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
2.869
Tracking Error
0.09
Treynor Ratio
0
Total Fees
$0.00
import pandas as pd
import numpy as np
import talib

class CalibratedResistanceAtmosphericScrubbers(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 1, 2)
        self.SetEndDate(2020, 1, 6) 
        self.SetCash(100000) 
        self.AddEquity("SPY", Resolution.Hour)
        
        self.rolling_window = pd.DataFrame()
        self.dema_period = 3
        self.sma_period = 3
        self.wma_period = 3
        self.window_size = self.dema_period * 2
        self.SetWarmUp(self.window_size)
        
    def OnData(self, data):
        if "SPY" not in data.Bars:
            return
        
        close = data["SPY"].Close
        
        if self.IsWarmingUp:
            # Add latest close to rolling window
            row = pd.DataFrame({"close": [close]}, index=[data.Time])
            self.rolling_window = self.rolling_window.append(row).iloc[-self.window_size:]
            
            # If we have enough closing data to start calculating indicators...
            if self.rolling_window.shape[0] == self.window_size:
                closes = self.rolling_window['close'].values
                
                # Add indicator columns to DataFrame
                self.rolling_window['DEMA'] = talib.DEMA(closes, self.dema_period)
                self.rolling_window['EMA'] = talib.EMA(closes, self.sma_period)
                self.rolling_window['WMA'] = talib.WMA(closes, self.wma_period)
            return
        
        closes = np.append(self.rolling_window['close'].values, close)[-self.window_size:]
        
        # Update talib indicators time series with the latest close
        row = pd.DataFrame({"close": close,
                            "DEMA" : talib.DEMA(closes, self.dema_period)[-1],
                            "EMA"  : talib.EMA(closes, self.sma_period)[-1],
                            "WMA"  : talib.WMA(closes, self.wma_period)[-1]},
                            index=[data.Time])
        
        self.rolling_window = self.rolling_window.append(row).iloc[-self.window_size:]

        
    def OnEndOfAlgorithm(self):
        self.Log(f"\nRolling Window:\n{self.rolling_window.to_string()}\n")
        self.Log(f"\nLatest Values:\n{self.rolling_window.iloc[-1].to_string()}\n")