Net Current Asset Value Effect Algorithm

Hello, I am studying NCAV too.

I found the same strategy but has a better return rate.

I also wrote code on my side but I have a way worse backtest result.

Thanks for sharing, I modified your OnData and fineSelection. and showed better

Compounding Annual Return from 10% to 25%

1. Your Ondata has error.

Your `OnData()`, will not hold the filtered_fine when we select a new Symbol that is not invested in the current Portfolio. The below code will avoid this error.

def OnData(self, data):
if not self.yearly_rebalance: return
if self.filtered_fine:
stocks_invested = [x.Key for x in self.Portfolio]
for i in stocks_invested:
#liquidate the stocks not in the filtered NCAV/MV list
if i not in self.filtered_fine:
self.Liquidate(i)
#purchase the stocks in the list
elif i in self.filtered_fine:
self.SetHoldings(i, 1/len(self.filtered_fine))
self.yearly_rebalance = False    

2. NCAV calc.

First, I am not sure `TotalNonCurrentLiabilitiesNetMinorityInterest` is the right value to select. This focus on NetMinorityInterest.

Second, this divides by BasicAverageSahres, which is not MarketCap.

total_liabilities = float(i.FinancialStatements.BalanceSheet.CurrentLiabilities.Value)+float(i.FinancialStatements.BalanceSheet.TotalNonCurrentLiabilitiesNetMinorityInterest.Value)
i.ncav = (float(i.FinancialStatements.BalanceSheet.CurrentAssets.Value) - total_liabilities)/float(i.EarningReports.BasicAverageShares.Value)

I used WorkingCaptial which is an asset - total liabilities.

            self.filtered_fine  =  [x.Symbol for x in fine if ((x.BalanceSheet.WorkingCapital) / x.MarketCap) > 1.5] 

I have tried to rewrite the algo using new fundamental universe:

from QuantConnect import Resolution
from AlgorithmImports import *


class NCAVsimple(QCAlgorithm):
    def Initialize(self):
        #rebalancing should occur in July
        self.SetStartDate(2010, 1, 1)  
        self.SetCash(100000) 
        self.UniverseSettings.Resolution = Resolution.Daily

        self.filtered_fine = None
        self.filtered_coarse = None
        self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol

        self.coarse_count = 3000
        
        self.yearly_rebalance = False
        # self.AddUniverse(self.CoarseSelectionFunction,self.FineSelectionFunction)
        self.AddUniverse(self.FundamentalFunction)
        self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.rebalance)

    def FundamentalFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
        if self.yearly_rebalance:
            # drop stocks which have no fundamental data or have low price
            filtered = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa']
            filtered = [x for x in filtered if x.EarningReports.BasicAverageShares.ThreeMonths > 0 
                            and x.MarketCap != 0 
                            and x.ValuationRatios.WorkingCapitalPerShare != 0]
            sorted_by_market_cap = sorted(filtered, key = lambda x:x.MarketCap, reverse=True)
            top_by_market_cap = [x for x in sorted_by_market_cap[:self.coarse_count]]
            self.filtered_fine = [x.Symbol for x in top_by_market_cap if ((x.FinancialStatements.BalanceSheet.CurrentAssets.TwelveMonths - x.FinancialStatements.BalanceSheet.TotalLiabilitiesAsReported.TwelveMonths) / x.MarketCap) > 1.5]
            return self.filtered_fine
        else: 
            return Universe.Unchanged
    
    def rebalance(self):
        #yearly rebalance
        if self.Time.month == 6:
            self.Debug("Rebalance" + str(self.Time))
            self.yearly_rebalance = True
        
    def OnData(self, data):
        if not self.yearly_rebalance: return 

        stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
        for symbol in stocks_invested:
            if symbol not in self.filtered_fine:
                self.Liquidate(symbol)

        if self.filtered_fine:
            for symbol in self.filtered_fine:
                if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable:  # Prevent error message.
                    self.SetHoldings(symbol, 1 / len(self.filtered_fine))

        self.yearly_rebalance = False

, but I got an error:

Runtime Error: N7parquet38ParquetInvalidOrCorruptedFileExceptionE (message: 'Invalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.')
  at 
    and x.ValuationRatios.WorkingCapitalPerShare != 0]
 in main.py: line 29
  at FundamentalFunction
    filtered = [x for x at filtered if x.EarningReports.BasicAverageShares.ThreeMonths > 0
 in main.py: line 27

Does anybody see what could be the cause ?

Hi Mislav

We cannot reproduce the issue, and we're able to run your code snippet without error. Can you retest please and see if the problem persists?

Best
Louis