| Overall Statistics |
|
Total Orders 194 Average Win 1.12% Average Loss -0.27% Compounding Annual Return 37.530% Drawdown 24.400% Expectancy 1.578 Start Equity 100000 End Equity 144143.37 Net Profit 44.143% Sharpe Ratio 1.198 Sortino Ratio 1.758 Probabilistic Sharpe Ratio 55.368% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 4.16 Alpha 0.228 Beta 0.881 Annual Standard Deviation 0.333 Annual Variance 0.111 Information Ratio 0.652 Tracking Error 0.314 Treynor Ratio 0.453 Total Fees $1605.05 Estimated Strategy Capacity $1500000.00 Lowest Capacity Asset USDCUSD 10B Portfolio Turnover 1.49% |
# region imports from AlgorithmImports import * from QuantConnect.DataSource import * from QuantConnect.Data.UniverseSelection import * # endregion class CryptoMomentum(QCAlgorithm): def Initialize(self): self.SetStartDate(2024, 1,1) # Set Start Date self.SetEndDate(2025, 3, 1) self.SetCash(100000) # Set Strategy Cash self.SetBenchmark("SPY") # SET UNIVERSE BASED ON MARKET CAP - top 25 (update weekly) # Configure brokerage and security initializer self.SetBrokerageModel(BrokerageName.Kraken, AccountType.Cash) self.SetSecurityInitializer(BrokerageModelSecurityInitializer(self.BrokerageModel, FuncSecuritySeeder(self.GetLastKnownPrice))) # Universe settings self.UniverseSettings.Resolution = Resolution.Daily self.UniverseSettings.Asynchronous = True # The asynchronous setting is a bool that defines whether or not LEAN can run universe selection asynchronously # utilizing concurrent execution to increase the speed of your algorithm. # Add crypto universe with a selection filter self.AddUniverse(CryptoUniverse.Kraken(self.UniverseSelectionFilter)) # Indicator dictionaries self.rsi = {} self.ema_short = {} self.ema_long = {} self.macd = {} self.entry_prices = {} def UniverseSelectionFilter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]: # only want top 10 traded based on volume - to ensure liquidty for momentum algo return [crypto.Symbol for crypto in sorted(universe_day, key=lambda x: x.VolumeInUsd, reverse=True)[:10]] def OnData(self, data): # Warm Up is a great way to prepare your algorithm and its indicators for trading. # if self.IsWarmingUp: # return # Sell securities that are no longer in the universe for symbol in list(self.entry_prices.keys()): if symbol not in self.ActiveSecurities.Keys: self.Debug(f"Selling {symbol} as it is no longer in the top 25.") self.Liquidate(symbol) del self.entry_prices[symbol] for symbol in self.ActiveSecurities.Keys: # no data, move on if not data.Bars.ContainsKey(symbol): continue # Retrieve indicator values current_price = data.Bars[symbol].Close # RSI rsi_value = self.rsi[symbol].Current.Value # EMA ema_short_value = self.ema_short[symbol].Current.Value ema_long_value = self.ema_long[symbol].Current.Value # MACD macd = self.macd[symbol] # The MACD line represents the distance between a shorter moving average and a longer moving average macd_line = macd.Current.Value # 9-day EMA of the MACD series signal_line = macd.Signal.Current.Value histogram = macd_line - signal_line # Exit Condition if self.entry_prices.get(symbol) is not None: # Already holding if ( ema_short_value < ema_long_value # Bearish crossover or macd_line < signal_line and histogram < 0 # macd less than signal and negative - bear trend ): self.Debug(f"Selling {symbol}") self.Liquidate(symbol) self.entry_prices[symbol] = None # Entry Condition if ( self.entry_prices.get(symbol) is None and # Not already holding ema_short_value > ema_long_value and # Bullish crossover rsi_value is not None and 55 <= rsi_value <= 85 and # RSI in range macd_line > signal_line and histogram > 0 # macd more than signal and positive - bull trend ): self.Debug(f"Buying {symbol}") self.SetHoldings(symbol, 0.15) # Allocate 15% of the portfolio self.entry_prices[symbol] = current_price def OnSecuritiesChanged(self, changes: SecurityChanges): for security in changes.AddedSecurities: symbol = security.Symbol # Initialize indicators for the symbol self.rsi[symbol] = self.RSI(symbol, 14, MovingAverageType.Simple, Resolution.Daily) self.ema_short[symbol] = self.EMA(symbol, 12, Resolution.Daily) self.ema_long[symbol] = self.EMA(symbol, 26, Resolution.Daily) self.macd[symbol] = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
