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).Symbol
        #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)
        if data.ContainsKey("BTCUSD") and data["BTCUSD"] is not None:
            data.Bars["BTCUSD"].Close
            # or via a ticks list:
            #data.Ticks["BTCUSD"][0].Close