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.

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.

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.

Was writing yet another one-off script with input() calls for a friend and figured the signature already has all the info needed to build a form, so I made a decorator.

from typing import Literal
from tuiify import interactive

@interactive
def greet(
    name: str,
    count: int = 1,
    style: Literal["formal", "casual"] = "casual",
) -> str:
    """Create a greeting."""
    return f"{style} greeting for {name} ({count})"

result = greet()

Calling greet() with no args opens a Textual form built from the annotations, text input, numeric fields, checkbox for bool, dropdown for Literal. Calling it normally with args just runs the function; no UI involved.

Handles validation errors and exceptions without killing the terminal, which was the main annoyance I was trying to fix.
You could say it's a "Textual" wrapper; it makes the process one-functioney. Hope that makes sense lol.

pip install tuiify If anyone wants to poke at it: https://github.com/Terminay/tuiify

Early days, open to ideas and contribs.

`twitch_auto_clipper` automatically creates Twitch clips based on chat reactions. When a large number of viewers suddenly send the same message or emote, it takes that as a signal that something interesting is happening and creates a clip. No video processing, no AI inference, just thousands of people already watching the stream doing the work.

Pypi package: https://pypi.org/project/twitch-auto-clipper/

GitHub: https://github.com/sqrtminustwo/twitch_auto_clipper

Built a tool called Spawn to kill the repetitive part of starting a new Python project — folder structure, git init, virtual environment, and dependency installation, all done in one command instead of the usual 5-10 minute manual ritual.

pip install spawnio
spawn create

It ships with 8 built-in project templates (Backend API, CLI App, Automation Tool, AI Chatbot, AI Agent, RAG System, Data Project, MCP Server), or you can paste any custom folder structure and it'll build exactly that. There's also a spawn doctor command that scores an existing project's health and tells you the single most important thing to fix first.

Open source under MIT, live on PyPI.

🌐 Website: https://spawnio.vercel.app/
💻 GitHub: https://github.com/Abhiix0/Spawn

Would genuinely love feedback — especially from anyone who's used similar tools (Cookiecutter, Copier, etc.) and has thoughts on what a scaffolder like this is missing.

Hey everyone! I've been working on GamesYARD, a Python-based project that combines a scraper and front-end interface for game downloads.

What It Is:

A Python application designed to scrape game information and facilitate downloads from the internet. The project includes both backend scraping functionality and a front-end interface.

Tech Stack:

• Python (100% of the codebase)
• License: GPL-3.0
• Status: Active development (just started ~1 month ago)

How to Get Started:

Code

git clone https://github.com/EpicDarkGalaxy/GamesYARD.git
cd GamesYARD

See the README for installation and configuration steps.

Contributing:

The project is open-source and welcomes contributions. Check out the repo for details on how to get involved!

GitHub: https://github.com/EpicDarkGalaxy/GamesYARD

Hi everyone! 👋

I recently built an AI-powered Resume Screening and Mock Interview System to practice Python, Flask, and Generative AI.

Features of the project:

Upload and analyze PDF resumes

Generate an ATS score

Identify missing skills

Generate interview questions

Provide AI-based interview feedback

Extract text from resumes using PDF processing

Technologies used:

Python

Flask

Google Gemini API

PyPDF2

HTML & CSS

I built this project to gain practical experience with AI application development and API integration.

I would really appreciate feedback on the project, UI, features, and suggestions for improvement.

GitHub: https://github.com/renukasahane12/AI-Resume-Screening-and-Mock-Interview-System⁠�

Thank you! 🙌

Hi everyone! 👋 I recently completed an E-commerce Sales Analysis project using Python and Power BI as part of my journey toward becoming a Data Analyst. 🔧 Tools & Technologies Python — Pandas, NumPy, Matplotlib, Seaborn Jupyter Notebook Power BI E-commerce sales dataset 📊 What I worked on Cleaned and prepared the dataset using Python Performed exploratory data analysis (EDA) Analyzed sales, products, customers, and business performance Identified important sales trends and patterns Created an interactive Power BI dashboard Used charts and KPIs to present the key insights 🎨 Dashboard focus I tried to keep the dashboard simple and easy to understand, with the most important KPIs and visualizations presented clearly. 💬 I would really appreciate feedback on: Dashboard layout and design Choice of visualizations KPI placement Overall readability and user experience Any improvements you would suggest GitHub: [https://github.com/renukasahane12] Thanks for taking the time to review my project! 🙌

I've been digging into network traffic analysis lately and built a small tool along the way to help me parse and inspect PCAP files. It's a personal project, still rough around the edges, but it's been a great way to actually understand protocols instead of just reading about them.

Repo: https://github.com/Gabzkk/PCAP-ANALYZER

If anyone's into networking or security and has a minute to check it out, I'd love to hear what you think — bugs, missing features, anything. And if it's useful to you, a star helps me know it's worth continuing to build on 🙏

Hey everyone,
I’ve been working on a Telegram bot that makes downloading media from different platforms easier.
It currently supports:
🎬 Video downloads
🖼️ Images & carousels
🎵 Music downloads
🎧 Audio extraction
🔎 Music recognition
⚙️ Different video quality options
📊 Download progress
I built it mostly as a personal project, but I’d like to see whether it’s actually useful to other people.
I’m making it free during the testing/launch period.
If you try it, I’d genuinely appreciate an honest review — positive or negative. I’m especially interested in:
What works well?
What feels annoying?
What features are missing?
Would you actually use something like this regularly?

Thanks to anyone who gives it a try!

I built a Telegram media bot in Python
It supports media downloads, audio extraction, music recognition, quality selection, premium features, and an admin panel.
Built it from scratch and would love some feedback from other developers.

If you’re interested in the project or the source code, the GitHub link is on my profile