Cheat Sheet P123 Factors

Cheat Sheet P123 Factors

In this post, I present a cheat sheet for my favorite P123 Formulas & Factors (will update in the future and send it out!) There are many more factors in the P123 Database, this is just a small part of it.

Factor & Function Reference

https://www.portfolio123.com/doc/doc_factors.jsp

Use that link for the search of a factor —> (also available when you build a screen or strategy or a ranking system).

Earnings estimates (powerfull!)

https://www.portfolio123.com/doc/doc_factors.jsp

After “//” the comment e.g. the explanation of the formula starts, you can copy it with the comment into buy and sell rules, it will be ignored.

%(CurQEPSMean, CurQEPS13WkAgo) // 13 Weeks EPS estimates growth [higher better]
%(CurQEPSMean, CurQEPS8WkAgo) // 8 Weeks [higher better]
%(CurQEPSMean, CurQEPS4WkAgo) // 4 Weeks [higher better]
%(CurQEPSMean, CurQEPS1WkAgo) // 1 Week [higher better]

CurQEPSStdDev/abs( CurQEPSMean) // EPS Estimate Variability current Quarter [lower better]

FRank (very powerfull function!)

Description: https://www.portfolio123.com/doc/doc_detail.jsp?factor=FRank

Use FRANK as a buy rule in a screen or strategy:

FRank("%(CurQEPSMean, CurQEPS13WkAgo)",#all,#DESC, #ExclNA) > 80 // all —> goes over all stocks and ranks them

FRank in a buy rule, will make sure, that only the top 20% of stocks in terms of
%(CurQEPSMean, CurQEPS13WkAgo) will pass the buy rule. If CurQEPS13WkAgo = 1 and CurQEPSMean 1.1, it would calculate 10% growth. But that is not all, all stocks will be ranked by the ratio %(CurQEPSMean, CurQEPS13WkAgo).

FRank("%(CurQEPSMean, CurQEPS13WkAgo)",#sector,#DESC, #ExclNA) > 80

FRank("%(CurQEPSMean, CurQEPS13WkAgo)",#subsector,#DESC, #ExclNA) > 80

FRank("%(CurQEPSMean, CurQEPS13WkAgo)",#industry,#DESC, #ExclNA) > 80

FRank("%(CurQEPSMean, CurQEPS13WkAgo)",#subindustry,#DESC, #ExclNA) > 80

FRank on Average True Range —>

FRank("ATR(20,0)",#subindustry,#DESC, #ExclNA) > 85 // —> subindustry that are in the top 15% of Average True Range last 20 Days, stuff that moves!

FRank("ATR(20,0)",#all,#DESC, #ExclNA) > 85 // —> stocks that are in the top 15% of Average True Range last 20 Days, stuff that moves!

Quick Rank on a screen —>one single factor ranking

%(CurQEPSMean, CurQEPS13WkAgo)

Other powerfull stuff I like (from “Small and Micro Cap Focus” and “Core: Sentiment” Ranking Systems)

LoopSum("EPSExclXor(Ctr,Qtr) > EPSExclXor(Ctr+4,Qtr)",6,0) // —> In how many of the last six quarters did the stock's EPS exceed its EPS a year earlier? [higher better]

(CurQEPSMean- HistQ4EPSActual)/Abs(HistQ4EPSActual) // —> This measures the growth of the company's most recent quarterly EPS estimate over the one a year earlier.

(FCFQ-FCFPYQ)/MktCap // —> This takes the familiar PEG ratio and applies it to the company's free cash flow, but using only values from the latest quarter and the same quarter a year ago.

CurFYEPSMean/Price // —> Forward earnings yield

ConsEstMean(#EBITDANTM,0)/EV // —> Forward EBITDA/EV

CurFYSalesMean/MktCap // —> Forward revenue yield

Abs(GrossPlantA/SalesA-FMedian("GrossPlantA/SalesA",#industry)) // —> This factor punishes companies whose ratio of gross plant to sales is quite different from the median in their industry. These are companies whose sales are incommensurate with their fixed assets and thus unsustainable, as well as companies whose sales are negligible considering how much capital they have. [lower better]

Aggregate("RSI(200)",#industry) // —> This is a useful barometer of industry momentum [higher better]

(InventoryA-InventoryPY)/AstTotPY // —> Companies that are decreasing their inventory are probably doing something right [lower better]

LoopMedian("ROE%(Ctr,TTM)",12) —> median ROE

Surprise%Q1 // —> Earnings Surprise last report [higher better]

Surprise%Q2 // —> Earnings Surprise last report before the last report ;-) [higher better]

AvgRec- AvgRec4WkAgo // —> Changes in Recommendation in the last 4 Weeks [higher better]

MedianVol(252)/SharesCur(126) // —> Share turnover is a standard metric which is very predictive of a stock's beta. The lower the share turnover, the lower the beta will be. [usally lower better, eps. with small caps]

MedianVol(65)/SharesCur(0) // —> Share turnover is a standard metric which is very predictive of a stock's beta. The lower the share turnover, the lower the beta will be. [usally lower better, eps. with small caps]

Aggregate("TotalReturn",#subindustry) // —> subindustry momentum

(OperCashFlTTM - CapExTTM + (1-TaxRate%TTMInd/100)*IntExpTTM)/EV // —> Unlevered free cash flow to EV This is a shorthand version of the ratio that powers intrinsic value calculations. Warning: it's relatively meaningless for companies in the financial sector.

(SMA(150,21)-SMA(150,252))/ATRN(150) // —> This is a momentum measure that takes the SMA a month ago, subtracts the SMA a year ago, and divides by the average true range. It favors stocks whose price has risen dramatically compared to their usual range. We don't look at the last month's worth of prices since we like to buy on the dip!

MedianDailyTot(126) // —> Median daily total amount traded (price * volume) for the past (in this example 126) number of bars.

AvgVol(13)/AvgVol(13,30) // —> stocks with a recent increase in volume are either exhibiting strong local momentum or strong mean reversion.

Momentum (from Core: Momentum)

close(0)/close(120) // —> 120 Days return
close(0)/close(140)
close(0)/close(160)
close(0)/close(180)

UpDownRatio(120,0) // —> up and down ratio last 120 Days
UpDownRatio(60,0)
UpDownRatio(20,0)

Close(60) / Close(120) // —> 3 Months returns 3 Months ago
Close(120) / Close(180) // —> 3MoRet6MoAgo
Close(180) / Close(240) // —> 3MoRet9MoAgo

Pr26W%ChgInd // —> 26 Week Price Percent Change Industry (%)
Pr52W%ChgInd // —> 52 Week Price Percent Change Industry (%)

FOrder("Ret1Y%Chg",#Previous) <= 25 // Execute the rules (in a screen) above and then buy the 25 stocks with the highest 1yr return.

Actuals Data (very powerfull as well!)

https://www.portfolio123.com/doc/doc_detail.jsp?factor=ActualGr%25PQ

“Actuals are companies' historical financial data, such as earnings, sales, and net income. Unlike fundamentals that come from official filings submitted to government agencies, Actuals are collected primarily from company press releases and/or from brokers.

Actuals are primarily used to calculate surprises vs the analyst estimates. However, integrating Actuals in fundamental analysis can offer a unique perspective compared to only using data from 10-K and 10-Q filings. This is particularly relevant when Actuals reflect different GAAP treatments, including one-off items or other specific financial occurrences.

Many semiannual companies release earning releases every quarter, and analysts follow suit with quarterly estimates. The main downside is that Actual data can be spotty and inconsistent through time, especially in the past. For example sometimes more complete actuals are the semiannual ones, and sometimes the quarterly.“

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ActualGr%ttm(#EPS) + ActualGr%PQ(#EPS) // —> Actual eps growth TTM + Last Quarter EPS Growth

%(CurQEPSMean, CurQEPS13WkAgo) // —> Earnings estimates growth last 13 Weeks, very strong on the industry and stock level

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Quality Filters

AltmanZOrig > 1.81 // good quality filter on small caps! https://www.portfolio123.com/doc/doc_detail.jsp?factor=AltmanZOrig

FCFq > 0 // free cash flow > 0

Net Income

NetIncBXorTTM > 0 // net income trailing 12 months
NetIncBXorPTM > 0 // net income prior trailing 12 months

Avoid Value Traps (in growthy systems)

PEExclXorQ * 0.75 > LTGrthMean // Price/Earnings * 0.75 > long term EPS growth

Insider Buying

InsNetShrPurch * price > 0.5 // 500k net (buys - sells) insider buying last 6 months

Example Screen

You can build this in 5-15 minutes!

mktcap > 1000 // —> Market Cap > 1 Billion

sma(50) > sma(150) // —> stock in some kind of uptrend

FRank("ATR(20,0)",#subindustry,#DESC, #ExclNA) > 85 // —> subindustry that are in the top 15% of Average True Range last 20 Days, stuff that moves!

AvgDailyTot(20) > 5000000 // —> Total Dollar Volume Average last 20 Days above 5 Million $

Do not forget the QuickRank —>

And run the backtest:

All right, here you have it :-)

All the best and best regards.

Andreas

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