Overall Statistics
Total Orders
389
Average Win
0.80%
Average Loss
-2.48%
Compounding Annual Return
26.148%
Drawdown
30.300%
Expectancy
-0.027
Start Equity
10000
End Equity
14590.39
Net Profit
45.904%
Sharpe Ratio
0.686
Sortino Ratio
0.739
Probabilistic Sharpe Ratio
40.874%
Loss Rate
26%
Win Rate
74%
Profit-Loss Ratio
0.32
Alpha
0.008
Beta
1.042
Annual Standard Deviation
0.217
Annual Variance
0.047
Information Ratio
0.074
Tracking Error
0.186
Treynor Ratio
0.143
Total Fees
$250.01
Estimated Strategy Capacity
$2600000.00
Lowest Capacity Asset
VXZB WRBPJAJZ2Q91
Portfolio Turnover
3.29%
 
from AlgorithmImports import *
from scipy.optimize import minimize

class LeverageEtfRiskParity(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2023, 1, 1)
        self.set_cash(10000)
        self.symbols = [self.add_equity(ticker, data_normalization_mode=DataNormalizationMode.RAW).symbol for ticker in ["TQQQ", "SVXY", "VXZ", "TMF", "EDZ", "UGL"]]
        self.dynamic_leverage = 1  # Start with base leverage of 1
        self.stop_loss_threshold = 0.1  # Define a 10% stop-loss limit
        self.schedule.on(self.date_rules.week_start(), self.time_rules.at(8, 0), self.rebalance)

    def rebalance(self):
        # Calculate average volatility across all symbols
        historical_data = self.history(self.symbols, 20, Resolution.DAILY).close.unstack(0)
        volatilities = historical_data.pct_change().std()  # Standard deviation of daily returns for each symbol
        avg_volatility = volatilities.mean()  # Take the mean across symbols
        self.dynamic_leverage = min(2.5, 1 / avg_volatility)  # Cap leverage at 1.5 for risk management

        # Calculate daily returns and optimize portfolio
        ret = self.history(self.symbols, 253, Resolution.DAILY).close.unstack(0).pct_change().dropna()
        x0 = [1 / ret.shape[1]] * ret.shape[1]
        constraints = {"type": "eq", "fun": lambda w: np.sum(w) - 1}
        bounds = [(0, 1)] * ret.shape[1]
        opt = minimize(lambda w: 0.5 * (w.T @ ret.cov() @ w) - x0 @ w, x0=x0, constraints=constraints, bounds=bounds, tol=1e-8, method="SLSQP")

        # Apply leverage and set holdings
        targets = [PortfolioTarget(symbol, weight * self.dynamic_leverage) for symbol, weight in zip(ret.columns, opt.x)]
        self.set_holdings(targets)

    def on_data(self, data):
        # Implement a stop-loss mechanism to limit losses
        for symbol in self.symbols:
            price = self.securities[symbol].price
            if price < (1 - self.stop_loss_threshold) * self.portfolio[symbol].average_price:
                self.liquidate(symbol)  # Exit position if it falls below stop-loss threshold