Overall Statistics |
Total Trades 366 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $4179.23 Estimated Strategy Capacity $71000.00 Lowest Capacity Asset AMMA WEHWVFZT6VZ9 |
class QuantumHorizontalRegulators(QCAlgorithm): def Initialize(self): self.SetStartDate(2021, 8, 9) # Set Start Date self.SetEndDate(2021, 8, 9) self.SetCash(333333) # Set Strategy Cash self.AddEquity("WORX", Resolution.Minute) self.scaning = False self.lastToggle = None self.needs_reset = False self.__numberOfSymbols = 1 self.UniverseSettings.Resolution = Resolution.Minute self.AddAlpha(ShortSqueezeModel(self)) self.SetExecution(ImmediateExecutionModel()) self.SetPortfolioConstruction(AccumulativeInsightPortfolioConstructionModel(lambda time: None)) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("WORX", 0), self.toggleScan) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("WORX", 375), self.toggleScan) def toggleScan(self): self.scaning = not self.scaning self.lastToggle = self.Time if not self.scaning: self.needs_reset = True class ShortSqueezeModel(AlphaModel): symbolData = {} def __init__(self, algo): self.algo = algo def Update(self, algorithm, slice): if algorithm.IsWarmingUp: return [] # If it's the end of the day, update the yesterday close of each indicator if not algorithm.Securities['WORX'].Exchange.ExchangeOpen: for symbol in self.symbolData: if symbol in slice.Bars: self.symbolData[symbol].yest_close = slice.Bars[symbol].Close if not self.algo.scaning: # Reset max indicator if self.algo.needs_reset: for symbol in self.symbolData: self.symbolData[symbol].max.Reset() self.algo.needs_reset = False return [] insights = [] insight_minutes = 1444 # Create insights for symbols up at least 10% on the day for symbol in self.symbolData: # If already invested, continue to next symbol if algorithm.Securities[symbol].Invested or symbol not in slice.Bars or self.symbolData[symbol].max.Samples == 0: continue # Calculate return sign yesterday's close yest_close = self.symbolData[symbol].yest_close close = slice[symbol].Close ret = (close - yest_close) / yest_close high_of_day_break = close > self.symbolData[symbol].max.Current.Value if ret >= 0.1 or high_of_day_break: # Up 10% on the day & breaks high of day hours = algorithm.Securities[symbol].Exchange.Hours # 5-minute before the close closeTime = hours.GetNextMarketClose(algorithm.Time, False) - timedelta(minutes=5) insights.append(Insight.Price(symbol, closeTime, InsightDirection.Up)) # Update max indicator for all symbols for symbol in self.symbolData: if symbol in slice.Bars: self.symbolData[symbol].max.Update(slice.Time, slice.Bars[symbol].High) # Constantly updating 7% Trailing Stop Order for symbol in self.symbolData: if symbol in slice.Bars and algorithm.Securities[symbol].Invested and slice[symbol].Close <= 0.99*self.symbolData[symbol].max.Current.Value: insights.append(Insight(symbol, timedelta(minutes=insight_minutes), InsightType.Price, InsightDirection.Flat)) return Insight.Group(insights) def OnSecuritiesChanged(self, algorithm, changes): if len(changes.AddedSecurities) > 0: # Get history of symbols over lookback window added_symbols = [x.Symbol for x in changes.AddedSecurities] history = algorithm.History(added_symbols, 1, Resolution.Daily) if history.empty: return history = history['close'] for added in changes.AddedSecurities: # Save yesterday's close closes = history.loc[[str(added.Symbol.ID)]].values if len(closes) < 1: continue self.symbolData[added.Symbol] = SymbolData(closes[0]) for removed in changes.RemovedSecurities: # Delete yesterday's close tracker self.symbolData.pop(removed.Symbol, None) class SymbolData: def __init__(self, yest_close): self.yest_close = yest_close self.max = Maximum(480*60) # 45 minutes