Overall Statistics
Total Orders
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Start Equity
100000
End Equity
100000
Net Profit
0%
Sharpe Ratio
0
Sortino Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
-0.822
Tracking Error
0.087
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
Portfolio Turnover
0%
Drawdown Recovery
0
 
# region imports
from AlgorithmImports import *
# endregion
class DeterminedBrownLemur(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2025, 12, 1)

        def select(constituents):
            msg = ','.join([f'{x.symbol.value} : {x.weight:.6%}' 
                for x in constituents if x.symbol.value in ["AAPL", "NVDA"] ])
            self.log(msg)
            return Universe.UNCHANGED
        self.add_universe(self.universe.etf("SPY", select))

        self._market_caps = {self.add_equity(ticker, Resolution.HOUR).symbol: 0 for ticker in ["AAPL", "NVDA"]}
        self.schedule.on(self.date_rules.every_day("AAPL"), self.time_rules.every(timedelta(hours=1)), self._log_market_cap)
        if self.live_mode:
            dt = datetime.now() - datetime(2025,12,1)
            self.set_warm_up(dt, Resolution.HOUR)
    def _log_market_cap(self):
        for symbol, previous in self._market_caps.items():
            current = self.securities[symbol].fundamentals.market_cap
            if previous != current:
                self.log(f'{self.utc_time:%y%m%d %H:%M:%S} Market Cap for {symbol.value} :: {previous=}, {current}')
                self._market_caps[symbol] = current