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4 books on AI for Trading [PDF]
Updated: May 10, 2024 | 30 |
Books on AI for Trading provide startups with essential insights into the convergence of artificial intelligence and financial markets, offering a comprehensive foundation. These resources delve into topics such as algorithmic trading, quantitative analysis, market prediction, and risk management, shedding light 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, enabling startups to understand and develop predictive trading systems. Moreover, these books often feature practical examples, case studies, and risk management strategies, allowing startups to learn from successful implementations in the trading world.
1. AI in the Financial Markets: New Algorithms and Solutions
2023 by Federico Cecconi
Can we envision the integration of AI technologies into financial markets to enhance their performance, not just in high-frequency trading or trend analysis, but also in terms of safety, stability, and transparency? This book delves into these questions, emphasizing a practical approach tied to real-world applications. It caters to three primary audiences: (1) professionals in financial markets, including those in banking, insurance, portfolio management, brokerage, risk assessment, investment, and debt management; (2) policymakers and regulators overseeing financial markets, ranging from government officials to legislators; and (3) individuals with an interest in technology, whether for personal or professional reasons, as well as those engaged in innovation and research across public and private sectors.
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2. Python for Algorithmic Trading
2020 by Yves Hilpisch
Algorithmic trading, once reserved for institutional players, has become accessible to small organizations and individual traders through online platforms. Python and its array of powerful packages have become the preferred tools for many traders. In this hands-on book, Yves Hilpisch, the author, guides students, academics, and practitioners on leveraging Python's capabilities in the exciting field of algorithmic trading. This book covers various applications of Python in algorithmic trading, including backtesting trading strategies and interfacing with online trading platforms. Many major buy- and sell-side institutions rely on Python extensively. By delving into systematic approaches to building and deploying automated algorithmic trading strategies, this book empowers readers to level the playing field. Topics covered include establishing a suitable Python environment for algorithmic trading, retrieving financial data from both public and proprietary sources, exploring vectorization techniques for financial analytics using NumPy and pandas, mastering vectorized backtesting of diverse algorithmic trading strategies, generating market predictions through machine learning and deep learning, and implementing automated algorithmic trading strategies with popular platforms like OANDA and FXCM.
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3. Artificial Intelligence in Finance
2020 by Yves Hilpisch
The rapid integration of AI and machine learning is causing a profound transformation across various industries. When these technologies are harnessed alongside the availability of historical and real-time financial data, the financial sector is poised for a significant upheaval. In this pragmatic book, Yves Hilpisch demonstrates how to leverage AI and machine learning to uncover statistical inefficiencies within financial markets and utilize them through algorithmic trading. Offering practical guidance to practitioners, students, and academics in finance and data science, the book provides a wealth of self-contained Python examples, ensuring readers can replicate all the presented results and visuals. Split into five parts, the guide introduces fundamental AI concepts and algorithms, delving into recent advancements on the path toward artificial general intelligence (AGI) and superintelligence (SI). It underscores the enduring impact of data-driven finance, AI, and machine learning on financial theory and practice. Readers will learn to apply neural networks and reinforcement learning to detect statistical market inefficiencies, discover and exploit economic inefficiencies through backtesting and algorithmic trading, and comprehend how AI will reshape competition within the financial sector and the potential implications of a financial singularity.
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4. Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
2018 by Stefan Jansen
Unlock the capabilities of an extensive array of supervised and unsupervised algorithms to uncover signals from diverse data sources and craft robust investment tactics with this book. It guides you through the process of accessing market, fundamental, and alternative data through APIs or web scraping, offering a comprehensive framework for assessing alternative data sources. By following the machine learning (ML) workflow, from designing models and defining loss metrics to parameter optimization and time series performance evaluation, you'll gain proficiency in ML algorithms like Bayesian and ensemble methods, as well as manifold learning techniques. You'll become adept at training and fine-tuning these models using tools such as pandas, statsmodels, scikit-learn, PyMC3, xgboost, lightgbm, and catboost. The book also equips you with the skills to extract meaningful features from text data using spaCy, perform news classification, and assign sentiment scores. Moreover, you'll learn to utilize gensim for topic modeling and word embedding extraction from financial reports. Additionally, you'll construct and assess neural networks, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs), employing Keras and PyTorch to leverage unstructured data for advanced trading strategies.
Download PDF
How to download PDF:
1. Install Google Books Downloader
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. AI in the Financial Markets: New Algorithms and Solutions
2023 by Federico Cecconi
Can we envision the integration of AI technologies into financial markets to enhance their performance, not just in high-frequency trading or trend analysis, but also in terms of safety, stability, and transparency? This book delves into these questions, emphasizing a practical approach tied to real-world applications. It caters to three primary audiences: (1) professionals in financial markets, including those in banking, insurance, portfolio management, brokerage, risk assessment, investment, and debt management; (2) policymakers and regulators overseeing financial markets, ranging from government officials to legislators; and (3) individuals with an interest in technology, whether for personal or professional reasons, as well as those engaged in innovation and research across public and private sectors.
Download PDF
2. Python for Algorithmic Trading
2020 by Yves Hilpisch
Algorithmic trading, once reserved for institutional players, has become accessible to small organizations and individual traders through online platforms. Python and its array of powerful packages have become the preferred tools for many traders. In this hands-on book, Yves Hilpisch, the author, guides students, academics, and practitioners on leveraging Python's capabilities in the exciting field of algorithmic trading. This book covers various applications of Python in algorithmic trading, including backtesting trading strategies and interfacing with online trading platforms. Many major buy- and sell-side institutions rely on Python extensively. By delving into systematic approaches to building and deploying automated algorithmic trading strategies, this book empowers readers to level the playing field. Topics covered include establishing a suitable Python environment for algorithmic trading, retrieving financial data from both public and proprietary sources, exploring vectorization techniques for financial analytics using NumPy and pandas, mastering vectorized backtesting of diverse algorithmic trading strategies, generating market predictions through machine learning and deep learning, and implementing automated algorithmic trading strategies with popular platforms like OANDA and FXCM.
Download PDF
3. Artificial Intelligence in Finance
2020 by Yves Hilpisch
The rapid integration of AI and machine learning is causing a profound transformation across various industries. When these technologies are harnessed alongside the availability of historical and real-time financial data, the financial sector is poised for a significant upheaval. In this pragmatic book, Yves Hilpisch demonstrates how to leverage AI and machine learning to uncover statistical inefficiencies within financial markets and utilize them through algorithmic trading. Offering practical guidance to practitioners, students, and academics in finance and data science, the book provides a wealth of self-contained Python examples, ensuring readers can replicate all the presented results and visuals. Split into five parts, the guide introduces fundamental AI concepts and algorithms, delving into recent advancements on the path toward artificial general intelligence (AGI) and superintelligence (SI). It underscores the enduring impact of data-driven finance, AI, and machine learning on financial theory and practice. Readers will learn to apply neural networks and reinforcement learning to detect statistical market inefficiencies, discover and exploit economic inefficiencies through backtesting and algorithmic trading, and comprehend how AI will reshape competition within the financial sector and the potential implications of a financial singularity.
Download PDF
4. Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
2018 by Stefan Jansen
Unlock the capabilities of an extensive array of supervised and unsupervised algorithms to uncover signals from diverse data sources and craft robust investment tactics with this book. It guides you through the process of accessing market, fundamental, and alternative data through APIs or web scraping, offering a comprehensive framework for assessing alternative data sources. By following the machine learning (ML) workflow, from designing models and defining loss metrics to parameter optimization and time series performance evaluation, you'll gain proficiency in ML algorithms like Bayesian and ensemble methods, as well as manifold learning techniques. You'll become adept at training and fine-tuning these models using tools such as pandas, statsmodels, scikit-learn, PyMC3, xgboost, lightgbm, and catboost. The book also equips you with the skills to extract meaningful features from text data using spaCy, perform news classification, and assign sentiment scores. Moreover, you'll learn to utilize gensim for topic modeling and word embedding extraction from financial reports. Additionally, you'll construct and assess neural networks, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs), employing Keras and PyTorch to leverage unstructured data for advanced trading strategies.
Download PDF
How to download PDF:
1. Install Google Books Downloader
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
