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Jan 24, 2023 11 tweets 3 min read Read on X
7 books for automated trading you should read in 2023:
Algorithmic Trading with Python: Quantitative Methods and Strategy Development

Lessons:

• Modern quant trading methods in Python
• Focus on pandas, numpy, and scikit-learn
Algorithmic Trading with Interactive Brokers (Python and C++)

Lessons:

• Developing applications based on TWS
• Implement full-scale trading systems
Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python

Lessons:

• Fundamentals of algorithmic trading
• Apply algorithms to real market data
Python for Algorithmic Trading: From Idea to Cloud Deployment

Lessons:

• Ways to apply Python to algorithmic trading
• Interacting with online trading platforms.
Machine Learning for Algorithmic Trading

Lessons:

• Leverage ML to design automated trading strategies
• Use pandas, TA-Lib, scikit-learn, TensorFlow, and Backtrader
Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies

Lessons:

• Build and backtest your algorithmic trading strategies
• How to gain a true advantage in the market
Python for Finance and Algorithmic Trading: Machine Learning, Deep Learning, Time Series Analysis, Risk and Portfolio Management, Quantitative Trading

Lessons:

• Connect Python algorithms to MetaTrader 5
• Run the strategies with a demo or live trading account
Reading is foundational to growth and learning.

You can read all these books before July by reading 20 minutes a day.

Make it a priority.
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I post Python code and tools for quant finance at 8:15 am EST and 8:15 pm EST every day.
The FREE 7-day masterclass that will get you up and running with Python for quant finance.

Here's what you get:

• Working code to trade with Python
• Frameworks to get you started TODAY
• Trading strategy formation framework

7 days. Big results.

pythonforquantfinancemasterclass.com

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More from @pyquantnews

Dec 31, 2025
17 free Python GitHub repos for quant finance and algo trading:
OpenBB-finance: Investment Research for Everyone, Anywhere.

github.com/OpenBB-finance…
Read 19 tweets
Dec 22, 2025
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…
PyQL

QuantLib's Python port.

github.com/enthought/pyql
Read 21 tweets
Dec 17, 2025
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
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…
Read 17 tweets
Dec 15, 2025
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:
First, you need data.

Use the OpenBB SDK to get it.

OpenBB is a leading open-source investment research software platform for accessing and analyzing financial market data.

Here’s an intro:
Read 13 tweets
Dec 9, 2025
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…
PyQL

QuantLib's Python port.

github.com/enthought/pyql
Read 22 tweets
Dec 2, 2025
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:
HMMs are particularly useful when dealing with sequential data, where the underlying process is governed by probabilities.
Read 8 tweets

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