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From the cofounder of the longest-running bitcoin exchange comes a compelling argument for how this digital currency will transform the global economy-and how it can work for you. Bitcoin may be the best investment opportunity of our time, yet most people have yet to understand its promise. In this book, Bobby Lee, one of the earliest, most successful pioneers in the cryptocurrency space, debunks myths and dispels fears that surround bitcoin, arguing that this rational, logical system is superior to traditional monetary systems. Lee cites signs of bitcoin's widening acceptance: a growing community of users worldwide and multiple initiatives for investing in and holding bitcoin among major financial services organizations and institutional investors who control trillions in assets. Lee offers a primer on the best strategies for purchasing and investing in this digital currency. He discusses the pros and cons, and covers the more complicated method of acquiring bitcoin, mining. He predicts developments in regulation, technology, business, and society that will lead to bitcoin's price increasing 500 percent over the next two decades. In the wake of the current economic crisis, Lee calls on consumers to embrace a technology that will not only increase their wealth but make their lives easier. .
(source: Nielsen Book Data)

• The effectiveness of applying Fintech application in Bahrain: Theoretical Perspective.- The Usage of Artificial Intelligence in Arab Financial Institutions.- Libra Currency and its Global Financial and Economic Impact.- Haitian cooperative of savings and credits: social and community dimensions of success.- The Impact of Corporate Social Responsibility Disclosure on the Financial Performance of Banks Listed on the PEX and the ASE.- Effect of Adopting the Criterion of Revenue from Contracts with Clients on Accounting Conservatism.- The Banking sector role against the risks of Currency Floating.- Shariah Resolutions and Issues on Islamic Repo.- Financial Performance Analysis of Firms: A Focus on Oil and Gas Industry Sustainable Practices in Oman.- Islamic banking strategies in the world of Fintech: Success story of Bahrain.- Computing Financial Performance of Road Freight Transportation (Trucking) Industry in India using Mathematical Tool.- Computing Causality Between Macro-Economic Indicators and Indian Financial Markets.- Crowdsoured Technology as A Collabarative Tool for Environmental Enforcement: A Critical Review of Current Applications.
• (source: Nielsen Book Data)

This book shows latest research on artificial intelligence for sustainable technology. ICGER 2021 was organized by the Accounting, Finance and Banking Department at Ahlia University, Bahrain, and was conducted on the 15th and 16th of September. The strategic partners included the University of Jordan, the Bahrain Economists Society, the Association of Chartered Certified Accountants: ACCA, Al-Barka Banking Group and the International Computer Auditing Education Association: ICAEA . The theme of the ICGER 2021 centered around artificial intelligence for sustainable finance and sustainable technology. Accordingly, the papers presented at the conference provided a holistic view of sustainable finance, sustainability, AI, financial technology, cybersecurity, blockchain, CSR, and governance. This book, unlike ever before, brings together intelligence applications of new technologies and the sustainability requirements in the era of the digital economy, with special attention given to the opportunities, challenges, for education, business growth, and economic progression of nations which will help societies (economists, financial managers, engineers, ICT specialists, digital managers, data managers, policymakers, regulators, researchers, academics, and students) to better understand, use, and control AI applications and financial technologies to develop future strategies and to achieve sustainable development goals.
(source: Nielsen Book Data)

• Introduction to Bitcoin / Lam Pak Nian, David Lee Kuo Chuen
• Is Bitcoin a real currency? An economic appraisal / David Yermack
• Bitcoin mining technology / Nirupama Devi Bhaskar, David Lee Kuo Chuen
• National Crytocurrencies / Andras Kristof
• Evaluating the potential of alternative cryptocurrencies / Bobby Ong, Teik Ming Lee, Guo Li, David Lee Kuo Chuen
• The effect of payment reversibility on e-commerce and postal quality / Christian Jaag, Christian Bach
• Blockchain and digital payments : an institutionalist analysis of cryptocurrencies / Georgios Papadopoulos
• Counterfeiting in cryptocurrency : an emerging problem / Ralph E. McKinney, Jr., Lawrence P. Shao, Duane C. Rosenlieb, Jr., Dale H. Shao
• Emergence, growth, and sustainability of Bitcoin : the network economics perspective / Ernie G.S. Teo
• Cryptocurrencies as distributed community experiments / Matthias Tarasiewicz, Andrew Newman
• Extracting market-implied Bitcoin's risk-free interest rate / Nicolas Wesner
• A microeconomic analysis of Bitcoin and illegal activities / Tetsuya Saito
• Legal issues in cryptocurrency / Vrajlal Sapovadia
• How to tax Bitcoin / Aleksandra Bal
• Cryptocurrency and virtual currency : corruption and money laundering/terrorism financing risks? / Kim-Kwang Raymond Choo
• A light tough of regulation for virtual currencies / Lam Pak Nian, David Lee Kuo Chuen
• Real regulation of virtual currencies / Richard B. Levin, Aaron A. O'Brien, Madiha M. Zuberi
• A facilitative model for cryptocurrency regulation in Singapore / Jonathan W. Lim
• Advancing egalitarianism / Gavin Wood, Aeron Buchanan
• How digital currencies will cascade up to global stable currency : the fundamental framework for the money of the future / Gideon Samid
• Bitcoin-like protocols and innovations / Ignacio Mas, David Lee Kuo Chuen
• Blockchain electronic vote / Pierre Noizat
• Translating commons-based peer production values into metrics : toward commons-based cryptocurrencies
• The confluence of bitcoin and the global sharing economy / Alyse Killeen
• What does cryptocurrency mean for the new economy? / David G.W. Birch
• Bitcoin : a look at the past and the future / Anton Cruysheer
• Bitcoin IPO, ETF, and crowdfunding / Nirupama Devi Bhaskar, Lam Pak Nian, David Lee Kuo Chuen
• Bitcoin exchanges / Nirupama Devi Bhaskar, David Lee Kuo Chuen.

Incorporating currencies, payment methods, and protocols that computers use to talk to each other, digital currencies are poised to grow in use and importance. The Handbook of Digital Currency gives readers a way to learn about subjects outside their specialties and provides authoritative background and tools for those whose primary source of information is journal articles. Taking a cross-country perspective, its comprehensive view of the field includes history, technicality, IT, finance, economics, legal, tax and regulatory environment. For those who come from different backgrounds with different questions in mind, The Handbook of Digital Currency is an essential starting point. 1. Discusses all major strategies and tactics associated with digital currencies, their uses, and their regulations. 2. Presents future scenarios for the growth of digital currencies. 3. Written for regulators, crime prevention units, tax authorities, entrepreneurs, micro-financiers, micro-payment businesses, cryptography experts, software developers, venture capitalists, hedge fund managers, hardware manufacturers, credit card providers, money changers, remittance service providers, exchanges, and academics.

• Introduction 1 Part I: Zen, Quicken, and the Big Picture 5
• Chapter 1: Setting Up Shop 7
• Chapter 2: Introduction to the Big Picture 23
• Chapter 3: Maximum Fun, Maximum Profits 45 Part II: The Absolute Basics 57
• Chapter 4: Checkbook on a Computer 59
• Chapter 5: Printing 101 87
• Chapter 6: Online and In Charge 99
• Chapter 7: Reports, Charts, and Other Cool Tools 107
• Chapter 8: A Matter of Balance 127
• Chapter 9: Housekeeping for Quicken 139
• Chapter 10: Compound Interest Magic and Other Mysteries 157 Part III: Home Finances 179
• Chapter 11: Credit Cards, Petty Cash, and PayPal 181
• Chapter 12: Other People's Money 203
• Chapter 13: Tracking Tax-Deferred Investments 227
• Chapter 14: Stocks and Bonds 253 Part IV: Very Serious Business 271
• Chapter 15: Mind Your Business 273
• Chapter 16: Managing Rentals 297 Part V: The Part of Tens 305
• Chapter 17: (Slightly More Than) Ten Questions I'm Frequently Asked about Quicken 307
• Chapter 18: (Almost) Ten Tips on How Not to Become a Millionaire 317
• Chapter 19: (Almost) Ten Troubleshooting Tips 327 Part VI: Appendixes 331 Appendix A: Quick-and-Dirty Windows 8.1 333 Appendix B: Glossary of Business, Financial, and Computer Terms 349 Index 359.
• (source: Nielsen Book Data)

Take control of your personal finances with this popular, easy guide! Quicken is the country's most popular and highly recommended personal finance software, and with Quicken For Dummies, you can harness the power of this practical financial tool to manage bills, reconcile bank accounts, track investments, and much more! Personal finance expert and CPA Stephen L. Nelson lends his expertise to this accessible, easy-to-read guide, which has been fully updated to reflect the new features of Quicken available in its newest version. You can quickly learn how to use Quicken to record credit card transactions, track mortgages and other liabilities, minimize taxes, maximize savings, maintain detailed records, and build a solid foundation for creating better financial plans. Covers updated features in the newest version of the increasingly popular Quicken software Empowers readers to fight the bad habits and decisions that lead to large debts and minimal or nonexistent savings Demystifies the software's features for tracking everyday finances, managing investments, and evaluating the tax implications of financial decisions Offers invaluable advice and expertise from author Stephen L. Nelson, a recognized authority on personal finance Every step toward better finances is a step in the right direction. Let Quicken For Dummies start your journey toward increased savings, lowered debt, and a more secure financial future.
(source: Nielsen Book Data)

• Table of Contents Financial Data and Preprocessing Technical Analysis in Python Time Series Modelling Multi-factor Models Modeling Volatility with GARCH class models Monte Carlo Simulations in Finance Asset Allocation in Python Identifying Credit Default with Machine Learning Advanced Machine Learning Models in Finance Deep Learning in Finance.
• (source: Nielsen Book Data)

Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key Features Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data Explore unique recipes for financial data analysis and processing with Python Estimate popular financial models such as CAPM and GARCH using a problem-solution approach Book DescriptionPython is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you'll have learned how to effectively analyze financial data using a recipe-based approach. What you will learn Download and preprocess financial data from different sources Backtest the performance of automatic trading strategies in a real-world setting Estimate financial econometrics models in Python and interpret their results Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment Improve the performance of financial models with the latest Python libraries Apply machine learning and deep learning techniques to solve different financial problems Understand the different approaches used to model financial time series data Who this book is forThis book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
(source: Nielsen Book Data)

• Python for finance : financial modeling and quantitative analysis explained
• Credits
• About the Author
• About the Reviewers
• Customer Feedback
• Table of Contents
• Preface
• Chapter 1: Python Basics
• Chapter 2: Introduction to Python Modules
• Chapter 3: Time Value of Money
• Chapter 4: Sources of Data
• Chapter 5: Bond and Stock Valuation
• Chapter 6: Capital Asset Pricing Model
• Chapter 7: Multifactor Models and Performance Measures
• Chapter 8: Time-Series Analysis
• Chapter 9: Portfolio Theory
• Chapter 10: Options and Futures
• Chapter 11: Value at Risk
• Chapter 12: Monte Carlo Simulation
• Chapter 13: Credit Risk Analysis
• Chapter 14: Exotic Options
• Chapter 15: Volatility, Implied Volatility, ARCH, and GARCH
• Index.

Learn and implement various Quantitative Finance concepts using the popular Python libraries About This Book * Understand the fundamentals of Python data structures and work with time-series data * Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib * A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance Who This Book Is For This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data. What You Will Learn * Become acquainted with Python in the first two chapters * Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models * Learn how to price a call, put, and several exotic options * Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options * Understand the concept of volatility and how to test the hypothesis that volatility changes over the years * Understand the ARCH and GARCH processes and how to write related Python programs In Detail This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. Style and approach This book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.
(source: Nielsen Book Data)

• Copyright
• Table of Contents
• Preface
• Conventions Used in This Book
• Using Code Examples
• Oâ#x80; #x99; Reilly Safari
• How to Contact Us
• Acknowledgments
• Chapter 1. Introduction
• Overview
• Section I: Accessing Financial Data
• Section II: Financial Data Analysis
• Section III: Creating Financial Reports
• Financial Markets
• Equities
• Corporate Loans (Bank Debt, Leveraged Loans)
• Corporate Bonds
• The Three Paths
• Path 1: Microsoft Excel
• Path 2: Microsoft Access
• Path 3: C#
• Online Files
• Summary

• Chapter 2. Organizing Financial DataPath 1: Excel
• Excel Range Versus Excel Table
• Adding Reference Columns
• Data Validation
• Paths 2 and 3: Tables in Access
• Connecting the Data with Queries
• Summary
• Chapter 3. Bloomberg
• Identifying the Fields
• The Mouse-Over
• The FLDS Screen
• Bloomberg Function Builder and Finding Fields in Excel
• If All Else Fails ...
• Excel Examples
• Pulling a Single Field (BDP)
• Pulling Bulk Data (BDS)
• Pulling Historical Data (BDH)
• Comparable Securities
• Indices
• Peers

• Related SecuritiesPaths 1 and 2: Excel and Access
• Corporate Bonds, Loans, and Indices
• Company Worksheet
• References and Overrides
• Path 3: Bloomberg C# API
• Setting Up Microsoft Access for Use with C#
• Bloomberg C# API
• Basic Reference Example
• Basic Historical Example
• Populating Access Database
• Summary
• Chapter 4. IHS Markit: Big Corporate Data
• Corporate Loans
• Data Request
• Facility Information
• Loan Pricing, Financials, and Analytics
• Corporate and Sovereign Bonds
• Path 1: Storing Markit Information in Excel

• Path 2: Importing Markit Data into Microsoft AccessPath 3: Importing Markit Data Using C#
• Summary
• Chapter 5. Financial Data Analysis
• Data Integrity
• Checking the Data
• Sample Size
• Outliers
• Portfolio
• Portfolio Worksheet
• Portfolio Database Table
• Linking Excel Worksheets to Microsoft Access
• Keeping a History
• Path 1: Excel
• Path 2: Microsoft Access
• Path 3: C#
• Summary
• Chapter 6. Relative-Value Analysis
• Path 1: Excel
• Correlation and Regression in Excel
• Peer Groups
• Ratings
• Stats Worksheets

• Side by SideIndices
• Weighted Z-Score
• Path 2: Access
• Correlation and Regression in Access
• Median in Access
• Path 3: C#
• Correlation and Regression
• Peer Groups
• Ratings
• Stats Tables
• Side by Side
• Weighted Z-Score
• Summary
• Chapter 7. Portfolio Risk Analysis
• Path 1: Excel
• Variance, Volatility, and Standard Deviation
• Sharpe Ratio with Historical or Forecasted Returns
• Portfolio Breakdown
• Warning Signs
• Path 2: Access
• Portfolio Breakdown
• Warning Signs
• Path 3: C#

Investors recognize that technology is a powerful tool for obtaining and interpreting financial data that could give them the one thing everyone on Wall Street wants: an edge. Yet, many don't realize that you don't need to be a programmer to access behind-the-scenes financial information from Bloomberg, IHS Markit, or other systems found at most banks and investment firms. This practical guide teaches analysts a useful subset of Excel skills that will enable them to access and interpret financial information-without any prior programming experience. This book will show analysts, step-by-step, how to quickly produce professional reports that combine their views with Bloomberg or Markit data including historical financials, comparative analysis, and relative value. For portfolio managers, this book demonstrates how to create professional summary reports that contain a high-level view of a portfolio's performance, growth, risk-adjusted return, and composition. If you are a programmer, this book also contains a parallel path that covers the same topics using C#. Topics include: Access additional data that isn't visible on Bloomberg screens Create tables containing corporate data that makes it possible to compare multiple companies, bonds, or loans side-by- side Build one-page analytic ("Tear Sheet") reports for individual companies that incorporates important financials, custom notes, relative value comparison of the company to its peers, and price trends with research analyst targets Build two-page portfolio summary report that contains a high-level view of the portfolio's performance, growth, risk-adjusted return, and composition Explore daily prices and facility information for most of the tradable corporate bond and loan market Determine the relationship between two securities (or index) using correlation and regression Compare each security's performance to a cohort made of up of securities with similar risk and return characteristics Measure portfolio risk-adjusted return by calculating variance, standard deviation, and Sharpe ratio Use Markit data to identify meaningful trends in prices, new issue spreads, and refinancings.
(source: Nielsen Book Data)

"This course will take you on a journey where you'll learn how to code in Python. You will learn how to use Python in a real working environment and explore how Python can be applied in the world of Finance to solve portfolio optimization problems. The first part of the course is ideal for beginners and people who want to brush up on their Python skills. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. The Finance block of this course will teach you in-demand, real-world skills employers are looking for. This explains topics such as how to work with Python's conditional statements, functions, sequences, and loops, build investment portfolios, and more."--Resource description page

• Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
• (source: Nielsen Book Data)

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 Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe 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 learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf 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.
(source: Nielsen Book Data)

• Basic Python for Data Management, Finance, and Marketing
• 1. Getting started with Python- Variables and numeric data types - Python containers to hold our data- Definite loops- Building a mortgage calculator
• 2. Writing your own Python scripts- Custom functions- Indefinite loops- Immutable containers - tuples- Pseudo code to problem solving
• 3. Extending Excel with Python- Dictionaries- Reading and writing csv files with Python- CSV and Urllib modules
• 4. Data Analysis with NumPy and Pandas- NumPy as an extension of Python - Series and DataFrame- Manipulate and plot data with Pandas
• 5. Solving common problems with Pandas - Concatinate and merge datasets - Group by keys- How to use logic in Pandas
• 6. Gathering data with Python- Web Scraping with Python - Reading PDF files with Python- Cleaning data- Storing data
• 7. Building predictive models - User authentication- Linear reression- K-nearest- CART - decision tree- Matplotlib to plot data
• 8. Automating tasks with Python- Sending emails- Deploying your script to a server- Running tasks at specific time.
• (source: Nielsen Book Data)

Learn how to gather, manipulate, and analyze data with Python. This book is a practical guide to help you get started with Python from ground zero and to the point where you can use coding for everyday tasks. Python, the most in-demand skill by employers, can be learned in a matter of months and a working knowledge will help you to advance your career. This book will teach you to crunch numbers, analyze big-data, and switch from spreadsheets to a faster and more efficient programming language. You'll benefit from the numerous real-life examples designed to meet current world challenges and from step-by-step guidance to become a confident Python user. Python is used in all aspects of financial industry, from algo trading, reporting and risk management to building valuations models and predictive machine learning programs. Basic Python for Data Management, Finance, and Marketing highlights how this language has become a useful skill with digital marketers, allowing them to analyze data more precisely and run more successful campaigns. What You'll Learn Get started with Python from square one Extend what's possible on excel with Python Automate tasks with Python Analyze data more precisely Who This Book Is For Professionals who want to find a job in the modern world or advance their careers within field of Python programming language. .
(source: Nielsen Book Data)

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Listeners will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Listeners become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

• Cover
• Contents
• Foreword
• Preface
• THERE AND BACK AGAIN
• COMPUTING
• THE INVESTMENT INDUSTRY
• TRADING STRATEGY DEVELOPMENT TOOLS
• THE RISE OF ADVANCED MATHEMATICAL CONCEPTS IN TRADING
• TRADING MEETS HIGHER EDUCATION
• Acknowledgments
• Introduction: Why a Second Edition?
• Chapter 1: On Trading Strategies
• WHY THIS BOOK WAS WRITTEN
• WHO WILL BENEFIT FROM THIS BOOK?
• THE GOALS OF THIS BOOK
• THE LAY OF THE LAND
• Chapter 2: The Systematic Trading Edge
• DISCRETIONARY TRADING
• RAISING THE BAR
• VERIFICATION
• QUANTIFICATION
• RISK AND REWARD
• THE PERFORMANCE PROFILE
• OBJECTIVITY
• CONSISTENCY
• EXTENSIBILITY
• THE BENEFITS OF THE HISTORICAL SIMULATION
• POSITIVE EXPECTANCY
• THE LIKELIHOOD OF FUTURE PROFIT
• THE PERFORMANCE PROFILE
• PROPER CAPITALIZATION
• A MEASURE OF REAL-TIME TRADING PERFORMANCE
• THE BENEFITS OF OPTIMIZATION
• THE BENEFITS OF THE WALK-FORWARD ANALYSIS
• THE ADVANTAGES OF A THOROUGH UNDERSTANDING
• CONFIDENCE
• STRATEGY REFINEMENT
• Chapter 3: The Trading Strategy Development Process
• TWO PHILOSOPHICAL APPROACHES TO STRATEGY DEVELOPMENT
• AN OVERVIEW OF THE TRADING STRATEGY DESIGN PROCESS
• Chapter 4: The Strategy Development Platform
• THE SCRIPTING LANGUAGE
• DIAGNOSTICS
• REPORTING
• OPTIMIZATION
• THE OBJECTIVE FUNCTION
• SPEED
• AUTOMATION
• WALK-FORWARD ANALYSIS
• PORTFOLIO ANALYSIS
• IN CONCLUSION
• Chapter 5: The Elements of Strategy Design
• THE THREE PRINCIPAL COMPONENTS OF A STRATEGY
• AN OVERVIEW OF A TYPICAL TRADING STRATEGY
• A TRADE EQUALS AN ENTRY AND AN EXIT
• THE MANAGEMENT OF RISK
• THE MANAGEMENT OF PROFIT
• POSITION SIZING
• ADVANCED STRATEGIES
• SUMMARY
• Chapter 6: The Historical Simulation
• THE ESSENTIAL REPORTS
• THE IMPORTANCE OF ACCURACY
• SOFTWARE LIMITATIONS
• REALISTIC ASSUMPTIONS
• LIMIT MOVES
• MAJOR EVENTS AND DATES
• HISTORICAL DATA
• STOCK PRICES
• CASH MARKETS
• FUTURES MARKETS
• THE CONTINUOUS CONTRACT
• THE PERPETUAL CONTRACT
• ADJUSTED CONTINUOUS CONTRACTS
• THE SIZE OF THE TEST WINDOW
• HOW MANY TRADES?
• STABILITY
• DEGREES OF FREEDOM
• FREQUENCY OF TRADING
• TYPES OF MARKETS
• EFFICIENT MARKETS
• THE LIFE CYCLE OF A TRADING STRATEGY
• WINDOW SIZE AND MODEL LIFE
• Chapter 7: Formulation and Specification
• FORMULATE THE TRADING STRATEGY
• SPECIFICATION-TRANSLATE THE IDEA INTO A TESTABLE STRATEGY
• MAKE A VAGUE IDEA PRECISE
• Chapter 8: Preliminary Testing
• VERIFICATION OF CALCULATIONS AND TRADES
• THEORETICAL EXPECTATIONS
• PRELIMINARY PROFITABILITY
• THE MULTIMARKET AND MULTIPERIOD TEST
• Chapter 9: Search and Judgment
• SEARCH METHODS
• ADVANCED SEARCH METHODS
• GENERAL PROBLEMS WITH SEARCH METHODS
• THE OBJECTIVE FUNCTION
• A REVIEW OF A VARIETY OF EVALUATION METHODS
• MULTIPLE EVALUATION TYPES
• Chapter 10: Optimization
• OPTIMIZATION CONTRA OVERFITTING
• A SIMPLE OPTIMIZATION
• THE OPTIMIZATION FRAMEWORK
• A MULTIMARKET AND MULTIPERIOD OPTIMIZATION --T$1.

Aims to give traders a way to develop and verify their trading strategy of various forms they are using including stochastics, moving averages, chart patterns, RSI, or breakout methods. This book offers practical instruction and advice on the development, evaluation, and application of winning mechanical trading systems.

"Financial services are increasingly deploying AI models and services for a wide range of applications in the credit lifecycle, such as credit onboarding and identifying transaction fraud and identity fraud. These models must be interpretable, explainable, and resilient to adversarial attacks. In some situations, regulatory requirements apply that prohibit black-box machine learning models. Jari Koister (FICO) shares forward-looking tools and infrastructure has developed to support these needs. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco."--Resource description page

• Table of Contents Algorithmic Trading Fundamentals Deciphering the Markets with Technical Analysis Predicting the Markets with basic Machine Learning Classical Trading Strategies Sophisticated Algorithmic Strategies Managing Risk of Algorithmic Strategies Building a Trading System in Python Connecting to trading exchanges Creating a Backtester in Python Adapting to market participants and changing financial markets.
• (source: Nielsen Book Data)

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading strategies Key Features Understand the power of algorithmic trading in financial markets with real-world examples Get up and running with the algorithms used to carry out algorithmic trading Learn to build your own algorithmic trading robots which require no human intervention Book DescriptionIt's now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate. You'll start with an introduction to algorithmic trading, along with setting up the environment required to perform the tasks in the book. You'll explore the key components of an algorithmic trading business and aspects you'll need to take into account before starting an automated trading project. Next, you'll focus on designing, building and operating the components required for developing a practical and profitable algorithmic trading business. Later, you'll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you'll create a trading bot from scratch using the algorithms built in the previous sections. By the end of this book, you'll be well-versed with electronic trading markets and have learned to implement, evaluate and safely operate algorithmic trading strategies in live markets. What you will learn Understand the components of modern algorithmic trading systems and strategies Apply machine learning in algorithmic trading signals and strategies using Python Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more Quantify and build a risk management system for Python trading strategies Build a backtester to run simulated trading strategies for improving the performance of your trading bot Deploy and incorporate trading strategies in the live market to maintain and improve profitability Who this book is forThis book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.
(source: Nielsen Book Data)

• Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Alpha Factor Research Strategy Evaluation The Machine Learning Process Linear Models Time Series Models Bayesian Machine Learning Decision Trees and Random Forests Gradient Boosting Machines Unsupervised Learning Working with Text Data Topic Modeling Word Embeddings Next Steps.
• (source: Nielsen Book Data)

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
(source: Nielsen Book Data)

• Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Alpha Factor Research Strategy Evaluation The Machine Learning Process Linear Models Time Series Models Bayesian Machine Learning Decision Trees and Random Forests Gradient Boosting Machines Unsupervised Learning Working with Text Data Topic Modeling Word Embeddings Next Steps.
• (source: Nielsen Book Data)

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
(source: Nielsen Book Data)

• Section 1. Machine Learning with Cross-Sectional Data
• Introduction
• Probabilistic Modeling
• Bayesian Regression & Gaussian Processes
• Feed Forward Neural Networks
• Interpretability
• Section 2. Sequential Learning
• Sequence Modeling
• Probabilistic Sequence Modeling
• Advanced Neural Networks
• Section 3. Introduction to Reinforcement learning
• Applications of Reinforcement Learning
• Inverse Reinforcement Learning and Imitation Learning
• Frontiers of Machine Learning and Finance.

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.
(source: Nielsen Book Data)

• Table of Contents Neural Networks and Gradient-Based Optimization Applying Machine Learning to Structured Data Utilizing Computer Vision Understanding Time Series Parsing Textual Data with Natural Language Processing Using Generative Models Reinforcement Learning for Financial Markets Privacy, Debugging, and Launching Your Products Fighting Bias Bayesian Inference and Probabilistic Programming.
• (source: Nielsen Book Data)

A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine learning and how to put them to work in financial industries Clear explanation and expert discussion of how machine learning works, with an emphasis on financial applications Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learning Book DescriptionMachine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas' experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learn Apply machine learning to structured data, natural language, photographs, and written text How machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Dig deep into neural networks, examine uses of GANs and reinforcement learning Debug machine learning applications and prepare them for launch Address bias and privacy concerns in machine learning Who this book is forThis book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.
(source: Nielsen Book Data)

• Foreword xv Introduction xvii
• PART 1: INFORMATION GAINS THROUGH DATA VISUALIZATIONS
• CHAPTER 1: Paving a Path Toward Visual Communications 3 Information Delivery Needs 5 Industry Demands 6 Enabling Factors 8 Summary 11
• CHAPTER 2: Benefits of Using Visual Methods 15 The Purpose of Charts 16 Making Comparisons 17 Establishing Connections 19 Drawing Conclusions 22 How to Leverage Charts 25 Summary 31
• PART 2: TRANSFORMING DATA FOR ACTIVE INVESTMENT DECISIONS
• CHAPTER 3: Security Assessment 35 Tile Framework 36 Stocks 39 Bonds 42 Mutual Funds 44 ETFs 50 Tile Collection 55 Summary 58
• CHAPTER 4: Portfolio Construction 61 Asset Allocation 62 Sector Analysis 67 Sector Leadership 67 Sectors and Alpha Factors 78 Risk Management 85 Overlap of Holdings 87 Stress Tests 99 Summary 106
• CHAPTER 5: Trading 109 Ticker 110 Quote 117 Watchlist 127 Visual System: Ticker, Quote, and Watchlist 138 Summary 140
• CHAPTER 6: Performance Measurement 143 Market Performance 144 Investment Firm Composite 151 Portfolio Gain/Loss 157 Attribution 161 Return Attribution 161 Risk Attribution 171 Summary 177
• PART 3: SHOWCASING DATA FOR EFFECTIVE COMMUNICATIONS
• CHAPTER 7: Financial Statements 183 Statement of Cash Flows 184 Nonprofit Organizations 184 For-Profit Organizations 191 Statement of Financial Activity 202 Operating Budget 206 Summary 212
• CHAPTER 8: Pension Funds 217 Plan Members 218 Members in Valuation 219 Post Retirement 227 Retirement Programs 232 Contributions versus Benefits 241 Additions by Source 241 Changes in Retirees & Beneficiaries 250 History of Member Salary 253 Funding Ratio 257 Summary 264
• CHAPTER 9: Mutual Funds 267 Core Components 268 Allocation Profile 269 Fees 277 Performance 285 Risk 293 Fund Fact Sheets 312 Mutual Fund Comparison 318 Total Returns 319 Ranking Against Benchmarks 324 Summary 332
• CHAPTER 10: Hedge Funds 335 Long/Short Positions 336 Long Positions and Benchmarking 344 Fund Characteristics 349 Strategy Rank 356 Strategy Rank and Ranges 361 Strategy Analysis 363 All Strategy Averages 364 Single Strategy Averages 367 Fund Level Returns 373 Summary 378
• PART 4: NEXT STEPS
• CHAPTER 11: Data Visualization Principles 383 Cater to Your Audience 384 Provide Clarity 394 Be Efficient 403 Summary 414
• CHAPTER 12: Implementing the Visuals 417 Business Value Assessment 418 Implementation Effort 422 Available Methods 427 Solution Score 428 Summary 432 Index 435.
• (source: Nielsen Book Data)

A fresh take on financial data visualization for greater accuracy and understanding Your data provides a snapshot of the state of your business and is key to the success of your conversations, decisions, and communications. But all of that communication is lost or incorrectly interpreted without proper data visualizations that provide context and accurate representation of the numbers. In Visualizing Financial Data, authors Julie Rodriguez and Piotr Kaczmarek draw upon their understanding of information design and visual communication to show you how to turn your raw data into meaningful information. Coverage includes current conventions paired with innovative visualizations that cater to the unique requirements across financial domains, including investment management, financial accounting, regulatory reporting, sales, and marketing communications. Presented as a series of case studies, this highly visual guide presents problems and solutions in the context of real-world scenarios. With over 250 visualizations, you ll have access to relevant examples that serve as a starting point to your implementations. Expand the boundaries of data visualization conventions and learn new approaches to traditional charts and graphs Optimize data communications that cater to you and your audience Provide clarity to maximize understanding Solve data presentation problems using efficient visualization techniques Use the provided companion website to follow along with examples The companion website gives you the illustration files and the source data sets, and points you to the types of resources you need to get started.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Time Series Analysis; Working with time series data; Linear time series modeling and forecasting; Modeling and forecasting UK house prices; Model identification and estimation; Model diagnostic checking; Forecasting; Cointegration; Cross hedging jet fuel; Modeling volatility; Volatility forecasting for risk management; Testing for ARCH effects; GARCH model specification; GARCH model estimation; Backtesting the risk model; Forecasting; Summary.

• Chapter 2: Portfolio OptimizationMean-Variance model; Solution concepts; Theorem (Lagrange); Working with real data; Tangency portfolio and Capital Market Line; Noise in the covariance matrix; When variance is not enough; Summary;
• Chapter 3: Asset Pricing Models; Capital Asset Pricing Model; Arbitrage Pricing Theory; Beta estimation; Data selection; Simple beta estimation; Beta estimation from linear regression; Model testing; Data collection; Modeling the SCL; Testing the explanatory power of the individual variance; Summary;
• Chapter 4: Fixed Income Securities.

• Measuring market risk of fixed income securitiesExample
• implementation in R; Immunization of fixed income portfolios; Net worth immunization; Target date immunization; Dedication; Pricing a convertible bond; Summary; Chapter 5: Estimating the Term Structure of Interest Rates; The term structure of interest rates and related functions; The estimation problem; Estimation of the term structure by linear regression; Cubic spline regression; Applied R functions; Summary; Chapter 6: Derivatives Pricing; The Black-Scholes model; The Cox-Ross-Rubinstein model; Connection between the two models.

• GreeksImplied volatility; Summary; Chapter 7: Credit Risk Management; Credit default models; Structural models; Intensity models; Correlated defaults
• the portfolio approach; Migration matrices; Getting started with credit scoring in R; Summary; Chapter 8: Extreme Value Theory; Theoretical overview; Application
• modeling insurance claims; Exploratory data analysis; Tail behavior of claims; Determining the threshold; Fitting a GPD distribution to the tails; Quantile estimation using the fitted GPD model; Calculation of expected loss using the fitted GPD model; Summary.

• Chapter 9: Financial NetworksRepresentation, simulation, and visualization of financial networks; Analysis of networks' structure and detection of topology changes; Contribution to systemic risk
• identification of SIFIs; Summary; Appendix: References; Time series analysis; Portfolio optimization; Asset pricing; Fixed income securities; Estimating the term structure of interest rates; Derivatives Pricing; Credit risk management; Extreme value theory; Financial networks; Index.

This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.
(source: Nielsen Book Data)