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Jan 24 • 11 tweets • 3 min read
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.
To keep this list of books handy:

1. Click the link below to hop to the top tweet
2. Retweet it so it stays on your timeline

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

PyQuant News 🐍 Profile picture
Jan 25
Every Python quant should have a library of analysis tools.

The problem?

Most people don’t know where to begin.

Here’s a quick walkthrough of the Kalman filter that might help.

↓
Download stock price data with the OpenBB SDK. Image
Create the filter and estimate the state means. Image
Read 6 tweets
PyQuant News 🐍 Profile picture
Jan 24
There is $482,000,000,000 invested in factor ETFs.

Factors can help you diversify risk and drive returns.

Unfortunately, most people don’t know how.

Until now.

Here’s a step-by-step guide for doing it 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…
A quick primer on factor investing:

• Used to target specific return drivers
• Helps manage risk outside diversification
• Important for active managers that get paid for performance

You can use the famous Fama-French 3-factor model for free.

Here’s how:
Read 16 tweets
PyQuant News 🐍 Profile picture
Jan 23
Trading the wrong strategy is a recipe for losing money.

Pick the right strategy for the market.

Use the Hurst exponent to help.

Here’s how with Python:
Get data with the OpenBB SDK.

I use 20 years for S&P 500 prices.
Calculate the Hurst exponent.

If it’s confusing, check out the recent newsletter for details (link at the bottom).
Read 5 tweets
PyQuant News 🐍 Profile picture
Jan 22
Most algorithmic traders only focus on the trade signal.

Then they wonder why they lose money.

It's not the signal that's most important.

It's the filter.

Here are 9 of the most popular filters everyone should know (with Python code):
Moving average filter

Uses a moving average of the data points to smooth out short-term fluctuations and highlight long-term trends.
Exponential smoothing filter

Gives more weight to recent data points, making it more responsive to changes in the underlying signal.
Read 13 tweets
PyQuant News 🐍 Profile picture
Jan 19
Missles, 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:
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
PyQuant News 🐍 Profile picture
Jan 18
7 blogs to help you generate trading ideas:
Hudson & Thames

Hudson & Thames is an engineering company that builds out quant python libraries consisting of the top algorithms found in the academic literature.

hudsonthames.org/research/
Quantstart

Learn systematic trading techniques to automate your trading, manage your risk and grow your account.

quantstart.com/articles/
Read 11 tweets

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