Overall Statistics
Total Orders
418
Average Win
8.57%
Average Loss
-2.12%
Compounding Annual Return
76.152%
Drawdown
39.100%
Expectancy
0.616
Start Equity
100000
End Equity
965825.76
Net Profit
865.826%
Sharpe Ratio
1.497
Sortino Ratio
1.973
Probabilistic Sharpe Ratio
70.603%
Loss Rate
68%
Win Rate
32%
Profit-Loss Ratio
4.04
Alpha
0.553
Beta
0.156
Annual Standard Deviation
0.382
Annual Variance
0.146
Information Ratio
1.108
Tracking Error
0.408
Treynor Ratio
3.67
Total Fees
$0.00
Estimated Strategy Capacity
$2300000.00
Lowest Capacity Asset
BTCUSD 2XR
Portfolio Turnover
28.51%
 
from AlgorithmImports import *

class TechnicalIndicatorsAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2018, 1, 1)  # Set Start Date
        self.SetEndDate(2022, 1, 1)    # Set End Date
        self.SetCash(100000)           # Set Strategy Cash
        
        # Add the cryptocurrency pair
        self.symbol = self.AddCrypto("BTCUSD", Resolution.Hour).Symbol
        
        # Define indicators
        self.hammer = self.CandlestickPatterns.Hammer(self.symbol)
        self.hanging_man = self.CandlestickPatterns.HangingMan(self.symbol)
        self.doji = self.CandlestickPatterns.Doji(self.symbol)
        self.spinning_top = self.CandlestickPatterns.SpinningTop(self.symbol)
        self.engulfing = self.CandlestickPatterns.Engulfing(self.symbol)
        self.rsi = self.RSI(self.symbol, 14, MovingAverageType.Wilders, Resolution.Hour)
        
        # Moving averages
        self.sma10 = self.SMA(self.symbol, 10, Resolution.Hour)
        self.sma05 = self.SMA(self.symbol, 5, Resolution.Hour)
        self.ema20 = self.EMA(self.symbol, 20, Resolution.Hour)
        self.sma30 = self.SMA(self.symbol, 30, Resolution.Hour)
        self.sma50 = self.SMA(self.symbol, 50, Resolution.Hour)
        self.sma200 = self.SMA(self.symbol, 200, Resolution.Hour)
        self.sma600 = self.SMA(self.symbol, 600, Resolution.Hour)
        self.sma40 = self.SMA(self.symbol, 40, Resolution.Hour)
        self.sma120 = self.SMA(self.symbol, 120, Resolution.Hour)
        self.ema05 = self.EMA(self.symbol, 5, Resolution.Hour)
        self.ema10 = self.EMA(self.symbol, 10, Resolution.Hour)
        self.ema30 = self.EMA(self.symbol, 30, Resolution.Hour)
        self.ema65 = self.EMA(self.symbol, 65, Resolution.Hour)
        self.ema100 = self.EMA(self.symbol, 100, Resolution.Hour)
        self.ema150 = self.EMA(self.symbol, 150, Resolution.Hour)
        self.ema500 = self.EMA(self.symbol, 500, Resolution.Hour)
        self.ema600 = self.EMA(self.symbol, 600, Resolution.Hour)
        
        self.entry_price = None
    
    def OnData(self, data):
        if not data.ContainsKey(self.symbol):
            return
        
        price = data[self.symbol].Close
        
        # Buy condition
        if (self.rsi.Current.Value > 55) and (self.ema10.Current.Value > self.ema20.Current.Value > self.ema65.Current.Value > self.ema150.Current.Value):
            if not self.Portfolio[self.symbol].Invested:
                self.SetHoldings(self.symbol, 1)
                self.entry_price = price
        
        # Sell condition
        elif self.rsi.Current.Value < 40 and ((self.ema10.Current.Value < self.ema65.Current.Value) or (self.ema20.Current.Value < self.ema65.Current.Value)):
            if self.Portfolio[self.symbol].Invested:
                self.Liquidate(self.symbol)
                self.entry_price = None
        
        # Stop-loss condition
        if self.Portfolio[self.symbol].Invested and self.entry_price is not None:
            if price < self.entry_price * 0.98:
                self.Liquidate(self.symbol)
                self.entry_price = None
    
    def PlotIndicators(self):
        self.Plot("RSI", "RSI", self.rsi.Current.Value)
        self.Plot("SMA", "SMA10", self.sma10.Current.Value)
        self.Plot("SMA", "SMA20", self.sma20.Current.Value)