Zetamac · 120s, default settings
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Read a version aloud three times, then put it away and say it from memory in whatever words arrive. Keep the structure; let the phrasing come out slightly different every time.
A verbatim answer is audible from across the table. At a firm screening for quick independent thinkers, rehearsed reads as brittle rather than sharp — which is exactly the opposite of what these answers are for.
Delivery rules · they cost you marks every time
Your raw material · ★ = strongest cards
- ★ Contract bridge, competitively at NUS. Their old ad literally asked for bridge or poker players. Bidding is market making: you state a level on incomplete information, opponents' bids are information you update on, and you're penalised for over- and under-bidding alike.
- ★ The Causeway Point bus bet — you bet yourself on how many stops before home. Someone who makes markets on uncertain quantities for entertainment. Deliver it lightly; it should make them smile.
- NUS, First Class Honours, BBA, Finance specialisation — one line, then move.
- ~3 years of internships in 4.5 years — private equity, M&A, fund analysis at an asset manager.
- PE was the favourite: "looking through the storytelling to put a number on what a company is actually worth," and "the spread is highest when the asset is illiquid."
- Graduated early, dropped a Statistics second major, to chase an AI company — referred in by two employees, reached the final round, then an acquisition triggered a hiring freeze.
- The ESG stint = helping your mum with her auditing work.
- Eight months building — self-taught AI engineering, a Singapore private-housing model, a cross-asset dashboard tracking credit spreads, options chains and an ETF sleeve against its benchmark.
- The drone bet — no firm has a moat, so you bought the bottleneck and are getting licensed. Part-time, shift-based, endable immediately.
- Triathlon · boardgames · 3D printing (a year).
The five set-pieces
1 · Why trading?
Done · 55s"I read Business Administration at NUS, First Class, finance specialisation, and interned for about three years across private equity, M&A and fund analysis.
PE was the one I loved — you're looking through the storytelling to put a number on what a company is actually worth, and the spread is widest exactly where the information is worst. The catch is you find out whether you were right in five years.
I graduated early to take a shot at an AI company. That didn't land, but the last eight months I've been building things and taking positions — the clearest one is drones: no firm has a real moat, so instead of trying to pick the winner I bought the bottleneck and I'm getting licensed as an operator.
Same wiring outside work. I played contract bridge competitively at NUS, and I still bet myself on how many stops the bus takes coming home from Causeway Point.
Trading is that same job with the feedback loop closed to seconds. Assumptions are the cornerstone of every decision and the biggest source of illusion in it — I'd rather be priced every day than be right in five years."
Why it works: PE is the hinge, not a résumé item — "look through the storytelling to put a number on real value" is a market maker's job description. You're not switching careers, you're speeding one up. The drone line shows trader reasoning instead of claiming it.
45-second cut: drop the assumptions sentence, end on "closed to seconds."
- "You liked wide spreads — ours are a tick." → it wasn't the width, it was that price and value diverge. PE pays for a few big gaps over years; market making for tiny ones thousands of times a day.
- "Tell me about bridge." → incomplete information, bidding as quoting, opponents' bids as information you update on.
- "Why didn't the AI thing work out?" → short, unbothered, no blame, land on what you took from it.
- "So are you doing drones or trading?" → the drone bet is a position, not a career.
2 · Why Fenix One?
Done · 40s"Two things. The flat structure — trader, developer and operator all report to the Managing Director. If I'm on the desk and the model prices something wrong, I walk up to the person who built it and it's fixed that afternoon, instead of filing a ticket and waiting around.
Second, breadth — futures, equities, ETFs, FX and crypto from one seat. Through my undergrad I deliberately moved through PE, M&A and fund analysis to understand the whole machine before committing, and it's the same instinct here. A trader who sees several products together spots relationships across them; that's an edge a single-product trader doesn't have."
The first draft ran ~200 words against an 80-word target because each revision was appended rather than swapped in. Revise by deletion.
The only test that matters: if you could say it to Optiver, IMC or Jane Street, it isn't an answer. The raw ingredients:
- Flat structure. 11–50 people; trader, developer and operator all report to the Managing Director. Flipped to the work's side: when a model prices something wrong, you walk to the person who wrote it and have it fixed that afternoon rather than filing a ticket.
- Breadth. Futures, equities, ETFs, FX and crypto from one seat. Flipped: a trader who sees four asset classes together spots relationships a siloed one can't — which is literally where stat arb and basis trades come from at a firm doing exactly that. And it continues a pattern you've already demonstrated: you deliberately sampled PE, M&A and fund analysis because you wanted to understand the whole machine before committing.
- Founder pedigree. Sjak Kuipers and Reymond Tse — Optiver → Flow Traders → their own firm. Market making learned from the Amsterdam lineage at source.
3 · Competitive comeback
Done · 40s"I was president of the boardgame club and got drafted into the college's mind sports team for contract bridge — about three weeks to learn a game I'd never played competitively.
Early on I kept over-bidding. I'd assume my partner's hand would complement mine and push on hands that didn't justify it. What I was missing was that the opponents' bidding was telling me what they held — I was playing my hand instead of playing the table. Once I started bidding defensively when the information said to, we stopped bleeding points.
We placed third. And I haven't touched it since, so I'd be useless at a table now."
The honest version is the better story. Drafted, three weeks from zero, placed third — that's learning velocity, and "quick with new complex concepts" is written into their JD. Years of practice would prove less. The closing line is deliberate: it's disarming and it permanently closes the follow-up risk.
I was playing my hand instead of playing the table.
Where it maps: in the McDonald's round you kept buying on your own estimate while being sold to six times. Same failure mode — acting on your own assumption instead of reading what the other side's behaviour was telling you. If a "greatest weakness" question comes, that's your answer, and it's strong because you can name the fix.
4 · Python & Excel
Done · 20s"Excel is my strongest tool — I'm fast in it, and I automate with Power Query and VBA. Most of my analytical work has been in R. Python I've done at module level: I can read it, and I've built working tools with AI assistance, but I wouldn't claim fluency yet. The concepts carry over from R and VBA, so for me it's syntax rather than thinking — it's the gap I'd close fastest."
Why this holds up: R is a real analytical language (data frames, regression, vectorised ops), VBA is real programming (loops, functions, conditionals), Power Query is ETL. You have every concept Python would ask of you — the gap is syntax, which is days not months. Credible because true, and it ends forward-looking rather than on the deficit.
Excel specifics to have ready: INDEX/MATCH and why it beats VLOOKUP (the lookup column's position can shift and silently break it) · SUMIFS / COUNTIFS · pivot tables · Power Query for anything that should refresh rather than be rebuilt.
40 minutes on Day 3 — highest remaining ROI. Converts "I did a module" into "I've been working through pandas this week," which is true and shows initiative. Be able to write these six from memory:
- A list comprehension —
[x * 2 for x in nums if x > 0] - A dict comprehension
df = pd.read_csv('file.csv')- A moving average —
df['ma'] = df['price'].rolling(20).mean() - A loop computing running P&L
- Filter,
groupby,sort_valueson a DataFrame
You're not trying to pass as an engineer — you're trying not to freeze if someone says "write me something simple."
5 · Pressure STAR
Done · two versionsLead with the work one if they ask for a professional example, or if the room feels formal. Lead with Inner Mongolia if you want to be the answer they remember at the end of a long day. Both are 60 seconds.
A · The LP call — the sharper trading parallel
"On an internship I was presenting the estimated CAGR for Korean e-commerce on a call with LPs. My figure was 25%. Partway through, my boss started quoting 33%.
I didn't know whether he had a more recent number than mine or had misremembered — and I wasn't going to correct him in front of the client. So while the call was still running, I added a sensitivity table with 33% highlighted as the bull case.
That worked either way. If his figure was the newer one, the deck now carried it. If it wasn't, the LPs saw it as an upside scenario rather than the base case. It turned out mine was current.
I couldn't resolve which of us was right in the time I had, so I looked for the move that was fine under both. That's the same instinct as hedging."
Why it lands: two possible states of the world, no way to resolve which inside the call — so you found the action that was correct under both. Not a compromise, a hedge. When you can't resolve the uncertainty in the time available, stop picking the right state and find the action robust across both.
- Frame the ambiguity honestly: "I didn't know whether he had a newer number or had misremembered." True, and generous for free.
- Never editorialise. Land the outcome flat — "it turned out mine was current" — then stop.
- Make the decision the hero, not being right. "I was right and he was wrong" reads as score-settling however you phrase it.
- Optional line of generosity: "he was moving fast across a lot of companies." Costs nothing, inoculates everything.
B · Inner Mongolia — the memorable one
"Earlier this year I was on a road trip through Inner Mongolia in the middle of winter — few tourists, and our rental had a Beijing plate, so we were visibly out-of-towners.
A car sat behind us for about thirty kilometres. What turned that from a feeling into a signal was that we missed our exit, U-turned, and they U-turned too.
We didn't confront them and we didn't try to outrun them on winter roads. We drove to the nearest town, parked in the busiest spot we could find, and walked into a restaurant alongside a stranger like we knew him. Awkward — but from outside we were three locals having lunch. Twenty minutes later the car was gone.
We never confirmed they meant harm. But being wrong about the precaution cost twenty awkward minutes; being wrong the other way was unbounded. That's an easy trade."
The U-turn is the best part — it's the test that separates coincidence from intent. Sharing a route for 30km is ambiguous; following through a reversal is not. That's how you confirm a signal.
The close is the trading answer: asymmetric payoff — a cheap hedge against an unbounded tail. You don't need certainty to justify a cheap precaution. Held in reserve: the local newspaper placed under the windshield before the trip, i.e. hedging in advance rather than only reacting.
Ask them this · lead with the desk question
- Which desk or market would I sit on first — Japan ETFs, crypto, futures — and what does the training path look like?
- How is trader compensation structured: formula-linked to desk P&L, or discretionary?
- What's the relationship between the Singapore HQ and the Austin office?
- How do traders and developers split strategy work here?
- What distinguishes the traders who thrive here in their first year?
The desk question first — it signals you think in specifics rather than in job titles.
Session 1 · Sun 2 Aug · learning the mechanics
No estimate before quoting, and the direction rule inverted — moved up while being sold to.
Direction correct. Spotted the flow disagreeing with a hard anchor — the round's real win.
Repriced decisively, widened 40 → 60. Estimate double-counted interchanges; first market too tight for the stated uncertainty.
Best estimate of the session, and confidence correctly converted into spread width. Lost on centring when squeezed tighter.
Session 2 · Mon 3 Aug · graded mock, aggressive counterparty
No size on the opening quote — I took ten, turning a −450 lesson into −4,500. Then cut the loss below fair value: holding would have cost 875 less.
Rebuilt the model under fire, bracketed the truth from flow, read small size as proximity. Final estimate within 10%. Lost on inverted skew — short, but the offer was the attractive side.
Where the sessions leave you
Session 1 taught the mechanics: −960 → +3 → −55 → −50 as the process tightened. Session 2 jumped estimation, flow-reading and size discipline a full grade each. Two things remain — inventory skew and position arithmetic under pressure.
The one unfixed error
You price your view, never your position
- Long and being sold to → kept a live bid all the way down.
- Short and being bought from → made the offer the attractive side and got shorter.
I'm long, so my offer is the attractive side and my bid retreats.
I'm short, so my bid is the attractive side and my offer retreats.
Both rounds ended bigger in the direction that was already hurting.
The direction rule
| They say | What they did | Means | You move |
|---|---|---|---|
| "I buy" | took your offer | truth is higher | UP |
| "I sell" | hit your bid | truth is lower | DOWN |
Whoever initiates thinks they're getting a bargain. Assume they're right and follow. Both quotes move, every time.
The seven rules
Lessons earned the hard way
Rebuild the estimate — don't retreat
When flow keeps hitting you, go back into the model and find the broken assumption. Retreating without re-estimating leaves you nowhere to stop — that's how a quote ends up below fair value.
McDonald's: retreated blindly, capitulated under fair. HDB: found the 200-households-per-block error, re-derived, landed within 10%. Same person, one round apart.
Anchored vs unanchored
Anchored (dice): you know the whole distribution — mean, floor, ceiling. Flow adjusts you; it doesn't panic you. Below fair value you should want the trade.
Unanchored (hawker centres): their flow is the only information in the room, so it moves you hard and fast.
Centre the market on your estimate
Estimated 450, quoted 450 / 500 — the estimate sat at the bottom, so the bid had no cushion and got taken. The right 50-wide market was 425 / 475.
Cutting a loss is not a price
It's a decision to stop being long. You still have to sell at a fair price — dumping below fair converts a bad position into a worse realised loss.
I'm long 25 and I now think fair is 150. I'll show 120 bid at 148 — my offer sits just under fair so buyers come to me, and my bid stands far back so I don't get longer.
Selling a touch under fair is paying for liquidity: ~2 a unit to drain 25 units of drift risk is a good trade — the McDonald's dump paid ~50 a unit for the same service. An earlier version of this card offered at 155, above fair — that's how you quote when you're happy to hold, not when you want out. You caught it (4 Aug): Rule 5, applied back at the coach.
Count the constraint, not the demand
McDonald's was anchored off 7-Eleven (~400) and adjusted up for demand. But the binding constraint is viable sites: a McDonald's needs a kitchen and seating, so it lives in ~170 malls plus standalone lots. A 7-Eleven fits in void decks, petrol stations, MRT stations and office lobbies — five to ten times the location set.
When they say "tighten it"
No new information has arrived — so demand the other dial back.
I'll go 50 wide — 425 at 475 — but only in one unit. If you want size, I need my spread back.
Know your breakeven, exactly
Sold 5 at 8,500, bought 2 at 12,000 → +18,500 cash against 3 short → breakeven 6,166.7. That's the price where you'd finish flat, and it's what risk limits key off.
Compute it. Don't estimate it.
No trade is a result
When your market properly straddles fair value, the informed counterparty has nothing to take. Getting no fill is often exactly what good market making looks like.
Deriving more than a price
- Bracket the truth from their flow. Sold at 450, passed at 350 → centre near 400. Saying that aloud is a very strong look.
- P&L = spread × volume − adverse selection − inventory cost. After each round, ask which term hurt.
- Break-even hit rate. 50 wide earns 25 per balanced trade, so one 50-loss needs two clean round-trips to repay.
- Think price × size, never price alone.
Story questions under fire
The dodge
Asked about the last two years (post-graduation), answered about student internships. The question felt like an attack, so rehearsed material came out instead of the asked-for material.
The last two years, so post-graduation…
Three words, and you can't drift. An interviewer who notices re-asks; one who doesn't just writes evasive.
Never under-sell an exonerating fact
- "Didn't break into the industry" → referred in by two employees, reached the final round, an acquisition froze hiring. Same event, completely different candidate.
- The ESG stint → "that was helping my mum with her auditing work." Eight words that kill the scatter read.
Compress the corporate drama to one sentence — it's someone else's story and invites tangents you can't control. Drop "I can prove it"; unprompted evidence reads as defensive.
Still owed: why you'll stay
You keep choosing domains with slow feedback and leaving them — PE pays out in five years, ESG audit in months, the drone bet in years. Trading scores you today.
It's the first thing I've picked that's scored on the timescale I actually want.
That's a reason. "I enjoy learning" is an argument for any hard job.
Habits to drill
Common fails
Quoting tight out of bravado · moving only the touched side · freezing after two hits · refusing to trade at all · losing track of your position.
You are the market maker
Fair value drifts and occasionally jumps on news. Uninformed flow trades more on whichever of your quotes is more attractive relative to fair; informed flow only shows up when your price is wrong — including just before news lands. Earn the spread, keep inventory near flat, survive.
The point: Rule 5 made physical. Long and want out? Move your centre below fair — the offer becomes the attractive side and the flow itself drains your inventory. That's quote skewing.
Read the two P&L lines
Edge at trade is what each fill earned versus fair at that moment — uninformed fills book positive edge (the half-spread); getting picked off books negative. Inventory drift is your position × every move in fair — the cost (or luck) of not being flat. A real desk lives on the first line and fights to keep the second near zero. If your drift line is bigger than your edge line, you're not market making — you're punting.
Nobody pushes price here
Every burst below is a fair coin — roughly half buys, half sells, in random order. The only thing you choose is where the depth sits. Each market order consumes one unit at the touch; when a level empties, price moves through it. Watch balanced flow walk price toward the thin side: the path of least resistance.
Run it several times per shape — it's a probability tilt, not a script. That's the honest version of the claim: depth biases where price can go; it doesn't command it.
Table stakes for this firm: an ex-Flow Traders / Optiver prop shop, official JPX ETF market maker, FPGA low-latency, market making and stat arb across futures, equities, ETFs, FX and crypto.
The business
Market making in one breath
Quote a bid and an offer; earn the spread on balanced flow.
Two enemies. Adverse selection — informed traders pick you off; the people most eager to trade with you know something you don't. Inventory risk — you accumulate a position the market then moves against.
Defences: widen, skew, hedge, size limits.
P&L is roughly spread times volume, minus adverse selection, minus hedging costs.
How a market maker loses money · a likely question
The paradox to lead with
A market maker doesn't lose money on the average trade. They lose on the trades they actually get filled on.
Your quotes are open to everyone, but you only trade with people who chose to — and they choose when your price is wrong in their favour. That selection is invisible in a naive EV calculation and it is the whole game.
1 · Adverse selection — the structural one
Someone knows more than you: real news, a large order about to land, or simply a faster machine.
- Stale quotes — fair value moved, your price hasn't, so you're lifted at yesterday's number.
- Queue position — late in the queue means you only get filled when the price is about to run away from you.
- Toxic counterparties — one client whose flow is systematically informed.
This is why Fenix One buys FPGAs. Latency isn't a vanity metric — it's the price of not being last to know.
2 · Inventory risk
You accumulate a position and the market moves before you can offload. Worst in the tails: overnight gaps, trading halts, limit moves.
Crowded exits — everyone widens at once under stress, so precisely when you most need a bid, nobody is there. Carrying inventory also costs real money in financing and borrow.
3 · Your fair value was simply wrong
Bad reference data, a mishandled corporate action, a stale dividend or FX assumption — and you quote confidently around a wrong centre, which is worse than quoting nervously around a right one.
Personal version: the 450 / 500 centring error, and the McDonald's estimate.
4 · The hedge fails
You quote the ETF and hedge with futures, but the hedge is never perfect — basis risk, timing gaps, correlation breaking down exactly when it's stressed.
In stat arb this is the entire risk: the pair stops converging.
5 · The spread is too thin for the risk
Competition compresses spreads, or a fee-and-rebate change flips the economics. You can be right about value and still lose because you weren't paid enough to take the other side.
6 · Operational blowups
Fat fingers, a runaway algo, connectivity loss leaving live quotes in a moving market.
Knight Capital, 2012 — about $440 million in 45 minutes on a bad deployment. Worth knowing by name; it's the canonical example.
Two sentences that sound senior
Adverse selection isn't a bug, it's the cost of goods sold. You will be picked off — the only question is whether the spread covers it.
Quoted spread minus realised spread. What you advertise, less what you keep once the price drifts against you after the trade — that gap IS adverse selection, and desks track it.
Shape of the business: many small gains, occasional large losses — negative skew. That's why risk management isn't a side activity at a prop firm, and why Kelly says bet half.
The 15-second answer
Three ways. I get picked off by someone who knows more than me — including someone who's just faster. I get stuck with inventory the market moves away from. Or my fair value was wrong to begin with. The first two I manage with spread, skew and hedging; the third is why the research matters.
Order types, rebates and the book
Resting limit orders — what you're actually doing
Market order: "buy 100 now, whatever the best price is." Executes immediately against what's already there.
Limit order: "buy 100, but pay no more than $10." If nobody's selling at $10 it doesn't execute — it goes into the book and waits, visible to everyone, until someone takes it or you cancel. That waiting is resting.
Maker and taker
Every trade has exactly one of each. The maker posted the resting order and waited. The taker sent the order that reached out and hit it.
In the game you were always the maker; the interviewer was always the taker.
Why exchanges pay you to be a maker
An exchange with an empty book is worthless — liquidity is the product it sells — so it bribes people into leaving orders there.
Like a marketplace that needs stalls: the operator pays stallholders to show up and display goods, charges shoppers an entry fee, and keeps the difference.
Maker-taker pricing, US equities scale: taker pays ≈$0.0030/share, maker receives ≈$0.0020/share, exchange keeps ≈$0.0010. Fractions of a cent — but on millions of shares it's real, and some strategies are roughly breakeven on the spread and profitable purely on rebates.
The part that sounds sharp
The rebate is compensation, not free money. As the maker you've handed the other side an option — they choose when to trade, and they'll choose the moment your price is wrong. You carry the adverse selection; the rebate partially pays for it.
Which is why queue position matters. At the back of the queue you're only filled after everyone ahead of you — usually meaning the price is about to move through your level. You get the bad fills and miss the good ones.
Not all venues work this way. Some run inverted (taker-maker) pricing, paying the aggressor to attract flow — so where you post is itself a strategic decision.
It's also genuinely contested: maker-taker creates a conflict for brokers routing client orders, since the best-paying venue isn't necessarily the best-filling one. Regulators have poked at it repeatedly.
The Fenix One connection
Per-share rebates are the retail-scale version. What Fenix has with JPX is the contractual form: a market-making incentive scheme where you take on quoting obligations — maximum spread, minimum size, minimum share of the day present at the touch — in exchange for fee discounts or rebates. A contract with the exchange, not just a pricing tier, and why they're on the published market-maker list.
One level deeper · the liquidity machine
Why the job exists — two problems
The anticipation problem. Buyers and sellers read each other's flow: the moment demand shows, sellers back away upward, and vice versa. Left alone, the spread gapes.
The depth problem. Natural two-way interest is thin, so medium orders cause outsized moves.
An MM's product is a tighter spread plus a deeper book — stability, sold for the spread.
The flash crash — liquidity is conditional
6 May 2010: a mutual fund's hedging algo (Waddell & Reed) sold 75,000 E-mini contracts (~$4.1bn) into an already-thin book. As volatility spiked, market makers pulled back rather than stand in front of what looked like informed flow — and prices collapsed in minutes.
The lesson isn't "algos are dangerous." It's that MMs quote when flow looks balanced and step away when it looks toxic — which is exactly why exchanges pay for quoting obligations. The JPX scheme Fenix sits on exists to keep quotes present in stress.
The full revenue stack — five, not one
- Spread capture — primary.
- Maker rebates — above.
- The hedge itself — delta-neutralising in a correlated instrument at good prices is a second edge. (Also why correlated markets stay correlated: everyone hedges in each other's products.)
- Order-flow information — full depth-of-book, market-by-order data. Not front-running (illegal) — better short-term probability models.
- Payment for order flow — brokers sell retail flow to MMs (US retail equities/options); MMs pay because it's uninformed, and price-improve it in return.
Toxic vs non-toxic flow
Desks grade counterparties, not just prices. Non-toxic = uninformed, statistically predictable — retail. Toxic = flow that's systematically right just before the market moves.
The winner's curse, the stranger's coin, and being repeatedly lifted in the game are all the same fact: eagerness to trade with you IS information.
Quote skewing — Rule 5's professional name
Long and want out? Shade both quotes down: your offer becomes the attractive side, flow tilts toward buying from you, inventory drains — sometimes at a small negative edge per fill, which is fine, because it beats carrying the drift risk.
From the outside the same move reads as a nudge: an asymmetric quote pulls flow toward the attractive side. You skew to rebalance; the tape sees a push. Try it live in the Sim tab.
Liquidity engineering — depth as a dial
Internal liquidity sits inside the spread; external is resting orders further out. Stacking depth on one side raises resistance there; thinning it opens a path of least resistance — flow that's already 50/50 walks price toward the thin side.
Skew is the incentive, depth is the resistance.
Second-order: MMs read each other, so one firm re-shaping its book reads as information to the rest — the cohort leans one way with nobody coordinating.
"So — are markets manipulated?"
A plausible culture-fit filter at a prop shop: they want to know you're not conspiracy-brained.
Any single market maker has narrow power, and competition strips it fast. What they actually do is skew quotes and manage depth to control inventory; summed across firms it can look like intentional stop-hunting, but it's mostly emergent. Actual manipulation — spoofing, layering, wash trading — is illegal and prosecuted.
The products
ETF arbitrage — their bread and butter
An ETF has a NAV, the value of its basket. Authorised participants can create or redeem ETF shares against that basket, which is the mechanism pinning price to NAV.
Market makers quote the ETF and hedge instantly with futures or the underlying basket; when price drifts from fair value — the live estimate is the iNAV — they trade the gap.
Japan specifics: TSE/JPX runs a market-making incentive scheme (fee rebates against quoting obligations — that's the list Fenix One is on), and the Bank of Japan historically bought enormous ETF volume, making Japan one of Asia's biggest ETF markets.
Futures
Basis = futures − spot, driven by cost of carry (rates minus dividends or yield). Upward-sloping curve is contango; downward is backwardation.
Asia staples: SGX Nikkei, FTSE China A50, MSCI Taiwan, iron ore.
FX
Majors and pips. Forward points come from the interest-rate differential (covered interest parity).
Prop firms trade FX mostly as fast price-taking and arbitrage, not macro punting.
Crypto
24/7 and fragmented across exchanges, so cross-exchange arbitrage. Perpetual futures track spot via a funding rate paid between longs and shorts; cash-and-carry basis trades are standard.
The real risks are exchange, custody and settlement — not just price.
The latency race
Colocation in the exchange data centre; FPGAs react in nanoseconds where software takes microseconds.
Why it matters: queue priority at the front of the order book, and being first to trade against stale quotes when news moves fair value.
Statistical arbitrage
Find instruments that move together — index versus constituents, dual listings, related futures — trade the divergence, and size by your confidence that it converges.
Regulation
Each exchange has its own rulebook, and market-abuse rules — spoofing, layering, wash trading — apply everywhere. As a prop firm trading its own capital Fenix doesn't need a MAS fund-management licence, but exchange rules and cross-border rules still bind every order.
Crash course · conditional probability → Bayes → Kelly → Monty Hall
Conditional probability — the one formula under everything
P(A|B) = P(A and B) / P(B)
In words: throw away every world where B didn't happen, then ask what fraction of the surviving worlds also have A. Conditioning = shrinking the universe. Every mistake in this family is someone shrinking to the wrong universe.
Worked: P(two dice sum ≥ 10, given the first die is 5)? Universe shrinks to "first die is 5" (6 worlds of 36). Of those, the second die must be 5 or 6 → 2 worlds. (2/36) / (6/36) = 1/3.
Rearranged, the same formula gives you the other two tools:
- Multiplication rule: P(A and B) = P(A|B)·P(B) = P(B|A)·P(A) — a joint probability is "chance of the condition × chance given the condition", from either direction.
- Total probability: P(E) = P(E|H)·P(H) + P(E|¬H)·P(¬H) — the chance of E is the sum over every route to E. This is the Bayes denominator, and the thing you've now wobbled on three times: it's the whole room, both branches.
- Independence test: A and B are independent exactly when P(A|B) = P(A) — learning B moved nothing.
Bayes — derive it in one line, run it three ways
Never memorise it. Write the multiplication rule from both directions and divide:
P(H|E)·P(E) = P(H and E) = P(E|H)·P(H) → P(H|E) = P(E|H)·P(H) / P(E)
Vocabulary, because interviewers use it: prior P(H) — belief before evidence · likelihood P(E|H) — how well the hypothesis explains the evidence · posterior P(H|E) — belief after · base rate — the prior when H is rare.
Way 1 — count people (most robust under pressure): turn probabilities into a population, count who's in the evidence room. Disease: 1,000 people → 1 sick tests positive, ~10 healthy do too → 1 of 11 ≈ 9%.
Way 2 — odds form (fastest, and how desks actually think):
posterior odds = prior odds × likelihood ratio, LR = P(E|H) / P(E|¬H)
Disease: prior odds 1:999 · LR = 0.99/0.01 = 99 → posterior odds 99:999 ≈ 1:10 → ≈9%. One multiplication, no denominator to fumble. The LR is the honest measure of evidence strength: "this signal is 99× more likely when sick" — and it still couldn't overcome a 1:999 prior.
Way 3 — the formula with the expanded denominator, for writing down and checking:
P(H|E) = P(E|H)·P(H) / [ P(E|H)·P(H) + P(E|¬H)·P(¬H) ]
A signal's hit rate isn't its value — a rarely-wrong signal on a rare event is still usually wrong when it fires. Prior times likelihood ratio, always both.
Kelly — where the formula comes from
The question it answers: a bet is in your favour and repeatable — what fraction f of your bankroll do you stake each round to maximise long-run growth?
Why not bet everything? Single-bet EV says stake 100% — and one loss ends you. Wealth compounds multiplicatively, so what matters is the growth rate of the log of wealth, not the EV of one round.
Even-money derivation, one line: growth per round g(f) = p·ln(1+f) + q·ln(1−f). Set g′(f) = p/(1+f) − q/(1−f) = 0 → f* = p − q (= 2p − 1). At 60/40: f* = 20%.
General form (odds not even): win b per 1 staked → f* = (bp − q)/b = p − q/b. Sanity checks: p = ½ at even money → bet 0 ✓ · p = 1 → bet everything ✓ · longer odds (bigger b) → same p justifies a bigger stake ✓.
- Betting 2× Kelly has ≈ zero growth — all the volatility, none of the drift. Beyond that you're compounding downward with positive EV per bet. That's the sharpest sentence in the room.
- The penalty is asymmetric: under-betting costs you a little growth; over-betting can cost you everything. Your p is an estimate → bet half-Kelly.
- Markets version: optimal size ≈ edge / variance. Same object — size with edge, shrink with uncertainty, then halve for humility.
Kelly maximises growth, not comfort — and since my edge is estimated, I size at half-Kelly: the cost of betting too small is linear, the cost of betting too big is ruin.
Monty Hall — it's about how the evidence was made
Why switching wins 2/3: your first pick was right 1/3 of the time, wrong 2/3. The host opening an empty door never changes those numbers — he can always open one, whatever you hold. In every wrong-pick world (2/3 of them), the door he leaves shut is the prize.
If it still itches, scale it: 100 doors, you pick one, the host knowingly opens 98 empty ones. Your door is still the 1-in-100 guess; the one he pointedly left shut carries the other 99%.
The real lesson: condition on the process, not the fact. What something means depends on how it came to be shown to you.
Monty Hall and adverse selection are the same lesson: an empty door from a host who knows, a price from a counterparty who chose to trade — filtered information isn't neutral information.
Numbers to know cold
Fractions and decimals
| Eighths | .125 .375 .625 .875 |
| Sixteenths | .0625 steps |
| Sevenths | .143 .286 .429 .571 .714 .857 |
| Ninths | .111 steps |
| 1/6 · 1/12 | .167 · .083 |
×5 = ×10 ÷2 · ×25 = ×100 ÷4 · ×125 = ×1000 ÷8 · numbers straddling a round one → difference of squares (48 × 52 = 50² − 2²)
Dice and coins
| One die | EV 3.5 |
| Two dice, sum | EV 7 · peak 6/36 · range 2–12 |
| Re-roll once / twice | 4.25 / 4.67 |
| Max of two dice | ≈4.47 · P(max=k) = (2k−1)/36 |
| 100 flips, $1 a head | EV 50 · SD 5 |
| Flips to first head | 2 |
| Flips to two in a row | 6 |
| Two aces from a deck | 1/221 ≈ 0.45% |
The option to retry always adds value, at a decreasing rate — say that sentence and they know you get it.
The winner's curse — their favourite question
Setup: a company worth V, uniform $0–100. The seller knows V; you don't. One bid B, filled if B ≥ V. It's worth 1.5V to you. What do you bid?
Answer: nothing. Being filled means V ≤ B, so E[V | filled] = B/2, not 50. Payoff when filled = 1.5(B/2) − B = −0.25B. Times the B/100 chance of a fill → EV = −B²/400, maximised at B = 0.
You'd need a multiplier above 2× just to break even. The 1.5× is bait.
Same mechanism as a stranger offering to flip their coin, and as being repeatedly sold to in the game. The counterparty's willingness to trade IS the information.
Three that collapse under pressure
Each stated in full so you can self-quiz: cover the answer, say the question back, work it aloud.
Bayes. A disease affects 1 in 1,000 people. The test is 99% accurate both ways — 99% of the sick test positive, 99% of the healthy test negative. You test positive. What's the probability you actually have it?
≈9%. Count people: of 1,000, the 1 sick person tests positive, and ~10 of the 999 healthy do too — you're one of ~11 positives and only 1 is sick → 1/11. Formula: (0.001 × 0.99) / (0.001 × 0.99 + 0.999 × 0.01) = 0.00099 / 0.01098 ≈ 9%. Base rates dominate: a rarely-wrong signal still fires wrongly most of the time when the event itself is rare.
Variant to re-derive: 10 coins, one double-headed. Pick one at random, flip three heads in a row. P(you hold the double-headed one)? → (0.1 × 1) / (0.1 × 1 + 0.9 × 0.125) = 0.1 / 0.2125 = 8/17 ≈ 47%.
Kelly. A coin comes up heads 60% of the time and pays even money. You have $1,000. How much do you bet per flip?
f* = 2p − 1 = 20% of bankroll — $200 on the first flip, always 20% of whatever the bankroll is now. In practice bet half-Kelly (~10%): over-betting punishes far worse than under-betting, and your edge estimate is itself uncertain.
Monty Hall. Three doors, one prize. You pick a door; the host — who knows where the prize is — opens one of the other two, always revealing nothing, and offers you a switch. Do you switch?
Yes — switching wins 2/3, staying wins 1/3. "My first pick was wrong two-thirds of the time, and in every one of those worlds switching wins." The host's knowledge is the whole trick: his door was never a random reveal.
The universal EV script
When any game is posed: 1 restate the payoff · 2 list outcomes and probabilities · 3 compute EV out loud · 4 mention variance or option value · 5 only then state your price.
Fermi in four steps
Decompose → round every input to an easy number → multiply → sanity-check the order of magnitude. Always end with a range, never a point.
Live drills — market-making rounds, EV firing, the graded mock, story feedback — happen in chat with Claude. This page is the solo cockpit.