| Overall Statistics |
|
Total Trades
1144
Average Win
1.99%
Average Loss
-0.78%
Compounding Annual Return
30.537%
Drawdown
76.200%
Expectancy
1.460
Net Profit
31466.001%
Sharpe Ratio
1.119
Probabilistic Sharpe Ratio
46.665%
Loss Rate
30%
Win Rate
70%
Profit-Loss Ratio
2.53
Alpha
0.292
Beta
-0.075
Annual Standard Deviation
0.256
Annual Variance
0.065
Information Ratio
0.664
Tracking Error
0.318
Treynor Ratio
-3.813
Total Fees
$9188.30
Estimated Strategy Capacity
$65000.00
Lowest Capacity Asset
LTPBV VTER7CYO778L
|
# https://quantpedia.com/strategies/net-current-asset-value-effect/
#
# The investment universe consists of all stocks on the London Exchange. Companies with more than one class of ordinary shares and foreign companies
# are excluded. Also excluded are companies on the lightly regulated markets and companies which belong to the financial sector. The portfolio of
# stocks is formed annually in July. Only those stocks with an NCAV/MV higher than 1.5 are included in the NCAV/MV portfolio. This Buy-and-hold
# portfolio is held for one year. Stocks are weighted equally.
#
# QC implementation changes:
# - Instead of all listed stock, we select top 3000 stocks by market cap from QC stock universe.
class NetCurrentAssetValueEffect(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.coarse_count = 3000
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))
security.SetLeverage(10)
def CoarseSelectionFunction(self, coarse):
if not self.selection_flag:
return Universe.Unchanged
selected = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa']
return selected
def FineSelectionFunction(self, fine):
fine = [x for x in fine if x.EarningReports.BasicAverageShares.ThreeMonths > 0 and x.MarketCap != 0 and x.ValuationRatios.WorkingCapitalPerShare != 0]
sorted_by_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
top_by_market_cap = [x for x in sorted_by_market_cap[:self.coarse_count]]
# NCAV/MV calc.
self.long = [x.Symbol for x in top_by_market_cap if ((x.ValuationRatios.WorkingCapitalPerShare * x.EarningReports.BasicAverageShares.ThreeMonths) / x.MarketCap) > 1.5]
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"))