| Overall Statistics |
|
Total Trades
801
Average Win
0.83%
Average Loss
-1.15%
Compounding Annual Return
6.491%
Drawdown
49.200%
Expectancy
0.306
Net Profit
329.634%
Sharpe Ratio
0.388
Probabilistic Sharpe Ratio
0.016%
Loss Rate
24%
Win Rate
76%
Profit-Loss Ratio
0.72
Alpha
0.017
Beta
0.675
Annual Standard Deviation
0.142
Annual Variance
0.02
Information Ratio
-0.019
Tracking Error
0.104
Treynor Ratio
0.081
Total Fees
$999.86
Estimated Strategy Capacity
$66000000.00
Lowest Capacity Asset
XLB RGRPZX100F39
|
#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/strategies/sector-momentum-rotational-system/
#
# Use ten sector ETFs. Pick 3 ETFs with the strongest 12-month momentum into your portfolio and weight them equally. Hold them for one month and then rebalance.
class SectorMomentumAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
# Daily ROC data.
self.data = {}
self.period = 12 * 21
self.SetWarmUp(self.period)
self.symbols = [
"VNQ", # Vanguard Real Estate Index Fund
"XLK", # Technology Select Sector SPDR Fund
"XLE", # Energy Select Sector SPDR Fund
"XLV", # Health Care Select Sector SPDR Fund
"XLF", # Financial Select Sector SPDR Fund
"XLI", # Industrials Select Sector SPDR Fund
"XLB", # Materials Select Sector SPDR Fund
"XLY", # Consumer Discretionary Select Sector SPDR Fund
"XLP", # Consumer Staples Select Sector SPDR Fund
"XLU" # Utilities Select Sector SPDR Fund
]
for symbol in self.symbols:
data = self.AddEquity(symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(5)
self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily)
self.data[self.symbols[0]].Updated += self.OnROCUpdated
self.recent_month = -1
self.rebalance_flag = False
def OnROCUpdated(self, sender, updated):
# set rebalance flag
if self.recent_month != self.Time.month:
self.recent_month = self.Time.month
self.rebalance_flag = True
def OnData(self, data):
if self.IsWarmingUp: return
# rebalance once a month
if self.rebalance_flag:
self.rebalance_flag = False
sorted_by_momentum = sorted([x for x in self.data.items() if x[1].IsReady and x[0] in data and data[x[0]]], key = lambda x: x[1].Current.Value, reverse = True)
long = [x[0] for x in sorted_by_momentum[:3]]
# Trade execution.
invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in long:
self.Liquidate(symbol)
for symbol in long:
self.SetHoldings(symbol, 1 / len(long))
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))