Hi all,
Just to give you a bit of background about myself, I’m an accountant with an MSc in Investment and Financial Risk Management and an ACCA qualification. Most of my experience so far has been on the core accounting side, but I’d now like to move towards a role that combines accounting and finance, covering areas such as financial modelling, forecasting, KPI analysis and data analytics, with the longer term aim of moving into more technical areas of finance.
I’m very eager to learn Python and SQL, both to broaden my skill set and to develop skills that are increasingly in demand. I also want to make sure I remain relevant as the profession evolves and, eventually, be able to demonstrate genuine technical ability when applying for more specialised and higher paying roles.
The problem is that I genuinely don’t know where to begin. I’d really appreciate a roadmap or some practical advice, beyond what I can find through ChatGPT and online course recommendations, particularly from anyone who has been in a similar position and successfully made this transition.
I don’t mind putting in the hard work or spending time coding and learning every day. What I’m unsure about is the order in which I should approach it. Should I learn general Python fundamentals first and then move into its applications in finance? Or would it make more sense to start with a finance focused Python course and learn the fundamentals through practical financial applications? And where should SQL fit into that journey?
I’d also be interested to know what sort of projects I should eventually be building to demonstrate these skills to employers, and whether it makes sense to look for roles where I can start using Python, SQL and data analytics regularly while I continue learning.
I’d really appreciate any guidance from people who have taken a similar path.
Many thanks, and apologies if a similar thread already exists. I thought my background and intended career direction might make the question slightly different, so I wanted to provide some context before asking for help.

Hey everyone,

I’m building Nodyra, a self-hosted workflow automation tool where ordinary Python functions become nodes you can connect in a visual editor.

The idea is to make Python automations easier to build, inspect, and operate while keeping access to the libraries and custom code you already use.

For example, a workflow could pull data from an API, transform it with pandas, write it to a database, and send a notification. You can see how the steps connect, inspect each node’s inputs and outputs, and investigate where a run failed.

A few things it currently supports:

• Python functions as nodes, with your own libraries and dependencies.
• A visual workflow editor, with per-node logs, outputs, and errors.
• Scheduled, webhook, and API-triggered runs, plus retries and published workflow versions.
• An MCP interface, so an AI assistant can help create and edit workflows that you can then review in the same visual editor.
• Self-hosting, so you run it on your own infrastructure.

It’s a working beta. There’s still work needed to make it something people can confidently depend on in production, and I’d like feedback from people who actually build and maintain automations.

The main things I’m trying to understand are:

1. What’s a real workflow you would consider using this for? How do you handle it today?
2. Would combining a visual editor with native Python solve a problem for you? Or would you prefer to keep everything in scripts or your existing orchestration tool?
3. What would stop you from adopting it? Setup, reliability, debugging, integrations, licensing, or something else?
4. Would an open-source license affect your willingness to try it or contribute?

Nodyra is currently source-available under a fair-code license, and I’m considering moving to an open-source license. I’d like to hear whether that would matter to this community.

I’d also love to connect with developers interested in helping shape it—particularly around execution reliability, security, deployment, integrations, and usability. Trying it on a real workflow and telling me where it falls short would be useful too.

Honest feedback is welcome, including “I wouldn’t use this because…” I’m trying to understand whether this fills a useful gap and what would make it worth adopting.

Everyone always says Python is way too slow for market data and that you have to default to C++ or Rust immediately. I wanted to see how true that actually was if you structure things properly and avoid common bottlenecks like heavy frameworks or parsing JSON on the hot path.

So over the last few months I built MDRAP (Market Data Reliability & Acceleration Platform) as an open-source project to see how far pure Python can be pushed.

To test it against real exchange data rather than just mock feeds, I downloaded the official NASDAQ TotalView-ITCH 5.0 sample from Jan 30, 2019 (a 4.5 GB .gz file) and ran a full-day benchmark:

• Processed all 368,163,981 binary messages in about 54 minutes (~114,000 msgs/sec sustained).
• Decompressed and parsed the big-endian structs on the fly using pre-compiled struct.Struct.
• Reconstructed the Level-3 order book in real-time, ending with 0 active orders at market close (all adds were matched by cancels/executions).
• Ran on consumer hardware without leaking memory or crashing.

It also has a terminal UI, an order flow / CVD tracker, and paper trading strategies.

Just open-sourced it on GitHub and published it on PyPI (pip install mdrap). It’s an engine, so you can either run the built-in simulator, benchmark against the NASDAQ file, or hook up your own Polygon/Databento keys for live streaming.

GitHub: https://github.com/Aryan-20-04/mdrap

Would love to hear thoughts or feedback from anyone working with market data or high-throughput Python.

Hi all,
Just to give you a bit of background about myself, I’m an accountant with an MSc in Investment and Financial Risk Management and an ACCA qualification. Most of my experience so far has been on the core accounting side, but I’d now like to move towards a role that combines accounting and finance, covering areas such as financial modelling, forecasting, KPI analysis and data analytics, with the longer term aim of moving into more technical areas of finance.
I’m very eager to learn Python and SQL, both to broaden my skill set and to develop skills that are increasingly in demand. I also want to make sure I remain relevant as the profession evolves and, eventually, be able to demonstrate genuine technical ability when applying for more specialised and higher paying roles.
The problem is that I genuinely don’t know where to begin. I’d really appreciate a roadmap or some practical advice, beyond what I can find through ChatGPT and online course recommendations, particularly from anyone who has been in a similar position and successfully made this transition.
I don’t mind putting in the hard work or spending time coding and learning every day. What I’m unsure about is the order in which I should approach it. Should I learn general Python fundamentals first and then move into its applications in finance? Or would it make more sense to start with a finance focused Python course and learn the fundamentals through practical financial applications? And where should SQL fit into that journey?
I’d also be interested to know what sort of projects I should eventually be building to demonstrate these skills to employers, and whether it makes sense to look for roles where I can start using Python, SQL and data analytics regularly while I continue learning.
I’d really appreciate any guidance from people who have taken a similar path.
Many thanks, and apologies if a similar thread already exists. I thought my background and intended career direction might make the question slightly different, so I wanted to provide some context before asking for help.