8 Interactive Brokers SQL Interview Questions (Updated 2024)
Interactive Brokers employees use SQL often for analyzing trading patterns and managing large databases of client trading histories. That's the reason behind why Interactive Brokers covers SQL problems during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you study for the Interactive Brokers SQL interview, we'll cover 8 Interactive Brokers Group SQL interview questions – scroll down to start solving them!
8 Interactive Brokers Group SQL Interview Questions
SQL Question 1: Analyze Trade Volumes
Interactive Brokers is a preeminent brokerage firm. Consider a hypothetical situation where you are given access to trading data that outlines each trade executed on the platform. In this case, you may want to analyze trade volumes for all stocks on a month-by-month basis and compare it with the previous month trade volume.
Create a SQL query using window functions to retrieve the trade volume per stock symbol per month, along with the percentage difference from the previous month's volume.
Example Input:
| trade_id | trade_date | symbol | volume |
|---|---|---|---|
| 111 | 2022-06-01 | AAPL | 1000 |
| 222 | 2022-06-15 | AAPL | 2000 |
| 333 | 2022-06-29 | GOOG | 1200 |
| 444 | 2022-07-01 | AAPL | 3000 |
| 555 | 2022-07-02 | GOOG | 3000 |
| 666 | 2022-07-20 | GOOG | 1500 |
| 777 | 2022-08-01 | AAPL | 2500 |
| 888 | 2022-08-15 | AAPL | 2000 |
| 999 | 2022-08-30 | AAPL | 1500 |
Example Output:
| mth | symbol | trade_volume | difference |
|---|---|---|---|
| June | AAPL | 3000 | - |
| June | GOOG | 1200 | - |
| July | AAPL | 3000 | 0 |
| July | GOOG | 4500 | 275.00% |
| August | AAPL | 6000 | 100.00% |
| August | GOOG | 0 | -100% |
Answer:
This query first aggregates the total trading volume for each stock symbol on a month-by-month basis. Then it uses a window function (the lag function) to calculate the trade volume difference between the current month and the previous month, expressed as a percentage. The COALESCE function is used to handle cases where there is no previous month data by returning '-' as the value. The result is sorted by month and symbol for ease of reading.
For more window function practice, try this Uber SQL problem within DataLemur's interactive SQL code editor:
SQL Question 2: Department Salaries
Imagine there was a table of Interactive Brokers employee salaries, along with which department they were in. Write a query to compare the average salary of employees in each department to the company's average salary for March 2024. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Write a SQL query for this interview question directly within the browser on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a detailed solution here: Department vs. Company Salary.
SQL Question 3: How does the RANK() window function differ from DENSE_RANK()?
While both and are used to rank rows, the key difference is in how they deal with ties.
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
Suppose we had data on how many deals different salespeople at Interactive Brokers:
To rank these salespeople, we could execute the following query:
The result of this query would be:
| name | deals_closed | rank | dense_rank |
|---|---|---|---|
| Akash | 50 | 1 | 1 |
| Brittany | 50 | 2 | 1 |
| Carlos | 40 | 3 | 2 |
| Dave | 40 | 4 | 3 |
| Eve | 30 | 5 | 3 |
| Farhad | 10 | 6 | 4 |
As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.
SQL Question 4: Active Traders Filter
Interactive Brokers is interested in identifying its active traders who have carried out more than 10 trades in the last month and have a total balance of over $50000. Create a SQL query to find out these traders.
Example Input:
| trader_id | full_name | account_balance |
|---|---|---|
| 1054 | John Doe | 60000 |
| 1873 | Jane Smith | 40000 |
| 2950 | James White | 75000 |
| 3852 | Emily Black | 85700 |
| 4901 | Michael Green | 30500 |
Example Input:
| trade_id | trader_id | trade_date | trade_amount |
|---|---|---|---|
| 6171 | 1054 | 06/08/2022 00:00:00 | 5000 |
| 7802 | 1873 | 06/10/2022 00:00:00 | 7000 |
| 5293 | 1054 | 06/18/2022 00:00:00 | 4000 |
| 6352 | 1054 | 06/25/2022 00:00:00 | 12000 |
| 4517 | 1873 | 06/30/2022 00:00:00 | 3000 |
| 9325 | 1054 | 07/02/2022 00:00:00 | 7000 |
| 6832 | 2950 | 07/05/2022 00:00:00 | 8000 |
| 1191 | 1054 | 07/06/2022 00:00:00 | 5500 |
| 0342 | 3852 | 07/10/2022 00:00:00 | 10000 |
| 4318 | 1054 | 07/12/2022 00:00:00 | 6000 |
| 9275 | 3852 | 07/15/2022 00:00:00 | 9000 |
| 9820 | 1054 | 07/18/2022 00:00:00 | 7800 |
| 0920 | 2950 | 07/26/2022 00:00:00 | 6800 |
Answer:
This query first joins the traders and trades tables on the trader_id. It then filters the trades to only those that have occurred in the last month using the WHERE clause. The GROUP BY clause groups the results by the trader_id and the HAVING clause filters the results to only show the traders who carried out more than 10 trades in the last month and have a total balance of over $50000.
SQL Question 5: Could you explain what a self-join is and provide examples of when it might be used?
A self-join is a operation in which a single table is joined to itself. To perform a self-join, you must specify the table name twice in the clause, giving each instance a different alias. You can then join the two instances of the table using a clause, and specify the relationship between the rows in a clause.
Think of using a self-joins whenever your data analysis involves analyzie pairs of the same things, like comparing the salaries of employees within the same department, or identifying pairs of products that are frequently purchased together (which you can do in this real SQL question from a Walmart interview).
For another self-join example, suppose you were conducting an HR analytics project and needed to examine how frequently employees within the same department at Interactive Brokers interact with one another, you could use a self-join query like the following to retrieve all pairs of Interactive Brokers employees who work in the same department:
This query returns all pairs of Interactive Brokers employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Interactive Brokers employee being paired with themselves).
SQL Question 6: Find the Largest Transaction for Each Client
Interactive Brokers is an electronic brokerage firm that deals with various financial services like stock trading, futures contracts, and more. Let's assume we have two tables, Clients (which maintains the client's details) and Transactions (which maintains all transactions details). Based on the provided data, the task is to find the client who made the largest transaction on each day.
Example Input:
| client_id | client_name |
|---|---|
| 1201 | Alice |
| 2506 | Bob |
| 3693 | Cathy |
| 4517 | Dan |
Example Input:
| transaction_id | client_id | transaction_date | amount |
|---|---|---|---|
| 9238 | 1201 | 06/08/2022 | 11000 |
| 6964 | 2506 | 06/08/2022 | 18000 |
| 5079 | 1201 | 06/09/2022 | 9500 |
| 8103 | 3693 | 06/10/2022 | 16000 |
| 4304 | 4517 | 06/10/2022 | 20000 |
We anticipate the output to have the date, client_id, client_name, and the maximum transaction amount for that day.
Answer:
This query first finds the maximum transaction for each client on each day from the Transactions table. It then joins this result with the Clients table to get the corresponding client name. The result is ordered by transaction_date and in the descending order of the transaction amount.
SQL Question 7: Can you explain the distinction between an inner and a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
To demonstrate each kind, Imagine you were working on a Advertising Analytics project at Interactive Brokers and had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
An retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
A retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 8: Finding Account Holders Residing in California
Interactive Brokers would like to target new promotional activities to clients residing in California. As a data analyst, your task is to filter down the customer database to find those customers whose addresses contain the string 'CA' (abbreviation for California).
Example Input:
| customer_id | first_name | last_name | account_balance | address |
|---|---|---|---|---|
| 1001 | John | Doe | 10000 | 123 Main St, Los Angeles, CA |
| 1002 | Jane | Smith | 20000 | 456 Pine Ave, New York, NY |
| 1003 | Tom | Brown | 15000 | 789 Oak St, San Francisco, CA |
| 1004 | Emma | Johnson | 12000 | 321 Willow Dr, Boston, MA |
| 1005 | Jack | Hill | 8000 | 654 Cherry Ln, Los Angeles, CA |
Your query is expected to produce the following output:
Example Output:
| customer_id | first_name | last_name | account_balance | address |
|---|---|---|---|---|
| 1001 | John | Doe | 10000 | 123 Main St, Los Angeles, CA |
| 1003 | Tom | Brown | 15000 | 789 Oak St, San Francisco, CA |
| 1005 | Jack | Hill | 8000 | 654 Cherry Ln, Los Angeles, CA |
Answer:
This query filters the table to find all customers residing in California. Here, we use the SQL LIKE operator with the wildcard character (%) before and after 'CA' to match any address that contains the string 'CA'. Thus, it will return all records where the field contains 'CA'.
Read about Interactive Broker's API solutions.
Interactive Brokers SQL Interview Tips
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Besides solving the above Interactive Brokers SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each SQL question has hints to guide you, step-by-step solutions and most importantly, there's an interactive coding environment so you can right in the browser run your SQL query and have it executed.
To prep for the Interactive Brokers SQL interview it is also wise to solve SQL problems from other stock exchange & brokerage companies like:
In case your SQL skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL concepts such as aggregate window functions and LEAD/LAG window functions – both of which pop up often during Interactive Brokers SQL interviews.
Interactive Brokers Group Data Science Interview Tips
What Do Interactive Brokers Data Science Interviews Cover?
Besides SQL interview questions, the other topics tested in the Interactive Brokers Data Science Interview include:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product-Sense Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for Interactive Brokers Data Science Interviews?
To prepare for Interactive Brokers Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
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