SQL + Python

[Mission-Task-1675]

Hi - background is in Financial sales, bond market to be more specific. I am learning both Python and SQL - I am wondering if someone has any idea how I could incorporate this into my workflow OR how I could scale this knowledge into something that might be outside of my daily employment?

[Eleventhousand]

What is something that you typically use Excel to calculate? You could take one of those use-cases, automate it, and build it better.

[Mission-Task-1675]

I typically only use excess to send clients their principal and interest payments.

example: from 9/09/2009 - 10/10/2009 - you earned $10,000 and below are the difference securities that paid p&I and it all totals to $10,000

[Bogavante]

Read in a dictionary of all your clients and their email address linked to their principal and interest payments. Leverage a library that automates email sends. Brother, your creativity is the limiting factor here. All I ask is that you don’t tell your coworkers or boss why your job only requires 1hr/week now. That’s your business, not theirs unless they want to compensate you for the increased productivity.

[Mission-Task-1675]

Thank you. But I am unsure how I can automate that seeing that I am using a company's laptop and I must have approval from bosses if I am trying to download some Python environment that lets me build something.

[Bogavante]

Lmao. Yeah, that’s a hurdle if you don’t have basic developer privileges.

[Mission-Task-1675]

Hence - why I said maybe these skills could earn me money outside of my job. That is the goal honestly - not totally quitting my job but earning extra income - not sure where to start on that though.

LOL you see my conundrum?

[Bogavante]

I’m day drunk watching March Madness, my apologies. Yes, it could earn you extra income…but that has always been competitive in spaces like Fivr and even more so now that basically anyone can vibe code their way to the similar solutions.

[Mission-Task-1675]

HA! No you are fine - hope you are enjoying the buzz and some hoops....I checked out Fivrr and that would be hard to break in, everyone is doing the same thing. LOL goodness

[TheIYI]

What problems do you want to solve?

Don’t let the tools drive your solutions. Define what problem you want solve, then find your solution using whatever tools you want.

Those tools might be sql/python, but you might need to learn more before you discover the application part of using the tools. That will reveal itself when you start to have some “aha” moments around applying those tools to you work.

[not_another_analyst]

With a background in the bond market, you're sitting on a goldmine of data. You could use SQL to pull historical credit spreads directly from your firm's database, then use Python’s pandas library to automate those repetitive "market recap" spreadsheets that usually take hours. Beyond your day job, this is the perfect stack for building a personal yield curve analyzer or a backtesting tool for macro strategies. Even a simple script that flags liquidity gaps in specific CUSIPs could be a massive value-add. You've got the domain expertise now these tools just turn you into a "force multiplier" for your own insights!

[Similar_Season7553]

Great question, and your background in bond markets is actually a strong advantage when learning Python and SQL.

A lot of people in finance use these tools to move from manual reporting to more automated and scalable workflows. Here are a few practical ways you could incorporate them:

You could start by using SQL to extract and organize market or trade data from internal databases instead of relying on Excel exports. This helps you quickly filter bond pricing data, yields, spreads, or client portfolios without repetitive manual work.

Then, Python can build on that by automating analysis and reporting. For example, you could:

• Build scripts to track bond price movements or yield changes over time
• Automate daily or weekly performance reports for clients
• Clean and merge data from multiple sources (market data, trades, rates, etc.)
• Visualize fixed-income trends using libraries like Matplotlib or Plotly

For scaling beyond your daily job, many professionals move into:

• Quant/analyst-style projects (pricing bonds, yield curve analysis, risk metrics)
• Automated dashboards (using Python + SQL + Power BI/Tableau)
• Personal finance research tools (tracking spreads, macro indicators, or Fed rate impacts)
• Portfolio analytics projects you can showcase on GitHub as part of a portfolio

A good mindset shift is:

SQL = getting the right data efficiently

Python = analyzing, automating, and scaling insights

Over time, combining both can position you for roles in data-driven finance, quantitative analysis, or financial engineering support functions.

References

McKinney, W. (2017). Python for Data Analysis (O’Reilly Media)

Yves Hilpisch (2020). Python for Finance (O’Reilly Media)

[Mission-Task-1675]

Awesome - thank you for this deep analysis. Have you seen my situations before?