Algorithmic trading is the domain of secretive hedge funds and banks.

Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool).

Use the same tools the professionals use.

Here are 17 Python libraries that open the black box: OpenBB Terminal

Terminal for investment research for everyone.

github.com/OpenBB-finance…

Missiles, robots, and traffic.

Nothing to do with quant finance, right?

Quants use the Kalman filter to predict future observations of hidden variables.

You can use it too-with Python.

Without the explosions: A quick primer on the Kalman filter if you’re unfamiliar:

• Invented to track missiles in space
• Uses noisy data to improve at each time step
• Traders use it to uncover the “true state” of a time series

Python makes it dead simple to use the Kalman filter.

Here’s how:

Save your $90,000 and skip the quant finance degree.

Dive into 17 code repos that will teach you more than all your professors at school.

All without costing you $90,000: OpenBB

Workspace for investment research for everyone.

github.com/OpenBB-finance…

RenTec uses Hidden Markov Models in trading.

The technique generated 60% returns per year over 30 years.

One of the co-founders of RenTec's name is in the algorithm!

Here's how it works: A Hidden Markov Model (HMM) is a statistical model used to represent systems that evolve over time with unobservable (hidden) states.

It is widely applied in areas such as natural language processing, speech recognition, and bioinformatics.

And in trading:

Quants use the volatility surface to price exotic options, calibrate models, and find mispricings.

You can build your own volatility surface with Python: First, get the options chain data.

During my master's degree, I read 65 books on quant finance.

Most of them were useless.

But these 4 are still on my shelf 10 years later.

And they should be on yours too: Linear and Nonlinear Programming by David G Luenberger and Yin Ye

What you learn:

Optimization theory and its applications, covering both linear and nonlinear programming with a focus on practical problem-solving techniques.

Some market data costs $1,400 per month.

The oil that makes the world's financial markets operate.

Unaffordable for 99% of us.

A profit center for countless Wall Street firms.

Fight back.

Here are the 12 Python libraries that give it to you free: yfinance

Data for stocks (historic, intraday, fundamental), FX, crypto, and options. Uses Yahoo Finance so any data available through Yahoo is available through yfinance.

github.com/ranaroussi/yfi…

Algorithmic trading is the domain of secretive hedge funds and banks.

Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool).

Use the same tools the professionals use.

Here are 17 Python libraries that open the black box: OpenBB

Workspace for investment research for everyone.

github.com/OpenBB-finance…

In the age of AI, Jupyter Notebook is the most powerful tool Python developers have.

But most people don’t know the hidden features.

Need a quick web app?

Or create REST APIs?

Here are the 6 ways to use Jupyter Notebook (you never knew existed): Jupyter Notebook is a web app for creating and sharing computational documents.

When I say powerful, here's what I mean:

• It supports 40 languages
• Produces rich, interactive output
• Leverages big data tools like Spark

So, what else can we do with Jupyter Notebook?

Myth:

You need a computer science degree to get started with Python, NumPy, and pandas.

Reality:

You need these 8 YouTube videos: "Python Pandas" by Corey Schafer

Learn the basics of creating and manipulating data frames, indexing and selecting data, and cleaning and manipulating data in Pandas.

Statistical arbitrage is scary sh*t.

That's what I used to think until I spent 25 hours studying pairs trading.

Now I realize it's a great way to get started with algorithmic trading.

Here’s how to build a pairs trading strategy in Python.

Step by step: By replicating this framework, you’ll be able to:

1. Get stock price data
2. Find cointegrated pairs
3. Model the spread
4. Trade the strategy

What is pairs trading anyway?

Python is the new Excel.

So don't be the only one stuck with 1,048,576 rows.

6 links to help you quickly get started with Python now: The Ultimate Beginner’s Guide to NumPy

What you'll learn: NumPy is the foundation for all data-driven libraries in Python. Understanding the basics will help you learn other libraries like Pandas and get an edge on your peers.

towardsdatascience.com/the-ultimate-b…

You can use options to predict stock price moves.

Here’s how: Options traders are well-informed. Their expectations of future stock price moves are often priced into options. We can use options prices to extract the options market’s expectations of stock price moves.

If we know where to look…

My PhD professors taught me MATLAB during my master's degree.

So I watched 200 YouTube videos to learn Python

96% of them were a complete waste of time.

But these 8 taught me more than all my PhD professors combined: Setting up Interactive Brokers API with Python

This video shows how to use Python with the Interactive Brokers API to automate a first strategy—cutting through its complexity in one clear walkthrough.

Every piece of trading advice I could think of after 20+ years trading, 20+ years writing code, and 15+ years as a quant:

1. Bulls make money. Bears make money. Pigs go to slaughter. (Thanks Dad.) 2. Never add to a losing trade.
3. The best way to burn out learning Python is to learn stuff you can't actually use.
4. Fight on the winning side and be willing to change sides when one side has gained the upper hand.

RenTec uses Hidden Markov Models in trading.

The technique generated 60% returns per year over 30 years.

One of the co-founders of RenTec's name is in the algorithm!

Here's how it works: A Hidden Markov Model (HMM) is a statistical model used to represent systems that evolve over time with unobservable (hidden) states.

It is widely applied in areas such as natural language processing, speech recognition, and bioinformatics.

And in trading:

There are 137,000 Python libraries.

But you only need 11 for quant finance: pandas

• Allows quick data cleaning and preprocessing
• Provides DataFrame structure for time-series data
• Enables complex data operations with simple syntax

Use it for:

Manipulating and transforming financial market data.

This Jane Street trader literally explains edge in this book.

Here's what's inside: The author starts by explaining how the professionals and hedge funds think about trading.

10 free Python PDF ebooks for download:


Think Python

greenteapress.com/thinkpython2/t…

OpenBB dropped the $0 cost financial AI agent workstation.

You can get 405 free data sources (and an AI Copilot).

Here's what you missed (in 90-seconds): In case you're unfamiliar:

OpenBB is the first AI financial workstation that combines data with an AI agent to transform investment research.

• Private (your data is not shared)
• 100s of free data sources
• Custom data backends
• Advanced AI Copilot

How it works:

My master's degree completely failed to teach me how to test trading strategies.

So I spent 40 hours looking for Python backtesting libraries.

Then I started using the best ones.

But unlike my quant finance degree, these won't cost you $90,000.

Here they are for free. Zipline

From Quantopian (acquired by Robinhood) the first to democratize quant trading, comes Zipline. It's a robust, fully-featured backtesting library which features slippage models, robust data handing and rich metrics.

github.com/quantopian/zip…