8 AQR SQL Interview Questions (Updated 2024)
AQR employees use SQL to analyze financial data, including historical stock prices and earnings reports, for investment strategies, as well as to manage vast relational databases, including those containing risk metrics and portfolio performance data, for risk assessment and management strategies. Because of this, AQR often usesSQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you study for the AQR SQL interview, we've curated 8 AQR Capital Management SQL interview questions in this blog.
8 AQR Capital Management SQL Interview Questions
SQL Question 1: Assessing Securities Performance Ranking within Asset Classes Over Time
In this question, we have a large dataset representing the daily performance of different securities in different asset classes over a period of time. Your task is to write a SQL query that calculates the percentile ranking of each security's daily return within its asset class for each day.
(sample data)
| security_id | date | asset_class | return |
|---|---|---|---|
| 1 | 2022-01-01 | Equity | 1.5% |
| 2 | 2022-01-01 | Equity | 2.0% |
| 3 | 2022-01-01 | Fixed Income | 1.0% |
| 1 | 2022-01-02 | Equity | 2.5% |
| 2 | 2022-01-02 | Equity | 1.0% |
| 3 | 2022-01-02 | Fixed Income | 1.5% |
We are interested in a table that shows for each security and each date, what percentile rank the security's return was within its asset class.
Answer:
Here is the PostgreSQL SQL query:
This query uses the PostgreSQL window function to calculate the relative rank of each security's daily return within its asset class. The clause ensures that the percentile rank is calculated separately for each date and asset class, and the clause within the function is used to rank the returns from lowest to highest.
To solve a related window function SQL problem on DataLemur's free interactive SQL code editor, solve this Amazon SQL question asked in a BI Engineer interview:
SQL Question 2: Second Highest Salary
Given a table of AQR employee salary data, write a SQL query to find the 2nd highest salary amongst all the employees.
AQR Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
You can solve this question directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution with hints here: 2nd Highest Salary.
SQL Question 3: Can you explain the concept of a cross-join, and their purpose?
A cross-join, also known as a cartesian join, is like a mad scientist's laboratory experiment gone wild. It takes two tables and mixes them together to create a crazy new table with every possible combination of rows from the original tables.
Here's an example:
If you have 20 products and 10 colors, that's 200 rows right there! Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. Just like a mad scientist, use your powers wisely!
SQL Question 4: Investment Portfolio Analytics
As a data analyst at AQR, a key responsibility of your role is to assist the Investment Team in analyzing the portfolio performance. You have been given two tables, and . The table has the following columns: , , , , and (where a positive quantity indicates a purchase and a negative quantity indicates a sell). The table has the following columns: , , , and .
Using these tables, write a PostgreSQL query to determine the total portfolio value for each client at the end of each month.
Example Input:
| transaction_id | client_id | asset_id | transaction_date | quantity |
|---|---|---|---|---|
| 1001 | 123 | 200 | 01/05/2022 | 5 |
| 1002 | 123 | 300 | 01/20/2022 | -1 |
| 1003 | 456 | 200 | 01/18/2022 | 3 |
| 1004 | 123 | 300 | 02/02/2022 | 3 |
| 1005 | 456 | 200 | 02/10/2022 | 2 |
Example Input:
| price_id | asset_id | price_date | price_per_unit |
|---|---|---|---|
| 2001 | 200 | 01/31/2022 | 10 |
| 2002 | 300 | 01/31/2022 | 15 |
| 2003 | 200 | 02/28/2022 | 12 |
| 2004 | 300 | 02/28/2022 | 20 |
Answer:
This query calculates the total quantity of each asset bought or sold by a client in a month, and then multiplies it by the end of month price to compute the total portfolio value, grouped by client and month. The function ensures that transactions occurring in the same month are consolidated together. Also, the multiplication and sum operation occurs for each client and each month separately.
SQL Question 5: What is the difference between a correlated subquery and non-correlated subquery?
While a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.
An example correlated sub-query:
This correlated subquery retrieves the names and salaries of AQR employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).
An example non-correlated sub-query:
This non-correlated subquery retrieves the names and salaries of AQR employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the AQR employees table.
Sure, here's an example of what you could ask relative to AQR. For clarity, AQR is a global investment management firm, so the data and the question align to a situation they might encounter.
SQL Question 6: Average Trading Volume Per Month
You are given a table named that keeps a record of all trades carried out by the investment firm. The table has columns for (unique identifier for each trade), (the id of the trader who made the trade), (the date when the trade was made), (the identifier for the stock traded), and (the number of shares traded).
Write a SQL query to find the average trading volume for each stock on a monthly basis.
Example Input:
| trade_id | trader_id | trade_date | stock_id | volume |
|---|---|---|---|---|
| 1012 | 45 | 2022-06-08 | 80001 | 250 |
| 1156 | 32 | 2022-06-20 | 90512 | 500 |
| 1035 | 27 | 2022-07-18 | 80001 | 400 |
| 1047 | 35 | 2022-07-26 | 90512 | 300 |
| 1078 | 16 | 2022-07-28 | 80001 | 350 |
Example Output:
| mth | stock | avg_volume |
|---|---|---|
| 6 | 80001 | 250.00 |
| 6 | 90512 | 500.00 |
| 7 | 80001 | 375.00 |
| 7 | 90512 | 300.00 |
Answer:
In this query, we're grouping by the month part of the and . We then calculate the average for each group.
SQL Question 7: What are the similarities and differences between a clustered index and non-clustered index?
Here is an example of a clustered index on the column of a table of AQR customer transactions:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 8: Calculating Adjusted Sales Growth
Consider a database containing information on hypothetical sales data for a company. The database has a table that contains monthly sales of different products for multiple years. We want to calculate the adjusted annual sales growth for a specific product.
Adjusted annual sales growth in this case is calculated as:
(POWER(ABS((this_year_sale - last_year_sale) / last_year_sale) + 1, 1/12) - 1) * 100
It means we want to calculate the monthly compounded growth rate and convert it to a percentage. We consider only the magnitude of sales difference ( function) while calculating monthly growth, but keep track of whether it's an increase or decrease compared to the last year (sign of expression ).
Note: Here we are ignoring the months with zero sales in the last year because we can't calculate growth rate for them.
We also want to round the result to two decimal places.
The table is as follows:
Example Input:
| sales_id | product_id | year_month | sales_qty |
|---|---|---|---|
| 1 | 987 | 201801 | 1400 |
| 2 | 987 | 201802 | 1600 |
| 3 | 987 | 201803 | 1500 |
| ... | ... | ... | ... |
| 24 | 987 | 201812 | 1800 |
| 25 | 987 | 201901 | 2000 |
| 26 | 987 | 201902 | 1750 |
| ... | ... | ... | ... |
| 48 | 987 | 201912 | 2100 |
We need to write a SQL query that calculates adjusted annual sales growth for the product 987 from 2018 to 2019.
Answer:
This query first calculates total sales in 2018 and 2019 in CTEs (). Then it uses these CTEs to compute adjusted annual sales growth. It uses the , , , and functions as per the given formula. function is used to avoid division by zero. Note that the in the function is written as a floating-point fraction to ensure correct decimal division.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating year-on-year growth rates in sales data or this Alibaba Compressed Mean Question which is similar for handling large data and calculating growth metrics.
Preparing For The AQR SQL Interview
The best way to prepare for a AQR SQL interview is to practice, practice, practice. In addition to solving the above AQR SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like FAANG tech companies and tech startups.
Each exercise has multiple hints, step-by-step solutions and crucially, there's an online SQL code editor so you can instantly run your query and have it graded.
To prep for the AQR SQL interview you can also be helpful to solve SQL questions from other finance companies like:
Discover how AQR is harnessing the power of Machine Learning to drive investment innovation!
However, if your SQL foundations are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers things like window functions like RANK() and ROW_NUMBER() and UNION vs. joins – both of which come up often in AQR SQL assessments.
AQR Capital Management Data Science Interview Tips
What Do AQR Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems covered in the AQR Data Science Interview include:
- Statistics and Probability Questions
- Python or R Coding Questions
- Product-Sense Questions
- ML Interview Questions
- Behavioral Questions based on AQR company values
How To Prepare for AQR Data Science Interviews?
To prepare for AQR Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Microsoft, Amazon & startups
- a refresher on Stats, SQL & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Also focus on the behavioral interview – prep for that using this behavioral interview question bank.