Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return -2.485% Drawdown 2.700% Expectancy 0 Net Profit 0% Sharpe Ratio -0.231 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.006 Beta 1 Annual Standard Deviation 0.09 Annual Variance 0.008 Information Ratio -3.112 Tracking Error 0.002 Treynor Ratio -0.021 Total Fees $2.02 |
# # QuantConnect Basic Template: # Fundamentals to using a QuantConnect algorithm. # # You can view the QCAlgorithm base class on Github: # https://github.com/QuantConnect/Lean/tree/master/Algorithm # import pandas as pd import numpy as np import talib class BasicTemplateAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2017,8,1) self.stocks = ["SPY","TQQQ"] for s in self.stocks: self.AddEquity(s) # History request using list of tickers hist = self.History(self.stocks, 1000, Resolution.Minute) # Check if returns an empty list. It should not. if hist.empty: self.Log(str(self.Time) + " empty hist") return # The History method returns a multi-index pandas.DataFrame, where the # symbols are index 0 and date/time is index 1. # We can use unstack to a column (close) and the index 0 will become column prices = hist.close.unstack(level=0) # Then we can .as_matrix normally ret = prices.pct_change()[1:].as_matrix(self.stocks) ret_mean = prices.pct_change().mean() ret_std = prices.pct_change().std() ret_norm = ret_mean / ret_std ret_norm = ret_norm.as_matrix(self.stocks) self.Log("ret_norm" + str(ret_norm)) def OnData(self, data): if not self.Portfolio.Invested: self.SetHoldings("SPY", 1)