| Overall Statistics |
|
Total Orders 1163 Average Win 0.40% Average Loss -0.42% Compounding Annual Return -1.480% Drawdown 31.500% Expectancy -0.006 Start Equity 10000000000 End Equity 9351472247.04 Net Profit -6.485% Sharpe Ratio -0.141 Sortino Ratio -0.121 Probabilistic Sharpe Ratio 0.502% Loss Rate 49% Win Rate 51% Profit-Loss Ratio 0.95 Alpha -0.031 Beta 0.12 Annual Standard Deviation 0.133 Annual Variance 0.018 Information Ratio -0.6 Tracking Error 0.206 Treynor Ratio -0.156 Total Fees $70162457.86 Estimated Strategy Capacity $62000000.00 Lowest Capacity Asset NKD Y94HV72ZKSN5 Portfolio Turnover 6.95% |
#region imports from AlgorithmImports import * from utils import * #endregion class BreakoutForecastAlphaModel(AlphaModel): def __init__(self, algorithm, symbols, periods, multiplier=1): self.algorithm = algorithm self.symbols = symbols self.periods = periods self.multiplier = multiplier self.day = -1 def update(self, algorithm, data): if self.day == algorithm.time.day: return [] self.day = algorithm.time.day signals = {period: {} for period in self.periods} period_weight = 1 / len(self.periods) # Get signals for each Future for period in self.periods: for symbol in self.symbols: if data.bars.contains_key(symbol): if self.get_breakout_signal(algorithm, symbol, period): signals[period][symbol] = 1 else: signals[period][symbol] = -1 return get_insights(algorithm, signals, period_weight, self.multiplier) def get_breakout_signal(self, algorithm, symbol, period): security = algorithm.securities[symbol.canonical] return security[f"EMA{period}"].current.value > 0 def on_securities_changed(self, algorithm, changes): for removed in changes.removed_securities: if removed.type != SecurityType.FUTURE or not removed.symbol.canonical in self.symbols: continue symbol = removed.symbol.canonical security = algorithm.securities[symbol] algorithm.subscription_manager.remove_consolidator(symbol, security["consolidator"]) for period in self.periods: security[f"Maximum{period}"].reset() security[f"Minimum{period}"].reset() security[f"EMA{period}"].reset() for added in changes.added_securities: if added.type != SecurityType.FUTURE or not added.symbol.canonical in self.symbols: continue security = algorithm.securities[added.symbol.canonical] security["consolidator"] = TradeBarConsolidator(timedelta(1)) for period in self.periods: security[f"Maximum{period}"] = Maximum(period) security[f"Minimum{period}"] = Minimum(period) security[f"EMA{period}"] = ExponentialMovingAverage(period // 4) security["consolidator"] = reset_and_warm_up_breakout(algorithm, security, self.periods) security["consolidator"].data_consolidated += self.on_consolidated algorithm.subscription_manager.add_consolidator(added.symbol.canonical, security["consolidator"]) def on_consolidated(self, _, bar): security = self.algorithm.securities[bar.symbol.canonical] for period in self.periods: max_ = security[f'Maximum{period}'].current.value min_ = security[f'Minimum{period}'].current.value if not max_ or not min_ or max_ == min_: continue mean = (max_ + min_) / 2 security[f'EMA{period}'].update(bar.end_time, 40 * (bar.close - mean) / (max_ - min_))
# region imports from AlgorithmImports import * from universe import FrontMonthFutureUniverseSelectionModel from breakout_alpha import BreakoutForecastAlphaModel from trend_following_alpha import TrendFollowingAlphaModel # endregion class BreakoutFuturesAlgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2019, 1, 1) self.set_end_date(2023, 6, 30) self.SetCash(10000000000) breakout_weight = self.get_parameter("breakout_weight", 0.5) trend_following_weight = self.get_parameter("trend_following_weight", 0.5) self.symbols = [ Symbol.create(Futures.Grains.WHEAT, SecurityType.FUTURE, Market.CBOT), Symbol.create(Futures.Grains.SOYBEANS, SecurityType.FUTURE, Market.CBOT), Symbol.create(Futures.Meats.FEEDER_CATTLE, SecurityType.FUTURE, Market.CME), Symbol.create(Futures.Softs.COTTON_2, SecurityType.FUTURE, Market.ICE), Symbol.create(Futures.Financials.Y_30_TREASURY_BOND, SecurityType.FUTURE, Market.CBOT), Symbol.create(Futures.Financials.Y_10_TREASURY_NOTE, SecurityType.FUTURE, Market.CBOT), Symbol.create(Futures.Financials.Y_2_TREASURY_NOTE, SecurityType.FUTURE, Market.CBOT), Symbol.create(Futures.Financials.FIVE_YEAR_USDMAC_SWAP, SecurityType.FUTURE, Market.CBOT), Symbol.create(Futures.Indices.SP_500_E_MINI, SecurityType.FUTURE, Market.CME), Symbol.create(Futures.Indices.MSCI_EUROPE_NTR, SecurityType.FUTURE, Market.NYSELIFFE), Symbol.create(Futures.Indices.NIKKEI_225_DOLLAR, SecurityType.FUTURE, Market.CME), Symbol.create(Futures.Currencies.BTC, SecurityType.FUTURE, Market.CME), Symbol.create(Futures.Currencies.ETH, SecurityType.FUTURE, Market.CME), Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX), Symbol.create(Futures.Metals.PLATINUM, SecurityType.FUTURE, Market.NYMEX), Symbol.create(Futures.Metals.PALLADIUM, SecurityType.FUTURE, Market.NYMEX), Symbol.create(Futures.Energies.NATURAL_GAS, SecurityType.FUTURE, Market.NYMEX), Symbol.create(Futures.Energies.CRUDE_OIL_WTI, SecurityType.FUTURE, Market.NYMEX), Symbol.create(Futures.Energies.BRENT_CRUDE, SecurityType.FUTURE, Market.ICE), Symbol.create(Futures.Indices.VIX, SecurityType.FUTURE, Market.CFE) ] self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE, AccountType.MARGIN) self.universe_settings.extended_market_hours = True self.universe_settings.data_normalization_mode = DataNormalizationMode.BACKWARDS_RATIO self.universe_settings.data_mapping_mode = DataMappingMode.OPEN_INTEREST self.universe_settings.contract_depth_offset = 0 self.universe_settings.resolution = Resolution.MINUTE # Seed initial price data self.set_security_initializer(BrokerageModelSecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices))) # We only want front month contract self.add_universe_selection(FrontMonthFutureUniverseSelectionModel(self.select_future_chain_symbols)) # breakout +/- trend following strategies breakout_periods = [5, 10, 20, 40, 80, 160, 320] self.add_alpha(BreakoutForecastAlphaModel(self, self.symbols, breakout_periods, breakout_weight)) trend_periods = [2, 4, 8, 16, 32, 64, 128] self.add_alpha(TrendFollowingAlphaModel(self.symbols, trend_periods, trend_following_weight)) # Position sizing is controlled by alpha model self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel()) # Avoid massive bid-ask spread friction self.set_execution(SpreadExecutionModel(0.03)) # Set benchmark: since it is a buy and hold portfolio, using SPY will be appropriate self.add_equity("SPY") self.set_benchmark("SPY") def select_future_chain_symbols(self, utcTime): return self.symbols
#region imports from AlgorithmImports import * from utils import * #endregion class TrendFollowingAlphaModel(AlphaModel): def __init__(self, symbols, periods, multiplier=1): self.symbols = symbols self.periods = periods self.multiplier = multiplier self.month = -1 def update(self, algorithm, data): if self.month == algorithm.time.month: return [] self.month = algorithm.time.month signals = {period: {} for period in self.periods} period_weight = 1 / len(self.periods) # Get signals for each Future for period in self.periods: for symbol in self.symbols: if data.bars.contains_key(symbol): if self.get_trend_signal(algorithm, symbol, period): signals[period][symbol] = 1 else: signals[period][symbol] = -1 return get_insights(algorithm, signals, period_weight, self.multiplier) def get_trend_signal(self, algorithm, symbol, period): security = algorithm.securities[symbol.canonical] return security[f"EMA{period}"].current.value < security.price def on_securities_changed(self, algorithm, changes): for removed in changes.removed_securities: if removed.type != SecurityType.FUTURE or not removed.symbol.canonical in self.symbols: continue symbol = removed.symbol.canonical security = algorithm.securities[symbol] for period in self.periods: security[f"EMA{period}"].reset() for added in changes.added_securities: if added.type != SecurityType.FUTURE or not added.symbol.canonical in self.symbols: continue security = algorithm.securities[added.symbol.canonical] security["consolidator"] = TradeBarConsolidator(timedelta(1)) for period in self.periods: security[f"EMA{period}"] = ExponentialMovingAverage(period) security["consolidator"] = reset_and_warm_up_trend_following(algorithm, security, self.periods)
#region imports from AlgorithmImports import * from Selection.FutureUniverseSelectionModel import FutureUniverseSelectionModel #endregion class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel): '''Creates futures chain universes that select the front month contract and runs a user defined futureChainSymbolSelector every day to enable choosing different futures chains''' def __init__(self, select_future_chain_symbols, rebalance_period = 7): super().__init__(timedelta(rebalance_period), select_future_chain_symbols) def filter(self, filter): '''Defines the futures chain universe filter''' return (filter.front_month().only_apply_filter_at_market_open())
#region imports from AlgorithmImports import * #endregion def reset_and_warm_up_breakout(algorithm, security, periods): consolidator = security['consolidator'] lookback = max(periods) # historical request to update the consolidator that will warm up the indicator history = algorithm.history[consolidator.input_type](security.symbol, lookback, Resolution.DAILY, data_normalization_mode = DataNormalizationMode.SCALED_RAW) # Replace the consolidator, since we cannot reset it # Not ideal since we don't the consolidator type and period algorithm.subscription_manager.remove_consolidator(security.symbol, consolidator) consolidator = TradeBarConsolidator(timedelta(1)) for period in periods: for indicator in [security[f'Maximum{period}'], security[f'Minimum{period}']]: indicator.reset() algorithm.register_indicator(security.symbol, indicator, consolidator) security[f'EMA{period}'].reset() for bar in list(history)[:-1]: consolidator.update(bar) for period in periods: max_ = security[f'Maximum{period}'].current.value min_ = security[f'Minimum{period}'].current.value mean = (max_ + min_) / 2 security[f'EMA{period}'].update(bar.end_time, 40 * (bar.close - mean) / (max_ - min_)) return consolidator def reset_and_warm_up_trend_following(algorithm, security, periods): indicators = [security[f'EMA{period}'] for period in periods] consolidator = security['consolidator'] lookback = max(periods) # historical request to update the consolidator that will warm up the indicator history = algorithm.history[consolidator.input_type](security.symbol, lookback, Resolution.DAILY, data_normalization_mode = DataNormalizationMode.SCALED_RAW) # Replace the consolidator, since we cannot reset it # Not ideal since we don't the consolidator type and period algorithm.subscription_manager.remove_consolidator(security.symbol, consolidator) consolidator = TradeBarConsolidator(timedelta(1)) for indicator in indicators: indicator.reset() algorithm.register_indicator(security.symbol, indicator, consolidator) for bar in list(history)[:-1]: consolidator.update(bar) return consolidator def get_insights(algorithm, signals, period_weight, alpha_weight=1): # Equal risk capital per signal group symbol_signals = {} for period, selected in signals.items(): if not selected: continue symbol_weight = 1 / len(selected) for symbol, sign in selected.items(): mapped = algorithm.securities[symbol].mapped multiplier = algorithm.securities[symbol].symbol_properties.contract_multiplier weight = sign * period_weight * symbol_weight * alpha_weight / multiplier if mapped not in symbol_signals: symbol_signals[mapped] = 0 symbol_signals[mapped] += weight return [ Insight.price(mapped, Expiry.END_OF_WEEK, InsightDirection.UP, weight = weight) for mapped, weight in symbol_signals.items() if abs(weight) >= 0.005 ]
