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.
Companies working in Fintech Trading
These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.
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 Distribution
Easy
15
17% of problems
Medium
35
40% of problems
Hard
33
38% of problems
Expert
4
5% of problems
What You'll Practice
Portfolio performance analysis
P&L attribution
Risk metrics & VaR
Trade execution analytics
Order flow analysis
Asset allocation reporting
Volatility calculations
Time-series financial analysis
Topics Covered
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
01
Active Verified Users by IncomePro
SQLEasy02High-Value Taxable PortfoliosPro
SQLEasy03Filled Market Buy OrdersPro
SQLEasy04Technology Stocks With High BetaPro
SQLEasy05High-Priority Watchlist AlertsPro
SQLEasy06User Total Portfolio ValuePro
SQLMedium07Filled Trades With Security SectorPro
SQLMedium08Portfolio Positions With Ticker SymbolPro
SQLMedium09Dividend History by SecurityPro
SQLMedium10Analyst Reports With Current PricePro
SQLMedium11Margin Accounts With User ProfilePro
SQLMedium12Trade Volume by SecurityPro
SQLMedium13Portfolio Count by Account TypePro
SQLEasy14Total Trading Fees by Order TypePro
SQLMedium15Average Portfolio Value by StrategyPro
SQLMedium16Top Securities by Position CountPro
SQLMedium17Rank Portfolios by Value Per UserPro
SQLMedium18Largest Position Per PortfolioPro
SQLHard19Trade Running Total Per PortfolioPro
SQLHard20Daily Price Rank by VolumePro
SQLHard21Previous Day Close and Daily ReturnPro
SQLHard22Top 3 Trades Per Security by AmountPro
SQLHard23Portfolios Above Average ValuePro
SQLMedium24Users With Three or More PortfoliosPro
SQLMedium25Securities Above Sector Average P/EPro
SQLHard26Users With Positions But No Recent TradesPro
SQLHard27Buy-Rated Positions Above Price TargetPro
SQLHard28Recent Filled Trades Last 30 DaysPro
SQLEasy29Dividend Payments Grouped by QuarterPro
SQLMedium30User Account Age With Login RecencyPro
SQLMedium31Trades With Fee Tier LabelPro
SQLMedium32Positions With P&L Performance CategoryPro
SQLMedium33Users With Risk and Income BucketPro
SQLEasy34Securities Held or on WatchlistsPro
SQLMedium35Securities Bought But Never SoldPro
SQLMedium36Portfolio Performance ScorecardPro
SQLHard37Fill Rate by Order Type and Security TypePro
SQLHard38Analyst Consensus vs Current PricePro
SQLHard39Margin Utilization Risk ReportPro
SQLHard40User Trading Behavior SummaryPro
SQLHard41Dividend Income Projection by PortfolioPro
SQLHard42Day Trade Pattern AnalysisPro
SQLHard43Active User ProfilesPro
PYTHONEasy44Security Sector CountsPro
PYTHONEasy45Portfolio Account Type SummaryPro
PYTHONMedium46Trade Status BreakdownPro
PYTHONMedium47Tradeable High-Cap SecuritiesPro
PYTHONEasy48Filled Buy Trades Above ThresholdPro
PYTHONMedium49Accredited High-Income InvestorsPro
PYTHONMedium50Margin Portfolios With Unrealized LossesPro
PYTHONHard51Count Portfolios by Strategy TypePro
PYTHONEasy52Total Trade Volume by SecurityPro
PYTHONMedium53Average Position Value by PortfolioPro
PYTHONMedium54Trade Statistics by Type and Order TypePro
PYTHONHard55Portfolio Holdings SummaryPro
PYTHONHard56Positions With Security NamesPro
PYTHONEasy57Trades With Portfolio DetailsPro
PYTHONMedium58Securities Without Dividend HistoryPro
PYTHONMedium59Trade Details With User InfoPro
PYTHONHard60Position Details With Full HierarchyPro
PYTHONHard61Rank Portfolios by Value Within UserPro
PYTHONMedium62Running Total Trade Amount per PortfolioPro
PYTHONMedium635-Day Moving Average Close PricePro
PYTHONHard64Daily Price Change vs Previous DayPro
PYTHONHard65Extract User Registration Month and YearPro
PYTHONEasy66Portfolio Age in DaysPro
PYTHONMedium67Monthly Trade Volume by Trade TypePro
PYTHONHard68Fill Missing User Income DataPro
PYTHONEasy69Clean Trade RecordsPro
PYTHONMedium70Clean and Enrich User Portfolio DataPro
PYTHONHard71Pivot Trade Counts by Type and StatusPro
PYTHONMedium72Portfolio Value Pivot by Strategy and AccountPro
PYTHONHard73Classify Securities by Market Cap TierPro
PYTHONMedium74Portfolio Risk ClassificationPro
PYTHONHard75Compute Daily Return and Spread FeaturesPro
PYTHONMedium76Trade Amount Quartile BucketingPro
PYTHONHard77Time-Based Trade FeaturesPro
PYTHONHard78Portfolio Feature MatrixPro
PYTHONExpert79Position Quality Feature EngineeringPro
PYTHONHard80Portfolio Value Descriptive Stats by StrategyPro
PYTHONMedium81Volume vs Return Correlation by SecurityPro
PYTHONHard82Anomalous Daily Price Moves (IQR)Pro
PYTHONHard83Portfolio Performance ScorecardPro
PYTHONHard84Investor Risk ReportPro
PYTHONHard85Full Trade Lifecycle ReportPro
PYTHONExpert86Portfolio Health ClassificationPro
PYTHONExpert87Cross-Security Performance AnalysisPro
PYTHONExpertReady to practice Fintech Trading?
87 SQL and Python challenges built from real fintech trading data. Graded instantly in your browser — no setup required.