Overall Statistics |
Total Trades 33 Average Win 1.21% Average Loss -3.93% Compounding Annual Return -85.239% Drawdown 68.700% Expectancy -0.891 Net Profit -43.511% Sharpe Ratio -0.322 Probabilistic Sharpe Ratio 20.349% Loss Rate 92% Win Rate 8% Profit-Loss Ratio 0.31 Alpha -0.552 Beta 0.246 Annual Standard Deviation 1.432 Annual Variance 2.05 Information Ratio -0.579 Tracking Error 1.435 Treynor Ratio -1.872 Total Fees $33.00 Estimated Strategy Capacity $290000000.00 |
from Alphas.MacdAlphaModel import MacdAlphaModel class WellDressedYellowGreenFish(QCAlgorithm): def Initialize(self): self.SetStartDate(2021, 1, 1) # Set Start Date self.SetCash(10000) # Set Strategy Cash #self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Simple, Resolution.Daily)) self.AddUniverse(self.CoarseSelectionFunction) self.UniverseSettings.Resolution = Resolution.Daily self.averages = { } def CoarseSelectionFunction(self, universe): selected = [] universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True) universe = [c for c in universe if c.Price > 5][:100] for coarse in universe: symbol = coarse.Symbol if symbol not in self.averages: # 1. Call history to get an array of 200 days of history data history = self.History(symbol, 200, Resolution.Daily) #2. Adjust SelectionData to pass in the history result self.averages[symbol] = SelectionData(history) self.averages[symbol].update(self.Time, coarse.AdjustedPrice) if self.averages[symbol].is_ready() and self.averages[symbol].fast > self.averages[symbol].slow: selected.append(symbol) return selected[:5] def OnData(self, data): for symbol, symbol_data in self.averages.items(): tolerance = 0.0025 holdings = self.Portfolio[symbol].Quantity if self.Portfolio.ContainsKey(symbol) else 0 signalDeltaPercent = (symbol_data.macd.Current.Value - symbol_data.macd.Signal.Current.Value)/symbol_data.macd.Fast.Current.Value if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01% # longterm says buy as well self.SetHoldings(symbol, 0.2) elif holdings >= 0 and signalDeltaPercent < -tolerance: self.Liquidate(symbol) class SelectionData(): #3. Update the constructor to accept a history array def __init__(self, history): self.slow = ExponentialMovingAverage(100) self.fast = ExponentialMovingAverage(20) self.macd = MovingAverageConvergenceDivergence(12, 26, 9, MovingAverageType.Exponential) #4. Loop over the history data and update the indicators for bar in history.itertuples(): self.fast.Update(bar.Index[1], bar.close) self.slow.Update(bar.Index[1], bar.close) self.macd.Update(bar.Index[1], bar.close) def is_ready(self): return self.slow.IsReady and self.fast.IsReady def update(self, time, price): self.fast.Update(time, price) self.slow.Update(time, price) self.macd.Update(time, price)