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PyQuant News 🐍 Profile picture
PyQuant News 🐍
@pyquantnews
Where finance practitioners get started with Python for quant finance, algorithmic trading, and data analysis | Tweets & threads with free Python code & tools.
38 subscribers
Jun 6 • 11 tweets • 3 min read
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: Image 1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…
May 28 • 12 tweets • 3 min read
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.
May 27 • 12 tweets • 4 min read
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.

May 13 • 11 tweets • 4 min read
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.

May 10 • 11 tweets • 4 min read
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.

May 4 • 24 tweets • 6 min read
The 21 cognitive biases that will sabotage your trading (and how to beat them to improve your results): Image Put your ego aside and attribute early trading success to luck. Image
May 1 • 11 tweets • 4 min read
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): The notebook to rule them all. 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?
Apr 29 • 14 tweets • 3 min read
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): Image 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
Apr 15 • 10 tweets • 3 min read
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): Image 1. Statistical Arbitrage

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

Apr 12 • 21 tweets • 4 min read
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.
Apr 1 • 16 tweets • 5 min read
12 Python libraries for free market data everyone should know: Image 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…
Mar 22 • 5 tweets • 2 min read
The NUMBER 1 issue for options traders:

Data.

The equity options market generates 3 terabytes of data every day.

And to stream it you need expensive broker feeds.

ThetaData changes that: First, import the client. Image
Feb 25 • 15 tweets • 4 min read
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 Terminal

Terminal for investment research for everyone.

github.com/OpenBB-finance…
Feb 22 • 12 tweets • 4 min read
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.

Feb 6 • 9 tweets • 3 min read
You can use options to predict stock price moves.

Here’s how: Image 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…
Feb 1 • 24 tweets • 6 min read
The cost behind the Python Quant Stack:

• SciPy: $0
• Zipline: $0
• Python: $0
• NumPy: $0
• PyFolio: $0
• Pandas : $0
• OpenBB: $0
• Empyrical: $0
• AlphaLens: $0
• Statsmodels: $0
• RiskFolio-Lib: $0

How to get started: Image By the end of this thread, you'll have the right libraries for:

• Numerical libraries & data structures
• Financial instruments & pricing
• Backtesting & trading
• Market data

Let's go!
Jan 28 • 14 tweets • 2 min read
Factor investing is what made me stand out at JPMorgan.

But it took me years to master the information coefficient.

In 1 minute, I'll teach you the 10 things you need to know (that took me 1 year to learn).

Let's go: Image The Information Coefficient (IC) is a statistical measure that reflects the correlation between predicted and actual asset returns.

It's the most important risk and performance measure for factor-based portfolios.

Let's see how it works: Image
Jan 20 • 13 tweets • 6 min read
10 free Python PDF ebooks for download: Image
Image
Image
Image Think Python

greenteapress.com/thinkpython2/t…Image
Jan 16 • 8 tweets • 2 min read
You can use options to predict stock price moves.

Here’s how: Image 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…
Jan 9 • 12 tweets • 4 min read
A Bloomberg Terminal costs $30,000 per year.

Unaffordable for 99.9% of us.

But Bloomberg is not AI-enabled.

OpenBB dropped the $0 replacement with AI agent workspaces.

Free market data and an AI Copilot:

The 90-second step-by-step guide: OpenBB Terminal Pro In case you're unfamiliar:

OpenBB is the first AI financial terminal that combines data integration 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:
Dec 31, 2024 • 11 tweets • 4 min read
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's the 6 ways to use Jupyter notebook you never knew existed: The notebook to rule them all. 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?