# 9 StoneX Group SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

Data Analytics, Data Science, and Data Engineering employees at StoneX Group use SQL for analyzing trading data for market trends and extracting financial data for risk management assessments. That's the reason behind why StoneX Group usually asks SQL interview questions.

## 9 StoneX Group SQL Interview Questions

### SQL Question 1: Identify High-Value Users at StoneX Group

StoneX Group is an international company that facilitates trading in a wide array of commodities. Let's define high-value users as those who have made more than 100 trades in a month and where the total value of their trades is over \$1,000,000.

In the provided table:

• identifies the user.
• is the date when the trade took place.
• is the amount of money that was traded.

Write a PostgreSQL query to identify high-value users for the month of August 2022. Provide both the user_id and the total trade value for each such user for August 2022. Results should be ordered by total trade value in descending order.

##### Example Input:
385221001/08/2022 00:00:0055000
726312302/08/2022 00:00:00125000
784221003/08/2022 00:00:0020000
187612303/08/2022 00:00:00250000
672121003/08/2022 00:00:0090000
726312304/08/2022 00:00:00500000

This query first filters for trades made in August 2022. It groups the remaining records by and calculates the total trade value for each user. Then it filters out groups where the number of trades is less than or equal to 100 or the total trade value is less than or equal to \$1,000,000. Finally, it orders the result by in descending order. This way, the users making highest total value of trades appear first in the result. They are the high-value users for the month of August 2022.

To practice a related customer analytics question on DataLemur's free interactive SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:

### SQL Question 2: Employees Earning More Than Their Boss

Given a table of StoneX Group employee salaries, write a SQL query to find all employees who make more money than their direct boss.

#### StoneX Group Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

#### Example Output:

employee_idemployee_name
3Olivia Smith

This is the output because Olivia Smith earns \$8,000, surpassing her manager, William Davis who earns 7,800.

You can solve this problem interactively on DataLemur:

First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.

If the code above is tough, you can find a step-by-step solution here: Employees Earning More Than Their Boss.

### SQL Question 3: What sets UNION apart from UNION ALL?

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at StoneX Group working on a Marketing Analytics project. If you needed to get the combined result set of both StoneX Group's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

### SQL Question 4: Daily Average Trade Volume

Given a simplified version of StoneX Group's trade history, write a PostgreSQL query to calculate the daily average volume of shares traded for each security. Assume we start and end trading at distinct timestamps everyday.

StoneX Group is interested in analysing trade patterns over time. The data will be used for internal reporting and to gain insights from trading trends on different securities.

##### Example Input:
10013452022-06-08 09:30:001000
10025672022-06-08 15:59:592000
10033452022-06-09 09:30:001500
10043452022-06-09 15:59:592500
10055672022-06-10 09:30:00800
10065672022-06-10 15:59:591200
##### Example Output:
datesecurity_idavg_daily_volume
2022-06-083451000
2022-06-085672000
2022-06-093452000
2022-06-105671000

This SQL query uses a window function to find the daily average volume of trades for each security. The clause groups records by trading day and security, so the function calculates the average over each partition separately. This provides a window into the trade pattern of each security on a daily basis, enabling StoneX Group to monitor volumes and identify unusual events or trends.

To solve another window function question on DataLemur's free interactive SQL code editor, try this Amazon SQL question asked in a BI Engineer interview:

### SQL Question 5: Why would you use the SQL constraint?

The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.

For example, say you had StoneX Group customer data. You could use a CHECK constraint to ensure that the email column contains only properly formatted email addresses, or that the age column contains only positive integer. Here's an example of that:

### SQL Question 6: Extracting Client Information Based on Transaction History

As a data analyst at StoneX Group, you have been given a task to evaluate client engagements. You are required to identify the clients with a total transaction amount of over \$10,000,000 in 2019, who have not conducted any transaction in 2020.

You have been given two tables - table which holds client details and table which holds transaction details.

The table has the following structure:

The table has the following structure:

You can get the desired result with the help of a subquery and using clause to filter the data based on the conditions.

PostgreSQL query for the problem would be as follows:

In the above query, first, a subquery is used to get the total transaction amount for each client for the year 2019. This subquery is then joined with the table to get client details. The clause is then applied to filter out clients whose total transaction amount is over \$10,000,000 and who have not conducted any transactions in the year 2020. The subquery is used for this last condition.

### SQL Question 7: How are and similar, and how are they different?

Both the and window functions are used to access a row at a specific offset from the current row.

However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.

Say you had a table of salary data for StoneX Group employees:

namesalary
Amanda130000
Brandon90000
Carlita80000

You could use the function to output the salary of each employee, along with the next highest-paid employee:

This would yield the following output:

namesalarynext_salary
Amanda13000090000
Brandon9000080000
Carlita80000NULL

Swapping for would get you the salary of the person who made just more than you:

namesalarynext_salary
Amanda130000NULL
Brandon90000130000
Carlita8000090000

### SQL Question 8: Average Transaction Value

StoneX Group is a global financial services company, providing access to global markets through a network of offices in more than 40 countries. For this question, let's assume that the company often performs transactions and would like to determine the average value of all transactions.

Consider the following table:

##### Example Input:
transaction_idclient_idtransaction_dateamount
475034506/05/20227500.50
682176506/15/202212000.00
783234507/10/20226500.00
912815907/20/20228500.30
543915907/25/20229200.20

As an analyst for StoneX Group, write a SQL query to find the average transaction value for each client.

##### Example Output:
client_idavg_transaction_value
3457000.25
76512000.00
1598850.25

The solution involves using the AVG function on the transactions' amount and grouping by the client_id.

This query calculates the average transaction value for each client. The function is used to compute the average amount and is used to separate the data into different groups based on client_id. The result is a list of clients with their respective average transaction values.

To practice a very similar question try this interactive Uber User's Third Transaction Question which is similar for using transactional data to answer the question or this Stripe Repeated Payments Question which is similar for analyzing transaction values for patterns.

Read about StoneX's marketing intelligence division and how they use data science to achieve their goals.

### SQL Question 9: Calculate Average Monthly Trading Volume per Broker

StoneX Group Inc. is a financial services organization that provides execution, risk management, market intelligence, and post-trade services across asset classes and markets worldwide. In this scenario, suppose you are given a table of broker trading records. Each record in the table represents a trade made by a broker, including the broker's id, the trade volume (the amount of stocks trade), and the date when the trade was made.

Your task is to write a SQL query that calculates the average monthly trade volume for each broker for the year 2022.

Here is the sample table:

##### Example Input:
101122022-01-151000000
102232022-02-201500000
103122022-02-282500000
104232022-03-013000000
105122022-03-15500000
106122022-04-152000000
##### Example Output:
2022-01121000000.00
2022-02122500000.00
2022-02231500000.00
2022-0312500000.00
2022-03233000000.00
2022-04122000000.00

This SQL query first filters the trades table to restrict the data to the year 2022 using the WHERE clause. Then it groups the data by the month of the trade date and the broker_id using the GROUP BY clause. The AVG function calculates the average trade volume for each broker each month. The date_trunc function truncates the trade_date to the month level, effectively grouping all trades that occurred in the same month and year together. The result is a table that shows the average monthly trade volume for each broker for the year 2022. The final outputs are ordered by month and broker id. This information can be helpful for the company to analyze and compare broker performance.

### How To Prepare for the StoneX Group SQL Interview

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. In addition to solving the earlier StoneX Group SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.

Each SQL question has hints to guide you, full answers and most importantly, there is an online SQL coding environment so you can right online code up your SQL query answer and have it executed.

To prep for the StoneX Group SQL interview it is also wise to solve SQL questions from other banking & finanacial services companies like:

In case your SQL foundations are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL tutorial.

This tutorial covers things like handling missing data (NULLs) and filtering data with WHERE – both of these pop up routinely during StoneX Group SQL assessments.

### StoneX Group Data Science Interview Tips

#### What Do StoneX Group Data Science Interviews Cover?

In addition to SQL query questions, the other types of questions to practice for the StoneX Group Data Science Interview are:

• Statistics and Probability Questions
• Python Pandas or R Coding Questions
• Data Case Study Questions
• ML Modelling Questions
• Behavioral Interview Questions

#### How To Prepare for StoneX Group Data Science Interviews?

The best way to prepare for StoneX Group Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
• A Refresher covering Python, SQL & ML
• Amazing Reviews (1000+ reviews, 4.5-star rating)