Python Safety Net: How to Prevent Sneaky Errors

I’ve been spending my recent free time in building an Event-Driven Backtesting Engine from scratch for Options. Backtesting complex option strategies requires processing massive amounts of market data, calculating Greeks, and tracking portfolio metrics simultaneously. To handle this without latency bottlenecks, I decided to architect the entire core engine in C++. for now I have mostly tried to make it very flexible like modular commission and slippage and ability to write custom strategies instead of editing the core engine itself I completely decoupled most of the core things so The entire C++ backend is compiled as a standalone library. I am also trying to Integrate a python bridge using pybind11 exposing this compiled library directly to Python. The goal for this is to make the engine to do all the computation in the background, allowing anyone to write, test, and plug in custom strategies dynamically using simple Python scripts without ever needing to modify the core engine files. Getting the C++ event loop to work good with Python scripting is proving to be a little complicated right now! I'll be pushing a final README and some sample strategies once I get the bindings fully stabilized. You guys can check out the code here : https://lnkd.in/gRSgd4gs #quantfinance #cpp #python #algorithmictrading #options #pybind11 #derivatives

Log 2/100: Telnetting into the Switch Python Library: telnetlib Today, I wrote a script to Telnet into my lab switch, but it kept crashing. After some troubleshooting, I found the exact issue: Python is just too fast. The script kept pasting the username before the switch had even generated the login prompt. The Fix: I added an extra line to the script (read_until) to force Python to wait and read the output until it actually saw the word "login:", and then asked it to paste the username. I applied the same logic to the Password prompt as well. Suddenly, the script started working flawlessly. The Takeaway: Writing the code is the easy part. Anticipating timing issues and making sure the script actually works without breaking in production is the tough part! The scirpts executes the show version command on the switch. Git Hub Repo: https://lnkd.in/gFkfNyWm Git hub Profile: https://lnkd.in/gjJJJQeT #Python #NetworkAutomation #NetDevOps #NetworkEngineering #100DaysOfCode #ArubaCX #CodingJourney

Day 2/30 – Building with Python Recently, I worked on a Vehicle Feedback System using Python The idea behind this project was to create a simple system where users can: 📝 Submit feedback about vehicles 📊 Store and manage responses efficiently Through this project, I learned: ✨ How to handle user input and data ✨ Basic logic building and structuring a program ✨ The importance of user-friendly systems This is just the beginning — I’m planning to improve it further by adding: OTP-based authentication for better security Database integration for scalability Possibly a simple UI for better user experience Building projects like this is helping me understand how real-world systems evolve step by step Would love your suggestions or ideas to improve this further! #Day2 #PythonProject #LearningInPublic #StudentDeveloper #BuildInPublic #TechJourney

VSCode workspace is a game changer! If you're writing Python codes for different versions (2.* and 3.*), then you should try the workspace in VSCode. Basically with workspace, you can define/set which CPython version to use when you click the play/run button in VSCode. This is very useful in cases where you might be trying out a logic in Python 2 and also on Python 3, or when you're migrating either upwards (from 2 to 3) or downwards (from 3 to 2), and since it's on the same IDE, the experience is really enjoyable. This also works on the same major version, so maybe you have Python 2.2 and 2.7, or 3.10 and 3.13; and it's not limited to just 2, you can have more. First time I tried it, had me smiling :) cause I work on different versions and it's not fun going to console and manually typing which python version to run on which folder --- (side note: python launcher is a great tool, but having an IDE that can support multiple versions seamlessly is just another level of ease). I really liked it, try it and see for yourself ;) #vscode #workspace #dcm #python

Built a multithreaded file cipher engine in C++17 and Python that handles thousands of files without choking; here's how. I just finished one of the most challenging projects I've built so far. A few weeks ago, I asked myself, what happens if you try to encrypt 10,000 files at once? So I actually tried it. My system nearly crashed. Turns out spawning a new thread for every file is a terrible idea at scale. So I went back to the drawing board and built the Parallel Cipher Engine in C++17 and Python. Honestly had to learn a lot of new concepts along the way: → Thread pools that adapt to your actual CPU cores → Reading files in 64KB chunks instead of byte by byte (genuine game changer) → Building a lightweight stream cipher from scratch → Created UI for better interaction → Managing threads safely without race conditions It's not perfect, and there's a lot I'd do differently now. But seeing it process thousands of files without breaking a sweat, that feeling makes it all worth it. If you're a beginner like me and haven't explored multithreading yet, just start. You'll break things, you'll get frustrated, but you'll learn so much faster than any course. Would love feedback from anyone who's been down this road. Github: https://lnkd.in/dJXCWuPF #CPP #Multithreading #BuildInPublic #SoftwareEngineering #LearningInPublic #Programming

My biggest mistake early in my Python journey wasn’t bad code. It was ignoring the small tools that make systems reliable. After 4+ years building automation projects, I found a handful of libraries that quietly transformed my side projects into production-ready products. I wrote about the exact 8 libraries I rely on today. Check out the full breakdown on my Medium account.

**"C and C++ are dead" is the ultimate "cutting the branch you're sitting on" moment in programming.** Python, JavaScript, Go, Rust… all the shiny high-level languages you love?   They’re literally **sitting on top of C/C++**. - CPython = written in C   - NumPy, pandas, PyTorch, TensorFlow, OpenCV = blazing-fast C/C++ cores   - Your browser, OS kernel, game engines, self-driving code… still C/C++ Scripting languages didn’t replace C/C++.   They **grew on top of them** and made the foundation invisible. Saying we should ditch C/C++ is like a bird claiming the tree is useless while chilling in the nest built on its branches. The low-level world still carries everything.   And it’s not going anywhere. #Cpp #C #Python #Programming

Built a mini C compiler from scratch in Python. No libraries. No shortcuts. Just pure compiler theory turned into working code. The pipeline: Source code → Lexer → Parser → Semantic Analyser → Code Generator → x86-64 Assembly What it covers: → Tokenizes raw C source into a typed token stream → Recursive-descent parser builds a full AST → Symbol table with type checking and use-before-declare detection → Emits real x86-64 AT&T assembly, linkable with gcc → Supports functions, arrays, pointers, if/else, while, for loops Also includes a sliding window test case (max sum subarray of size k) compiled and run entirely through the custom compiler all 6 test cases pass. It's a great exercise if you want to actually understand what happens between writing C and running a binary. Full source + README on GitHub. https://lnkd.in/gj5A3-xb #C #Compilers #Python #SystemsProgramming #LowLevelProgramming #OpenSource

In real, THREADS don’t actually run in parallel in Python… Here’s what really happens: >They just act like they are running in parallel. >Thread A starts working like it owns the system. Thread B is ready to jump in, but Python says, “Wait for your turn.” So, B waits. Then A pauses, B runs, then C gets a chance. It’s super-fast switching, so it looks like everyone is working together. >It’s basically one laptop shared by multiple users. Everyone looks busy, but only one is typing at a time. >This happens because of the Global Interpreter Lock in Python, which allows only one thread to execute at a time. >However, for I/O-bound tasks like API calls or file handling, threads still improve performance because while one waits, another runs. #Important_Point And here’s the key: in languages like Java, C++, and Go, threads can truly run in parallel across multiple CPU cores. #python #threads #multithreading #java #c++ #coding

Just finished building an Automated Data Firewall in Python! The Problem: Manually checking incoming CSVs for errors is slow and prone to human error. The Solution: I built a system using Python and Watchdog that monitors folders in real-time. It runs parallel quality checks (null-value detection & outlier analysis) before promoting clean data to a MySQL warehouse via SQLAlchemy. Key Features: ✅ Real-time file monitoring ✅ Fail-safe reporting with ReportLab (PDFs) ✅ Concurrent processing with ThreadPoolExecutor 📂 Check out the repo here:https://lnkd.in/e8p7Aefq #Python #DataEngineering #Automation #Github #SoftwareDevelopment