Leveraging AI can help improve operational efficiency and decision-making. AI can help with risk assessment, sentiment analysis of real-time news streams, optimizing asset allocation based on risk tolerance, and many more use cases. But where does anyone start to learn about all this?!? This is one of the most common questions I get. So, I'm curating a list of resources that folks, new or experienced, might find useful in the journey. Since finance is a very broad field, let's focus our tools on trading, which is one of the most popular use cases.

Theory/Research Papers

For the curious who want to understand why some of these techniques work and the prior art related to the topics.

• FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance: Introduces a deep reinforcement learning (DRL) library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.

• Machine learning for trading: With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case.

• Financial Trading as a Game: The effectiveness of applying deep reinforcement learning algorithms to the financial trading domain.

• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem: Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine-learning solution to the portfolio management problem.

• Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy: An ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return.

Data Sets

Because you really can't learn to explore much without proper data!

• NASDAQ: Access millions of financial and economic data points from hundreds of publishers via the Nasdaq Data Link suite of APIs.

• Yahoo Finance: One of the most popular data sources, the link has a Python module to get stock data from Yahoo! Finance.

Existing Libraries/Strategies

• stockpredictionai: This is a course in itself! A complete process for predicting stock price movements.

• Personae: Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.

• Ensemble-Strategy: Deep Reinforcement Learning for Automated Stock Trading.

• FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. If you haven’t already, check out the linked paper in the theory/research section to understand the FinRL architecture.

• Asset News Sentiment Analyzer - Sentiment analysis and report generation for financial assets and securities utilizing GPT models.

Courses

If the above list has piqued your interest, there are also some good online courses that you can follow.

• Coursera: Machine Learning and Reinforcement Learning in Finance Specialization:  Extend your expertise in algorithms and tools needed to predict financial markets.

  • Since it’s a four part series, this is the most relevant: Overview of Advanced Methods of Reinforcement Learning in Finance.

• Udacity: AI for trading: Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization.

• Quantopian: - Webinars about Machine Learning for trading.

• QuantInsti Quantitative Learning - Videos in Quantitative finance & Algorithmic trading.

• AI in finance: Understand the foundations and applications of AI technology as well as the opportunities and risks associated with it.

Community

• Financial Machine Learning and Data Science: A curated list of practical financial machine learning (FinML) tools and applications

LLMs

Since LLMs are a hot topic, we obviously have to mention some material related to it!

• Financial Statement Analysis with Large Language Models - Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes.

• FinGPT: Open-Source Financial Large Language Models: A lot of resources, including papers, analysis and open source hugging face models for LLM in finance! I would categorize this as a community!

• Hands-on LLMs: Train and Deploy a Real-time Financial Advisor: Hands on LLMs course to Train and Deploy a Real-Time Financial Advisor.

Disclaimer: All investments and trading involve risk. The above list is for educational purpose only.  Users should perform their own due diligence before making any trading decisions.