| Overall Statistics |
|
Total Trades 1610 Average Win 0.15% Average Loss -0.25% Compounding Annual Return -19.712% Drawdown 31.800% Expectancy 0.048 Net Profit -6.630% Sharpe Ratio -0.039 Probabilistic Sharpe Ratio 24.409% Loss Rate 35% Win Rate 65% Profit-Loss Ratio 0.60 Alpha 0.016 Beta 1.006 Annual Standard Deviation 0.509 Annual Variance 0.259 Information Ratio 0.033 Tracking Error 0.462 Treynor Ratio -0.02 Total Fees $0.00 Estimated Strategy Capacity $240000.00 Lowest Capacity Asset BTCUSD XJ |
from QuantConnect.Indicators import *
from AlgorithmImports import *
class BollingerMomentum(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 9, 1)
self.SetCash("BTC",100)
self.ticker="BTCUSD"
self.symbol=self.AddCrypto(self.ticker, Resolution.Minute).Symbol
self.per_order_proportion=0.5
period_BB = 50
std=2
n_MinuteConsolidator = TradeBarConsolidator(timedelta(minutes=5))
n_MinuteConsolidator.DataConsolidated += self.N_MinuteBarHandler
self.SubscriptionManager.AddConsolidator(self.ticker, n_MinuteConsolidator)
self.Bolband = self.BB(self.ticker, period_BB, std, MovingAverageType.Simple)
self.RegisterIndicator(self.ticker, self.Bolband, timedelta(minutes=5))
self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.1))
self.SetWarmUp(period_BB)
def N_MinuteBarHandler(self, sender, bar):
self.Debug(str(self.Time) + " " + str(bar))
if self.IsWarmingUp != False:
return
holdings = self.Portfolio[self.ticker].Quantity
price = self.Securities[self.ticker].Close
quantity = self.CalculateOrderQuantity(self.ticker,self.per_order_proportion)
if holdings <= 0:
if price <= self.Bolband.LowerBand.Current.Value:
self.MarketOrder(self.ticker, quantity*1)
if holdings > 0:
if price >= self.Bolband.UpperBand.Current.Value:
self.LiquidateAll()
def OnData(self, data):
pass
def LiquidateAll(self):
self.Liquidate()
self.Transactions.CancelOpenOrders(self.ticker)