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
-1.639
Tracking Error
0.127
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
Portfolio Turnover
0%
 
# region imports
from AlgorithmImports import *
# endregion

class DEBUG04(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2025, 1, 1)  # Set Start Date
        self.set_cash(100000)  # Set Strategy Cash

        self.nvda = self.add_equity("NVDA", Resolution.DAILY).symbol
        self.nvda_ema = self.ema(self.nvda, 100, 0.02, Resolution.DAILY)
        self.nvda_sma = self.sma(self.nvda, 100, Resolution.DAILY)

        self.meta = self.add_equity("META", Resolution.DAILY).symbol
        self.meta_ema = self.ema(self.meta, 100, 0.02, Resolution.DAILY)
        self.meta_sma = self.sma(self.meta, 100, Resolution.DAILY)

        self.set_warmup(600, Resolution.DAILY)

    def on_data(self, data: Slice):
        if not self.nvda_ema.is_ready or not self.nvda_sma.is_ready or not self.meta_ema.is_ready or not self.meta_sma.is_ready:
            return
        else:
            if self.Time.year == 2025:
                self.Log(f"{self.Time} [NVDA] ema_100: {round(self.nvda_ema.current.value,2)} / sma_100: {round(self.nvda_sma.current.value,2)}")
                self.Log(f"{self.Time} [META] ema_100: {round(self.meta_ema.current.value,2)} / sma_100: {round(self.meta_sma.current.value,2)}")