Overall Statistics |
Total Trades
431
Average Win
1.97%
Average Loss
-2.37%
Compounding Annual Return
1.284%
Drawdown
30.300%
Expectancy
0.078
Net Profit
30.284%
Sharpe Ratio
0.157
Probabilistic Sharpe Ratio
0.001%
Loss Rate
41%
Win Rate
59%
Profit-Loss Ratio
0.83
Alpha
0.016
Beta
-0.014
Annual Standard Deviation
0.099
Annual Variance
0.01
Information Ratio
-0.24
Tracking Error
0.206
Treynor Ratio
-1.101
Total Fees
$15498.46
|
class EarningsQualityFactor(QCAlgorithm): def Initialize(self): self.SetStartDate(2000,1,1) #Set Start Date self.SetCash(1000000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.previous_fine = None self.long = None self.short = None self.AddUniverse(self.CoarseSelectionFunction,self.FineSelectionFunction) self.AddEquity("SPY", Resolution.Daily) # monthly scheduled event but will only rebalance once a year self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(23, 0), self.rebalance) self.yearly_rebalance = False def CoarseSelectionFunction(self, coarse): if self.yearly_rebalance: # drop stocks which have no fundamental data filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")] return filtered_coarse else: return [] def FineSelectionFunction(self, fine): if self.yearly_rebalance: #filters out the non-financial companies that don't contain the necessary data fine = [x for x in fine if (x.CompanyReference.IndustryTemplateCode != "B") and (x.FinancialStatements.BalanceSheet.CurrentAssets.Value != 0) and (x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value != 0) and (x.FinancialStatements.BalanceSheet.CurrentLiabilities.Value != 0) and (x.FinancialStatements.BalanceSheet.CurrentDebt.Value != 0) and (x.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value != 0) and (x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value != 0)] if not self.previous_fine: # will wait one year in order to have the historical fundamental data self.previous_fine = fine self.yearly_rebalance = False return [] else: # calculate the accrual for each stock fine = self.CalculateAccruals(fine, self.previous_fine) filtered_fine = [x for x in fine if (x.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value!=0) and (x.EarningReports.BasicEPS.Value!=0) and (x.EarningReports.BasicAverageShares.Value!=0) and (x.OperationRatios.DebttoAssets.Value!=0) and (x.OperationRatios.ROE.Value!=0)] for i in filtered_fine: # cash flow to assets i.CFA = i.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value/(i.EarningReports.BasicEPS.Value * i.EarningReports.BasicAverageShares.Value) # debt to assets i.DA = i.OperationRatios.DebttoAssets.Value # return on equity i.ROE = i.OperationRatios.ROE.Value # sort stocks by four factors respectively sortedByAccrual = sorted(filtered_fine, key=lambda x: x.Accrual, reverse=True) # high score with low accrual sortedByCFA = sorted(filtered_fine, key=lambda x: x.CFA) # high score with high CFA sortedByDA = sorted(filtered_fine, key=lambda x: x.DA, reverse=True) # high score with low leverage sortedByROE = sorted(filtered_fine, key=lambda x: x.ROE) # high score with high ROE # create dict to save the score for each stock score_dict = {} # assign a score to each stock according to their rank with different factors for i,obj in enumerate(sortedByAccrual): scoreAccrual = i scoreCFA = sortedByCFA.index(obj) scoreDA = sortedByDA.index(obj) scoreROE = sortedByROE.index(obj) score = scoreAccrual + scoreCFA + scoreDA + scoreROE score_dict[obj.Symbol] = score sortedByScore = sorted(score_dict, key = lambda x: score_dict[x], reverse = True) # long stocks with the top score (>30%) and short stocks with the bottom score (<70%) self.long = sortedByScore[:int(0.3*len(sortedByScore))] self.short = sortedByScore[-int(0.3*len(sortedByScore)):] # save the fine data for the next year's analysis self.previous_fine = fine return self.long + self.short else: return [] def CalculateAccruals(self, current, previous): accruals = [] for stock_data in current: #compares this and last year's fine fundamental objects try: prev_data = None for x in previous: if x.Symbol == stock_data.Symbol: prev_data = x break #calculates the balance sheet accruals and adds the property to the fine fundamental object delta_assets = float(stock_data.FinancialStatements.BalanceSheet.CurrentAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentAssets.Value) delta_cash = float(stock_data.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value)-float(prev_data.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value) delta_liabilities = float(stock_data.FinancialStatements.BalanceSheet.CurrentLiabilities.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentLiabilities.Value) delta_debt = float(stock_data.FinancialStatements.BalanceSheet.CurrentDebt.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentDebt.Value) delta_tax = float(stock_data.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value)-float(prev_data.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value) dep = float(stock_data.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value) avg_total = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)+float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/2 #accounts for the size difference stock_data.Accrual = ((delta_assets-delta_cash)-(delta_liabilities-delta_debt-delta_tax)-dep)/avg_total accruals.append(stock_data) except: #value in current universe does not exist in the previous universe pass return accruals def rebalance(self): #yearly rebalance at the end of June (start of July) if self.Time.month == 7: self.yearly_rebalance = True def OnData(self, data): if not self.yearly_rebalance: return if self.long and self.short: long_stocks = [x.Key for x in self.Portfolio if x.Value.IsLong] short_stocks = [x.Key for x in self.Portfolio if x.Value.IsShort] # liquidate the stocks not in the filtered long/short list for long in long_stocks: if long not in self.long: self.Liquidate(long) for short in short_stocks: if short not in self.short: self.Liquidate(short) long_weight = 0.8/len(self.long) for i in self.long: self.SetHoldings(i, long_weight) short_weight = 0.8/len(self.short) for i in self.short: self.SetHoldings(i, -short_weight) self.yearly_rebalance = False self.long = False self.short = False
import numpy as np from scipy.optimize import minimize import statsmodels.api as sm sp100_stocks = ['AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM','CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI','COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'] def MonthDiff(d1, d2): return (d1.year - d2.year) * 12 + d1.month - d2.month def Return(values): return (values[-1] - values[0]) / values[0] def Volatility(values): values = np.array(values) returns = (values[1:] - values[:-1]) / values[:-1] return np.std(returns) def MultipleLinearRegression(x, y): x = np.array(x).T x = sm.add_constant(x) result = sm.OLS(endog=y, exog=x).fit() return result # Custom fee model class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD")) # Quandl free data class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "settle" # Quandl "value" data class QuandlValue(PythonQuandl): def __init__(self): self.ValueColumnName = 'Value' # Quandl short interest data. class QuandlFINRA_ShortVolume(PythonQuandl): def __init__(self): self.ValueColumnName = 'SHORTVOLUME' # also 'TOTALVOLUME' is accesible # Commitments of Traders data. # NOTE: IMPORTANT: Data order must be ascending (datewise). # Data source: https://commitmentsoftraders.org/cot-data/ # Data description: https://commitmentsoftraders.org/wp-content/uploads/Static/CoTData/file_key.html class CommitmentsOfTraders(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/cot/{0}.PRN".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) # File example. # DATE OPEN HIGH LOW CLOSE VOLUME OI # ---- ---- ---- --- ----- ------ -- # DATE LARGE SPECULATOR COMMERCIAL HEDGER SMALL TRADER # LONG SHORT LONG SHORT LONG SHORT def Reader(self, config, line, date, isLiveMode): data = CommitmentsOfTraders() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(',') # Prevent lookahead bias. data.Time = datetime.strptime(split[0], "%Y%m%d") + timedelta(days=1) data['LARGE_SPECULATOR_LONG'] = int(split[1]) data['LARGE_SPECULATOR_SHORT'] = int(split[2]) data['COMMERCIAL_HEDGER_LONG'] = int(split[3]) data['COMMERCIAL_HEDGER_SHORT'] = int(split[4]) data['SMALL_TRADER_LONG'] = int(split[5]) data['SMALL_TRADER_SHORT'] = int(split[6]) data['open_interest'] = int(split[1]) + int(split[2]) + int(split[3]) + int(split[4]) + int(split[5]) + int(split[6]) data.Value = int(split[1]) return data # Quantpedia bond yield data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaIndices(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/index/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): data = QuantpediaIndices() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(',') data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1) data['close'] = float(split[1]) data.Value = float(split[1]) return data # Quantpedia bond yield data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaBondYield(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/bond_yield/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): data = QuantpediaBondYield() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(',') data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1) data['yield'] = float(split[1]) data.Value = float(split[1]) return data # Quantpedia data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): data = QuantpediaFutures() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(';') data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1) data['back_adjusted'] = float(split[1]) data['spliced'] = float(split[2]) data.Value = float(split[1]) return data # Commitments of Traders data. # NOTE: IMPORTANT: Data order must be ascending (datewise). # Data source: https://commitmentsoftraders.org/cot-data/ # Data description: https://commitmentsoftraders.org/wp-content/uploads/Static/CoTData/file_key.html class CommitmentsOfTraders(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/cot/{0}.PRN".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) # File example. # DATE OPEN HIGH LOW CLOSE VOLUME OI # ---- ---- ---- --- ----- ------ -- # DATE LARGE SPECULATOR COMMERCIAL HEDGER SMALL TRADER # LONG SHORT LONG SHORT LONG SHORT def Reader(self, config, line, date, isLiveMode): data = CommitmentsOfTraders() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(',') # Prevent lookahead bias. data.Time = datetime.strptime(split[0], "%Y%m%d") + timedelta(days=1) data['LARGE_SPECULATOR_LONG'] = int(split[1]) data['LARGE_SPECULATOR_SHORT'] = int(split[2]) data['COMMERCIAL_HEDGER_LONG'] = int(split[3]) data['COMMERCIAL_HEDGER_SHORT'] = int(split[4]) data['SMALL_TRADER_LONG'] = int(split[5]) data['SMALL_TRADER_SHORT'] = int(split[6]) data.Value = int(split[1]) return data # NOTE: Manager for new trades. It's represented by certain count of equally weighted brackets for long and short positions. # If there's a place for new trade, it will be managed for time of holding period. class TradeManager(): def __init__(self, algorithm, long_size, short_size, holding_period): self.algorithm = algorithm # algorithm to execute orders in. self.long_size = long_size self.short_size = short_size self.long_len = 0 self.short_len = 0 # Arrays of ManagedSymbols self.symbols = [] self.holding_period = holding_period # Days of holding. # Add stock symbol object def Add(self, symbol, long_flag): # Open new long trade. managed_symbol = ManagedSymbol(symbol, self.holding_period, long_flag) if long_flag: # If there's a place for it. if self.long_len < self.long_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, 1 / self.long_size) self.long_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Open new short trade. else: # If there's a place for it. if self.short_len < self.short_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, - 1 / self.short_size) self.short_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Decrement holding period and liquidate symbols. def TryLiquidate(self): symbols_to_delete = [] for managed_symbol in self.symbols: managed_symbol.days_to_liquidate -= 1 # Liquidate. if managed_symbol.days_to_liquidate == 0: symbols_to_delete.append(managed_symbol) self.algorithm.Liquidate(managed_symbol.symbol) if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 # Remove symbols from management. for managed_symbol in symbols_to_delete: self.symbols.remove(managed_symbol) def LiquidateTicker(self, ticker): symbol_to_delete = None for managed_symbol in self.symbols: if managed_symbol.symbol.Value == ticker: self.algorithm.Liquidate(managed_symbol.symbol) symbol_to_delete = managed_symbol if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 break if symbol_to_delete: self.symbols.remove(symbol_to_delete) else: self.algorithm.Debug("Ticker is not held in portfolio!") class ManagedSymbol(): def __init__(self, symbol, days_to_liquidate, long_flag): self.symbol = symbol self.days_to_liquidate = days_to_liquidate self.long_flag = long_flag class PortfolioOptimization(object): def __init__(self, df_return, risk_free_rate, num_assets): self.daily_return = df_return self.risk_free_rate = risk_free_rate self.n = num_assets # numbers of risk assets in portfolio self.target_vol = 0.05 def annual_port_return(self, weights): # calculate the annual return of portfolio return np.sum(self.daily_return.mean() * weights) * 252 def annual_port_vol(self, weights): # calculate the annual volatility of portfolio return np.sqrt(np.dot(weights.T, np.dot(self.daily_return.cov() * 252, weights))) def min_func(self, weights): # method 1: maximize sharp ratio return - self.annual_port_return(weights) / self.annual_port_vol(weights) # method 2: maximize the return with target volatility #return - self.annual_port_return(weights) / self.target_vol def opt_portfolio(self): # maximize the sharpe ratio to find the optimal weights cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) bnds = tuple((0, 1) for x in range(2)) + tuple((0, 0.25) for x in range(self.n - 2)) opt = minimize(self.min_func, # object function np.array(self.n * [1. / self.n]), # initial value method='SLSQP', # optimization method bounds=bnds, # bounds for variables constraints=cons) # constraint conditions opt_weights = opt['x'] return opt_weights