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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.
To keep this list of books handy:

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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

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
Feb 16
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.
We’ll import pandas and OpenBB for data and NumPy for the math. Image
Read 11 tweets
Feb 7
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
Next, we configure the language model and set it up to work with tokenization. Image
Read 7 tweets
Feb 5
10 free Python PDF ebooks for download: Image
Image
Image
Image
Python Data Science Handbook

github.com/terencetachion…Image
Read 13 tweets
Feb 3
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 9 tweets
Jan 23
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…
xlwings

xlwings is a BSD-licensed Python library that makes it easy to call Python from Excel and vice versa.

github.com/xlwings/xlwings
Read 19 tweets

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