Quant interview preparation

Questions

100

Traced to a firm

53

Firms

15

Updated

September 2026

Asked at

All firmsOld Mission Capital12Tower Research Capital10Jump Trading7Akuna Capital5Citadel4DED.E. Shaw3Jane Street3ACAQR Capital Management2DRW2Millennium Management2Schonfeld2SCSquarepoint Capital2Susquehanna International Group2Belvedere Trading1Optiver1

Topic

All topicsProbability10Coins, cards and games6Expected value8Statistics11Market making15Estimation and mental maths4Stochastic processes4Regression5Machine learning6Time series6Programming10Options and derivatives8Fit and motivation7

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1. 078Given a stream of numbers, return the median after each element arrives.ProgrammingHardtechnicalOld Mission CapitalEquities · Boston · 2024

   Say this

   Two heaps. A max heap for the lower half and a min heap for the upper half, kept balanced so their sizes differ by at most one. The median is the top of the larger heap, or the average of the two tops. Insert is O(log n), query is O(1).

   Then walk it

   1. Insert rule: if the new value is at most the max of the lower heap, push it there, otherwise push to the upper heap. Then rebalance by moving one element across if the sizes differ by more than one.
   2. Query: if the sizes are equal, the median is the average of the two tops. Otherwise it is the top of the larger heap. Constant time either way.
   3. Total cost for n elements is n log n, and memory is O(n) because you must retain everything. That memory cost is the honest limitation, and it is the first thing an interviewer will probe.
   4. If the median must be over a sliding window rather than the whole prefix, the two-heap approach needs deletions from the middle. Use an indexed multiset or two heaps with lazy deletion and a hash of pending removals. That is the version that comes up in practice on a tick stream.
   5. And if approximate is acceptable, which on a trading system it usually is, the right answer is a streaming quantile sketch: t-digest or the Greenwald-Khanna algorithm, giving you any quantile in bounded memory rather than O(n). Naming that unprompted is what turns a correct interview answer into a practical one.

   Where candidates lose it

   Sorting on every element, which is O(n squared log n) overall, or maintaining a sorted list with insertion, which is O(n) per element because of the shifting even though the binary search is fast. Say two heaps immediately, then volunteer the sliding-window and bounded-memory variants, because that is where the conversation is heading.

   Expect next

   • Now do it over a sliding window of the last thousand values.
   • What if you cannot store all the data?
   • How would you get the 99th percentile instead of the median?

   Reported by candidates at Old Mission Capital (Equities, Boston, 2024). Source: Wall Street Oasis.

2. 081Write me an unordered_map class. What is actually inside a hash map?ProgrammingHardsuperdayOld Mission CapitalTrading · Chicago · 2021

   Say this

   An array of buckets, a hash function mapping keys to bucket indices, a collision resolution strategy, and a resize policy driven by load factor. The three decisions that define the implementation are the hash, the collision handling, and when you grow.

   Then walk it

   1. Core operations: index equals hash of key modulo bucket count, then search within that bucket comparing keys for equality. Insert, find and erase all follow that pattern, and all are O(1) expected under a good hash.
   2. Collision resolution, and this is the main design choice. Separate chaining stores a list per bucket, which is simple and is what the C++ standard effectively mandates for unordered_map because of its iterator and reference stability guarantees. Open addressing stores entries inline and probes forward, which is far more cache-friendly but complicates erase, since you need tombstones or backward shifting.
   3. Resize: track load factor as elements over buckets, and when it exceeds a threshold, typically 0.75 for chaining or 0.5 to 0.7 for open addressing, allocate a bigger array and rehash everything. Use a power-of-two bucket count so the modulo is a bitmask, but then your hash must mix the high bits or a weak hash collides badly.
   4. The correctness details an interviewer will probe: key equality is separate from the hash, two equal keys must hash the same, iterator invalidation on rehash, and what happens when the key type has a bad hash. A hash that is the identity on integers plus power-of-two buckets means sequential keys with a stride collide catastrophically.
   5. If I were writing this for a trading system I would use open addressing with linear probing over a pre-allocated power-of-two array, reserve capacity up front so no rehash ever happens in the hot path, and store keys and values in separate arrays if the values are large. The reason is tail latency: one rehash mid-session is a millisecond spike, and a millisecond is forever.

   Where candidates lose it

   Describing the interface rather than the internals. The question is about buckets, hashing, collisions and resizing. Also be ready for why is std::unordered_map slow, whose answer is node-per-element allocation and the standard's stability guarantees forcing chaining. And never forget that erase under open addressing needs tombstones, which is the bug candidates ship.

   Expect next

   • How does erase work under open addressing?
   • What load factor would you choose and why?
   • What makes a good hash function, and what happens with a bad one?

   Reported by candidates at Old Mission Capital (Trading, Chicago, 2021). Source: Wall Street Oasis.

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.