Backtest

Overall Statistics
Total Orders 4201 Average Win 0.23% Average Loss -0.29% Compounding Annual Return 1.998% Drawdown 5.900% Expectancy 0.002 Start Equity 100000 End Equity 110411.67 Net Profit 10.412% Sharpe Ratio -0.062 Sortino Ratio -0.071 Probabilistic Sharpe Ratio 4.683% Loss Rate 45% Win Rate 55% Profit-Loss Ratio 0.81 Alpha -0.006 Beta 0.034 Annual Standard Deviation 0.038 Annual Variance 0.001 Information Ratio -0.648 Tracking Error 0.156 Treynor Ratio -0.069 Total Fees $4444.40 Estimated Strategy Capacity $31000000.00 Lowest Capacity Asset BRKB R735QTJ8XC9X Portfolio Turnover 69.49%

from AlgorithmImports import *
from QuantConnect.DataSource import *

class ETFConstituentUniverseAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2016, 1, 1)
        self.set_end_date(2021, 1, 1)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        self.universe_settings.resolution = Resolution.MINUTE
        
        # Requesting data
        self.spy = self.add_equity("SPY").symbol
        self._universe = self.add_universe(self.universe.etf(self.spy, self.universe_settings, self.etf_constituents_filter))
        
        # Historical Universe data
        history = self.history(self._universe, 30, Resolution.DAILY)
        for (universe_symbol, time), constituents in history.items():
            for constituent in constituents:
                self.debug(f'{constituent.symbol}: {constituent.weight}')

        self.weight_by_symbol = {}
        
        self.schedule.on(
            self.date_rules.every_day(self.spy),
            self.time_rules.after_market_open(self.spy, 1),
            self.rebalance)

    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
        # Get the 10 securities with the largest weight in the index
        selected = sorted([c for c in constituents if c.weight],
            key=lambda c: c.weight, reverse=True)[:10]
        self.weight_by_symbol = {c.symbol: c.weight for c in selected}
        
        return list(self.weight_by_symbol.keys())

    def rebalance(self) -> None:
        spy_weight = sum(self.weight_by_symbol.values())

        if spy_weight > 0:
            for symbol in self.portfolio.Keys:
                if symbol not in self.weight_by_symbol:
                    self.liquidate(symbol)
    
            for symbol, weight in self.weight_by_symbol.items():
                self.set_holdings(symbol, 0.5 * weight / spy_weight)
                
            self.set_holdings(self.spy, -0.5)

    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.removed_securities:
            if security.invested:
                self.liquidate(security.symbol, 'Removed From Universe')

        for security in changes.added_securities:
            # Historical data
            history = self.history(security.symbol, 7, Resolution.DAILY)
            self.debug(f'We got {len(history)} from our history request for {security.symbol}')