minute) def OnData(self, data): # via a tradebar dictionary (symbol - bar) data.Bars["BTCUSD"].Close # or via a ticks list: data.Ticks["BTCUSD"][0].Close