| Overall Statistics |
|
Total Trades
1000
Average Win
0.46%
Average Loss
-0.53%
Compounding Annual Return
7.110%
Drawdown
53.800%
Expectancy
0.425
Net Profit
340.687%
Sharpe Ratio
0.438
Probabilistic Sharpe Ratio
0.159%
Loss Rate
24%
Win Rate
76%
Profit-Loss Ratio
0.87
Alpha
0.08
Beta
-0.091
Annual Standard Deviation
0.167
Annual Variance
0.028
Information Ratio
-0.008
Tracking Error
0.254
Treynor Ratio
-0.805
Total Fees
$119.60
Estimated Strategy Capacity
$110000000.00
Lowest Capacity Asset
RDSB TAGTSHOFSMLH
|
# https://quantpedia.com/strategies/net-payout-yield-effect/
#
# The investment universe consists of all stocks on NYSE, AMEX, and NASDAQ. At the end of June of each year t, ten portfolios are formed based on ranked
# values net payout yield. The net payout yield is the ratio of dividends plus repurchases minus common share issuances in year t to year-end market
# capitalization. There are two measures of payout yield, one based on the statement of cash flows, the other based on the change in Treasury stocks.
# For the net payout yield, we use the cash flow-based measure of repurchases. The portfolio with the highest net payout yield is bought and held for
# one year, after which it is rebalanced.
#
# QC implementation changes:
# - Instead of all listed stock, we select 500 most liquid stocks traded on NYSE, AMEX, or NASDAQ.
class NetPayoutYieldEffect(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.coarse_count = 500
self.long = []
self.selection_flag = False
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel(self))
def CoarseSelectionFunction(self, coarse):
if not self.selection_flag:
return Universe.Unchanged
selected = sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa'],
key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in selected[:self.coarse_count]]
def FineSelectionFunction(self, fine):
fine = [x for x in fine if x.MarketCap !=0 and x.ValuationRatios.TotalYield != 0 and x.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths != 0 and
((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))]
# Sorting by net payout.
sorted_by_payout = sorted(fine, key = lambda x: ( (x.ValuationRatios.TotalYield * (x.MarketCap)) - \
(x.FinancialStatements.CashFlowStatement.CommonStockIssuance.TwelveMonths / (x.MarketCap))), reverse=True)
decile = int(len(sorted_by_payout) / 10)
self.long = [x.Symbol for x in sorted_by_payout[:decile]]
return self.long
def OnData(self, data):
if not self.selection_flag:
return
self.selection_flag = False
stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in stocks_invested:
if symbol not in self.long:
self.Liquidate(symbol)
for symbol in self.long:
if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable: # Prevent error message.
self.SetHoldings(symbol, 1 / len(self.long))
self.long.clear()
def Selection(self):
if self.Time.month == 6:
self.selection_flag = True
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))