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3 books on AI for Trading [PDF]
July 28, 2025 | 30 |
These books explain such topics as algorithmic trading, quantitative analysis, market prediction and risk management and focus on how AI can enhance trading strategies and decision-making. They also explore various machine learning techniques and quantitative models used in the financial industry.
1. Hands-On AI Trading with Python, QuantConnect, and AWS
2025 by Jiri Pik, Ernest P. Chan, Jared Broad, Philip Sun, Vivek Singh
This book is written for students interested in finance and professional traders looking to enrich their strategy with artificial intelligence algorithms. The book requires basic knowledge of Python, including pandas, numpy and sci-kit libraries. It provides an overview of key ML algorithms, methods and best practices used in modern financial trading. Unlike other books on trading, this book does not focus on setting up market data, backtesting and trading infrastructure. Such setups are usually complex, expensive and require significant investment. Instead, the book relies on using QuantConnect, an open-source platform that allows you to create trading algorithms and test hypotheses using market data. Also, the book does not focus on the complex details of training large language models or advanced financial ML models. Instead, it relies on already trained models such as AWS Bedrock, MLFinLab and PredictNow.
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2. AI-Powered Bitcoin Trading: Developing an Investment Strategy with Artificial Intelligence
2024 by Eoghan Leahy
The author of this book focuses on the fact that when it comes to using AI for trading, you should clearly distinguish between forecasting and classification tasks. AI copes well with the first task. For example, it can be used to classify whether the market is in an uptrend or a downtrend. Research has shown that AI does this with a high degree of success (in the short term). But forecasting is a more difficult task. The reason for this is the huge number of potential variables that can affect market prices. Moreover, stock market prices are the result of speculation. Different traders process a huge amount of heterogeneous information differently depending on their preferences, emotions and tactics. Prices in financial markets are the result of their collective play. Sharp price changes occur when unexpected information or events in the real world appear. By their nature, these factors are exogenous to the input parameters of the neural network. This is why it is so difficult to create price forecasting models, despite the rapid pace of innovation in the field of AI.
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3. AI in the Financial Markets: New Algorithms and Solutions
2023 by Federico Cecconi
Federico Cecconi, R&D manager of QBT Sagl, decided to explore the questions: How can AI be useful in the context of credit markets? What are the benefits of applying AI in finance? In short, his thinking is as follows: AI is a technology based on the idea that a machine can imitate the human brain and that, given enough data, it can learn to think like a human. As a result, we can apply artificial intelligence to understand financial processes and predict future trends. A machine can also be trained to recognize patterns in the dynamics of buying behavior and use this knowledge to predict future prices. As a result, ML can help in predicting market closing prices, market opening prices and give the opportunity to make more profitable trades. Also, AI can be used to improve customer service in financial institutions. For example, it can be used to help customers with their inquiries about account operations or market analysis. In addition, AI can help in reducing the cost of financial management. It can be used to automate processes and improve the operational efficiency of financial institution.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Hands-On AI Trading with Python, QuantConnect, and AWS
2025 by Jiri Pik, Ernest P. Chan, Jared Broad, Philip Sun, Vivek Singh
This book is written for students interested in finance and professional traders looking to enrich their strategy with artificial intelligence algorithms. The book requires basic knowledge of Python, including pandas, numpy and sci-kit libraries. It provides an overview of key ML algorithms, methods and best practices used in modern financial trading. Unlike other books on trading, this book does not focus on setting up market data, backtesting and trading infrastructure. Such setups are usually complex, expensive and require significant investment. Instead, the book relies on using QuantConnect, an open-source platform that allows you to create trading algorithms and test hypotheses using market data. Also, the book does not focus on the complex details of training large language models or advanced financial ML models. Instead, it relies on already trained models such as AWS Bedrock, MLFinLab and PredictNow.
Download PDF
2. AI-Powered Bitcoin Trading: Developing an Investment Strategy with Artificial Intelligence
2024 by Eoghan Leahy
The author of this book focuses on the fact that when it comes to using AI for trading, you should clearly distinguish between forecasting and classification tasks. AI copes well with the first task. For example, it can be used to classify whether the market is in an uptrend or a downtrend. Research has shown that AI does this with a high degree of success (in the short term). But forecasting is a more difficult task. The reason for this is the huge number of potential variables that can affect market prices. Moreover, stock market prices are the result of speculation. Different traders process a huge amount of heterogeneous information differently depending on their preferences, emotions and tactics. Prices in financial markets are the result of their collective play. Sharp price changes occur when unexpected information or events in the real world appear. By their nature, these factors are exogenous to the input parameters of the neural network. This is why it is so difficult to create price forecasting models, despite the rapid pace of innovation in the field of AI.
Download PDF
3. AI in the Financial Markets: New Algorithms and Solutions
2023 by Federico Cecconi
Federico Cecconi, R&D manager of QBT Sagl, decided to explore the questions: How can AI be useful in the context of credit markets? What are the benefits of applying AI in finance? In short, his thinking is as follows: AI is a technology based on the idea that a machine can imitate the human brain and that, given enough data, it can learn to think like a human. As a result, we can apply artificial intelligence to understand financial processes and predict future trends. A machine can also be trained to recognize patterns in the dynamics of buying behavior and use this knowledge to predict future prices. As a result, ML can help in predicting market closing prices, market opening prices and give the opportunity to make more profitable trades. Also, AI can be used to improve customer service in financial institutions. For example, it can be used to help customers with their inquiries about account operations or market analysis. In addition, AI can help in reducing the cost of financial management. It can be used to automate processes and improve the operational efficiency of financial institution.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
