| Overall Statistics |
|
Total Orders 9691 Average Win 5.53% Average Loss -1.15% Compounding Annual Return 58.160% Drawdown 37.100% Expectancy 0.639 Start Equity 100000.00 End Equity 221997.95 Net Profit 121.998% Sharpe Ratio 0.984 Sortino Ratio 1.114 Probabilistic Sharpe Ratio 36.832% Loss Rate 72% Win Rate 28% Profit-Loss Ratio 4.80 Alpha 0.64 Beta 0.356 Annual Standard Deviation 0.701 Annual Variance 0.491 Information Ratio 0.784 Tracking Error 0.703 Treynor Ratio 1.939 Total Fees $0.00 Estimated Strategy Capacity $59000.00 Lowest Capacity Asset LTCUSD 2XR Portfolio Turnover 42.96% |
from AlgorithmImports import *
class TechnicalIndicatorsAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2023, 1, 1) # Set Start Date
self.SetEndDate(2024, 12, 12) # Set End Date
self.SetCash(100000) # Set Strategy Cash
# Add the cryptocurrency pairs
self.symbols = [
self.AddCrypto("BTCUSD", Resolution.HOUR).Symbol,
self.AddCrypto("ETHUSD", Resolution.HOUR).Symbol,
self.AddCrypto("LTCUSD", Resolution.HOUR).Symbol,
self.AddCrypto("XRPUSD", Resolution.HOUR).Symbol
]
self.indicators = {}
for symbol in self.symbols:
self.indicators[symbol] = {
"hammer": self.CandlestickPatterns.Hammer(symbol),
"hanging_man": self.CandlestickPatterns.HangingMan(symbol),
"doji": self.CandlestickPatterns.Doji(symbol),
"spinning_top": self.CandlestickPatterns.SpinningTop(symbol),
"engulfing": self.CandlestickPatterns.Engulfing(symbol),
"rsi": self.RSI(symbol, 14, MovingAverageType.Wilders, Resolution.HOUR),
"sma10": self.SMA(symbol, 10, Resolution.HOUR),
"sma05": self.SMA(symbol, 5, Resolution.HOUR),
"ema20": self.EMA(symbol, 20, Resolution.HOUR),
"sma30": self.SMA(symbol, 30, Resolution.HOUR),
"sma50": self.SMA(symbol, 50, Resolution.HOUR),
"sma200": self.SMA(symbol, 200, Resolution.HOUR),
"sma600": self.SMA(symbol, 600, Resolution.HOUR),
"sma40": self.SMA(symbol, 40, Resolution.HOUR),
"sma120": self.SMA(symbol, 120, Resolution.HOUR),
"ema05": self.EMA(symbol, 5, Resolution.HOUR),
"ema10": self.EMA(symbol, 10, Resolution.HOUR),
"ema30": self.EMA(symbol, 30, Resolution.HOUR),
"ema65": self.EMA(symbol, 65, Resolution.HOUR),
"ema100": self.EMA(symbol, 100, Resolution.HOUR),
"ema150": self.EMA(symbol, 150, Resolution.HOUR),
"ema500": self.EMA(symbol, 500, Resolution.HOUR),
"ema600": self.EMA(symbol, 600, Resolution.HOUR),
"entry_price": None,
"stop_price": None
}
def OnData(self, data):
for symbol in self.symbols:
if not data.ContainsKey(symbol):
continue
price = data[symbol].Close
indicators = self.indicators[symbol]
# Buy condition
if (indicators["rsi"].Current.Value > 40) and (indicators["ema10"].Current.Value > indicators["ema20"].Current.Value > indicators["ema65"].Current.Value > indicators["ema150"].Current.Value):
if not self.Portfolio[symbol].Invested:
self.SetHoldings(symbol, 1)
indicators["entry_price"] = price
indicators["stop_price"] = price * 0.90
# Sell condition
elif indicators["rsi"].Current.Value < 50 and ((indicators["ema10"].Current.Value < indicators["ema65"].Current.Value) or (indicators["ema20"].Current.Value < indicators["ema65"].Current.Value)):
if self.Portfolio[symbol].Invested:
self.Liquidate(symbol)
indicators["entry_price"] = None
indicators["stop_price"] = None
# Stop-loss condition
#if self.Portfolio[symbol].Invested and indicators["entry_price"] is not None:
# if price < indicators["entry_price"] * 0.85:
# self.Liquidate(symbol)
# indicators["entry_price"] = None
# indicators["stop_price"] = None
#
# Trailing stop-loss and target
#if self.Portfolio[symbol].Invested:
# indicators["stop_price"] = max(indicators["stop_price"], price * 0.90)
# if price < indicators["stop_price"]:
# self.Liquidate(symbol)
# indicators["stop_price"] = None
# if price > indicators["entry_price"] * 1.15: # Example target
# self.Liquidate(symbol)
# indicators["entry_price"] = None
# indicators["stop_price"] = None