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")