why I said you looked too dissatisfied

聊天每 10次 觉得你有5 次(太多 🙂 流露对自己的处境严重不满。这方面我们俩类似, 所以我也有同感。正因如此, 我觉得你没必要这么不满意, 更不必苦闷。

从没听你提到你父亲。我父亲这方面给我宝贵的指点. 更重要是, 反复指点 — 我的思维习惯好难改变, 我一直有独立思考的性格和信心, 真固执 , 甚至顽固不化。我感激他不厌其烦指出我的愚人自扰.

光感激没啥用. 更重要的是 我被他的智慧和耐心逐渐地感化, 认识到自己并非顽固不化。

你我对很多问题的看法差异都与我父亲相关。比如学区;比如名校招生偏向弱族;比如各国教育系统哪个更成功; 比如对孩子评估过早…

还是说个人事业吧. 我深感自己 IQ/EQ 有限, 实在没必要和高薪的技术人员比.(更不要去比管理型人才). 所以我说目前处境不错, 偷笑还来不及.

刷题并不一定要有经济效益 — 比如拿个硅谷 或是高频 顶级公司聘约. 我比较重视能力提高,技能积累. 几年候 就算积累效果不佳, 我也希望能做到心安理得.

我的 UChicago 硕士读下来这个状况, 心安理得 着实不容易 . 我的总结 — 金融数学职位太少而且要求比我能力高, 薪水不一定比程序员高多少, 也没有 Contract 可言. 没法发挥我 (和 CSDoctor) coding 方面的特长和经验. 所以说 2013 年选择这个硕士课程, 实情了解得不够. 上了船才知道。

这次惨痛的经历决定了我对各种新技术新领域的谨慎, 徘徊, 举足不前.

既然我不看好这些领域的”钱”途, 我也没你那么不满现状. 话说回来,

* i’m good at scripting/SQL/data-processing compared to other developers I know;
* I like analyzing complex data, with attention to details;
* I have formal math training including statistics

So IF there’s some high-paying domain for me, I am open to it. That’s a big IF. The way I see it, most of those data analyst jobs are not paying well. If it pays well, it would be too hard to get in.

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overvalued analytics applications #CSDoctor

Is GPS navigator necessary? Are side signal lights necessary? Some things are more necessary than others.

Trading system, risk systems, market data systems are mainstream. In contrast, I have found out that pricing analytics system is not really mainstream. Many buy-side and most smaller sell-side firms don’t use any pricing analytics beyond rudimentary derivations from raw market data.

There are also a number of sophisticated derivative and fixed-income analytics vendors including Bloomberg, Murex, Numerix.. These vendors focus on analytics so their customers don’t need deep expertise. OCBC’s quant team’s main job was validating the analytics offered by Murex.

Pricing analytics tools are “advisory” and not mandatory. The creators of those tools (like CSDoctor) tend to over-value their creations as if they are going to make the traders faster, safer, more profitable. In reality, traders can always choose not to use them.

As a contrast, take market data as example – 80% of trading shops need to build or buy market data systems as they can’t operate without it.

Math+intelligence ] trading != high value-add

  • — Examples of math applied outside traditional proven quant domains like VaR
  • Trade analytics, execution analytics systems — analyzing past executions, and uses statistic tools to derive some empirical or parametric distribution.
  • Sell-side pre-trade analytics to evaluate a proposed trade….
  • Real-time risk analytics

Q: How much math in these systems? Not that much.

  • Fundamentally, trading domain is math-lite…
  • Risk management is slightly more mathematical due to large data set, relaxed latency requirement, many scenarios
  • The fancier and more advanced math, the more dubious

Q: Value-add? Questionable. That’s one reason why most financial institutions don’t spend billions building such systems. They do spend billions on traditional automation systems.

Q: Who would want to pay to use these systems? Rather Few.

Q: Python? Possibly.

case study: CSDoctor’s — value-add@analytics #CSDoctor

how is mkt data used ] buy-side FI analytics@@

This is a BIG bond asset manager… They use 2-factor HJM model, among others.

They use EOD market data for risk measure + risk sensitivity calculations. No real time.

Models were written by 40+ quants untrained in c++. The 16-strong IT team integrates the models

I asked “Do you use liquid fixed income market data mostly to calibrate models and use the model to price illiquid instruments?”

A: both

  • To calibrate model — every day, as explained in [[complete guide]] P436
  • To derive valuation directly on existing positions if the instruments are comparable (between ref data instrument and position instrment)

python usage in FI quant lib #Pimco

In one of world’s biggest fixed income buy-side firms’ quant library, the codebase is 3/4 c++ and ¼ python including pandas, numpy, machine learning, grid computing modules. I think this is similar to Macquarie FICC quant lib.

C++ is much faster, but data structures are very limited including STL containers.

I think the funds hold mostly bonds and mortgages. How about futures, IRS? Perhaps for hedging?

##orgro lens:which past accu proved long-term # !! quant

(There’s a recoll on this accumulation lens concept…. )

This post is Not focused on IV or GTD. More like zbs.

Holy grail is orgro, thin->thick->thin…, but most of my endeavors fell short. I have no choice but keep shifting focus. A focus on apache+mysql+php+javascript would have left me with rather few options.

  • —-hall of famers
  • 1) [T] data structure theory + implementation in java, STL, c# for IV — unneeded in projects
  • 2) [CRT] core java knowledge including java OO has seen rather low churn,
    • comparable to c++
    • much better than j2EE and c#
  • 3) [T] threading? Yes insight and essential techniques. Only for interviews. C# is adding to the churn.
  • 4) [j] java/c++/c# instrumentation using various tools. Essential for real projects and indirectly helps interviews
  • [C] core C++ knowledge
  • [C] GTD knowledge in perl/python/sh scripting
  • [j] google-style algo quiz — Only for high-end tech interviews. Unneeded in any project
  • [R] SQL? yes but not a tier one skill like c++ or c#
  • coding IV — improved a lot at RTS
  • ————————also-ran :
  • devops
  • [C] personal productivity scripts
  • [T] probability IV
  • [C] regex – needed for many coding interviews and real projects
  • [C] low level C skills@RTS {static; array; cStr; reinterpret_cast;  enum; typedef; namespace; memcpy}
  • [!T] bond math? Not really my chosen direction, so no serious investment
  • [!T] option math?
  • SQL tuning? not much demand in the trading interviews, but better in other interviews
  • [R] Unix — power-user GTD skills.. instrumentation, automation? frequently used but only occasionally quizzed
  • [R] Excel + VBA? Not my chosen direction

–strengths
C= churn rate is comfortable
D = has depth, can accumulate
R= robust demand
T= thin->thick->thin achieved
j|J = relevant|important to job hunting

22surprises since Apr2017 #work+family #mostly positive

  1. regained self-esteem — in tech IV/GTD, after bleeding self-confidence for 5 years
  2. my c++ competence (including sockets) finally gained traction, thanks to the interviews.
  3. [c] my peers didn’t “leave me in the slow track”. Most of them are still regular developers.
  4. [c] concentration window — proved to be extremely important to my learning, career planning and reflections. Parenting and household chores are real drags.
  5. retire — I have decided to retire in Singapore not U.S. I see my Singapore citizenship as a huge strategic advantage over my Chinese/Indian peers.
  6. quant career and math aptitude — broken dream. Disillusioned. deepest pain
  7. coding test — continues to spread. I improved progressively, gained traction — I even find it enjoyable.
    • dnlg — all 3 types of domain knowledge are losing weight in interviews. Note half my recent interviews are outside ibanks.
  8. “strategic technology bet” — is thoroughly discredited, through repeated introspection
  9. [c] java remains robust and dominant in ibanks. c++ is robust too. There are still many c++ roles in U.S.
  10. Tristate housing — school-district housing is more expensive than I thought, but Edison/Bayonne can be quite affordable

–Next 20

  1. [c] I didn’t lose all of my java strength…
  2. U.S. high-end contract rate — has grown from $90 to $100
  3. [c] ibanks interviews — (including coding IV) continue to play to my advantage, after 5 years
  4. [c] aging developers — I see good examples in Shubin, Paul, Shanyou, Alan, Daniel etc
  5. [c] U.S. job market — didn’t lose steam. I think it is growing. U.S. economy continues to grow
  6. [c] wife was competent at her job and continues to keep the kids in the current condition without deterioration
  7. kids — my daughter didn’t become alienated; my son didn’t get out of control.
  8. [c] I continue to take unpaid leaves to learn from interviews
  9. start-ups — There are many interesting start-ups both in U.S. and Singapore, able to pay.
  10. mkt data — enjoys growing demand and I gained traction more than I gained a new defensible territory.
  11. U.S. investment yield is typically 6%, higher than what I observe in Singapore.
  12. [c] ibanks didn’t reduce IT budget or go offshore as some predicted
  13. [c] HFT — is robust
  14. rise of west coast
  15. BGC — delays
  16. apps and coding jobs … are becoming more important, more wide-spread than I anticipated.
  17. [c = continuation, but unexpected]

j4 stick2c++: Score big{losing@quant/c#

See also vindicative specializations , what if I transition to desk quant role but don’t rise up@@ and j4 c#: hind sight

I already give up several “investments”. If I take a java job, I would again forgo so many years of investment in c++. Now after I got more c++ offers, I feel /triumphant/vindicative/.

swing py c# quant 2010~13 quant af 2013 c/c++  (Zoom out …)
 $0 $0  $0 S$70k $ invested
 $0 $0 S$5k/Y  $0 $1k/Y cf
nonQuant job
up to USD20k/Y pretax opportunity cost
6M 2Y since barc 2Y  1Y 3Y 6Y since 1998 nominal effort
3M 4M 1Y  6M 2.5Y 3Y serious effort incl. STS
3M 2M 1M  2M 2Y 1.5Y spare time sacrificed(STS)
-2 -3 -6  -5 #more than py -15 -15 points invested
Barx passed some IVs OC, Bbg, Reuters 95G/OC Stirt/Mac/CVA ~14 offers job “offers”
Trex,bbg..  -> DRW; Nomura; Mako; Trex; Pimco analytics many help interviews
helps my WPF xx value@algo IV deepens java nlg  -> brain teasers; math cfd; contrarian
insight into bigData/quantTrading;
deepens java nlg other ROTI
2 more than
invested
3 #built real
professional xp
 3 9 #50%+ more than invested points SCORED
 no loss? no loss -3 -2 -7 no loss net points lost

[11]quant library in java/c# Not catching on

XR,

You once told me java can emulate the same quant lib functionality of C/C++. I asked quants in GS, MS, ML, Barcap and a few other banks. I don’t remember anyone saying their quant lib is in java. I now feel there’s no industry momentum behind such a migration. Further, I feel there’s no justification either. I’d go out on a limb and say there’s justification for sticking to C++.

A java implementation is less accessible from dotnet, python, and other scripting languages that could be making (slow) inroads into trading floors.  In contrast, all major languages support an decent interface to integrate with a C library. C is the common denominator.

More importantly, the sponsors of the quant lib are business users (not only traders) and they
know none of the languages but they know MSExcel. I’d say Excel integration is a must for every quant lib, otherwise traders may refuse to use it. C implementations easily integrate with MSExcel, via the
Microsoft COM interface and other interfaces. C# also integrates well with Excel.

Some quant libs are used in visualization and GUI. Dotnet and WPF are a market leader in GUI.

I also feel C implementation tends to be faster, at least no slower, than java quant lib. In pre-trade real time apps, a quant lib needs to be fast. A Barcap veteran told me the most important justification for C++ in quant lib is speed/performance.

In 2018 I asked an Executive Director in MS CVA team why java is not used. He said performance is the main reason.