PyQuant News 🐍

Algorithmic trading with Python AMA x.com/i/broadcasts/1…

I know people making $1,000/month trading with: • One strategy • Multiple ways to implement it • An API connection to their broker • Python That’s it. Do yourself a favor and don't overcomplicate it.

Newsflash: Python is the new Excel. Don't be the only one stuck with 1,048,576 rows. So to avoid this fate, here's a free 7-day crash course to help you finally quit the green icon: pyquantnews.com/free-course

Advanced techniques for quantitative trading using stat arb. (By a veteran trader.) Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage. Here's what's inside:

I know people making $12,000/month trading with: • One strategy • An API connection to Interactive Brokers • Python That’s it. Want to learn how? 👉 Join Our Free Algorithmic Trading Workshop: learn.quantscience.io/join 

Caveat: this book is packed! • Machine learning methods of forecasting stock returns • Combine multiple forecasts into a single model • How to avoid the pitfalls of overfitting A book everyone needs on their bookshelf.

How to make a simple algorithmic trading strategy with a 472% return using Python. A thread. 🧵

Using Mathematics to Make Money by legend Jim Simons. 8 page PDF free for download:

Newsflash: Python is the new Excel. Don't be the only one stuck with 1,048,576 rows. So to avoid this fate, here's a free 7-day crash course to help you finally quit the green icon: pyquantnews.com/free-course

😬😬😬

Outcomes-Based Python The 5-step framework to find a problem worth solving: 1. Write down all your blockers 2. Group them and add specificity 3. Define the problem and the outcome 4. Refine how your workflow will change 5. Build, test, and iterate on your solution Repeat.

Manual trading (Track 1 trading) takes everything. Your mornings. Your focus. Your emotional bandwidth. And it still doesn't scale. Algorithmic trading (Track 2) runs while you do other things. You build it once. You test it. You deploy it. That's not passive income. That's infrastructure. Free live workshop on how to build yours (Track 1 --> Track 2). Live Python demo + Q&A. 500 seats. 👉Register here (500 seats): learn.quantscience.io/join 

JP Morgan's Python training. Available 100% for free:

OpenBB: A free alternative to the $20,000 Bloomberg Terminal Available 100% free on GitHub:

I love it when people are this f****** pumped about Python for algorithmic trading. Especially when I help them get there.

5 things you can do with Python now: 1. Automate Excel 2. Replace old VBA 3. Interact with APIs 4. Explore market data 5. Manipulating CSV files When learning Python, forget puzzles. Do something useful.

Looking to start using Python for quant finance? Here's a free Crash Course with everything you need to get started. Join the 1,000s of people who finally started with Python after reading it: pyquantnews.com/free-course

Machine learning is magic for building portfolios. The problem? You need a Ph.D. to figure out how to use it. With RiskFolio-Lib, you can build your own cutting-edge portfolio with machine learning. No Ph.D. required:

We only scratched the surface of HRP. As a next step, add the assets in your portfolio and optimize it for a risk measure other than variance. You can try “MAD”, “CVaR”, or “VaR.” Check the documentation for details.

The risk contribution of each asset in a portfolio quantifies how much individual assets contribute to the total risk, considering both their own volatility and their correlation with other assets. We can see the highest risk contribution is from OIH which is an oil ETF.

Machine learning is magic for building portfolios. The problem? You need a Ph.D. to figure out how to use it. With RiskFolio-Lib, you can build your own cutting-edge portfolio with machine learning. No Ph.D. required: