exposure: semi-automatic(shallow)Accu #$valuable contexx

Opening eg — In RTS team, granted I didn’t get deep[2] socket experience or latency /engineering/ experience, but over the years semi-automatically I would get some valuable exposures, by raising good questions about .. sockets; reliable orderbook replication; error recovery; throughput engineering…

eg — in mvea team, I can get some valuable exposures to FIX; large scale and reliable equity OMS; low-latency (caching); order routing; automatic hedging; partial fills; limit orders; order cancels/amends; busts… Even if I don’t get deep experience on this job, my resume could claim genuine experience! Strategic positioning … (shallow) Accumulation

eg — in citi-muni, I got exposure to mkt-making; event-driven limit order repricing; PnL roll-up; mark-to-market; swing; JMS; Gemfire…

Key points about the “pattern”:

  • thanks to the strategic contexx, you get to accumulate (semi)automatically
  • robust commercial value in the skill
  • shallow [2] accumulation — I call it “exposure”, enough to impress some interviewers.
  • [1] small amount of effort — much lower than GTD, getting a job/certificate, losing weight
  • consistent effort ..

However, as the years go by, many developers stop digging with questions and others ignore the opportunities to dig into the difficult codebase because … they don’t have to:(. The automatic learning is a valuable option if you put in some legwork [1]. In contrast, some jobs don’t offer much automatic learning —

  • OC team: not so mainstream. I could still learn some WCF; reliable windows servers;
  • Qz team: poor portability. I could still learn some curve building; ticking risk;

[2] In contrast, here are examples of “deep” experience —

  1. from-scratch (95G) wait/notify solution
  2. from-scratch (95G) sybase stored proc to manage inventory in the face of competing orders
  3. home-prj order book replication in 2 coding interviews — Jump + iRage
  4. home-prj FIX client/server https://github.com/tiger40490/repo1/tree/jProj/java/com/tanbinFIX
  5. home-prj swing GUI to auto-update a table viewer

 

Advertisements

most(new/old)specializations turn out non-strategic

Look at my past vindicative specializations vs 

The Singapore government make technology bets. Venture capitalist make bets. Many organizations also make technology bets.. Every technology bet can lose or win.

In this post, I’m not advocating trySomethingNew. I am advocating specialization, which often requires accumulation and sticking to something not so new, like FIX, threading, SQL.

If you play safe and stay within the comfort zone of java/SQL/Perl, then don’t under-estimate the negative consequences such as

  • reactive
  • doldrums — see post on “y re-enter c++”
  • no deepening your understanding — a zbs
  • remain afraid and /uninitiated/ with the lower-level details below JVM

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

## past vindicative specializations

Quant and other Unsuccessful diversifications are not the primary focus today, but they are listed below the table.

see also — For the “domains nlg” (first 3 rows), marketable_domain_xp spreadsheet is more comprehensive but not necessarily more updated.

 (scales 0-5)  ⇘ mkt value given my direction robust
demand
entry barrier accu achieved %%expertise among peers i was surprised wage ROI
mktData #socket  4 growing not everyone has xp  2~3  2  4 none
bondMath  3 robust math is not natural
to most dev
 2  3~4  3 none
orderBook, OMS, FIX #Eq/FX  3-4 robust medium 1 #cod`IV 1  3 none
c++  5 ok not
growing
higher than I thought  5  3  2 minimal
Algo practice  5 Growing high 4 #XR disagrees  5  5 none
python  4~3 growing low  2  3 #unknown  3 none
bash scripting + commands  2 robust medium  4  4  5~3 none
threading xLang 5 growing high  5  5 2 minimal
(abstract?) container internals 5 unexpected strength medium  5  5 4 none
RDBMS 3~4 shrinking low  5  4 5 #under-whelming none
MOM 2 robust medium  1  1 4 none

–algo practice for IV
^ good amount of accumulation
^ my confidence is boosted esp. in c++
^ I’m rising to the challenge of coding test growing popular

–Python
^ boosts my IV confidence
▼no critical mass yet
▼low traction
▼don’t know my expertise relative to peers

–option domain knowledge including BS:
▼ much lower demand than bond math

— C# and XAML
^ Microsoft is a major force
▼not aligned to my current direction

–MSVS:
^ was my Achilles heel, now slowly gaining confidence

–C++ GTD and IV
^ I made real progress in 1) GTD and 2) IV but not during Mac days — wrong job nature
^ deepens my java understanding
▼ those high paying domains (HFT, quant) are too hard to break into

–MOM architecture, products…
▼ not as widespread as I perceived

–javascript, php, mysql
▼not aligned to my direction

–gemfire
▼ market too fragmented

–EJB, Spring, Hibernate
▼falling out of favor

##[17]%%agile advantages: b aggressive

  • healthy job market
  • advantage: short commute. Job not too demanding. Plenty of time available for self-study
  • advantage: I can work day and night to get things done
  • advantage: I’m not aiming for a promotion, so I can try many interviews
  • advantage: my quant credentials and zbs is probably top tier among techies
  • advantage: domain nlg
  • advantage: health and stamina
  • advantage? data analysis aptitude
  • advantage: I have Singapore + java as safety net –> risk capacity.
  • advantage: am open to relocation
  • advantage: am open to short term contracts
  • advantage: am open to pay cuts
  • advantage: no family.
  • advantage: c++/c# in addition to java
  • advantage: above-average SQL, Unix, python/perl/bash

If I hit $220k as a contractor, my self-image and self-esteem would improve. I would feel confident, no longer inferior. In fact, I would feel better than the managers since I don’t rely on that one employer.

##[17] 4 tech career growth directions #aggressive

In spite of the threats and pains, I remain hopeful about my technical growth. Ideally, I hope to

  1. maintain competitiveness in java. Consider body-building
  2. selectively build up my c++ IV competitiveness, in terms of QQ/BP/ECT, from 6 to 7, over 2 years
    1. sockets, concurrency, sharedMem, templates ..
  3. try to recover the /sunk cost/ in quant domain
  4. slowly branch out to data analytics or data science

[1 java] feels achievable, based on my recent interviews. [4 data] is secondary and nice to have, not a must at age 43, so [2 cpp] is my main drive.

The interviews are crucial. Without IV, I can improve my GTD or zbs but painfully slow improvement of IV competitiveness.

Q: Suppose we don’t know java. Based on our c++ IV performance, can we survive as a c++ developer?
A: I will survive. My family needs me.

quant^HFT^WestCoast, again

After I felt fairly established on Wall St (around 2011), I looked for greener pastures:

  1. quant dev
  2. HFT
  3. high-end positions in the west coast. Not sure what positions exactly — mobile? machine learning? cloud?
  4. regular Wall St c++  job

In fact my java jobs on Wall St is not that inferior, and has the advantage of reachability. In contrast, those greener pastures still look rather distant. They look closer when I’m in a positive mood. But let’s put on the black hat and be critical and skeptical:

Quant dev is low-churn but demand is kinda shrinking.

HFT is rather niche skillset, possibly less relevant to west coast than java and regular c++.

1) 2) 3) are mostly FTE, not contracts. Regular c++ is contract-friendly and more reachable.

I used to feel the quant domain is hardest. Now I feel it’s shrinking. Now I feel I’m in much better shape. I made the decision to focus on quant early, assuming I could self-study and break into HFT later.

Data science? Math is much easier than quant finance, and I have some training in it.

C++? I now have more hands-on experience

##[18] Algo problem solving as hobby@@

  • how does this compare to board game?
  • how does this compare to jigsaw puzzles
  • how does this compare to small mobile app development as a hobby?
    • small mobile game development
  • how does this compare to photography as a hobby?
  • how does this compare to blogging as a hobby
  • how does this compare with DIY home improvement
    • woodwork
  • how does this compare to auto tuning and bike building
  • how does this compare with hi-fi tuning

Every one of them is better than TV, gaming, sight-seeing or drinking. Each one requires consistent effort, sustained focus. Each one will become more frustrating less exciting before you reach the next level.