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PyQuant News 🐍 Profile picture
@pyquantnews
Jan 24, 2023 • 11 tweets • 3 min read • Read on X
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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
May 8
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.
Large Information Sets

The vast scope of information influencing market prices, highlights the need for complex models.
Read 11 tweets
PyQuant News 🐍 Profile picture
May 4
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…
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 13 tweets
PyQuant News 🐍 Profile picture
May 3
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
Algorithmic Trading with Interactive Brokers by Matthew Scarpino

Introduces readers to algorithmic trading through Interactive Brokers' Trader Workstation (TWS) programming interface.

• Guide to using IB TWS API
• Focuses on Python and C++
• No prior experience needed Image
Read 11 tweets
PyQuant News 🐍 Profile picture
Apr 26
17 Python GitHub repos for quant finance:
Goldman Sachs

github.com/goldmansachs
OpenBB-finance: Investment Research for Everyone, Anywhere.

github.com/OpenBB-finance…
Read 19 tweets
PyQuant News 🐍 Profile picture
Apr 13
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!
First, import the libraries we need.

Optopsy is light with very few dependencies. Image
Read 6 tweets
PyQuant News 🐍 Profile picture
Mar 14
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!
You’ll need an OpenAI API key to run this code. Once you have it, create a .env file in your working directly. Add the following line:

OPENAI_API_KEY=YOUR_API_KEY

Once that's done, move on:
Read 8 tweets

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