Python Quant Finance Tools and Resources

Claude Code Leak: the whole code leaked today and rewritten to Python today... All public. A security researcher Chaofan Shou discovered that Anthropic had accidentally included a large .map source map file (around 57–60 MB) in their publicly published npm package for Claude Code (version 2.1.88). This file allowed anyone to reconstruct the full original TypeScript source — roughly 1,900 files and over 512,000 lines of code. The package was tied to a release from March 30, but the exposure went public and spread rapidly on March 31. https://lnkd.in/dkU9B9U3

Built a Python tool that automates equity risk-return analysis for S&P 500 stocks — Sharpe ratios, cumulative return rankings, and risk-return scatter plots. Data sourced live from Yahoo Finance and FRED. GitHub: https://lnkd.in/dyrMXFim #Python #Finance #DataAnalysis #FinancialModeling

Machine Learning Time Series Data using pmdarima #machinelearning #datascience #timeseriesdata #pmdarima pmdarima brings R’s beloved auto.arima to Python, making an even stronger case for why you don’t need R for data science. pmdarima is 100% Python + Cython and does not leverage any R code, but is implemented in a powerful, yet easy-to-use set of functions & classes that will be familiar to scikit-learn users. pmdarima is essentially a Python & Cython wrapper of several different statistical and machine learning libraries (statsmodels and scikit-learn), and operates by generalizing all ARIMA models into a single class (unlike statsmodels). It does this by wrapping the respective statsmodels interfaces (ARMA, ARIMA and SARIMAX) inside the pmdarima.ARIMA class, and as a result there is a bit of monkey patching that happens beneath the hood. The auto_arima function itself operates a bit like a grid search, in that it tries various sets of p and q (also P and Q for seasonal models) parameters, selecting the model that minimizes the AIC (or BIC, or whatever information criterion you select). To select the differencing terms, auto_arima uses a test of stationarity (such as an augmented Dickey-Fuller test) and seasonality (such as the Canova-Hansen test) for seasonal models. https://lnkd.in/gjnJVA5T

Machine Learning Financial Data using finmarketpy #machinelearning #datascience #financialdata #finmarketpy finmarketpy is a Python based library that enables you to analyze market data and also to backtest trading strategies using a simple to use API. finmarketpy is a Python based library that enables you to analyze market data and also to backtest trading strategies using a simple to use API, which has prebuilt templates for you to define backtest. Included in the library. Prebuilt templates for backtesting trading strategies. https://lnkd.in/gh-dNpZV

Machine Learning Financial Data using backtesting py #machinelearning #datascience #financialdata #backtestingpy Backtesting.py is a Python framework for backtesting trading strategies on historical candlestick data. Backtesting.py is a fast, lightweight Python framework for testing trading strategies against historical market data. It gives you a clean, minimal API to define a strategy, run a simulation, inspect detailed statistics, and explore interactive charts — all without locking you into a particular data provider or indicator library. backtesting.py simulates a trading strategy running bar-by-bar through a historical OHLCV dataset. You write a Strategy subclass, declare your indicators in init(), and place orders in next(). The Backtest engine handles order execution, position tracking, commissions, and statistics. The library is indicator-library-agnostic: you can use NumPy, pandas, TA-Lib, Tulipy, scikit-learn, or any other tool to compute indicator values. If it returns a NumPy array the same length as your data, it works. https://lnkd.in/gC_dz6xG

From the leaked Claude source code, people are porting the architecture and logic to a different language (python) to avoid legal issues. One example is this one: https://lnkd.in/eVpnWN-n I remember using claude code to reverse engineering it itself, the intention? to enable a custom llm proxy, and it worked! The llm proxy didn't follow the regular authentication mechanism (required extra headers, custom url pattern) plus a custom change in the response. It was the opencode before opencode 😅🤣

Working with Apache Iceberg in Python is getting more practical and a bit less fragmented. At DataEngBytes Auckland, Alex Merced will walk through how the Python Iceberg ecosystem is starting to come together in a way that actually supports real workflows. Instead of stitching tools together, you'll see how PyIceberg handles core table operations and metadata, iceframe brings a more natural DataFrame-style experience for queries and transformations, and iceberg-cli supports the operational side, like debugging, inspection, and day-to-day management. The goal: a clearer mental model of when to use each tool and how a Python-first approach can simplify both development and operations in a lakehouse environment. Catch the session on March 24th at 4:00pm NZDT: https://hubs.la/Q047Qs0g0 Not in Auckland? His book Architecting an Apache Iceberg Lakehouse is available at 45% off with code 3merced26 through April 14th: https://hubs.la/Q047Qjs30

🚀 Just Published My First Python Library on PyPI! Excited to share that I’ve built and published "common-fun" — a modular Python utility library designed to simplify everyday development tasks. 📦 Install: pip install common-fun 🖥️ Try CLI: common-fun help 🔗 GitHub: https://lnkd.in/gjWRyhpq 🔧 What it includes: • Number utilities (prime, gcd, factorial, etc.) • String processing (palindrome, slugify, etc.) • Array helpers (flatten, chunk, rotate) • Validators (email, URL, password) • File utilities • Performance decorators (timer, retry, caching) • 🔥 CLI support for direct terminal usage 💡 Why I built this: While working on multiple projects, I realized I was repeatedly writing similar utility functions. So I decided to consolidate everything into a clean, reusable, and structured library. ⚙️ Key highlights: • Fully modular architecture • Optimized implementations • CLI tool for quick access • PyPI-ready packaging • Clean documentation This project helped me understand: ✔️ Library design ✔️ Packaging & publishing ✔️ CLI development ✔️ Clean code practices Would love your feedback and suggestions! #Python #OpenSource #Developer #Programming #PyPI #SoftwareDevelopment

I was 10 minutes in a tutorial video when I had to pause and stare at the ceiling.   He showed this:   def add_record(record, records=[]): # WRONG    records.append(record)    return records   The bug: that empty list is created ONCE when the function is defined. Every call to this function shares the SAME list. Call it 10 times — the list has 10 items, not 1.   I have written this exact pattern. More than once.   The correct version:   def add_record(record, records=None):    if records is None:        records = []    records.append(record)    return records   Never use a mutable object as a default argument. Use None and create the object inside the function.   The reason I am posting this:   I thought I knew Python functions. I have been writing them for 9 years. And a beginner tutorial just fixed a bug pattern I had been repeating.   There is no embarrassment in going back to basics. The embarrassment is in assuming you do not need to.   Has this one caught you before?   ---- #Python #LearningInPublic #DataEngineering #MLOps #CodingMistakes #PythonTips

Python shows up everywhere — data science, web apps, automation, infrastructure. Its simplicity and ecosystem make it one of the most practical languages to work with. But when performance starts to matter, many teams run into the same question: how do you keep Python's flexibility while making it significantly faster? That's the focus of tomorrow's PySTL Meetup (March 19th at 6pm CT). The session explores how Rust can be paired with Python to unlock performance gains without sacrificing the developer experience that makes Python so appealing in the first place. Register here: https://hubs.la/Q047b6PQ0 If you can't attend, Fast Python by Tiago Antao walks through practical ways to write Python that’s not just functional, but efficient and production-ready: https://hubs.la/Q047bgh10