SWAG a J/EXCEL/GIT Personal Cash Flow Forecasting Mob

While browsing in a favorite bookstore with my son, I spotted a display of horoscope themed Christmas tree ornaments. The ornaments were glass balls embossed with golden birth signs like Aquarius, Gemini, Cancer, et cetera, and a descriptive phrase that “summed up” the character of people born under a sign. Below my birth sign golden text declared, “Imaginative and Suspicious.”

I said to my son, “I hate it when astrological rubbish is right.”

I am imaginative and suspicious; it’s a curse. When it comes to money my “suspicious dial” is permanently set on eleven. I assume everyone is out to cheat and defraud me until there is overwhelming evidence to the contrary. Paranoia is generally crippling but when it comes to cold hard cash it’s a sound retention strategy.

Prompted by an eminent life move, I found myself in need of a cash flow forecasting tool. Normal people deal with forecasting problems by buying standard finance programs or cranking up spreadsheets; imaginative and suspicious people roll their own.

SWAG

SWAG, (Silly Wild Ass Guess), is a hybrid J/EXCEL/GIT mob¹ that meets my eccentric needs. I wanted a tool that:

1. Abstracted away accounting noise.
2. Was general and flexible.
3. Used highly portable, durable, and version control friendly inputs and outputs.
4. Reflected what ordinary people, not tax accountants, actually do with money.
5. Is open source and unencumbered by parasitic software patents.

Amazingly, my short list of no-brainer requirements eliminates most standard finance programs. Time to code!

SWAG Inputs

The bulk of SWAG is a JOD generated self-contained J script. You can peruse the script here. SWAG inputs and outputs are brain-dead simple TAB delimited text tables. Inputs consist of monthly, null-free, numeric time series tables, scenario tables, and name cross-reference tables. Outputs are simple, null-free, numeric time series tables. Input and output time series tables have identical formats.

A few examples will make this clear. The following is a typical SWAG input and output time series table. Note: the following tables have been truncated for this blog. The complete text files are available on GitHub here.

Date	E0	E1	E2	E3	E4	E5	E6	E7	E8	EC	EF	Etotal	I0	I1	I2	I3	I4	I5	IC	Itotal	R0	R1	R2	R3	Rtotal	D0	D1	D2	D3	D4	Dtotal	BB	NW	U0	U1	U2	U3
2015-09-01	912	1650.000000000	100	0	50	0.000000000	0	0	0	0	0	2712.000000000	4800.000000000	0	0	0	0	0	0	4800.000000000	130000.000000000	25000.000000000	0.000000000	0	155000.000000000	0.00000000	0.000000000	0	0	0	0.000000000	2088.000000000	157088.000000000	155000	0	0.0000000	0.000000000
2015-10-01	912	1656.875000000	100	0	50	0.000000000	0	0	0	0	0	2718.875000000	4806.000000000	0	0	0	0	0	0	4806.000000000	130054.166666667	25062.500000000	0.000000000	0	155116.666666667	0.00000000	0.000000000	0	0	0	0.000000000	2087.125000000	159291.791666667	0	0	0.0000000	0.000000000
2015-11-01	912	1663.778645833	100	0	50	0.000000000	0	0	0	0	0	2725.778645833	4812.007500000	0	0	0	0	0	0	4812.007500000	130054.166666667	25062.500000000	0.000000000	0	155116.666666667	0.00000000	0.000000000	0	0	0	0.000000000	2086.228854167	161378.020520833	0	0	0.0000000	0.000000000
2015-12-01	912	1670.711056858	100	0	50	0.000000000	0	0	0	0	0	2732.711056858	4818.022509375	0	0	0	0	0	0	4818.022509375	130054.166666667	25062.500000000	0.000000000	0	155116.666666667	0.00000000	0.000000000	0	0	0	0.000000000	2085.311452517	163463.331973351	0	0	0.0000000	0.000000000

The first header line is a simple list of names. The first name “Date” heads a column of first of month dates in YYYY-MM-DD format. The SWAG clock has month resolution and dates are the only nonnumeric items. Names beginning with “E” like E0, E1, …, are aggregated expenses. Names beginning with “I” like I0, I1, I2 … are income totals. “R” names are reserves: basically savings, investments, equity and so forth. “D” names are various debts. BB is basic period balance, NW is period net worth and “U” names are utility series. Utility series facilitate calculations. Remaining names are self-explanatory totals. Be aware that this table has been formatted for this blog. Examples of raw input and output tables can be found here.

The next ingredient in the SWAG stew is what many call a scenario. A scenario is a collection of prospective assumptions and actions. In one scenario you buy a Mercedes and assume interest rates remain low. In another, you take the bus and rates explode. When forecasting I evaluate five basic scenarios, grim, pessimistic, expected, optimistic, and exuberant. Being a negative Debbie Downer type I rarely invest time in exuberant scenarios. I concentrate on grim and pessimistic scenarios because once you are mentally prepared for the worst anything better feels like a lottery win.

The following is a typical SWAG scenario table. Scenario tables, like time series tables, are simple TAB delimited text files.

 Name         Scenario On Group       Value  OnDate     OffDate    Method   MethodArguments                                                Description                                                                     
 reservetotal s0          assumptions 0      2015-09-01 2015-10-01 assume   RSavings=. 0.5 [ RInvest=. 3 [ REquity=. 3 [ ROther=. 1        annual nominal percent reserve growth or decline during period                  
 car          s0                      50     2015-09-01 2035-08-01 history                                                                 annualized car maintenance until first death                                    
 house        s0                      912    2015-09-01 2016-08-01 history  BackPeriods=.1                                                 current rent until move                                                         
 insurance    s0                      100    2015-09-01 2035-08-01 history                                                                 car insurance                                                                   
 living       s0                      1650   2015-09-01 2044-01-01 history  YearInflate=.5                                                 normal monthly living expenses                                                  
 salary       s0                      4800   2015-09-01 2016-08-01 history  BackPeriods=.4 [ YearInflate=.1.5                              maintain net monthly income until move                                          
 reservetotal s0                      25000  2015-09-01 2015-10-01 reserve  Initial=.1 [ RInvest=. 1                                       stock value at model start                                                      
 reservetotal s0                      130000 2015-09-01 2015-10-01 reserve  Initial=.1                                                     savings at model start                                                          
 salary       s0                      4200   2016-08-01 2023-07-01 history  BackPeriods=.4 [ YearInflate=.1.5                              reduced net income after move until retirement                                  
 house        s0          move        2000   2016-08-01 2016-09-01 history                                                                 moving expenses                                                                 
 house        s0                      100    2016-08-01 2044-01-01 history                                                                 incidental housing expenses after move                                          
 house        s0                      100    2016-08-01 2044-01-01 history                                                                 home owners association payments                                                
 house        s0                      150    2016-08-01 2044-01-01 history                                                                 property taxes                                                                  
 reservetotal s0          buy house   110000 2016-08-01 2016-09-01 reserve  Initial=.1 [ REquity=. 1                                       down payment added to house equity initial setting prevents double spend        
 loan         s0          buy house   150000 2016-08-01 2023-02-01 borrow   Interest=. 4.5 [ YearTerm=. 30 [ DHouse=.1  [ LoanEquity=.1    30 year mortgage rate on house until inheritance                                
 reservetotal s0          buy house   110000 2016-08-01 2016-09-01 spend                                                                   down payment on house from savings                                              
 annuities    s0                      250    2018-07-01 2035-08-01 history                                                                 monthly retirement and other annuity payments end date is unknown               
 annuities    s0                      50     2018-07-01 2035-08-01 history                                                                 any government pension payments                                                 
 reservetotal s0          assumptions 0      2020-01-01 2044-01-01 assume   RSavings=. -2.5 [ RInvest=. -5.0 [ REquity=. -5.0 [ ROther=. 2 market tanks government introduces negative interest                            
 loan         s0          buy car     10000  2020-07-01 2025-07-01 borrow   Interest=. 5 [ YearTerm=. 5 [ DCar=.1                          pay balance of car at 5% for 5 years                                            
 reservetotal s0          buy car     7000   2020-07-01 2020-08-01 spend                                                                   car down payment from savings                                                   
 reservetotal s0          inherit     180000 2023-01-01 2023-02-01 reserve  Initial=.1                                                     inheritance to savings                                                          
 reservetotal s0          buy house   150000 2023-03-01 2023-04-01 transfer Fee=. 1500 [ DHouse=.1                                         pay off remaining mortgage balance after inheritance fee is closing cost        
 salary       s0                      1400   2023-07-01 2035-08-01 history                                                                 estimated social security payments spread over expected life                    
 insurance    s0                      700    2023-07-01 2024-12-01 history                                                                 medical insurance in the gap between retirement and spouse medicare eligibility 
 annuities    s0                      100    2024-12-01 2044-01-01 history                                                                 any retirement pension payments to spouse                                       
 annuities    s0                      100    2035-08-01 2044-01-01 history                                                                 any us social security survivor benefit after first death                       

Again the first header row is a simple list of names. Most scenario names are self-explanatory but four OnDate, OffDate, Method, and MethodArguments merit some explanation. SWAG series methods assume, history, reserve, transfer, borrow, and spend are modeled on what people typically do with cash.

1. assume sets expected interest rates and other global assumptions for a given time period. SWAG series methods operate over a well-defined time period. The period is defined by OnDate and OffDate.
2. history looks at past periods and estimates a numeric value that is projected into the future. Currently, history computes simple means but the underlying code can use arbitrary time series verbs.
3. reserve manages savings, investments, equity and other cash-like instruments.
4. borrow borrows money and sets future loan payments. borrow supports simple amortization loans but is also capable of reading an arbitrary payment schedule that can be used for exotic² loans.
5. transfer moves money between reserves, debts, expenses and income series.
6. spend does just what you expect.

SWAG series methods adjust all the series affected by the method. As you might expect SWAG arguments methods are detailed. MethodArguments uses a restricted J syntax to set SWAG arguments. Argument order does not matter but only supported names are allowed. Many examples of SWAG MethodArguments can be found in the EXCEL spreadsheet tp.xlsx. I use EXCEL as a scenario editor. By setting EXCEL filters, you can manage many scenarios.

The final SWAG input is a name cross-reference table. It is another TAB delimited text file that defines SWAG names. You can inspect a typical cross-reference table here.

Running SWAG

To run SWAG you:

1. Prepare input files.
2. Start J, any front-end jconsole, JQT or JHS will do, and load the Swag script.
3. Execute RunTheNumbers.
4. Open the EXCEL spreadsheet swag.xlsx, click on the data ribbon and then press the “Refresh All” button.

Let’s work through the steps.

Prepare Input Files

By far the most difficult step is the first. Here you review your financial status which means checking bank balances, stock values, loan balances and so on. Depending on your holdings this could take anywhere from minutes to hours. I call this updating actuals. Not only is updating actuals the most difficult and time-consuming step it is also the most valuable. Money that is not closely watched leaks away.

I store my actuals in a simple tabbed spreadsheet. Each tab maintains an image of a text file. I enter my data and then cut and paste the sheets into a text editor where I apply final tweaks and then save the sheets as TAB delimited text files.

Monthly income, expenses and debts are easy to update but some of my holdings do not offer monthly statements. The verb RawReservesFromLast in Swag.ijs fills in missing months with the last known values. When I’m finished preparing input files I’m left with four actual TAB delimited files, RawIncome.txt, RawExpenses.txt, RawReserves.txt, and RawDebts.txt. You can inspect example actual files here.

Start J and load the Swag script.

The SWAG script is relatively self-contained. It can be run in any J session that loads the standard J profile. Load Swag with the standard load utility.

 load 'c:/pd/fd/swag/swag.ijs'

Here SWAG is loaded in JHS.

Execute RunTheNumbers

RunTheNumbers sets the SWAG configuration, loads scenarios, copies actuals to each scenario, and then evaluates each scenario. Scenarios are numbered. I use positive numbers for “production” scenarios and negative numbers for test scenarios. It sounds more complicated that it is. This is all you have to do to execute RunTheNumbers

 RunTheNumbers 0 1 2 3 4 

The code is simple and shows what’s going on.

RunTheNumbers=:3 : 0

NB.*RunTheNumbers v-- compute all scenarios on list (y).
NB.
NB. monad:  blclFiles =. RunTheNumbers ilScenarios
NB.
NB.   RunTheNumbers 0 1 2 3 4

NB. parameters sheet is the last config sheet
ModelConfiguration_Swag_=:MainConfiguration_Swag_
parms=. ".;{:LoadConfig 0
scfx=. ScenarioPrefix

ac=. toHOST fmttd ActualSheet 0
ac write TABSheetPath,'MainActuals',SheetExt

sf=. 0$a:
for_sn. y do.
    ac write TABSheetPath,scfx,(":sn),'Actuals',SheetExt
    sf=. sf , parms Swag sn [ LoadSheets sn
end.

sf 
)

RunTheNumbers writes a pair of TAB delimited forecast and statistics files for each scenario it evaluates.

Open swag.xlsx and press “Refresh All”

The spreadsheet swag.xlsx loads SWAG TAB delimited text files and plots results.³ I plot monthly cash flow, estimated net worth and debt/equity for each scenario. The following is a typical cash flow plot. It estimates mean monthly cash balance over the scenario time range.

The polynomial displayed on the graph is an estimated trend line. Things are looking bleak.

Here’s a typical net worth plot.

In this happy scenario, we die broke and leave a giant bill for the government.⁴

So far SWAG has met my basic needs and forced me to pay more attention to the proverbial bottom line. As I use the system I will fix bugs, refine rough spots, and add strictly necessary features. Feel free to use or modify SWAG for your own purposes. If you find SWAG useful please leave a note on this blog or follow the SWAG repository on GitHub.