Robo Advisor With Python

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Robo-Advisor with Python

Author : Aki Ranin
Publisher : Packt Publishing Ltd
ISBN 13 : 1801819009
Total Pages : 250 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Robo-Advisor with Python by : Aki Ranin

Download or read book Robo-Advisor with Python written by Aki Ranin and published by Packt Publishing Ltd. This book was released on 2023-02-28 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your own robo-advisor in Python to manage your investments and get up and running in no time Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesExplore the use cases, workflow, and features that make up robo-advisorsLearn how to build core robo-advisor capabilities for goals, risk questions, portfolios, and projectionsDiscover how to operate the automated processes of a built and deployed robo-advisorBook Description Robo-advisors are becoming table stakes for the wealth management industry across all segments, from retail to high-net-worth investors. Robo-advisors enable you to manage your own portfolios and financial institutions to create automated platforms for effective digital wealth management. This book is your hands-on guide to understanding how Robo-advisors work, and how to build one efficiently. The chapters are designed in a way to help you get a comprehensive grasp of what Robo-advisors do and how they are structured with an end-to-end workflow. You'll begin by learning about the key decisions that influence the building of a Robo-advisor, along with considerations on building and licensing a platform. As you advance, you'll find out how to build all the core capabilities of a Robo-advisor using Python, including goals, risk questionnaires, portfolios, and projections. The book also shows you how to create orders, as well as open accounts and perform KYC verification for transacting. Finally, you'll be able to implement capabilities such as performance reporting and rebalancing for operating a Robo-advisor with ease. By the end of this book, you'll have gained a solid understanding of how Robo-advisors work and be well on your way to building one for yourself or your business. What you will learnExplore what Robo-advisors do and why they existCreate a workflow to design and build a Robo-advisor from the bottom upBuild and license Robo-advisors using different approachesOpen and fund accounts, complete KYC verification, and manage ordersBuild Robo-advisor features for goals, projections, portfolios, and moreOperate a Robo-advisor with P&L, rebalancing, and fee managementWho this book is for If you are a finance professional or a data professional working in wealth management and are curious about how robo-advisors work, this book is for you. It will be helpful to have a basic understanding of Python and investing concepts. This is a great handbook for developers interested in building their own robo-advisor to manage personal investments or build a platform for their business to operate, as well as for product managers and business leaders in financial services looking to lease, buy, or build a robo-advisor.

Build a Robo Advisor with Python (From Scratch)

Author : Rob Reider
Publisher : Manning
ISBN 13 : 9781633439672
Total Pages : 0 pages
Book Rating : 4.4/5 (396 download)

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Book Synopsis Build a Robo Advisor with Python (From Scratch) by : Rob Reider

Download or read book Build a Robo Advisor with Python (From Scratch) written by Rob Reider and published by Manning. This book was released on 2024-01-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take control of your wealth management by building your own reliable, effective, and automated financial advisor tool. Summary In Build a Robo Advisor with Python (From Scratch) you’ll learn how to: Measure returns and estimate the benefits of robo advisors Use Monte Carlo simulations to build and test financial planning tools Construct diversified, efficient portfolios using optimization and other advanced methods Implement and evaluate rebalancing methods to track a target portfolio over time Decrease taxes through tax-loss harvesting and optimized withdrawal sequencing Use reinforcement learning to find the optimal investment path up to, and after, retirement Every day automated digital advisors, also called robo advisors, make financial decisions worth millions of dollars. Build a Robo Advisor with Python (From Scratch): Automate your financial and investment decisions teaches you how to construct a Python-based financial advisor of your very own! You’ll develop a flexible tool that’s capable of managing a real investing strategy—all with popular free Python libraries. About the technology Automated “robo advisors” are commonplace in financial services, thanks to their ability to give high-quality investment advice at a fraction of the cost of human advisors. Your own robo advisor will be a real asset for your financial planning, whether you’re saving for retirement, creating a diversified portfolio, or trying to ensure your tax efficiency. About the book In Build a Robo Advisor with Python (From Scratch), you’ll design and develop a working financial advisor that can manage a real investing strategy. You’ll add new features to your advisor chapter-by-chapter, including determining the optimal weight of cryptocurrency in your portfolio, rebalancing to keep your investments on target while minimizing taxes, and using reinforcement learning to find a “glide path” that can maximize how long your money will last in retirement. Best of all, the skills you learn in reinforcement learning, convex optimization, and Monte Carlo methods can be applied to numerous lucrative fields beyond the domain of finance. About the reader The book is accessible to anyone with a basic knowledge of Python and finance—no special skills required. About the author Rob Reider has been a quantitative hedge fund portfolio manager for over 15 years. He holds a PhD in Finance from The Wharton School and is an Adjunct Professor at NYU, where he teaches a graduate course in the Math-Finance department called “Time Series Analysis and Statistical Arbitrage.” He has built asset allocation models, financial planning tools, and optimal tax strategies for a robo advisor. Rob has given numerous lectures that combine Python with finance, as well as developing an online course entitled “Time Series Analysis in Python.” As a hedge fund manager, Rob has been involved in all aspects of the investment process, from discovering new trading strategies to backtesting, executing, and managing the risk. Alex Michalka has worked in finance and technology since 2006. He began his career developing weather derivative pricing models at Weatherbill, spent six years conducting research on quantitative equity portfolio construction at AQR Capital Management, and currently leads the investments research group at Wealthfront. He holds a BA in applied mathematics from UC Berkeley and a PhD in operations research from Columbia University.

Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492073008
Total Pages : 432 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Robo-Advisory

Author : Peter Scholz
Publisher : Springer Nature
ISBN 13 : 3030408183
Total Pages : 302 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Robo-Advisory by : Peter Scholz

Download or read book Robo-Advisory written by Peter Scholz and published by Springer Nature. This book was released on 2020-12-28 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robo-Advisory is a field that has gained momentum over recent years, propelled by the increasing digitalization and automation of global financial markets. More and more money has been flowing into automated advisory, raising essential questions regarding the foundations, mechanics, and performance of such solutions. However, a comprehensive summary taking stock of this new solution at the intersection of finance and technology with consideration for both aspects of theory and implementation has so far been wanting. This book offers such a summary, providing unique insights into the state of Robo-Advisory. Drawing on a pool of expert authors from within the field, this edited collection aims at being the vital go-to resource for academics, students, policy-makers, and practitioners alike wishing to engage with the topic. Split into four parts, the book begins with a survey of academic literature and its key insights paired with an analysis of market developments in Robo-Advisory thus far. The second part tackles specific questions of implementation, which are complemented by practical case studies in Part III. Finally, the fourth part looks ahead to the future, addressing questions of key importance such as artificial intelligence, big data, and social networks. Thereby, this timely book conveys both a comprehensive grasp of the status-quo as well as a guiding outlook onto future trends and developments within the field.

Robo-Advisors in Management

Author : Gupta, Swati
Publisher : IGI Global
ISBN 13 :
Total Pages : 426 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Robo-Advisors in Management by : Gupta, Swati

Download or read book Robo-Advisors in Management written by Gupta, Swati and published by IGI Global. This book was released on 2024-04-22 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ever-evolving landscape of management, the introduction of robo-advisors has introduced challenges and opportunities that require careful examination. Organizations grapple with the profound impact of these automated systems on decision-making processes, resource allocation, and strategic planning. The need for a comprehensive understanding of how robo-advisors integrate into various management functions and sectors has become paramount. Decision-makers, researchers, and students seeking clarity in this transformative period are faced with a shortage of literature that bridges theoretical insights with practical applications. Robo-Advisors in Management stand out as a pioneering solution to this crucial gap in the existing body of knowledge. This book does not merely explore the challenges presented by robo-advisors; it delves into the heart of these challenges and navigates the diverse applications of these technologies in sectors ranging from wealth management to healthcare and real estate. By seamlessly blending theoretical foundations with real-world scenarios, the book equips both professionals and academics with the tools needed to comprehend and harness the potential of robo-advisors. It is an invaluable resource for decision-makers looking to optimize their strategies, researchers seeking in-depth insights, and students aspiring to navigate the intersection of management and fintech.

Financial Modeling Using Quantum Computing

Author : Anshul Saxena
Publisher : Packt Publishing Ltd
ISBN 13 : 1804614874
Total Pages : 292 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Financial Modeling Using Quantum Computing by : Anshul Saxena

Download or read book Financial Modeling Using Quantum Computing written by Anshul Saxena and published by Packt Publishing Ltd. This book was released on 2023-05-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Achieve optimized solutions for real-world financial problems using quantum machine learning algorithms Key Features Learn to solve financial analysis problems by harnessing quantum power Unlock the benefits of quantum machine learning and its potential to solve problems Train QML to solve portfolio optimization and risk analytics problems Book DescriptionQuantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems. This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you’ll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing. By the end of this book, you’ll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.What you will learn Explore framework, model and technique deployed for Quantum Computing Understand the role of QC in financial modeling and simulations Apply Qiskit and Pennylane framework for financial modeling Build and train models using the most well-known NISQ algorithms Explore best practices for writing QML algorithms Use QML algorithms to understand and solve data mining problems Who this book is for This book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite.

Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat
Publisher : O'Reilly Media
ISBN 13 : 1492073024
Total Pages : 432 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by O'Reilly Media. This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Python for Finance

Author : Dmytro Zherlitsyn
Publisher : BPB Publications
ISBN 13 : 9355516894
Total Pages : 480 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Python for Finance by : Dmytro Zherlitsyn

Download or read book Python for Finance written by Dmytro Zherlitsyn and published by BPB Publications. This book was released on 2024-07-30 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis. This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. KEY FEATURES ● Comprehensive guide to Python for financial data analysis and modeling. ● Practical examples and real-world applications for immediate implementation. ● Covers advanced topics like regression, Machine Learning and time series forecasting. WHAT YOU WILL LEARN ● Learn financial data analysis using Python data science libraries and techniques. ● Learn Python visualization tools to justify investment and trading strategies. ● Learn asset pricing and portfolio management methods with Python. ● Learn advanced regression and time series models for financial forecasting. ● Learn risk assessment and volatility modeling methods with Python. WHO THIS BOOK IS FOR This book is designed for financial analysts and other professionals interested in the financial industry with a basic understanding of Python programming and statistical analysis. It is also suitable for students in finance and data science who wish to apply Python tools to financial data analysis and decision-making. TABLE OF CONTENTS 1. Getting Started with Python for Finance 2. Python Tools for Data Analysis: Primer to Pandas and NumPy 3. Financial Data Manipulation with Python 4. Exploratory Data Analysis for Finance 5. Investment and Trading Strategies 6. Asset Pricing and Portfolio Management 7. Time Series Analysis and Financial Data Forecasting 8. Risk Assessment and Volatility Modelling 9. Machine Learning and Deep Learning in Finance 10. Time Series Analysis and Forecasting with FB Prophet Library Appendix A: Python Code Examples for Finance Appendix B: Glossary Appendix C: Valuable Resources

Digital Transformation

Author : Jacek Maślankowski
Publisher : Springer Nature
ISBN 13 : 3031230124
Total Pages : 146 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Digital Transformation by : Jacek Maślankowski

Download or read book Digital Transformation written by Jacek Maślankowski and published by Springer Nature. This book was released on 2022-12-07 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th PLAIS EuroSymposium 2022 which was held in Sopot, Poland, on December 15, 2022. The objective of the PLAIS EuroSymposium is to promote and develop high quality research on all issues related to digital transformation. It provides a forum for IS researchers and practitioners in Europe and beyond to interact, collaborate, and develop this field. The leading topic for the EuroSymposium this year was “Digital Transformation”. The 8 papers presented in this volume were carefully reviewed and selected from 23 submissions. They were organized in topical sections named: artificial intelligence; creativity and innovations; big data, internet of things and blockchain technologies.

Probabilistic Machine Learning for Finance and Investing

Author : Deepak K. Kanungo
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492097632
Total Pages : 287 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Probabilistic Machine Learning for Finance and Investing by : Deepak K. Kanungo

Download or read book Probabilistic Machine Learning for Finance and Investing written by Deepak K. Kanungo and published by "O'Reilly Media, Inc.". This book was released on 2023-08-14 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether based on academic theories or discovered empirically by humans and machines, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory. Unlike conventional AI systems, probabilistic machine learning (ML) systems treat errors and uncertainties as features, not bugs. They quantify uncertainty generated from inexact model inputs and outputs as probability distributions, not point estimates. Most importantly, these systems are capable of forewarning us when their inferences and predictions are no longer useful in the current market environment. These ML systems provide realistic support for financial decision-making and risk management in the face of uncertainty and incomplete information. Probabilistic ML is the next generation ML framework and technology for AI-powered financial and investing systems for many reasons. They are generative ensembles that learn continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, prediction and counterfactual reasoning. By moving away from flawed statistical methodologies (and a restrictive conventional view of probability as a limiting frequency), you can embrace an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you why and how to make that transition.

Machine Learning Applications Using Python

Author : Puneet Mathur
Publisher : Apress
ISBN 13 : 1484237870
Total Pages : 384 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning Applications Using Python by : Puneet Mathur

Download or read book Machine Learning Applications Using Python written by Puneet Mathur and published by Apress. This book was released on 2018-12-12 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will LearnDiscover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

Personal Finance with Python

Author : Max Humber
Publisher : Apress
ISBN 13 : 1484238028
Total Pages : 130 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Personal Finance with Python by : Max Humber

Download or read book Personal Finance with Python written by Max Humber and published by Apress. This book was released on 2018-07-20 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deal with data, build up financial formulas in code from scratch, and evaluate and think about money in your day-to-day life. This book is about Python and personal finance and how you can effectively mix the two together. In Personal Finance with Python you will learn Python and finance at the same time by creating a profit calculator, a currency converter, an amortization schedule, a budget, a portfolio rebalancer, and a purchase forecaster. Many of the examples use pandas, the main data manipulation tool in Python. Each chapter is hands-on, self-contained, and motivated by fun and interesting examples. Although this book assumes a minimal familiarity with programming and the Python language, if you don't have any, don't worry. Everything is built up piece-by-piece and the first chapters are conducted at a relaxed pace. You'll need Python 3.6 (or above) and all of the setup details are included. What You'll Learn Work with data in pandas Calculate Net Present Value and Internal Rate Return Query a third-party API with Requests Manage secrets Build efficient loops Parse English sentences with Recurrent Work with the YAML file format Fetch stock quotes and use Prophet to forecast the future Who This Book Is For Anyone interested in Python, personal finance, and/or both! This book is geared towards those who want to manage their money more effectively and to those who just want to learn or improve their Python.

Applied Quantitative Finance

Author : Mauricio Garita
Publisher : Springer Nature
ISBN 13 : 3030291413
Total Pages : 240 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Applied Quantitative Finance by : Mauricio Garita

Download or read book Applied Quantitative Finance written by Mauricio Garita and published by Springer Nature. This book was released on 2021-09-03 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners.

Python Machine Learning By Example

Author : Yuxi (Hayden) Liu
Publisher : Packt Publishing Ltd
ISBN 13 : 178355312X
Total Pages : 249 pages
Book Rating : 4.7/5 (835 download)

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Book Synopsis Python Machine Learning By Example by : Yuxi (Hayden) Liu

Download or read book Python Machine Learning By Example written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2017-05-31 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take tiny steps to enter the big world of data science through this interesting guide About This Book Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work—giving you a deep insight into the world of machine learning. With simple yet rich language—Python—you will understand and be able to implement the examples with ease.

Mastering Python for Finance

Author : James Ma Weiming
Publisher : Packt Publishing Ltd
ISBN 13 : 1789345278
Total Pages : 414 pages
Book Rating : 4.7/5 (893 download)

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Book Synopsis Mastering Python for Finance by : James Ma Weiming

Download or read book Mastering Python for Finance written by James Ma Weiming and published by Packt Publishing Ltd. This book was released on 2019-04-30 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications Key FeaturesExplore advanced financial models used by the industry and ways of solving them using PythonBuild state-of-the-art infrastructure for modeling, visualization, trading, and moreEmpower your financial applications by applying machine learning and deep learningBook Description The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis. What you will learnSolve linear and nonlinear models representing various financial problemsPerform principal component analysis on the DOW index and its componentsAnalyze, predict, and forecast stationary and non-stationary time series processesCreate an event-driven backtesting tool and measure your strategiesBuild a high-frequency algorithmic trading platform with PythonReplicate the CBOT VIX index with SPX options for studying VIX-based strategiesPerform regression-based and classification-based machine learning tasks for predictionUse TensorFlow and Keras in deep learning neural network architectureWho this book is for If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.

Mastering Python for Finance

Author : James Ma Weiming
Publisher : Packt Publishing Ltd
ISBN 13 : 1784397873
Total Pages : 340 pages
Book Rating : 4.7/5 (843 download)

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Book Synopsis Mastering Python for Finance by : James Ma Weiming

Download or read book Mastering Python for Finance written by James Ma Weiming and published by Packt Publishing Ltd. This book was released on 2015-04-29 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required.

Hands-On Financial Trading with Python

Author : Jiri Pik
Publisher : Packt Publishing Ltd
ISBN 13 : 1838988807
Total Pages : 360 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis Hands-On Financial Trading with Python by : Jiri Pik

Download or read book Hands-On Financial Trading with Python written by Jiri Pik and published by Packt Publishing Ltd. This book was released on 2021-04-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and backtest your algorithmic trading strategies to gain a true advantage in the market Key FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python's data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook Description Creating an effective system to automate your trading can help you achieve two of every trader's key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it's the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you'll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets. What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is for If you're a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You don't have to be a fully-fledged programmer to dive into this book, but knowing how to use Python's core libraries and a solid grasp on statistics will help you get the most out of this book.