| Overall Statistics |
|
Total Orders 2236 Average Win 0.19% Average Loss -0.17% Compounding Annual Return 0.511% Drawdown 13.600% Expectancy 0.038 Start Equity 1000000 End Equity 1058604.54 Net Profit 5.860% Sharpe Ratio -0.236 Sortino Ratio -0.256 Probabilistic Sharpe Ratio 0.016% Loss Rate 51% Win Rate 49% Profit-Loss Ratio 1.14 Alpha -0.009 Beta -0.009 Annual Standard Deviation 0.038 Annual Variance 0.001 Information Ratio -0.379 Tracking Error 0.169 Treynor Ratio 0.997 Total Fees $6163.04 Estimated Strategy Capacity $12000.00 Lowest Capacity Asset SSNT WJTX1OBQ409X Portfolio Turnover 0.26% |
#region imports
from AlgorithmImports import *
#endregion
class NetCurrentAssetValue(QCAlgorithm):
def initialize(self):
#rebalancing should occur in July
self.set_start_date(2007, 5, 15)
self.set_end_date(2018, 7, 15)
self.set_cash(1_000_000)
self.settings.minimum_order_margin_portfolio_percentage = 0
self.universe_settings.resolution = Resolution.DAILY
self._previous_fine = None
self._filtered_fine = None
self.add_universe(self._coarse_selection_function, self._fine_selection_function)
spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
#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._months = -1
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 and x.market == Market.USA]
return []
def _fine_selection_function(self, fine):
if self._yearly_rebalance:
#filters out the companies that don't contain the necessary data
fine = [x for x in fine if (float(x.financial_statements.balance_sheet.current_assets.value) > 0)
and (float(x.financial_statements.balance_sheet.cash_and_cash_equivalents.value) > 0)
and (float(x.financial_statements.balance_sheet.current_liabilities.value) > 0)
and (float(x.financial_statements.balance_sheet.current_debt.value) > 0)
and (float(x.financial_statements.balance_sheet.income_tax_payable.value) > 0)
and (float(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:
#will calculate and sort the balance sheet accruals
self._filtered_fine = self._calculate_accruals(fine, self._previous_fine)
sorted_filter = sorted(self._filtered_fine, key=lambda x: x.bs_acc)
self._filtered_fine = [i.symbol for i in sorted_filter]
#we save the fine data for the next year's analysis
self._previous_fine = fine
return self._filtered_fine
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.bs_acc = ((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
self._months += 1
if self._months % 12 == 0:
self._yearly_rebalance = True
def on_data(self, data):
if not self._yearly_rebalance or self.is_warming_up:
return
if self._filtered_fine:
filtered_fine = [s for s in self._filtered_fine if s in data]
portfolio_size = int(len(filtered_fine)/10)
#pick the upper decile to short and the lower decile to long
targets = []
longs = filtered_fine[:portfolio_size]
long_weight = 0.25/len(longs)
for symbol in longs:
targets.append(PortfolioTarget(symbol, long_weight))
shorts = filtered_fine[-portfolio_size:]
short_weight = -0.25/len(shorts)
for symbol in shorts:
targets.append(PortfolioTarget(symbol, short_weight))
self.set_holdings(targets, True)
self._yearly_rebalance = False
self._filtered_fine = False