| 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]