Overall Statistics
Total Orders
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
13.280%
Drawdown
33.600%
Expectancy
0
Start Equity
100000
End Equity
348395.52
Net Profit
248.396%
Sharpe Ratio
0.553
Sortino Ratio
0.558
Probabilistic Sharpe Ratio
10.319%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
-0
Beta
0.998
Annual Standard Deviation
0.146
Annual Variance
0.021
Information Ratio
-0.453
Tracking Error
0
Treynor Ratio
0.081
Total Fees
$3.06
Estimated Strategy Capacity
$1400000000.00
Lowest Capacity Asset
SPY R735QTJ8XC9X
Portfolio Turnover
0.03%
 
# THIS IS JUST A DEFAULT PROJECT
# In order to check the 10 Best Days research you need to select ...
#
# on the right side in the EXPLORER select --> Best Days.ipynb


from AlgorithmImports import *

class BuyAndHoldSPY(QCAlgorithm):
    def Initialize(self):
        # Set the start and end date for a 10-year backtest
        self.SetStartDate(2014, 10, 1)  # Start date 10 years ago
        self.SetEndDate(2024, 10, 1)    # End date
        self.SetCash(100000)            # Set the initial cash balance

        # Add SPY (S&P 500 ETF) to the universe
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol

        # Set warm-up period for indicators or anything else if needed
        self.SetWarmUp(TimeSpan.FromDays(30))

    def OnData(self, data):
        # Check if the portfolio does not already have SPY holdings
        if not self.Portfolio[self.spy].Invested:
            # Buy SPY with 100% of the available portfolio cash
            self.SetHoldings(self.spy, 1)
            self.Debug(f"Purchased SPY on {self.Time}")