| Overall Statistics |
|
Total Trades 1076 Average Win 0.85% Average Loss -0.76% Compounding Annual Return 5.394% Drawdown 22.000% Expectancy 0.057 Net Profit 21.913% Sharpe Ratio 0.389 Probabilistic Sharpe Ratio 7.544% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 1.12 Alpha 0.016 Beta 0.216 Annual Standard Deviation 0.112 Annual Variance 0.012 Information Ratio -0.474 Tracking Error 0.173 Treynor Ratio 0.201 Total Fees $4523.67 Estimated Strategy Capacity $17000000.00 Lowest Capacity Asset MSFT R735QTJ8XC9X |
class EmotionalFluorescentPinkSalmon(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 1, 1)
self.SetEndDate(2021, 10, 8)
self.SetCash(100000)
self.atr = {};
self.atr_norm = {};
symbols = ["AMZN", "MSFT", "AAPL"]
self.stocks = [self.AddEquity(ticker, Resolution.Minute).Symbol for ticker in symbols]
for sec in self.stocks:
self.atr[sec] = self.ATR(sec, 14, MovingAverageType.Simple, Resolution.Daily)
self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
self.SetWarmUp(15, Resolution.Daily)
self.yest_close = self.SMA(self.spy, 1, Resolution.Daily, Field.Close)
def OnData(self, data):
if self.IsWarmingUp or not self.yest_close.IsReady or not len(data.Bars) > 0: return
for sec in self.stocks:
if not self.atr[sec].IsReady: continue
atr_curr = self.atr[sec].Current.Value
price = self.Securities[sec].Price
self.atr_norm[sec] = atr_curr/price
self.Plot("ATR", sec, self.atr_norm[sec])
selected_atrs = {k: v for k, v in sorted(self.atr_norm.items(), key=lambda item: item[1], reverse=True)[-1:]}
portfolio_targets = []
for sec, volt in self.atr_norm.items():
if sec in selected_atrs:
portfolio_targets.append(sec)
if self.Time.hour == 9 and self.Time.minute == 45:
for sec in portfolio_targets:
price = self.Securities[self.spy].Price
yest_close = self.yest_close.Current.Value
if price > yest_close:
self.SetHoldings(sec, 1/len(portfolio_targets))
if self.Time.hour == 15 and self.Time.minute == 45:
self.Liquidate()