| Overall Statistics |
|
Total Trades 2518 Average Win 0.25% Average Loss -0.27% Compounding Annual Return 69.260% Drawdown 9.100% Expectancy 0.015 Net Profit 4.722% Sharpe Ratio 1.267 Probabilistic Sharpe Ratio 51.358% Loss Rate 48% Win Rate 52% Profit-Loss Ratio 0.94 Alpha 0.382 Beta 0.605 Annual Standard Deviation 0.253 Annual Variance 0.064 Information Ratio 1.727 Tracking Error 0.244 Treynor Ratio 0.529 Total Fees $0.00 Estimated Strategy Capacity $100000.00 Lowest Capacity Asset BTCUSD 2MN |
# CRYPTO curr_price and price_paid
# --------------------------
CRYPTO = "BTCUSD"; BARS = 2;
# --------------------------
class CryptoConsolidator(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 3, 21)
self.SetEndDate(2022, 4, 21)
self.SetCash(1000)
self.crypto = self.AddCrypto(CRYPTO, Resolution.Minute, Market.FTX).Symbol
self.window = RollingWindow[TradeBar](BARS)
self.Consolidate(self.crypto, Resolution.Minute, self.CustomBarHandler)
def CustomBarHandler(self, bar):
self.window.Add(bar)
def OnData(self, data):
if not self.window.IsReady: return
curr_close = np.array([self.window[i].Close for i in range(BARS)])[0]
past_close = np.array([self.window[i].Close for i in range(BARS)])[1]
if not self.Portfolio[self.crypto].Invested:
if curr_close <= past_close*0.999:
self.SetHoldings(self.crypto, 1.00 )
elif self.Portfolio[self.crypto].Invested:
curr_price = self.Securities[self.crypto].Price
price_paid = self.Securities[self.crypto].Holdings.AveragePrice
if curr_price >= price_paid*1.002:
self.Liquidate(self.crypto, "Take Profit")
elif curr_price < price_paid*0.998:
self.Liquidate(self.crypto, "Stop Loss")