Building Fintech App in Java: Tips & Trick

The talk is for JVM Meetup #18 featured here: https://www.eventbrite.com/e/jvm-meetup-18-with-tunai-kita-late-night-tech-tickets-58411026954

We discuss the basics of calculating big numbers and do numerical analysis on Java in this presentation.

Source code for the talk can be found at: https://github.com/JVMDeveloperID/jvm-fintech-tips-trick

Transcript

1. λ Building Fintech App in Java Tips & Trick
2. THE SPEAKER › Alex Xandra Albert Sim › Principal R&D Engineer at Blibli.com › @bertzzie(.sim)
3. λ Disclaimer Presentations are intended for educational purposes only and not to replace independent professional judgment. The views and opinions expressed in this presentation do not necessarily reflect the official policy or position of blibli.com. Audience discretion is advised.
4. λ Fundamentals of Financial Computation
5. λ Foundational Knowledge › Computational Finance (Keuangan Komputasional) › Emphasis on: – Mathematical Finance (Matematika Keuangan; Teknik Finansial) – Numerical Method (Metode Numerik) › Layman terms: – Analysis – Simulation – Approximation
6. λ Practical Concerns › Calculations in fintech needs to be accurate and precise › Calculations needs to be FAST
7. λ How?
8. λ Accuracy and Precision Java Fintech Tip & Trick #1
9. λ #1 Floating-point Numbers are FAKE NEWS
10. λ Let’s look at some code Click for code
11. λ Output
12. λ Notice › When the fraction is power of 2 (1/2, 1/4) we got correct result › 1/5 is correct before being summed › 1/3, 1/6, and 1/7 have tiny roundoff-error › 1/3 has round-up error on fraction, but round-down error on sum. Vice versa for 1/6 and 1/7
13. λ But HOW?
14. λ The Float Number Format Sign Exponent Fraction Sign: positive (0) or negative (1) Exponent: 127 or -126 to +127 Fraction: the actual fraction value with leading 1 for positive (normalized) and leading 0 for 0 and others (denormalized) -5.5 = 1 1000 0001 011 0000 0000 0000 0000 0000
15. λ The Float Number Format Sign Exponent Fraction Normalized value: = −1 × 2 × 1. Denormalized value: = −1 × 2−126 × 0.
16. λ The Float Number Format Sign Exponent Fraction -5.5 = 1 1000 0001 011 0000 0000 0000 0000 0000 011 0000 0000 0000 0000 0000 1 + 0 + 1 21 + 1 22 + 0 + 0 + 0 + ⋯ 0 1 1 0 0 0 …
17. λ Example! Let’s do some math!
18. λ Conclusion › Float (and double) should not be used to count money (unless you’re counting my debt – then by all means) › They are still useful because it’s fast and quite accurate. And sometimes the mistakes are funny.
19. λ Conclusion (cont.) Use Float (and Double) with Discretion! When in doubt, use BigDecimal
20. λ Overflow Java Fintech Tip & Trick #2
21. λ #2 Watch out for Integer Overflows!
22. λ Java’s Integer Integer Type Size (bits) Min Value Max Value byte 8 -128 127 short 16 -32.768 32.767 char 16 0 65.535 int 32 -2.147.483.648 2.147.483.647 long 64 -9.223.372.036.854.775.808 9.223.372.036.854.775.807
23. λ The Fly-Trap › Notice how max value is always smaller than the positive of min-value? What’s the output? -2147483648 -2147483648
24. λ The Fly-Trap (2) › Notice how max value is always smaller than the positive of min-value? What’s the output? -2147483648 -2147483648
25. λ The Fly-Trap (3) › Notice how max value is always smaller than the positive of min-value? What’s the output? 2147483640 + 10000 = -2147473656
26. λ More Unsafe Code Example
27. λ Safe Code (Java 8+) There’s also Math.subtractExact and Math.negateExact
28. λ Safe Code (Bound Check)
29. λ Safe Code (Bound Check) (2)
30. λ Safe Code (Bound Check) (3) How to do bound check for abs?
31. λ Safe Code (Casting)
32. λ Safe Code Simply: Use BigInteger
33. λ As Binary As Possible Java Fintech Tip & Trick #3
34. λ Binary › Everything inside our computer is represented by binary › There’s usually a way to efficiently calculate stuff with binary, since both CPU and Memory works in Binary
35. λ Binary Example: Power › Simple example, to calculate: = › The simple and naïve approach: = ∙ ∙ ∙ ∙ … › Finishes in linear time:
36. λ Power: the Better Approach = = 14 Get binary rep. of 1110 Convert to power of 2 (right to left) 1 ∙ 2 ∙ 4 ∙ 8 Remove the 0 bit 2 ∙ 4 ∙ 8 Calculate!
37. λ Power Comparison! Click for code
38. λ Power Comparison Result! Result "tech.namas.sharing.numbers.PowerComparison.powBinary": 1188449.776 ±(99.9%) 133027.111 ops/ms [Average] (min, avg, max) = (938259.958, 1188449.776, 1379370.603), stdev = 153194.219 CI (99.9%): [1055422.665, 1321476.887] (assumes normal distribution) Result "tech.namas.sharing.numbers.PowerComparison.powMath": 11479.744 ±(99.9%) 429.096 ops/ms [Average] (min, avg, max) = (9987.543, 11479.744, 12010.515), stdev = 494.148 CI (99.9%): [11050.648, 11908.840] (assumes normal distribution) Benchmark Mode Cnt Score Error Units PowerComparison.powBinary thrpt 20 1188449.776 ± 133027.111 ops/ms PowerComparison.powMath thrpt 20 11479.744 ± 429.096 ops/ms
39. λ Benchmarking Questions › ~10x difference? Is this real life? Or is this fantasy? › Why do we need the (min, max, avg) numbers? › Why is the std-dev differs so much? › What does “normal distribution means?
40. λ Interpolation & Approximation Java Fintech Tip & Trick #4
41. λ Prediction › A lot of fintech use case is analyzing / predicting future values › Will person x, given his/her history, pay his/her debt in time and consistently? › Will investing in share A yield good value given its share value history?
42. λ Interpolation
43. λ Interpolation (n) /in-​ˌtər-pə-​ˈlā-shən/ a method of constructing new data points within the range of a discrete set of known data points Approximation (n) /əˌpräksəˈmāSH(ə)n/ a process of matching two functions that closely match, usually from the same class for specific task
44. λ The Common Polynomial Form › Most function to calculate stuff like this will be shown in polynomial form: = 0 + 1 + 2 2 + ⋯ + › Which are very expensive to calculate (look at all the Math.pow)
45. λ The Newton Form › To simplify, we could use the Newton Form: = 0 + 1 − 0 + 2 − 0 − 1 + … + − 0 − 1 … − −1 › which allows us to cache calculations and apply dynamic programming techniques
46. λ Polynomial to Newton form
47. λ Let’s look at some code Click for code
48. λ Final Result
49. λ Closing
50. λ Summary › Don’t use float. It’s not accurate and it adds up. › Beware of overflows, specifically for big calculations. › Optimize by exploiting the fact that our computer uses binary internally. › Approximate and interpolate where possible.
51. λ Further Discussion › How to store transactions and values? › How to do reporting / analytics fast and real-time? › How to create instant decision-support?
52. λ Question and Answer