| Overall Statistics |
|
Total Trades 954 Average Win 4.00% Average Loss -0.83% Compounding Annual Return 118.472% Drawdown 24.700% Expectancy 0.629 Net Profit 865.148% Sharpe Ratio 2.094 Probabilistic Sharpe Ratio 86.932% Loss Rate 72% Win Rate 28% Profit-Loss Ratio 4.80 Alpha 0.338 Beta 0.399 Annual Standard Deviation 0.395 Annual Variance 0.156 Information Ratio -0.842 Tracking Error 0.475 Treynor Ratio 2.075 Total Fees $2349860.46 Estimated Strategy Capacity $450000.00 Lowest Capacity Asset ETHUSD E3 |
# Multi-asset, 4 Hour multi-indicators crypto portfolio with risk management
# ----------------------------------------------------------------------------------
CRYPTOS = ['BTCUSD', 'LTCUSD', 'ETHUSD']; LEV = 1;
MA_1 = 120; MA_2 = 12; RSIUP = 70; RSIDOWN = 30; a = RSIUP - RSIDOWN; b = RSIUP - a;
c = ((RSIUP + 10) - (RSIDOWN - 10 )) / 100; d = (RSIUP + 10) - c * 100;
# ----------------------------------------------------------------------------------
class UpgradedApricotPony(QCAlgorithm):
def Initialize(self):
#Grunddaten für Indikator
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2021, 11, 24)
self.SetCash(1000000)
self.SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin)
self.SetTimeZone("Europe/Berlin")
self.profit_percentage = 0.3
self.loss_percentage = -0.1
self.pairs = [Pair(self, ticker) for ticker in CRYPTOS]
self.SetWarmUp(5*24*(MA_1 + MA_2), Resolution.Hour)
def OnData(self, data):
for pair in self.pairs:
if self.IsWarmingUp: return
symbol = pair.symbol
price = self.Securities[symbol].Price
rsi = pair.rsi.Current.Value
rsi_ema = pair.rsi_ema.Current.Value
diff = rsi_ema - rsi
nma = pair.nma.Current.Value
nma_ema = pair.nma_ema.Current.Value
bbr = (self.Securities[symbol].Price - pair.bb.LowerBand.Current.Value) / (pair.bb.UpperBand.Current.Value - pair.bb.LowerBand.Current.Value) if (pair.bb.UpperBand.Current.Value - pair.bb.LowerBand.Current.Value) > 0 else 0
bbnorm = (a * bbr + b)
sto = pair.sto.Current.Value
sto_stochd = (c * pair.sto.StochK.Current.Value + d)
if not self.Portfolio[symbol].Invested:
if self.Securities[symbol].Close > nma and pair.rsi.Current.Value > pair.rsi_ema.Current.Value and bbnorm > pair.rsi.Current.Value and pair.rsi.Current.Value > sto_stochd:
quantity = (self.Portfolio.Cash * LEV/len(self.pairs))/ self.Securities[symbol].Price
self.MarketOrder(symbol, quantity)
elif self.Securities[symbol].Close < nma:
self.Liquidate(symbol)
profit = self.Portfolio[symbol].UnrealizedProfitPercent
if self.Portfolio[symbol].Invested:
if self.Portfolio[symbol].IsLong:
if profit > self.profit_percentage:
self.Liquidate(symbol, "Take Profit reached")
elif profit < self.loss_percentage:
self.Liquidate(symbol, "Stop Price reached")
class Pair:
def __init__(self, algorithm, ticker):
self.symbol = algorithm.AddCrypto(ticker, Resolution.Hour, Market.Bitfinex).Symbol
algorithm.consolidator = TradeBarConsolidator(timedelta(hours = 4))
# RSI over EMA
self.rsi = RelativeStrengthIndex(14)
algorithm.RegisterIndicator(self.symbol, self.rsi, algorithm.consolidator)
self.rsi_ema = IndicatorExtensions.EMA(self.rsi, 13)
# New Moving Average
ema_1 = ExponentialMovingAverage(MA_1)
algorithm.RegisterIndicator(self.symbol, ema_1, algorithm.consolidator, Field.High)
ema_2 = ExponentialMovingAverage(MA_2)
algorithm.RegisterIndicator(self.symbol, ema_2, algorithm.consolidator, Field.Low)
self.nma = IndicatorExtensions.Over(IndicatorExtensions.Plus(ema_1, ema_2), 2)
self.nma_ema = IndicatorExtensions.Of(ExponentialMovingAverage(MA_2), self.nma)
# Bollinger Bands
self.bb = BollingerBands(20, 2)
algorithm.RegisterIndicator(self.symbol, self.bb, algorithm.consolidator)
# Stochastic
self.sto = Stochastic(14, 5, 1)
algorithm.RegisterIndicator(self.symbol, self.sto, algorithm.consolidator)