| Overall Statistics |
|
Total Orders 455 Average Win 0.41% Average Loss -0.25% Compounding Annual Return 24.485% Drawdown 8.700% Expectancy 0.275 Start Equity 1000000.00 End Equity 1180703.47 Net Profit 18.070% Sharpe Ratio 1.06 Sortino Ratio 1.231 Probabilistic Sharpe Ratio 67.796% Loss Rate 51% Win Rate 49% Profit-Loss Ratio 1.61 Alpha 0.101 Beta 0.237 Annual Standard Deviation 0.109 Annual Variance 0.012 Information Ratio 0.313 Tracking Error 0.171 Treynor Ratio 0.489 Total Fees $1002.14 Estimated Strategy Capacity $44000000.00 Lowest Capacity Asset XRPUSD 2XR Portfolio Turnover 9.44% Drawdown Recovery 175 |
# region imports
from AlgorithmImports import *
import time
# endregion
class MultiAssetMomentum(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2025, 7, 1) # Set Start Date
self.SetEndDate(2025, 9, 17)
self.SetCash(100000) # Set Strategy Cash
self.SetWarmUp(30, Resolution.Daily) # 30 days of historical data
# Set sector allocation at initialization (25% each sector)
# self.sector_allocation = 0.25 # 25% for each sector
# self.asset_allocation = 0.05 # 5% of the sector allocation per asset (i.e., 1.25% of total portfolio per asset)
self.rsi_buy_top = self.GetParameter("rsi_buy_top", 80) # Default 70
self.rsi_buy_bot = self.GetParameter("rsi_buy_bot", 50) # Default 30
self.rsi_sell_top = self.GetParameter("rsi_sell_top", 90) # Default 80
self.rsi_sell_bot = self.GetParameter("rsi_sell_bot", 40) # Default 40
self.ema_short_period = self.GetParameter("ema_short_period", 12) # Default 12
self.ema_long_period = self.GetParameter("ema_long_period", 26) # Default 26
self.atr_multiplier = 2 # ATR Stop Loss Multiplier
self.max_holding_days = 10 # Time-based Stop Loss (Exit after 10 days)
# Configure brokerage and security initializer
# Universe settings
self.UniverseSettings.Resolution = Resolution.Daily
self.UniverseSettings.Asynchronous = True
# Define fixed universes
# BTCUSD - Bitcoin, ETHUSD - Ethereum , SOLUSD - Solana, XRPUSD - XRP (Ripple), ADAUSD - Cardano , DOGEUSD - Dogecoin
self.crypto_symbols = ["BTCUSD", "ETHUSD", "SOLUSD", "XRPUSD", "ADAUSD", "DOGEUSD"]
# apple, microsoft, nvidia, google, amazon, meta, tesla, boardcome, taiwan semiconductor, ali baba
self.tech_symbols = ["AAPL", "MSFT", "NVDA", "GOOGL", "AMZN", "META", "TSLA", "AVGO", "TSM", "BABA"]
# XOM - ExxonMobil, CVX - Chevron Corporation, BP - BP Plc , TOT - TotalEnergies, COP - ConocoPhillips , SHEL - Shell Plc
self.energy_symbols = ["XOM", "CVX", "BP", "TOT", "COP", "SHEL"]
# EURUSD - Euro / US Dollar, USDJPY - US Dollar / Japanese Yen, GBPUSD - British Pound / US Dollar, AUDUSD - Australian Dollar / US Dollar
# USDCAD - US Dollar / Canadian Dollar, EURJPY - Euro / Japanese Yen, GBPJPY - British Pound / Japanese Yen, EURGBP - Euro / British Pound
self.fx_symbols = ["EURUSD", "USDJPY", "GBPUSD", "AUDUSD", "USDCAD", "EURJPY", "GBPJPY", "EURGBP"]
# Add securities and initialize indicators
self.rsi = {}
self.ema_short = {}
self.ema_long = {}
self.macd = {}
self.atr = {}
self.entry_prices = {}
self.entry_dates = {} # Track entry time for time-based stop loss
self.stop_losses = {} # Track ATR-based stop loss levels
self.trailing_stop_losses = {} # Track trailing stop losses
for symbol in self.crypto_symbols:
self.AddCrypto(symbol, Resolution.Daily)
for symbol in self.tech_symbols + self.energy_symbols:
self.AddEquity(symbol, Resolution.Daily)
for symbol in self.fx_symbols:
self.AddForex(symbol, Resolution.Daily)
for symbol in self.crypto_symbols + self.tech_symbols + self.energy_symbols + self.fx_symbols:
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)
self.atr[symbol] = self.ATR(symbol, 14, MovingAverageType.Simple, Resolution.Daily)
def OnData(self, data):
if self.IsWarmingUp:
return
current_time = self.Time
for symbol in list(self.entry_prices.keys()):
if symbol not in data.Bars:
continue
current_price = data.Bars[symbol].Close
atr_value = self.atr[symbol].Current.Value
# Time-based stop-loss
if (current_time - self.entry_dates[symbol]).days >= self.max_holding_days:
self.Debug(f"Time stop-loss: {symbol}")
self.Liquidate(symbol)
self.RemovePositionTracking(symbol)
continue
# ATR-based stop-loss
if current_price < self.stop_losses[symbol]:
self.Debug(f"ATR stop-loss: {symbol}")
self.Liquidate(symbol)
self.RemovePositionTracking(symbol)
continue
# Update trailing stop
if current_price > self.entry_prices[symbol]:
self.trailing_stop_losses[symbol] = max(
self.trailing_stop_losses[symbol],
current_price - 1.5 * atr_value
)
if current_price < self.trailing_stop_losses[symbol]:
self.Debug(f"Trailing stop-loss: {symbol}")
self.Liquidate(symbol)
self.RemovePositionTracking(symbol)
for symbol in self.rsi.keys():
if symbol not in data.Bars:
continue
current_price = data.Bars[symbol].Close
rsi_value = self.rsi[symbol].Current.Value
ema_short_value = self.ema_short[symbol].Current.Value
ema_long_value = self.ema_long[symbol].Current.Value
macd_line = self.macd[symbol].Current.Value
signal_line = self.macd[symbol].Signal.Current.Value
histogram = macd_line - signal_line
atr_value = self.atr[symbol].Current.Value
if (
self.entry_prices.get(symbol) is None and
ema_short_value > ema_long_value and
self.rsi_buy_bot <= rsi_value <= self.rsi_buy_top and
histogram > 0
):
allocation = self.GetDynamicAllocation(symbol, atr_value, histogram)
self.Debug(f"Buying {symbol} with allocation {allocation:.2%}")
self.SetHoldings(symbol, allocation)
self.entry_prices[symbol] = current_price
self.entry_dates[symbol] = current_time
self.stop_losses[symbol] = current_price - self.atr_multiplier * atr_value
self.trailing_stop_losses[symbol] = current_price - 1.5 * atr_value
def GetDynamicAllocation(self, symbol, atr_value, histogram):
if symbol in self.crypto_symbols:
base_weight = 0.02
elif symbol in self.tech_symbols:
base_weight = 0.05
elif symbol in self.energy_symbols:
base_weight = 0.04
elif symbol in self.fx_symbols:
base_weight = 0.03
else:
base_weight = 0.02
vol_adjustment = 1.0
if atr_value > 0:
vol_adjustment = min(1.0, 1.0 / (atr_value / 100))
momentum_adjustment = 1.0 + min(0.5, histogram / 0.02)
allocation = min(base_weight * vol_adjustment * momentum_adjustment, base_weight * 2)
return allocation
def RemovePositionTracking(self, symbol):
"""Removes tracking data for a symbol when a position is closed."""
del self.entry_prices[symbol]
del self.entry_dates[symbol]
del self.stop_losses[symbol]
del self.trailing_stop_losses[symbol]
''' self.Debug(f"{symbol}: rsi {rsi_value} emaS{ema_short_value} emaL {ema_long_value} histogram {histogram} ")
# Exit condition: bearish crossover or MACD reversal
if ema_short_value < ema_long_value or histogram < 0:
self.Debug(f"Selling {symbol}")
self.Liquidate(symbol)
del self.entry_prices[symbol]
# Entry conditions
for symbol in self.rsi.keys():
if symbol not in data.Bars:
self.Debug(f"{symbol} No data available at this time.")
continue # Skip if no data
current_price = data.Bars[symbol].Close
rsi_value = self.rsi[symbol].Current.Value
ema_short_value = self.ema_short[symbol].Current.Value
ema_long_value = self.ema_long[symbol].Current.Value
macd_line = self.macd[symbol].Current.Value
signal_line = self.macd[symbol].Signal.Current.Value
histogram = macd_line - signal_line
self.Debug(f"{symbol}: rsi {rsi_value} emaS{ema_short_value} emaL {ema_long_value} histogram {histogram} ")
if (
self.entry_prices.get(symbol) is None and # Not already holding
ema_short_value > ema_long_value and
self.rsi_buy_bot <= rsi_value <= self.rsi_buy_top and
histogram > 0
):
self.Debug(f"Buying {symbol}")
self.SetHoldings(symbol, 0.05)
self.entry_prices[symbol] = current_price'''