About the service
I have been writing production code in Dyalog APL for nearly ten years — for banks, actuarial divisions and independent quant desks. Array-oriented programming is not exotic for me, but a natural way to think about data. When a calculation involves millions of rows, nested tables of rates or a price surface that needs to be revalued every few seconds, Dyalog APL lets me express the entire logic in a few dense and verifiable lines instead of bulky loops where errors hide. That is exactly what I offer: less code, fewer defects and numerical procedures that run at speed.
My typical projects are actuarial reserve and cash flow forecasting models, Monte Carlo pricing engines, portfolio risk and VaR calculations, time series analytics, and porting of obsolete APL logic or spreadsheet calculations into maintained Dyalog APL workspaces. I work confidently with what matters in practice: rank and leading axis operators, key and stencil, dfns and tacit chains, isolate utilities and parallel application for multi-core computation, coupling to databases through SQAPL, Conga and dotnet bridges. When needed I connect Dyalog APL with Python and native libraries.
The result is neat and well-documented. You get a versioned workspace or a set of source scripts, a brief explanatory note for each function, a set of tests with expected values and a walkthrough so your team can maintain the code themselves. I profile hot spots and tune them so that the concise expression is also fast. If you inherited opaque APL code that nobody dares touch, I will read it, explain it and carefully rework it. Describe the task, the data shape and the performance target — I will offer a concrete plan with stages.
My typical projects are actuarial reserve and cash flow forecasting models, Monte Carlo pricing engines, portfolio risk and VaR calculations, time series analytics, and porting of obsolete APL logic or spreadsheet calculations into maintained Dyalog APL workspaces. I work confidently with what matters in practice: rank and leading axis operators, key and stencil, dfns and tacit chains, isolate utilities and parallel application for multi-core computation, coupling to databases through SQAPL, Conga and dotnet bridges. When needed I connect Dyalog APL with Python and native libraries.
The result is neat and well-documented. You get a versioned workspace or a set of source scripts, a brief explanatory note for each function, a set of tests with expected values and a walkthrough so your team can maintain the code themselves. I profile hot spots and tune them so that the concise expression is also fast. If you inherited opaque APL code that nobody dares touch, I will read it, explain it and carefully rework it. Describe the task, the data shape and the performance target — I will offer a concrete plan with stages.
