| Overall Statistics |
|
Total Orders 6157 Average Win 0.14% Average Loss -0.11% Compounding Annual Return -1.148% Drawdown 22.000% Expectancy -0.078 Start Equity 10000000 End Equity 9521095.29 Net Profit -4.789% Sharpe Ratio -0.378 Sortino Ratio -0.433 Probabilistic Sharpe Ratio 0.516% Loss Rate 58% Win Rate 42% Profit-Loss Ratio 1.22 Alpha -0.035 Beta -0.034 Annual Standard Deviation 0.097 Annual Variance 0.009 Information Ratio -0.537 Tracking Error 0.171 Treynor Ratio 1.067 Total Fees $215945.89 Estimated Strategy Capacity $15000.00 Lowest Capacity Asset ALTR R735QTJ8XC9X Portfolio Turnover 1.83% |
# region imports from AlgorithmImports import * # endregion class GeekyAsparagusViper(QCAlgorithm): def initialize(self): self.set_start_date(2021, 1, 1) self.set_end_date(2025, 4, 1) self.set_cash(10_000_000) # Add a universe of the 100 smallest assets in IWM. etf = Symbol.create('IWM', SecurityType.EQUITY, Market.USA) date_rule = self.date_rules.week_start(etf, 1) self.universe_settings.schedule.on(date_rule) self._week = 0 self._universe = self.add_universe(self.universe.etf(etf, universe_filter_func=self._select_assets)) # Rebalance the portfolio every 2 weeks. self.schedule.on(date_rule, self.time_rules.after_market_open(etf, 1), self._rebalance) def _select_assets(self, constituents): # Only update the universe every 2 weeks. week = self.time.isocalendar()[1] if abs(week - self._week) < 2: return [] self._week = week # Split assets into long/short groups based on ETF weight. sorted_by_weight = [c.symbol for c in sorted(constituents, key=lambda c: c.weight)] self._longs = sorted_by_weight[-100:] self._shorts = sorted_by_weight[:100] return self._longs + self._shorts def _rebalance(self): if not self._longs: return self.set_holdings([PortfolioTarget(s, 0.5/len(self._longs)) for s in self._longs] + [PortfolioTarget(s, -0.5/len(self._shorts)) for s in self._shorts], True) self._longs = [] self._shorts = []
