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| coinhub ai quantitative trading: AI-Powered Bitcoin Trading Eoghan Leahy, 2024-05-14 Survive and thrive amongst the professional traders using sophisticated cryptocurrency analysis and trading techniques The purpose of this book is to provide a concise yet comprehensive background of some effective methods for analyzing markets and creating fully automated AI-optimized trading systems. The book outlines some easy-to-replicate yet highly effective quant trading techniques that can be used for analyzing asset prices and then apply them to Bitcoin prices, showing how to generate actionable insights from data that can be used to create fully automated trading signals and systems. Big data analytics can be enhanced with artificial intelligence techniques. Back testing and optimization methods are presented with a special emphasis placed on the use of distributed genetic algorithms for parameter optimization. Finally, a case study of a fully automated trend-following trading strategy that leverages artificial intelligence is presented. Bitcoin AlphaBotTM combines human insight with AI-driven optimization to build profit table trend trading strategies. |
| coinhub ai quantitative trading: Cryptoassets Chris Brummer, 2019 Cryptoassets represent one of the most high profile financial products in the world, and fastest growing financial products in history. From Bitcoin, Etherium and Ripple's XRP-so called utility tokens used to access financial services-to initial coin offerings that in 2017 rivalled venture capital in money raised for startups, with an estimated $5.6 billion (USD) raised worldwide across 435 ICOs. All the while, technologists have hailed the underlying blockchain technology for these assets as potentially game changing applications for financial payments and record-keeping. At the same time, cryptoassets have produced considerable controversy. Many have turned out to be lacklustre investments for investors. Others, especially ICOs, have also attracted noticeable fraud, failing firms, and alarming lapses in information-sharing with investors. Consequently, many commentators around the world have pressed that ICO tokens be considered securities, and that concomitant registration and disclosure requirements attach to their sales to the public. This volume assembles an impressive group of scholars, businesspersons and regulators to collectively write on cryptoassets. This volume represents perspectives from across the regulatory ecosystem, and includes technologists, venture capitalists, scholars, and practitioners in securities law and central banking. |
| coinhub ai quantitative trading: Quantitative Finance with R and Cryptocurrencies Dean Fantazzini, 2019-05-20 The main objective of this book is to provide the necessary background to analyze cryptocurrencies markets and prices. To this end, the book consists of three parts: the first one is devoted to cryptocurrencies markets and explains how to retrieve cryptocurrencies data, how to compute liquidity measures with these data, how to calculate bounds for Bitcoin (and cryptocurrencies) fundamental value and how competing exchanges contribute to the price discovery process in the Bitcoin market. The second part is devoted to time series analysis with cryptocurrencies and presents a large set of univariate and multivariate time series models, tests for financial bubbles and explosive price behavior, as well as univariate and multivariate volatility models. The third part focuses on risk and portfolio management with cryptocurrencies and shows how to measure and backtest market risk, how to build an optimal portfolio according to several approaches, how to compute the probability of closure/bankruptcy of a crypto-exchange, and how to compute the probability of death of crypto-assets.All the proposed methods are accompanied by worked-out examples in R using the packages bitcoinFinance and bubble.This book is intended for both undergraduate and graduate students in economics, finance and statistics, financial and IT professionals, researchers and anyone interested in cryptocurrencies financial modelling. Readers are assumed to have a background in statistics and financial econometrics, as well as a working knowledge of R software. |
| coinhub ai quantitative trading: Bitcoin, Blockchain, and Cryptoassets Fabian Schar, Aleksander Berentsen, 2020-09-01 An introduction to cryptocurrencies and blockchain technology; a guide for practitioners and students. Bitcoin and blockchain enable the ownership of virtual property without the need for a central authority. Additionally, Bitcoin and other cryptocurrencies make up an entirely new class of assets that have the potential for fundamental change in the current financial system. This book offers an introduction to cryptocurrencies and blockchain technology from the perspective of monetary economics. |
| coinhub ai quantitative trading: Applied Data Mining for Business and Industry Paolo Giudici, Silvia Figini, 2009-04-15 The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance. |
| coinhub ai quantitative trading: Machine Learning in Finance Matthew F. Dixon, Igor Halperin, Paul Bilokon, 2020-07-01 This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. |
| coinhub ai quantitative trading: Analysis of Categorical Data with R Christopher R. Bilder, Thomas M. Loughin, 2024-07-31 Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The second edition is a substantial update of the first based on the authors’ experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter—group testing and splines. The computing has been completely updated, with the emmeans package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated. Features: Requires no prior experience with R, and offers an introduction to the essential features and functions of R Includes numerous examples from medicine, psychology, sports, ecology, and many other areas Integrates extensive R code and output Graphically demonstrates many of the features and properties of various analysis methods Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study Supplemented by a website with data sets, code, and teaching videos Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise. |
| coinhub ai quantitative trading: ROC Curves for Continuous Data Wojtek J. Krzanowski, David J. Hand, 2009-05-21 Since ROC curves have become ubiquitous in many application areas, the various advances have been scattered across disparate articles and texts. ROC Curves for Continuous Data is the first book solely devoted to the subject, bringing together all the relevant material to provide a clear understanding of how to analyze ROC curves.The fundamenta |
| coinhub ai quantitative trading: Corruption and Fraud in Financial Markets Carol Alexander, Douglas Cumming, 2020-06-22 Identifying malpractice and misconduct should be top priority for financial risk managers today Corruption and Fraud in Financial Markets identifies potential issues surrounding all types of fraud, misconduct, price/volume manipulation and other forms of malpractice. Chapters cover detection, prevention and regulation of corruption and fraud within different financial markets. Written by experts at the forefront of finance and risk management, this book details the many practices that bring potentially devastating consequences, including insider trading, bribery, false disclosure, frontrunning, options backdating, and improper execution or broker-agency relationships. Informed but corrupt traders manipulate prices in dark pools run by investment banks, using anonymous deals to move prices in their own favour, extracting value from ordinary investors time and time again. Strategies such as wash, ladder and spoofing trades are rife, even on regulated exchanges – and in unregulated cryptocurrency exchanges one can even see these manipulative quotes happening real-time in the limit order book. More generally, financial market misconduct and fraud affects about 15 percent of publicly listed companies each year and the resulting fines can devastate an organisation's budget and initiate a tailspin from which it may never recover. This book gives you a deeper understanding of all these issues to help prevent you and your company from falling victim to unethical practices. Learn about the different types of corruption and fraud and where they may be hiding in your organisation Identify improper relationships and conflicts of interest before they become a problem Understand the regulations surrounding market misconduct, and how they affect your firm Prevent budget-breaking fines and other potentially catastrophic consequences Since the LIBOR scandal, many major banks have been fined billions of dollars for manipulation of prices, exchange rates and interest rates. Headline cases aside, misconduct and fraud is uncomfortably prevalent in a large number of financial firms; it can exist in a wide variety of forms, with practices in multiple departments, making self-governance complex. Corruption and Fraud in Financial Markets is a comprehensive guide to identifying and stopping potential problems before they reach the level of finable misconduct. |
| coinhub ai quantitative trading: Forensic Accounting, Global Edition Robert Rufus, Laura Miller, William Hahn, 2015-01-26 For courses in Forensic Accounting As a result of increased litigation and regulatory enforcement, the demand for forensic accountants has never been higher. This area of specialty is considered the top niche market in the accounting profession. The new Forensic Accounting is the first text of its kind to provide a comprehensive view of what forensic accountants actually do and how they do it. With experience as both practitioners and educators, authors Robert Rufus, Laura Miller, and William Hahn offer a unique perspective that bridges the gap between theory and practice. They present concepts in the context of a scientific approach, emphasising critical thinking, reasoning, and problem solving—skills that are useful in a wide variety of academic and professional environments. And because its content is consistent with the AICPA curriculum for the Certified in Financial Forensics (CFF) credential, this text gives your students a head start on the path toward career advancement. Forensic Accounting facilitates an outstanding teaching and learning experience—for you and your students. It will help you to: Introduce the requisite forensic accounting skills: The text identifies a three-layer skill set and provides students instruction in the key areas of forensic accounting expertise. Offer an inside view into forensic accounting practice: Integrated case studies and sample documents give students a glimpse into the actual practice of forensic accounting. Highlight the importance of a scientific approach: The authors explain the benefits of utilising a scientific approach and provide opportunities for students to practice its application. Foster thorough understanding via learning aids: Various tools, throughout the text and at the end of each chapter, support students as they learn and review. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. |
| coinhub ai quantitative trading: Credit Risk Management Tony Van Gestel, Bart Baesens, 2009 This first of three volumes on credit risk management, providing a thorough introduction to financial risk management and modelling. |
| coinhub ai quantitative trading: Advanced Credit Risk Analysis and Management Ciby Joseph, 2013-04-22 Credit is essential in the modern world and creates wealth, provided it is used wisely. The Global Credit Crisis during 2008/2009 has shown that sound understanding of underlying credit risk is crucial. If credit freezes, almost every activity in the economy is affected. The best way to utilize credit and get results is to understand credit risk. Advanced Credit Risk Analysis and Management helps the reader to understand the various nuances of credit risk. It discusses various techniques to measure, analyze and manage credit risk for both lenders and borrowers. The book begins by defining what credit is and its advantages and disadvantages, the causes of credit risk, a brief historical overview of credit risk analysis and the strategic importance of credit risk in institutions that rely on claims or debtors. The book then details various techniques to study the entity level credit risks, including portfolio level credit risks. Authored by a credit expert with two decades of experience in corporate finance and corporate credit risk, the book discusses the macroeconomic, industry and financial analysis for the study of credit risk. It covers credit risk grading and explains concepts including PD, EAD and LGD. It also highlights the distinction with equity risks and touches on credit risk pricing and the importance of credit risk in Basel Accords I, II and III. The two most common credit risks, project finance credit risk and working capital credit risk, are covered in detail with illustrations. The role of diversification and credit derivatives in credit portfolio management is considered. It also reflects on how the credit crisis develops in an economy by referring to the bubble formation. The book links with the 2008/2009 credit crisis and carries out an interesting discussion on how the credit crisis may have been avoided by following the fundamentals or principles of credit risk analysis and management. The book is essential for both lenders and borrowers. Containing case studies adapted from real life examples and exercises, this important text is practical, topical and challenging. It is useful for a wide spectrum of academics and practitioners in credit risk and anyone interested in commercial and corporate credit and related products. |
| coinhub ai quantitative trading: Decision Trees and Random Forests Mark Koning, Chris Smith, 2017-10-04 If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services. They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you. |
| coinhub ai quantitative trading: Credit Risk Models and Management David C. Shimko, 2004 |
| coinhub ai quantitative trading: Quantitative Trading Ernest P. Chan, 2021-07-27 Master the lucrative discipline of quantitative trading with this insightful handbook from a master in the field In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm. You'll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as: Updated back tests on a variety of trading strategies, with included Python and R code examples A new technique on optimizing parameters with changing market regimes using machine learning. A guide to selecting the best traders and advisors to manage your money Perfect for independent retail traders seeking to start their own quantitative trading business, or investors looking to invest in such traders, this new edition of Quantitative Trading will also earn a place in the libraries of individual investors interested in exploring a career at a major financial institution. |
| coinhub ai quantitative trading: Applications of Computational Intelligence in Data-Driven Trading Cris Doloc, 2019-11-05 “Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic. |
| coinhub ai quantitative trading: Machine Learning for Algorithmic Trading Stefan Jansen, 2020-07-31 Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required. |
| coinhub ai quantitative trading: AI-Powered Profits Abebe-Bard Ai Woldemariam, 2024-02-14 AI-Powered Profits: Leveraging Technology in the Crypto Market with Bitcoin Crack the code of the crypto market with the power of AI! Conversational Chat Informative Book (Abe and Gemini) By Abebe-Bard AI Woldemariam (Pen Name) Abebe Gebre Woldemariam (Real Name) Tired of battling the volatile world of Bitcoin and other cryptocurrencies? AI-Powered Profits offers a revolutionary approach, harnessing the power of artificial intelligence to guide your investment decisions and unlock consistent profits. Join Abe and Gemini, an innovative duo, on a journey into the exciting world of AI-powered crypto trading. This engaging, conversational guide explains: The challenges of the crypto market: Understand the inherent volatility and learn how AI can overcome it. AI's game-changing potential: Discover how machine learning, deep learning, and other AI techniques analyze vast data sets, identify hidden patterns, and predict market movements. Specific AI tools and platforms: Explore platforms designed for Bitcoin trading, see real-life success stories, and learn how to choose the right tool for your goals. Balancing AI with human expertise: Understand the limitations of AI and why human judgment remains crucial for success. Building your own AI-powered strategy: Learn valuable tips on backtesting, risk management, and crafting a personalized trading approach. The future of AI in crypto: Dive into the exciting possibilities of advanced portfolio management and personalized recommendations. Bonus Section: Harnessing the Power of AI for Smarter Crypto Trading Go beyond the basics: Deepen your understanding with practical insights on sentiment analysis and automated trading. Learn from the best: Gain inspiration from case studies of successful AI-powered traders. Build your own AI edge: Follow a step-by-step guide to creating a winning strategy. Get ready for the future: Explore the cutting-edge advancements shaping the future of AI-powered crypto trading. AI-Powered Profits is your indispensable guide to navigating the ever-evolving crypto landscape. Whether you're a seasoned investor or a curious newcomer, this book empowers you to make informed decisions, leverage the latest technology, and unlock the full potential of your crypto journey. Disclaimer: Information may not always be accurate or reflect Google's views. Dedication: To Merab Abebe, and all who believe in the power of AI to improve our world. Copyright (c) February 2024 by GEMINI AND ABEBE GEBRE WOLDEMARIAM ( |
| coinhub ai quantitative trading: Artificial Intelligence in Finance Yves Hilpisch, 2020-10-14 The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about |
| coinhub ai quantitative trading: Hands-On AI Trading with Python, QuantConnect and AWS Jiri Pik, 2025-01-29 Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Alpha by Regression: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab. Alpha by PCA: Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM. Alpha by Hidden Markov Models: Predict market volatility regimes and allocate funds accordingly. Alpha by Gaussian Naive Bayes: Predict daily returns of tech stocks using classifiers. Alpha by Support Vector Machine Regression: Forecast Forex pairs' future prices using Support Vector Machines and wavelets. Alpha by Essential Neural Networks: Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs. Alpha by GenAI for Trading: Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications. LLM Real-Trading: Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization. Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch. AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS. |
| coinhub ai quantitative trading: Advanced Techniques For Using AI to Master Crypto Jeffery W Long, 2024-08-21 Advanced Techniques For Using AI to Master Crypto Chapter 1: Introduction to Cryptocurrency Introduction to Cryptocurrency Cryptocurrency has emerged as one of the most revolutionary financial innovations of the 21st century. At its core, cryptocurrency represents digital or virtual currencies that rely on cryptographic principles to secure transactions, regulate issuance, and authenticate transfer of assets. Established in 2009 with the introduction of Bitcoin by an enigmatic figure known as Satoshi Nakamoto, cryptocurrency has since evolved into a multifaceted domain encompassing various aspects of finance and technology. Cryptocurrency owes its existence to the failure and limitations of traditional financial systems. In 2008, the financial crisis exposed the inherent vulnerabilities of centralized banking and the mistrust surrounding monetary institutions. It was against this backdrop that Bitcoin, the first decentralized cryptocurrency, was conceived. By implementing blockchain technology, Bitcoin established the foundation for a new kind of currency free from the control of central authorities and immune to the pitfalls of traditional financial institutions. The impact of cryptocurrencies on modern financial systems cannot be overstated. Cryptocurrencies facilitate peer-to-peer transactions independent of intermediaries, bringing about cost efficiency and speed in transferring funds. As the public becomes increasingly aware of the benefits and potential of cryptocurrencies, the landscape of global finance is undergoing a transformative shift, heralding the advent of a decentralized economy. |
| coinhub ai quantitative trading: Day Trade with AI Shunyu Tang, 2023-08-31 A book where finance theory meets data science. Sound strategies supported by the efficient market theory and behavioral finance. Minimum math for a clear explanation of machine learning and deep learning algorithms for trading. Reusable code snippets for easy deployment in algorithmic trading. A comprehensive hands-on guide to making AI your personal assistant to trading. |
| coinhub ai quantitative trading: High-Performance Algorithmic Trading Using AI Melick R. Baranasooriya, 2024-08-08 DESCRIPTION High-Performance Algorithmic Trading using AI is a comprehensive guide designed to empower both beginners and experienced professionals in the finance industry. This book equips you with the knowledge and tools to build sophisticated, high-performance trading systems. It starts with basics like data preprocessing, feature engineering, and ML. Then, it moves to advanced topics, such as strategy development, backtesting, platform integration using Python for financial modeling, and the implementation of AI models on trading platforms. Each chapter is crafted to equip readers with actionable skills, ranging from extracting insights from vast datasets to developing and optimizing trading algorithms using Python's extensive libraries. It includes real-world case studies and advanced techniques like deep learning and reinforcement learning. The book wraps up with future trends, challenges, and opportunities in algorithmic trading. Become a proficient algorithmic trader capable of designing, developing, and deploying profitable trading systems. It not only provides theoretical knowledge but also emphasizes hands-on practice and real-world applications, ensuring you can confidently navigate and leverage AI in your trading strategies. KEY FEATURES ● Master AI and ML techniques to enhance algorithmic trading strategies. ● Hands-on Python tutorials for developing and optimizing trading algorithms. ● Real-world case studies showcasing AI applications in diverse trading scenarios. WHAT YOU WILL LEARN ● Develop AI-powered trading algorithms for enhanced decision-making and profitability. ● Utilize Python tools and libraries for financial modeling and analysis. ● Extract actionable insights from large datasets for informed trading decisions. ● Implement and optimize AI models within popular trading platforms. ● Apply risk management strategies to safeguard and optimize investments. ● Understand emerging technologies like quantum computing and blockchain in finance. WHO THIS BOOK IS FOR This book is for financial professionals, analysts, traders, and tech enthusiasts with a basic understanding of finance and programming. TABLE OF CONTENTS 1. Introduction to Algorithmic Trading and AI 2. AI and Machine Learning Basics for Trading 3. Essential Elements in AI Trading Algorithms 4. Data Processing and Analysis 5. Simulating and Testing Trading Strategies 6. Implementing AI Models with Trading Platforms 7. Getting Prepared for Python Development 8. Leveraging Python for Trading Algorithm Development 9. Real-world Examples and Case Studies 10. Using LLMs for Algorithmic Trading 11. Future Trends, Challenges, and Opportunities |
| coinhub ai quantitative trading: The Quant Trader's Handbook Josh Luberisse, In The Quant Trader's Handbook, Josh masterfully navigates the intricate world of algorithmic trading, shedding light on its various complexities and revealing the secrets that drive the success of some of the most prominent quantitative hedge funds and traders. Through a blend of captivating storytelling and rigorous analysis, this guide offers readers an unparalleled opportunity to delve into the mechanics of quantitative trading, exploring the strategies, technologies, and practices that have transformed the financial landscape. As modern markets continue to be shaped by the silent precision of algorithms, it becomes essential for traders and investors to understand the underlying mechanics that drive these systems. This book promises to immerse its readers in the rich tapestry of the algorithmic trading realm, stretching from its nascent beginnings in the 1970s to the AI-integrated strategies of the 21st century. Inside, you'll embark on a chronological journey starting with the pioneering days of electronic stock markets and culminating in the sophisticated high-frequency trading systems of today. Alongside this, Josh takes you through the ins and outs of popular quantitative trading strategies, illustrated with intuitive pseudocode examples, like the Moving Average Crossover and the Pair Trading Strategy, ensuring even those new to the domain can grasp the nuances. But this isn't just a book about code and numbers. The Quant Trader's Handbook paints the bigger picture. With detailed network diagrams, you'll gain insights into the architectural complexity and beauty of modern trading systems, understanding how various components seamlessly intertwine to make real-time decisions in the blink of an eye. As you embark on this journey with Josh, you'll discover the foundational concepts of algorithmic trading, unravel the mysteries of quantitative analysis and modeling, and gain valuable insights into the inner workings of execution and order management. From the depths of data mining techniques to the heights of infrastructure and technology, each chapter is meticulously crafted to provide a thorough understanding of the various aspects that contribute to a successful algorithmic trading business. In addition to its wealth of practical knowledge, The Quant Trader's Handbook also delves into the regulatory and compliance considerations that are essential for navigating today's financial markets. With a keen eye for detail and a remarkable ability to contextualize even the most technical topics, Josh brings to life the fascinating stories of industry giants like Renaissance Technologies, DE Shaw, and Two Sigma, painting a vivid picture of the rise of quantitative finance. Whether you're an aspiring quant looking to make your mark in the world of finance, an investor trying to demystify the black box of algorithmic trading, or merely a curious soul eager to understand how bits and bytes are silently shaping the financial world, The Quant Trader's Handbook is an indispensable resource that will captivate, inform, and inspire you. Join Josh as he unravels the secrets of the world's most successful traders and embark on a journey that may just change the way you see the markets forever. |
| coinhub ai quantitative trading: Unveiling the Future of Finance Abebe-Bard Ai Woldemariam, 2024-05-19 Unveiling the Future of Finance: A Journey from Algorithmic Trading to Quantum Strategies By Abebe-Bard AI Woldemariam Are you ready to unlock the secrets of the financial future? This book takes you on a captivating exploration of the ever-evolving world of finance. We begin by demystifying the algorithmic trading strategies that have made hedge funds like Renaissance Technologies famous (The Quant Mecca). Next, we delve deep into the inner workings of The Quantitative Trading Engine, exposing the intricate logic and data analysis that power these algorithms. But our journey doesn't stop there. We explore the cutting-edge technologies like artificial intelligence and blockchain that are poised to revolutionize finance in Beyond the Horizon. Finally, we reach the true frontier: Unveiling the Future of Finance: Quantum Algorithmic Trading. Here, we explore the mind-bending potential of quantum computing to transform the way we analyze markets, optimize portfolios, and unlock entirely new investment strategies. Whether you're a seasoned investor or simply curious about the future of finance, this book is your comprehensive roadmap. It equips you with the knowledge to navigate the exciting and potentially lucrative landscape that lies ahead. |
| coinhub ai quantitative trading: Machine Trading Ernest P. Chan, 2017-02-06 Dive into algo trading with step-by-step tutorials and expert insight Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level. Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools. Utilize the newer, easier algorithmic trading platforms Access markets previously unavailable to systematic traders Adopt new strategies for a variety of instruments Gain expert perspective into the human side of trading The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation; decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions. |
| coinhub ai quantitative trading: Hands-On Deep Learning for Finance Luigi Troiano, Pravesh Kriplani, Elena Mejuto Villa, 2020-02-28 |
| coinhub ai quantitative trading: Alpha Machines: Inside the AI-Driven Future of Finance Gaurav Garg, The world of finance has been transformed by the emergence of artificial intelligence and machine learning. Advanced algorithms are now routinely applied across the industry for everything from high frequency trading to credit risk modeling. Yet despite its widespread impact, AI trading remains an often misunderstood field full of misconceptions. This book aims to serve as an accessible introduction and guide to the real-world practices, opportunities, and challenges associated with applying artificial intelligence to financial markets. Across different chapters, we explore major applications of AI in algorithmic trading, common technologies and techniques, practical implementation considerations, and case studies of successes and failures. Key topics covered include data analysis, feature engineering, major machine learning models, neural networks and deep learning, natural language processing, reinforcement learning, portfolio optimization, algorithmic trading strategies, backtesting methods, and risk management best practices when deploying AI trading systems. Each chapter provides sufficient technical detail for readers new to computer science and machine learning while emphasizing practical aspects relevant to practitioners. Code snippets and mathematical derivations illustrate key concepts. Significant attention is dedicated to real-world challenges, risks, regulatory constraints, and procedures required to operationalize AI in live trading. The goal is to provide readers with an accurate picture of current best practices that avoids overstating capabilities or ignoring pitfalls. Ethics and responsible AI development are highlighted given societal impacts. Ultimately this book aims to dispel myths, ground discussions in data-driven evidence, and present a balanced perspective on leveraging AI safely and effectively in trading. Whether an experienced practitioner looking to enhance trading strategies with machine learning or a curious student interested in exploring this intriguing field, readers across backgrounds will find an accessible synthesis of core topics and emerging developments in AI-powered finance. The book distills decades of research and industry lessons into a compact guide. Complimented by references for further reading, it serves as a valuable launchpad for readers seeking to gain a holistic understanding of this future-oriented domain at the nexus of computing and financial markets. |
| coinhub ai quantitative trading: AI and Financial Markets Shigeyuki Hamori, Tetsuya Takiguchi, 2020-07-01 Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets. |
| coinhub ai quantitative trading: Artificial Intelligence in Finance Yves Hilpisch, 2020-11-10 Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic availability of historical and real-time financial data in combination with techniques from AI and machine learning will also change the financial industry in a fundamental way. This practical book explains how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science how machine and deep learning algorithms can be applied to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. Examine how data is reshaping finance from a theory-driven to a data-driven discipline Understand the major possibilities, consequences, and resulting requirements of AI-first finance Get up to speed on the tools, skills, and major use cases to apply AI in finance yourself Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Delve into the concepts of the technological singularity and the financial singularity |
| coinhub ai quantitative trading: The Future of AI-Driven Crypto Investing Herman Strange, 2023-06-08 Are you tired of using outdated trading strategies and feeling like you're always one step behind in the fast-paced world of cryptocurrency investing? Look no further than The Future of AI-Driven Crypto Investing: Advanced Strategies for Building and Deploying AI Trading Systems. This comprehensive guide offers insights and practical advice on how to leverage the power of artificial intelligence to revolutionize your investment approach. With a focus on advanced strategies for building and deploying AI trading systems, this book will teach you how to stay ahead of the curve and make smarter, data-driven investment decisions. From algorithmic trading strategies to reinforcement learning and predictive modeling for cryptocurrency prices, this book covers all the essential topics you need to know to take your crypto investment game to the next level. You'll learn how to incorporate alternative data sources into your risk management and portfolio optimization strategies, as well as how to use AI to optimize mining operations and improve efficiency. But it doesn't stop there. The book also provides a step-by-step guide to building your own AI-driven crypto trading system, complete with tips and best practices for choosing the right AI tools and technologies for your system. With this knowledge, you'll be able to confidently navigate the complex world of cryptocurrency investing and make informed decisions that drive your success. Of course, with any investment strategy, there are challenges and limitations to consider. That's why the book also provides insights into the potential risks and drawbacks of AI-driven investing, as well as advice on how to mitigate those risks. The future of cryptocurrency investing is here, and it's time to embrace it. Whether you're a seasoned investor or just getting started, The Future of AI-Driven Crypto Investing is a must-read for anyone looking to stay ahead of the curve and achieve success in this rapidly evolving market. Don't miss out on this opportunity to transform your investment approach and reach new heights in your crypto investment journey. |
| coinhub ai quantitative trading: Algorithmic Trading Methods Robert Kissell, 2020-09-08 Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. - Provides insight into all necessary components of algorithmic trading including: transaction cost analysis, market impact estimation, risk modeling and optimization, and advanced examination of trading algorithms and corresponding data requirements - Increased coverage of essential mathematics, probability and statistics, machine learning, predictive analytics, and neural networks, and applications to trading and finance - Advanced multiperiod trade schedule optimization and portfolio construction techniques - Techniques to decode broker-dealer and third-party vendor models - Methods to incorporate TCA into proprietary alpha models and portfolio optimizers - TCA library for numerous software applications and programming languages including: MATLAB, Excel Add-In, Python, Java, C/C++, .Net, Hadoop, and as standalone .EXE and .COM applications |
| coinhub ai quantitative trading: AI in the Financial Markets Federico Cecconi, 2023-03-24 This book is divided into two parts, the first of which describes AI as we know it today, in particular the Fintech-related applications. In turn, the second part explores AI models in financial markets: both regarding applications that are already available (e.g. the blockchain supply chain, learning through big data, understanding natural language, or the valuation of complex bonds) and more futuristic solutions (e.g. models based on artificial agents that interact by buying and selling stocks within simulated worlds). The effects of the COVID-19 pandemic are starting to show their financial effects: more companies in a liquidity crisis; more unstable debt positions; and more loans from international institutions for states and large companies. At the same time, we are witnessing a growth of AI technologies in all fields, from the production of goods and services, to the management of socio-economic infrastructures: in medicine, communications, education, and security. The question then becomes: could we imagine integrating AI technologies into the financial markets, in order to improve their performance? And not just limited to using AI to improve performance in high-frequency trading or in the study of trends. Could we imagine AI technologies that make financial markets safer, more stable, and more comprehensible? The book explores these questions, pursuing an approach closely linked to real-world applications. The book is intended for three main categories of readers: (1) management-level employees of companies operating in the financial markets, banks, insurance operators, portfolio managers, brokers, risk assessors, investment managers, and debt managers; (2) policymakers and regulators for financial markets, from government technicians to politicians; and (3) readers curious about technology, both for professional and private purposes, as well as those involved in innovation and research in the private and public spheres. |
| coinhub ai quantitative trading: Detecting Regime Change in Computational Finance Jun Chen, Edward P K Tsang, 2020-09-14 Based on interdisciplinary research into Directional Change, a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction (zigzags). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002. |
| coinhub ai quantitative trading: An Introduction To Machine Learning In Quantitative Finance Hao Ni, Xin Dong, Jinsong Zheng, Guangxi Yu, 2021-04-07 In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data! |
| coinhub ai quantitative trading: The Predictive Edge Alejandro Lopez-Lira, 2024-07-11 Use ChatGPT to improve your analysis of stock markets and securities In The Predictive Edge: Outsmart the Market Using Generative AI and ChatGPT in Financial Forecasting, renowned AI and finance researcher Dr. Alejandro Lopez-Lira delivers an engaging and insightful new take on how to use large language models (LLMs) like ChatGPT to find new investment opportunities and make better trading decisions. In the book, you’ll learn how to interpret the outputs of LLMs to craft sounder trading strategies and incorporate market sentiment into your analyses of individual securities. In addition to a complete and accessible explanation of how ChatGPT and other LLMs work, you’ll find: Discussions of future trends in artificial intelligence and finance Strategies for implementing new and soon-to-come AI tools into your investing strategies and processes Techniques for analyzing market sentiment using ChatGPT and other AI tools A can’t-miss playbook for taking advantage of the full potential of the latest AI advancements, The Predictive Edge is a fully to-date and exciting exploration of the intersection of tech and finance. It will earn a place on the bookshelves of individual and professional investors everywhere. |
| coinhub ai quantitative trading: Mastering AI-Powered Trading Bots for Options: Jeffery Long, 2024-08-15 Mastering AI-Powered Trading Bots for Options Analyzing Large Amounts of Data Faster Than Humans Can Read AI can help traders make more informed decisions by analyzing large amounts of data and identifying patterns that humans may miss. Some ways AI can help in trading options include: 1. Predictive analytics: AI algorithms can analyze historical market data and predict future price movements, helping traders make more accurate decisions on which options to buy. 2. Sentiment analysis: AI can analyze news articles, social media posts, and other sources of information to gauge market sentiment and identify potential trading opportunities. 3. Risk management: AI can help traders manage risk by analyzing their portfolio and identifying potential risks and opportunities for hedging. 4. Automation: AI can automate the trading process, executing trades based on predetermined criteria and removing human emotion from the decision-making process. 5. Machine learning: AI can continuously learn from past trading data and optimize trading strategies over time, adapting to changing market conditions and improving performance. Overall, AI can help traders make more informed decisions, reduce risk, and potentially increase returns when trading options. Chapter 1: Introduction to AI and Option Trading Welcome to the exciting world of AI-powered trading bots for executing options trades. In this subchapter, we will explore the fundamentals of AI and option trading, providing you with a solid foundation to begin your journey into the world of trading stocks and options. Whether you are a novice trader looking to learn the basics or an experienced investor seeking to leverage the power of AI technology in your trading strategies, this subchapter is designed to help you understand the key concepts and principles that drive success in the world of option trading. First and foremost, it is important to understand what AI is and how it is revolutionizing the way we approach financial markets. Artificial intelligence, or AI, refers to the simulation of human intelligence processes by machines, particularly computer systems. In the context of option trading, AI can be used to analyze vast amounts of data, identify patterns and trends, and make informed decisions about when to buy or sell options. By harnessing the power of AI technology, traders can gain a competitive edge in the market and increase their chances of success. |
| coinhub ai quantitative trading: Quantitative Trading Ernie Chan, 2009-01-12 While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is yes, and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent retail trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed. |
| coinhub ai quantitative trading: TRADING STRATEGIES WITH ARTIFICIAL INTELLIGENCE Marcel Souza, In today's rapidly evolving financial markets, artificial intelligence (AI) is becoming an indispensable tool for traders looking to stay ahead of the curve. Trading Strategies with Artificial Intelligence is an essential guide for both beginner and advanced traders who are eager to explore the power of AI to enhance their trading performance. This book demystifies complex AI concepts and demonstrates how they can be applied to create profitable trading strategies in a variety of market conditions. Readers will learn how AI algorithms, such as machine learning, deep learning, and neural networks, are transforming traditional trading approaches. Through detailed explanations and real-world case studies, this book offers a comprehensive understanding of how AI is used to analyze vast amounts of market data, detect patterns, and make precise predictions that human traders alone cannot achieve. Whether you're trading stocks, forex, or cryptocurrencies, these AI-driven strategies can optimize your decisions and increase your profitability. The book provides practical examples of AI-powered trading systems, complete with step-by-step instructions for developing your own algorithms. With insights into backtesting, risk management, and automated execution, readers will gain hands-on experience in building and deploying AI trading models. The strategies outlined in this book are designed to adapt to different market conditions, ensuring traders remain competitive even in the most volatile environments. Trading Strategies with Artificial Intelligence is more than just a technical manual; it explores the future of trading and the ethical considerations of using AI in financial markets. From regulatory challenges to the impact on market efficiency, this book encourages traders to think critically about the implications of AI-driven trading. If you're ready to take your trading to the next level and harness the full potential of AI, this book is your roadmap to success. |
| coinhub ai quantitative trading: Alternative Data and Artificial Intelligence Techniques Qingquan Tony Zhang, Beibei Li, Danxia Xie, 2022-10-31 This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation. |
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