Overall Statistics |

Total Trades 7903 Average Win 0.14% Average Loss -0.14% Compounding Annual Return 11.846% Drawdown 24.800% Expectancy 0.260 Net Profit 313.142% Sharpe Ratio 0.827 Loss Rate 36% Win Rate 64% Profit-Loss Ratio 0.96 Alpha 0.072 Beta 0.475 Annual Standard Deviation 0.14 Annual Variance 0.019 Information Ratio 0.171 Tracking Error 0.146 Treynor Ratio 0.243 Total Fees $8851.63 |

import numpy as np import math class StandardizedUnexpectedEarnings(QCAlgorithm): '''Step 1. Calculate the change in quarterly EPS from its value four quarters ago Step 2. Calculate the st dev of this change over the prior eight quarters Step 3. Get standardized unexpected earnings (SUE) from dividing results of step 1 by step 2 Step 4. Each month, sort universe by SUE and long the top quantile Reference: [1] Foster, Olsen and Shevlin, 1984, Earnings Releases, Anomalies, and the Behavior of Security Returns, The Accounting Review. URL: https://www.jstor.org/stable/pdf/247321.pdf?casa_token=KHX3qwnGgTMAAAAA: ycgY-PzPfQ9uiYzVYeOF6yRDaNcRkL6IhLmRJuFpI6iWxsXJgB2BcM6ylmjy-g6xv-PYbXySJ_VxDpFETxw1PtKGUi1d91ce-h-V6CaL_SAAB56GZRQ [2] Hou, Xue and Zhang, 2018, Replicating Anomalies, Review of Financial Studies, URL: http://theinvestmentcapm.com/HouXueZhang2019RFS.pdf ''' def Initialize(self): self.SetStartDate(2007, 1, 1) # Set Start Date. Warm up for first 36 months self.SetEndDate(2019, 9, 1) # Set End Date self.SetCash(100000) # Set Strategy Cash self.months_eps_change = 12 # Number of months of lag to calculate eps change self.months_count = 36 # Number of months of rolling window object self.num_coarse = 1000 # Number of new symbols selected at Coarse Selection self.top_percent = 0.05 # Percentage of symbols selected based on SUE sorting self.eps_by_symbol = {} # Contains RollingWindow objects for all stocks self.new_fine = [] # Contains new symbols selected at Coarse Selection self.long = [] # Contains symbols that we will long self.next_rebalance = self.Time # Define next rebalance time self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionAndSueSorting) def CoarseSelectionFunction(self, coarse): '''Get dynamic coarse universe to be further selected in fine selection ''' # Before next rebalance time, just remain the current universe if self.Time < self.next_rebalance: return Universe.Unchanged ### Run the coarse selection to narrow down the universe # filter by fundamental data and price & Sort descendingly by daily dollar volume sorted_by_volume = sorted([ x for x in coarse if x.HasFundamentalData and x.Price > 5 ], key = lambda x: x.DollarVolume, reverse = True) self.new_fine = [ x.Symbol for x in sorted_by_volume[:self.num_coarse] ] # Return all symbols that have appeared in Coarse Selection return list( set(self.new_fine).union( set(self.eps_by_symbol.keys()) ) ) def FineSelectionAndSueSorting(self, fine): '''Select symbols to trade based on sorting of SUE''' sue_by_symbol = dict() for stock in fine: ### Save (symbol, rolling window of EPS) pair in dictionary if not stock.Symbol in self.eps_by_symbol: self.eps_by_symbol[stock.Symbol] = RollingWindow[float](self.months_count) # update rolling window for each stock self.eps_by_symbol[stock.Symbol].Add(stock.EarningReports.BasicEPS.ThreeMonths) ### Calculate SUE if stock.Symbol in self.new_fine and self.eps_by_symbol[stock.Symbol].IsReady: # Calculate the EPS change from four quarters ago rw = self.eps_by_symbol[stock.Symbol] eps_change = rw[0] - rw[self.months_eps_change] # Calculate the st dev of EPS change for the prior eight quarters new_eps_list = list(rw)[:self.months_count - self.months_eps_change:3] old_eps_list = list(rw)[self.months_eps_change::3] eps_std = np.std( [ new_eps - old_eps for new_eps, old_eps in zip( new_eps_list, old_eps_list ) ] ) # Get Standardized Unexpected Earnings (SUE) sue_by_symbol[stock.Symbol] = eps_change / eps_std # Sort and return the top quantile sorted_dict = sorted(sue_by_symbol.items(), key = lambda x: x[1], reverse = True) self.long = [ x[0] for x in sorted_dict[:math.ceil( self.top_percent * len(sorted_dict) )] ] # If universe is empty, OnData will not be triggered, then update next rebalance time here if not self.long: self.next_rebalance = Expiry.EndOfMonth(self.Time) return self.long def OnSecuritiesChanged(self, changes): '''Liquidate symbols that are removed from the dynamic universe ''' for security in changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol, 'Removed from universe') def OnData(self, data): '''Monthly rebalance at the beginning of each month. Form portfolio with equal weights. ''' # Before next rebalance, do nothing if self.Time < self.next_rebalance or not self.long: return # Placing orders (with equal weights) equal_weight = 1 / len(self.long) for stock in self.long: self.SetHoldings(stock, equal_weight) # Rebalance at the beginning of every month self.next_rebalance = Expiry.EndOfMonth(self.Time)