Pro87 ProblemsSQL + Python

Fintech Trading SQL & Python Interview Questions

Trading platforms and fintech firms track portfolio performance, risk exposure, and market data across asset classes. These SQL and Python challenges are modeled after work at Robinhood, Fidelity, Charles Schwab, Coinbase, Interactive Brokers, Citadel, Bloomberg, Jane Street, Betterment, Acorns, and more. Build skills in P&L attribution, volatility metrics, trade execution analysis, options analytics, and portfolio risk reporting.

Fintech Trading

87 total problems

SQL42

Python45

Practice SQL Practice Python

Companies working in Fintech Trading

These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.

Robinhood

Fidelity

Charles Schwab

Coinbase

Citadel

Two Sigma

Interactive Brokers

Jane Street

Virtu Financial

Bloomberg

eToro

Betterment

Acorns

Morningstar

FactSet

Refinitiv

SoFi

Kraken

Gemini

Apex Fintech

Company names and logos are trademarks of their respective owners, used here only to describe the kind of data these companies work with. Let's Data Science is not affiliated with, endorsed by, or sponsored by any company shown. Practice problems are original works and are not real interview questions from these companies. Rights & takedowns.

Difficulty Breakdown

87 problems

15 Easy(17%)

35 Medium(40%)

33 Hard(38%)

4 Expert(5%)

What You'll Practice

Portfolio performance analysisP&L attributionRisk metrics & VaRTrade execution analyticsOrder flow analysisAsset allocation reportingVolatility calculationsTime-series financial analysis

Topics Covered

21 topics

SQL · 9

aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions

Python · 12

eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window

All Problems87 total

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