Overall Statistics
Total Orders
845
Average Win
0.33%
Average Loss
-0.01%
Compounding Annual Return
21.103%
Drawdown
29.000%
Expectancy
45.369
Start Equity
1000000
End Equity
6837125.63
Net Profit
583.713%
Sharpe Ratio
0.922
Sortino Ratio
0.998
Probabilistic Sharpe Ratio
56.518%
Loss Rate
3%
Win Rate
97%
Profit-Loss Ratio
46.73
Alpha
0.058
Beta
0.774
Annual Standard Deviation
0.136
Annual Variance
0.018
Information Ratio
0.497
Tracking Error
0.079
Treynor Ratio
0.162
Total Fees
$1225.25
Estimated Strategy Capacity
$1600000.00
Lowest Capacity Asset
IYW RUTTRZ1RC7L1
Portfolio Turnover
0.21%
Drawdown Recovery
600
 
# ================================================================================
# REB01 - Rebalancing Strategy
# ================================================================================
# Author: Angus Li
# Date: January 14, 2026
# 
# ⚠️ DISCLAIMER - FOR EDUCATIONAL & BACKTESTING PURPOSES ONLY ⚠️
# 
# This code is provided for demonstration and educational purposes only.
# It is NOT intended for live trading without proper due diligence, risk 
# management, and thorough testing in paper trading environments.
# 
# Past performance does not guarantee future results. The author assumes 
# NO LIABILITY for any financial losses, damages, or adverse outcomes 
# resulting from the use of this code in live trading environments.
# 
# By using this code, you acknowledge that:
# - You trade at your own risk
# - You are solely responsible for your trading decisions
# - The author is not a financial advisor
# - This is not financial advice
# 
# Always consult with qualified financial professionals before making 
# investment decisions.
# ================================================================================


# region imports
from AlgorithmImports import *
# endregion


# lean project-create --language python "REB01"
# lean cloud backtest "REB01" --push --open
class REB01(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2016, 1, 1)
        self.set_cash(1000000)
        self.set_benchmark("SPY")
        self._stock = "IYW"
        self._gold = "GLD"
        self.assets = [self._stock, self._gold] 
        
        # Add Equity ------------------------------------------------ 
        for ticker in self.assets:
            self.add_equity(ticker, Resolution.HOUR).symbol
        
        self.schedule.on(self.date_rules.week_start("SPY"), 
            self.time_rules.after_market_open("SPY", 1), 
            self.every_day_before_market_close)
            
            
    def every_day_before_market_close(self):
        self.set_holdings([PortfolioTarget(self._gold, 0.4), PortfolioTarget(self._stock, 0.6)])