Overall Statistics |
Total Trades
6321
Average Win
0.08%
Average Loss
-0.14%
Compounding Annual Return
2.572%
Drawdown
29.600%
Expectancy
0.061
Net Profit
35.300%
Sharpe Ratio
0.292
Loss Rate
32%
Win Rate
68%
Profit-Loss Ratio
0.56
Alpha
0.029
Beta
0.088
Annual Standard Deviation
0.103
Annual Variance
0.011
Information Ratio
0.103
Tracking Error
0.103
Treynor Ratio
0.344
Total Fees
$7927.81
|
# https://quantpedia.com/Screener/Details/1 # Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, BND - bonds, VNQ - REITs, # GSG - commodities), equal weight the portfolio. Hold asset class ETF only when # it is over its 10 month Simple Moving Average, otherwise stay in cash. import numpy as np from datetime import datetime class BasicTemplateAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2007, 5, 1) self.SetEndDate(datetime.now()) self.SetCash(100000) self.data = {} period = 10*21 self.SetWarmUp(period) self.symbols = ["SPY", "EFA", "BND", "VNQ", "GSG"] for symbol in self.symbols: self.AddEquity(symbol, Resolution.Daily) self.data[symbol] = self.SMA(symbol, period, Resolution.Daily) def OnData(self, data): if self.IsWarmingUp: return isUptrend = [] for symbol, sma in self.data.items(): if self.Securities[symbol].Price > sma.Current.Value: isUptrend.append(symbol) else: self.Liquidate(symbol) for symbol in isUptrend: self.SetHoldings(symbol, 1/len(isUptrend))