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…

9 trading strategies everyone should know (with Python code): Bollinger Bands Pattern Recognition

The mid band is the moving average on the price series.

The upper and lower bands are two moving standard deviations away from the mid band.

Jupyter Notebook is the most powerful tool for Python.

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?

I lost $9,000 "technical trading."

So I watched 200 YouTube videos to learn algorithmic trading.

96% of them were a complete waste of time.

But these 6 turned me from a loser into a winner: Build a powerful AI finance agent with Python

OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
• Building agents
• AI in business
• 601 AI use cases

9 of the best guides you can't miss: 1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…

If you use it right, Twitter is the most powerful information platform in the world.

Unfortunately, most people get lost in the noise.

Here are 8 threads for Python and quant finance to get you started today (#4 is a game changer): Level the playing field with the pros.

https://twitter.com/3187132960/status/1549911563821260801

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.

My master's degree completely failed to teach me Python for quant finance (they taught me MATLAB).

And Octave (WTF?)

So I watched 200 YouTube videos.

And the truth is, 96% of them were a complete waste of time.

But these 8 taught me more than all my PhD professors combined: Algorithmic Trading Using Python (4.5 hours)

Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions.

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: Algorithmic Trading Using Python (4.5 hours)

Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions.

The 21 cognitive biases that will sabotage your trading (and how to beat them to improve your results): Put your ego aside and attribute early trading success to luck.

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:

AI bots are taking over the markets.

Truth:

Most shops still run Excel '97.

Some shops use machine learning.

10 ways to use machine learning in trading (with the Python library): Reinforcement Learning

Optimizing portfolio management using rewards.

Uses agent-based rewards for dynamic portfolio management. It learns to balance risk and reward by trading different stocks over time.

Use: Stable Baselines3's A2C model

This is Edward Thorp.

The genius mathematician who returned 20%+ over 30 years.

He traded for 19 years, with his worst loss being 1%.

He beat Vegas dealers at blackjack.

His top 7 trading strategies (and how they work): 1. Statistical Arbitrage

He used mathematical models to identify price discrepancies between different markets or securities, buying undervalued assets and selling overvalued ones.

I'm 43.

If you're still in your 20s (or 30s), read this: 1. The compounding effect of doing the same thing over and over for a long period of time is a force of nature. Learn how to compound.

2. Learn how to be comfortable with being uncomfortable. Wealth doesn't accrue to people solving easy problems.

12 Python libraries for free market data everyone should know: 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…