Overall Statistics |
Total Orders 219 Average Win 0.96% Average Loss -1.51% Compounding Annual Return -6.038% Drawdown 48.200% Expectancy -0.043 Start Equity 100000 End Equity 93945.68 Net Profit -6.054% Sharpe Ratio 0.102 Sortino Ratio 0.108 Probabilistic Sharpe Ratio 14.360% Loss Rate 42% Win Rate 58% Profit-Loss Ratio 0.64 Alpha -0.147 Beta 1.181 Annual Standard Deviation 0.427 Annual Variance 0.182 Information Ratio -0.425 Tracking Error 0.278 Treynor Ratio 0.037 Total Fees $450.93 Estimated Strategy Capacity $1200000.00 Lowest Capacity Asset PFG S93T4RZVA1D1 Portfolio Turnover 6.50% |
from AlgorithmImports import * class conservative_reblancing(AlphaModel): def __init__(self, benchmark, v_lookback, m_lookback): self.benchmark = benchmark self.v_lookback = v_lookback self.m_lookback = m_lookback self.symbols = [] self.month = -1 def on_securities_changed(self, algorithm, changes): for added in changes.added_securities: self.symbols.append(added.symbol) for removed in changes.removed_securities: symbol = removed.symbol if symbol in self.symbols: self.symbols.remove(symbol) def update(self, algorithm, data): if algorithm.time.month == self.month: return [] self.month = algorithm.time.month # Initialize the data alphas = dict() # Fetch indicator data for symbol in self.symbols: if symbol not in data.Keys: continue # Create the indicators roc = algorithm.roc(symbol, 1, Resolution.Daily) std = algorithm.std(symbol, self.v_lookback, Resolution.DAILY) momp = algorithm.momp(symbol, self.m_lookback, Resolution.DAILY) # Get historical data for warm-up history = algorithm.History(symbol, 40, Resolution.DAILY) # Warm up the indicators for idx, row in history.loc[symbol].iterrows(): roc.Update(idx, row["close"]) std.Update(idx, roc.current.value) momp.Update(idx, row["close"]) # Compute the rank value alphas[symbol] = momp.Current.Value / std.Current.Value # Rank the symbol by the value of mom/vol selected = sorted(alphas.items(), key=lambda x: x[1], reverse=True)[:5] selected_symbols = [x[0] for x in selected] return [ Insight.price(symbol, Expiry.END_OF_MONTH, InsightDirection.UP) for symbol in selected_symbols ]
#region imports from AlgorithmImports import * from universe import * from alpha import * #endregion class ConservativeApgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2020, 1, 1) self.set_end_date(2021, 1, 1) self.set_cash(100000) # self.set_warm_up(60) # Set number days to trace back v_lookback = self.get_parameter("v_lookback", 36) m_lookback = self.get_parameter("m_lookback", 12) self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE, AccountType.MARGIN) # SPY 500 companies spy = self.add_equity("SPY", resolution = self.universe_settings.resolution, data_normalization_mode = self.universe_settings.data_normalization_mode).symbol self.set_benchmark(spy) self.set_universe_selection(etf_constituents_universe(spy, self.universe_settings)) self.add_alpha(conservative_reblancing(spy, v_lookback, m_lookback)) self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH)) self.set_risk_management(NullRiskManagementModel()) self.set_execution(ImmediateExecutionModel())
from AlgorithmImports import * class etf_constituents_universe(ETFConstituentsUniverseSelectionModel): def __init__(self, benchmark, universe_settings: UniverseSettings = None) -> None: super().__init__(benchmark, universe_settings, self.etf_constituents_filter) def etf_constituents_filter(self, constituents): return [c.symbol for c in constituents]