| Overall Statistics |
|
Total Orders 5535 Average Win 0.10% Average Loss -0.12% Compounding Annual Return -2.159% Drawdown 31.400% Expectancy -0.120 Start Equity 1000000 End Equity 719183.91 Net Profit -28.082% Sharpe Ratio -0.91 Sortino Ratio -0.847 Probabilistic Sharpe Ratio 0.000% Loss Rate 51% Win Rate 49% Profit-Loss Ratio 0.78 Alpha -0.034 Beta -0.009 Annual Standard Deviation 0.038 Annual Variance 0.001 Information Ratio -0.585 Tracking Error 0.153 Treynor Ratio 3.861 Total Fees $8043.43 Estimated Strategy Capacity $2000.00 Lowest Capacity Asset AMS R735QTJ8XC9X Portfolio Turnover 0.22% |
#region imports
from AlgorithmImports import *
#endregion
class EarningsQualityFactor(QCAlgorithm):
def initialize(self):
self.set_start_date(2003, 6, 30)
self.set_end_date(2018, 8, 1)
self.set_cash(1_000_000)
self.settings.minimum_order_margin_portfolio_percentage = 0
self.set_security_initializer(BrokerageModelSecurityInitializer(
self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices)))
self.universe_settings.resolution = Resolution.DAILY
self._previous_fine = None
self._long = None
self._short = None
self.add_universe(self._coarse_selection_function, self._fine_selection_function)
self.add_equity("SPY", Resolution.DAILY)
# monthly scheduled event but will only rebalance once a year
self.schedule.on(self.date_rules.month_start("SPY"), self.time_rules.at(23, 0), self._rebalance)
self._yearly_rebalance = False
self.set_warmup(timedelta(365))
def _coarse_selection_function(self, coarse):
if self._yearly_rebalance:
# drop stocks which have no fundamental data
return [x.symbol for x in coarse if x.has_fundamental_data]
else:
return []
def _fine_selection_function(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.company_reference.industry_template_code != "B")
and (x.financial_statements.balance_sheet.current_assets.value != 0)
and (x.financial_statements.balance_sheet.cash_and_cash_equivalents.value != 0)
and (x.financial_statements.balance_sheet.current_liabilities.value != 0)
and (x.financial_statements.balance_sheet.current_debt.value != 0)
and (x.financial_statements.balance_sheet.income_tax_payable.value != 0)
and (x.financial_statements.income_statement.depreciation_and_amortization.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._calculate_accruals(fine, self._previous_fine)
filtered_fine = [x for x in fine if (x.financial_statements.cash_flow_statement.operating_cash_flow.value != 0)
and (x.earning_reports.basic_eps.value != 0)
and (x.earning_reports.basic_average_shares.value != 0)
and (x.operation_ratios.debt_to_assets.value != 0)
and (x.operation_ratios.roe.value != 0)]
for i in filtered_fine:
# cash flow to assets
i.cfa = i.financial_statements.cash_flow_statement.operating_cash_flow.value/(i.earning_reports.basic_eps.value * i.earning_reports.basic_average_shares.value)
# debt to assets
i.da = i.operation_ratios.debt_to_assets.value
# return on equity
i.roe = i.operation_ratios.roe.value
# sort stocks by four factors respectively
sorted_by_accrual = sorted(filtered_fine, key=lambda x: x.accrual, reverse=True) # high score with low accrual
sorted_by_cfa = sorted(filtered_fine, key=lambda x: x.cfa) # high score with high CFA
sorted_by_da = sorted(filtered_fine, key=lambda x: x.da, reverse=True) # high score with low leverage
sorted_by_roe = 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(sorted_by_accrual):
score_accrual = i
score_cfa = sorted_by_cfa.index(obj)
score_da = sorted_by_da.index(obj)
score_roe = sorted_by_roe.index(obj)
score = score_accrual + score_cfa + score_da + score_roe
score_dict[obj.symbol] = score
sorted_by_score = sorted(score_dict, key=lambda x: score_dict[x], reverse=True)
# long stocks with the top score (>10%) and short stocks with the bottom score (<10%)
self._long = sorted_by_score[:int(0.10*len(sorted_by_score))]
self._short = sorted_by_score[-int(0.10*len(sorted_by_score)):]
# save the fine data for the next year's analysis
self._previous_fine = fine
return self._long + self._short
else:
return []
def _calculate_accruals(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.financial_statements.balance_sheet.current_assets.value)-float(prev_data.financial_statements.balance_sheet.current_assets.value)
delta_cash = float(stock_data.financial_statements.balance_sheet.cash_and_cash_equivalents.value)-float(prev_data.financial_statements.balance_sheet.cash_and_cash_equivalents.value)
delta_liabilities = float(stock_data.financial_statements.balance_sheet.current_liabilities.value)-float(prev_data.financial_statements.balance_sheet.current_liabilities.value)
delta_debt = float(stock_data.financial_statements.balance_sheet.current_debt.value)-float(prev_data.financial_statements.balance_sheet.current_debt.value)
delta_tax = float(stock_data.financial_statements.balance_sheet.income_tax_payable.value)-float(prev_data.financial_statements.balance_sheet.income_tax_payable.value)
dep = float(stock_data.financial_statements.income_statement.depreciation_and_amortization.value)
avg_total = (float(stock_data.financial_statements.balance_sheet.total_assets.value)+float(prev_data.financial_statements.balance_sheet.total_assets.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 on_data(self, data):
if not self._yearly_rebalance or self.is_warming_up:
return
if self._long and self._short:
longs = [s for s in self._long if s in data.Bars]
shorts = [s for s in self._short if s in data.Bars]
long_weight = 0.5/len(longs)
short_weight = -0.5/len(shorts)
targets = [PortfolioTarget(i, long_weight) for i in longs]
targets.extend([PortfolioTarget(i, short_weight) for i in shorts])
self.set_holdings(targets, True)
self._yearly_rebalance = False
self._long = False
self._short = False