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
-1.501
Tracking Error
0.103
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
class JumpingOrangeTermite(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 6, 21)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.Securities["SPY"].SetDataNormalizationMode(DataNormalizationMode.Adjusted)
        
        self.ema = self.EMA(self.spy,10,Resolution.Daily)
        history = self.History(self.spy,10,Resolution.Daily)
        for time , row in history.loc[self.spy].iterrows():
            self.ema.Update(time,row.close)
        self.Securities["SPY"].SetDataNormalizationMode(DataNormalizationMode.Raw)
        
        
        


    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
            Arguments:
                data: Slice object keyed by symbol containing the stock data
        '''

        if self.spy in data.Splits:
            self.ema.Reset()
            split = data.Splits[self.spy]
            history = self.History(self.spy,10,Resolution.Daily)
            history = history/split.SplitFactor
            for time , row in history.loc[self.spy].iterrows():
                self.ema.Update(time,row.close)