| Overall Statistics |
|
Total Orders
930
Average Win
0.61%
Average Loss
-1.34%
Compounding Annual Return
6.075%
Drawdown
29.400%
Expectancy
0.230
Start Equity
100000
End Equity
441751.56
Net Profit
341.752%
Sharpe Ratio
0.238
Sortino Ratio
0.234
Probabilistic Sharpe Ratio
0.047%
Loss Rate
16%
Win Rate
84%
Profit-Loss Ratio
0.46
Alpha
0.012
Beta
0.307
Annual Standard Deviation
0.104
Annual Variance
0.011
Information Ratio
-0.126
Tracking Error
0.143
Treynor Ratio
0.08
Total Fees
$3243.16
Estimated Strategy Capacity
$650000.00
Lowest Capacity Asset
GSG TKH7EPK7SRC5
Portfolio Turnover
1.09%
|
# https://quantpedia.com/strategies/asset-class-trend-following/
#
# Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, IEF - 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.
#
# QC implementation changes:
# - SMA with period of 210 days is used.
#region imports
from AlgorithmImports import *
#endregion
class AssetClassTrendFollowing(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
tickers: List[str] = ["SPY", "EFA", "IEF", "VNQ", "GSG"]
period: int = 10 * 21
self.sma: Dict[Symbol, SimpleMovingAverage] = {
self.AddEquity(ticker, Resolution.Minute).Symbol : self.SMA(ticker, period, Resolution.Daily) for ticker in tickers
}
self.recent_month: int = -1
self.SetWarmUp(period, Resolution.Daily)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
def OnData(self, data: Slice) -> None:
if self.IsWarmingUp: return
if not (self.Time.hour == 9 and self.Time.minute == 31):
return
# rebalance once a month
if self.Time.month == self.recent_month:
return
self.recent_month = self.Time.month
long: List[Symbol] = [ symbol for symbol, sma in self.sma.items()
if symbol in data
and data[symbol]
and sma.IsReady
and data[symbol].Value > sma.Current.Value
]
portfolio: List[PortfolioTarget] = [PortfolioTarget(symbol, 1. / len(long)) for symbol in long]
self.SetHoldings(portfolio, True)