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 -0.736 Tracking Error 0.114 Treynor Ratio 0 Total Fees $0.00 |
class TachyonQuantumAutosequencers(QCAlgorithm): def Initialize(self): self.SetStartDate(2015, 7, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash # string list of symbols we want to trade # self.symbolList = ["BTCUSD","ETHUSD","LTCUSD","BCHUSD", # "XRPUSD","BCHUSD","XLMUSD","EOSUSD", # "REPUSD","XTZUSD","ETCUSD","ZRXUSD"] self.symbolList = ["BTCUSD","ETHUSD","LTCUSD","BCHUSD","BCHUSD","XLMUSD","EOSUSD","REPUSD","XTZUSD","ETCUSD","ZRXUSD"] # self.symbolList = ["BTCUSD","LTCUSD"] # dictionary to hold rolling window for each symbol self.daily = {} # list of symbol objects self.cryptoSymbols = [] for name in self.symbolList: # get symbol object symbol = self.AddCrypto(name, Resolution.Hour, Market.GDAX).Symbol #symbol = self.AddCrypto(name, Resolution.Minute, Market.GDAX).Symbol # append to list self.cryptoSymbols.append(symbol) # create rolling window of len 2 self.daily[symbol] = RollingWindow[float](2) self.SetWarmup(5) def OnData(self, data): # iterate through each object in our list for symbol in self.cryptoSymbols: if data.ContainsKey(symbol): #self.Debug("{}".format(symbol)) # get OHLC values to use later o = data.Bars[symbol].Open h = data.Bars[symbol].High l = data.Bars[symbol].Low c = data.Bars[symbol].Close # update our rolling window self.daily[symbol].Add(c) # check if rolling window is full if not (self.daily[symbol].IsReady): return prev_hour_price = self.daily[symbol][1]