Overall Statistics
Total Orders
225
Average Win
1.54%
Average Loss
-1.65%
Compounding Annual Return
16.008%
Drawdown
22.200%
Expectancy
0.312
Start Equity
100000
End Equity
210161.94
Net Profit
110.162%
Sharpe Ratio
0.552
Sortino Ratio
0.644
Probabilistic Sharpe Ratio
24.484%
Loss Rate
32%
Win Rate
68%
Profit-Loss Ratio
0.93
Alpha
0.018
Beta
0.875
Annual Standard Deviation
0.154
Annual Variance
0.024
Information Ratio
0.09
Tracking Error
0.094
Treynor Ratio
0.097
Total Fees
$601.50
Estimated Strategy Capacity
$45000000.00
Lowest Capacity Asset
VGT SVS2QA8SPHET
Portfolio Turnover
2.63%
Drawdown Recovery
570
 
#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/Screener/Details/3
# Use 10 sector ETFs. Pick 3 ETFs with strongest 3 month momentum into your portfolio 
# and weigh them equally. Hold for 1 month and then rebalance.

class SectorMomentumAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(self.end_date - timedelta(5*365))
        self.set_cash(100000)
        self.settings.automatic_indicator_warm_up = True

        tickers = ["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
                   "KBE",  # SPDR S&P Bank ETF
                   "VAW",  # Vanguard Materials ETF
                   "XLY",  # Consumer Discretionary Select Sector SPDR Fund
                   "XLP",  # Consumer Staples Select Sector SPDR Fund
                   "VGT"]  # Vanguard Information Technology ETF
                        
        for ticker in tickers:
            equity = self.add_equity(ticker, Resolution.DAILY)
            # Three-month momentum (3 x 22 days)
            equity.momp = self.momp(equity.symbol, 3*22, Resolution.DAILY)

        # schedule the function to fire at the month start 
        self.schedule.on(
            self.date_rules.month_start("VNQ"), 
            self.time_rules.midnight, 
            self._rebalance)

    def _rebalance(self):
        # Pick 3 ETFs with strongest momentum and weight them equally.
        to_long = [x.symbol for x in sorted(self.securities.values(), key=lambda s: s.momp)[-3:]]
        targets = [PortfolioTarget(symbol, 1/3) for symbol in to_long]
        self.set_holdings(targets, liquidate_existing_holdings=True)