Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe 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
0.146
Tracking Error
0.167
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
# region imports
from AlgorithmImports import *
# endregion

import numpy as np

class AdaptableBrownJaguar(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 5, 8)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        self.AddEquity("SPY", Resolution.Daily)

        self.legnth = 5
        self.SetWarmUp(timedelta(self.legnth))
        self.arrary1 = RollingWindow[float](self.legnth)
        self.arrary2 = RollingWindow[float](self.legnth)

    def OnData(self, data: Slice):
        x = 1.0
        y = 2.0
        self.arrary1.Add(x)
        self.arrary1.Add(y)
        if not self.arrary1.IsReady:
            return
        
        element2 = np.sum(list(self.arrary1))
        self.arrary2.Add(element2)
        if not self.arrary2.IsReady:
            return