| 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 -2.16 Tracking Error 0.112 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |
import numpy as np
import pandas as pd
from numba import jit, float64
class SwimmingBrownEagle(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2021, 2, 15) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
def OnData(self, data):
history = self.History(self.spy, 200, Resolution.Daily).close.unstack("symbol")
ret = NumbaJITLogReturn(history.values) # need to use np array
@jit(float64(float64))
def NumbaJITLogReturn(df):
ret = np.zeros(df.shape)
ret[0] = 0
for i in range(1, len(df)):
ret[i] = (df[i]-df[i-1])/df[i-1]
return ret