Share this page!

Enter URL or ID to Unroll

You can paste full URL like: https://x.com/threadreaderapp/status/1644127596119195649
or just the ID like: 1644127596119195649

How to get URL link on X (Twitter) App

  1. On the Twitter thread, click on or icon on the bottom
  2. Click again on or Share Via icon
  3. Click on Copy Link to Tweet
  4. Paste it above and click "Unroll Thread"!
  5. More info at Twitter Help
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.
May 8 • 11 tweets • 2 min read
I read the 161-page Financial Machine Learning paper.

It took me 3 days.

Here are the key topics (in 30 seconds): Image Understanding Asset Prices

Prices as predictions, reflecting future payoffs and investor valuations.
May 4 • 13 tweets • 3 min read
There is $664,000,000,000 invested in factor strategies.

Factors can help you manage risk and amplify returns.

I spent 3 years figuring it out.

Now you can do it in 10 minutes.

Here’s how in Python. By reading this thread, you’ll be able to:

• Download historic factor data
• Compute the sensitivities to the factors
• Figure out the risk contribution of the factors

But first…
May 3 • 11 tweets • 4 min read
7 of the best books on algorithmic trading with Python you can buy: Image Trading Evolved by Andreas Clenow

Professional backtesting environment using Python, and provides strategies for trading both futures and equities.

• Professional backtesting with Python
• Source code and strategy explanation
• Focus on futures and equities trading Image
Apr 26 • 19 tweets • 5 min read
17 Python GitHub repos for quant finance: Goldman Sachs

github.com/goldmansachs
Apr 13 • 6 tweets • 2 min read
Backtesting options strategies is impossible.

Why?

• Millions of contracts
• Weekly expirations
• Assignment if ITM

Not anymore.

Now, you can backtest options strategies in a few lines of code (link to free options data below).

Here’s how: Image Optopsy is an analysis tool for options trading strategies.

• Generates strategies from raw historic data
• Uncover performance statistics for combos
• 12 strategies built in including strangles

We’ll use 5 years of historic options data on RUT for the analysis.

Let’s go!
Mar 14 • 8 tweets • 3 min read
OpenAI.

OpenBB.

LangChain.

Cutting-edge sentiment analysis using generative pre-trained transformers.

All in a few lines of Python.

Here's the code: By reading this thread, you’ll use the OpenBB SDK to download news for a topic.

Then, you’ll build a prompt to predict the sentiment of a news headline.

You’ll bring it all together with LangChain.

Let's go!
Feb 16 • 11 tweets • 3 min read
How to do multi-factor interest rate analysis with Python, step-by-step (bookmark this one for later): Interest rates are the driving force behind the economy.

Quants model how interest rate changes impact portfolios using principal component analysis (PCA).

Here’s how in Python.
Feb 7 • 7 tweets • 2 min read
I built an AI agent that can download stock price data and analyze the performance.

Here's how to do it in 30 seconds: Image When integrated with LLMs, Python can bring new levels of power and scalability to our analysis.

That's where LlamaIndex comes in.

Start with the imports: Image
Feb 5 • 13 tweets • 6 min read
10 free Python PDF ebooks for download: Image
Image
Image
Image Think Python

greenteapress.com/thinkpython2/t…Image
Feb 3 • 9 tweets • 3 min read
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: Image 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:
Jan 23 • 19 tweets • 5 min read
Python and Excel:

A potent combination for working with data.

Here are the 17 Python libraries to help you unlock the power. XlsxWriter

XlsxWriter is a Python module for writing files in the Excel 2007+ XLSX file format.

github.com/jmcnamara/Xlsx…
Jan 20 • 12 tweets • 4 min read
Backtesting options strategies sucks.

• Millions of contracts
• Weekly expirations
• Assignment if ITM

Now, you can backtest options strategies in a few lines of code.

Here’s how (with a link to historical options data): Image Optopsy is an analysis tool for options trading strategies.

• Generates strategies from raw historic data
• Uncover performance statistics for combos
• 12 strategies built in including strangles

We’ll use 5 years of historic options data on RUT for the analysis.

Let’s go!
Jan 10 • 12 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.

Dec 31, 2025 • 19 tweets • 5 min read
17 free Python GitHub repos for quant finance and algo trading: Goldman Sachs

github.com/goldmansachs
Dec 22, 2025 • 21 tweets • 6 min read
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: Image OpenBB Terminal

Terminal for investment research for everyone.

github.com/OpenBB-finance…
Dec 17, 2025 • 17 tweets • 5 min read
13 Python libraries for free market data everyone should know: Theta Data

Real-time and historic, high-resolution, tick data for stocks and options. Theta Data is not free but there is a generous free tier and it's one of the cheapest sources of options data on the market.

thetadata.net
Dec 15, 2025 • 13 tweets • 4 min read
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: Image 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:
Dec 9, 2025 • 22 tweets • 6 min read
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: Image OpenBB

Workspace for investment research for everyone.

github.com/OpenBB-finance…
Dec 2, 2025 • 8 tweets • 2 min read
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: Image 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:
Nov 30, 2025 • 10 tweets • 3 min read
Quants use the volatility surface to price exotic options, calibrate models, and find mispricings.

You can build your own volatility surface with Python: Image First, get the options chain data. Image
Nov 27, 2025 • 7 tweets • 2 min read
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: Image 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. Image