| Overall Statistics |
|
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 147.421% Drawdown 36.500% Expectancy 0 Net Profit 57.085% Sharpe Ratio 2.544 Probabilistic Sharpe Ratio 67.058% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 1.531 Beta 0 Annual Standard Deviation 0.602 Annual Variance 0.362 Information Ratio 1.838 Tracking Error 0.614 Treynor Ratio 17831.505 Total Fees $1.18 Estimated Strategy Capacity $470000000.00 |
import numpy as np
import pandas as pd
class JumpingBlackArmadillo(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 11, 4)
self.SetCash(100000)
self.symbol = self.AddEquity("TSLA", Resolution.Daily).Symbol
self.equity_curve = pd.Series()
def OnData(self, data):
if not (data.ContainsKey(self.symbol) and data[self.symbol] is not None):
return
if not self.Portfolio.Invested:
self.SetHoldings(self.symbol, 1)
self.equity_curve = self.equity_curve.append(pd.Series(data[self.symbol].Price, index=[data.Time]))
self.Plot('Drawdown Percent', 'Current', self.current_drawdown_percent(self.equity_curve))
max_dd_percent = self.max_drawdown_percent(self.equity_curve)
if not np.isnan(max_dd_percent):
self.Plot(f'Drawdown Percent', 'Max', max_dd_percent)
def current_drawdown_percent(self, equity_curve):
hwm = max(equity_curve)
current = equity_curve[-1]
return (hwm - current) / hwm * 100
def max_drawdown_percent(self, equity_curve):
i = np.argmax(np.maximum.accumulate(equity_curve) - equity_curve)
if equity_curve[:i].size == 0:
return np.nan
j = np.argmax(equity_curve[:i])
return abs((equity_curve[i]/equity_curve[j]) - 1) * 100