Advertisement
| best python library for technical analysis: Financial Data Analysis Using Python Dmytro Zherlitsyn, 2025-05-20 This book will introduce essential concepts in financial analysis methods & 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 also 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 the Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. FEATURES • Illustrates financial data analysis using Python data science libraries & techniques • Uses Python visualization tools to justify investment and trading strategies • Covers asset pricing & portfolio management methods with Python |
| best python library for technical analysis: Python for Finance Cookbook Eryk Lewinson, 2020-01-31 Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python 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 learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This 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. |
| best python library for technical analysis: Python for Finance Dmytro Zherlitsyn, 2024-07-30 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 |
| best python library for technical analysis: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| best python library for technical analysis: ALGORITHMIC TRADING MASTERMIND SHIKHAR SINGH (THE ZENITH), Go beyond the technical aspects of coding and dive deep into the strategic thinking that fuels successful algorithmic trading. Algorithmic Trading Mastermind is not just about writing code; it's about developing the mindset of a master strategist. This book explores: The psychology of trading: Understanding biases and emotional pitfalls that often derail even the most promising strategies. Market analysis for algorithmic traders: Learning to identify profitable patterns and opportunities within market data. Strategy development frameworks: Discover proven methodologies for crafting robust and adaptable trading algorithms. Advanced concepts in algorithmic trading: Explore machine learning, statistical modeling, and other cutting-edge techniques. Building a complete trading ecosystem: Managing risk, optimizing performance, and staying ahead of the curve. This book is for the ambitious trader who seeks not only to understand the how of algorithmic trading but also the why. Learn to think like a master strategist and develop algorithms that adapt and thrive in ever-changing markets. |
| best python library for technical analysis: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| best python library for technical analysis: Quantitative Technical Analysis Howard Bandy, 2014-01-02 Techniques for design, testing, validation and analysis of systems for trading stocks, futures, ETFs, and FOREX. Includes techniques for assessing system health, dynamical determining maximum safe position size, and estimating profit potential. |
| best python library for technical analysis: High-Performance Algorithmic Trading Using AI Melick R. Baranasooriya, 2024-08-08 DESCRIPTION High-Performance Algorithmic Trading using AI is a comprehensive guide designed to empower both beginners and experienced professionals in the finance industry. This book equips you with the knowledge and tools to build sophisticated, high-performance trading systems. It starts with basics like data preprocessing, feature engineering, and ML. Then, it moves to advanced topics, such as strategy development, backtesting, platform integration using Python for financial modeling, and the implementation of AI models on trading platforms. Each chapter is crafted to equip readers with actionable skills, ranging from extracting insights from vast datasets to developing and optimizing trading algorithms using Python's extensive libraries. It includes real-world case studies and advanced techniques like deep learning and reinforcement learning. The book wraps up with future trends, challenges, and opportunities in algorithmic trading. Become a proficient algorithmic trader capable of designing, developing, and deploying profitable trading systems. It not only provides theoretical knowledge but also emphasizes hands-on practice and real-world applications, ensuring you can confidently navigate and leverage AI in your trading strategies. KEY FEATURES ● Master AI and ML techniques to enhance algorithmic trading strategies. ● Hands-on Python tutorials for developing and optimizing trading algorithms. ● Real-world case studies showcasing AI applications in diverse trading scenarios. WHAT YOU WILL LEARN ● Develop AI-powered trading algorithms for enhanced decision-making and profitability. ● Utilize Python tools and libraries for financial modeling and analysis. ● Extract actionable insights from large datasets for informed trading decisions. ● Implement and optimize AI models within popular trading platforms. ● Apply risk management strategies to safeguard and optimize investments. ● Understand emerging technologies like quantum computing and blockchain in finance. WHO THIS BOOK IS FOR This book is for financial professionals, analysts, traders, and tech enthusiasts with a basic understanding of finance and programming. TABLE OF CONTENTS 1. Introduction to Algorithmic Trading and AI 2. AI and Machine Learning Basics for Trading 3. Essential Elements in AI Trading Algorithms 4. Data Processing and Analysis 5. Simulating and Testing Trading Strategies 6. Implementing AI Models with Trading Platforms 7. Getting Prepared for Python Development 8. Leveraging Python for Trading Algorithm Development 9. Real-world Examples and Case Studies 10. Using LLMs for Algorithmic Trading 11. Future Trends, Challenges, and Opportunities |
| best python library for technical analysis: Python for Finance Yves Hilpisch, 2014-12-11 The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies |
| best python library for technical analysis: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| best python library for technical analysis: Forecasting: principles and practice Rob J Hyndman, George Athanasopoulos, 2018-05-08 Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
| best python library for technical analysis: Advances in Financial Machine Learning Marcos Lopez de Prado, 2018-02-21 Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual 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. |
| best python library for technical analysis: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| best python library for technical analysis: Python Data Science Essentials: Tools, Techniques and Applications Dr.R.Kavitha, Dr.S.Ponmaniraj, Mrs.D.Poovizhi, Ms.R.Vinodharas, Mrs.C.Ramya, 2024-11-22 Dr.R.Kavitha, Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Dr.S.Ponmaniraj, Professor, Department of Computational Intelligence, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India. Mrs.D.Poovizhi, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Ms.R.Vinodharasi, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. Mrs.C.Ramya, Assistant Professor, Department of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamil Nadu, India. |
| best python library for technical analysis: Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023) Faruk Balli, Hui Nee Au Yong, Sikandar Ali Qalati, Ziqiang Zeng, 2023-10-10 This is an open access book.The relationship between international trade and economic development is mutual: foreign trade is the driving force of economic growth, and higher export level means that a country has the means to improve its import level. The growth of exports also tends to change the investment fields of the countries concerned. Exports make a country gain the benefits of economies of scale, and competition in the world market will put pressure on a country's export industry, A growing export sector will also encourage domestic and foreign investment. The concept of financial development actually means that the financial structure has changed to a certain extent. This change is not only the change of time, but also the change of internal transaction flow. International trade is known as the driving force of the development of human science and technology, and has created countless employment opportunities worldwide. It is also international trade that has led to the formation of industrial division worldwide. International trade, from its name, can be seen as trade between different countries, and the financial development level of a country will have a direct impact on the trend of international trade, so the purchasing power will be stronger. In this case, more countries are willing to increase import and export trade, which can not only increase their income, but also increase the relationship between countries. The 2nd International Academic Conference on Economics, Smart Finance, and Contemporary Trade (ESFCT 2023) will be held on July 28–30, 2023 in Dali, China. The purpose of ESFCT 2023 is to explore the relationship between economy, smart finance and contemporary trade. Experts and scholars in relevant fields are welcome to participate in ESFCT 2023. |
| best python library for technical analysis: Python for Mechanical and Aerospace Engineering Alex Kenan, 2021-01-01 The traditional computer science courses for engineering focus on the fundamentals of programming without demonstrating the wide array of practical applications for fields outside of computer science. Thus, the mindset of “Java/Python is for computer science people or programmers, and MATLAB is for engineering” develops. MATLAB tends to dominate the engineering space because it is viewed as a batteries-included software kit that is focused on functional programming. Everything in MATLAB is some sort of array, and it lends itself to engineering integration with its toolkits like Simulink and other add-ins. The downside of MATLAB is that it is proprietary software, the license is expensive to purchase, and it is more limited than Python for doing tasks besides calculating or data capturing. This book is about the Python programming language. Specifically, it is about Python in the context of mechanical and aerospace engineering. Did you know that Python can be used to model a satellite orbiting the Earth? You can find the completed programs and a very helpful 595 page NSA Python tutorial at the book’s GitHub page at https://www.github.com/alexkenan/pymae. Read more about the book, including a sample part of Chapter 5, at https://pymae.github.io |
| best python library for technical analysis: Python for Finance Yves Hilpisch, 2014-12-11 The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies |
| best python library for technical analysis: Business Analytics with R and Python David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi, 2024-07-30 This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence. |
| best python library for technical analysis: Python Data Analysis Cookbook Ivan Idris, 2016-07-22 Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained. |
| best python library for technical analysis: Machine Learning for Algorithmic Trading - Second Edition Stefan Jansen, 2020-07-31 |
| best python library for technical analysis: Python Algorithmic Trading Cookbook Pushpak Dagade, 2020-08-28 Build a solid foundation in algorithmic trading by developing, testing and executing powerful trading strategies with real market data using Python Key FeaturesBuild a strong foundation in algorithmic trading by becoming well-versed with the basics of financial marketsDemystify jargon related to understanding and placing multiple types of trading ordersDevise trading strategies and increase your odds of making a profit without human interventionBook Description If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help. Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders. By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem. Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice. What you will learnUse Python to set up connectivity with brokersHandle and manipulate time series data using PythonFetch a list of exchanges, segments, financial instruments, and historical data to interact with the real marketUnderstand, fetch, and calculate various types of candles and use them to compute and plot diverse types of technical indicatorsDevelop and improve the performance of algorithmic trading strategiesPerform backtesting and paper trading on algorithmic trading strategiesImplement real trading in the live hours of stock marketsWho this book is for If you are a financial analyst, financial trader, data analyst, algorithmic trader, trading enthusiast or anyone who wants to learn algorithmic trading with Python and important techniques to address challenges faced in the finance domain, this book is for you. Basic working knowledge of the Python programming language is expected. Although fundamental knowledge of trade-related terminologies will be helpful, it is not mandatory. |
| best python library for technical analysis: Data Analysis with Python David Taieb, 2018-12-31 Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development. |
| best python library for technical analysis: Applied Quantitative Finance Mauricio Garita, 2021-09-03 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. |
| best python library for technical analysis: Applied Text Analysis with Python Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda, 2018-06-11 From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity |
| best python library for technical analysis: Publishing Python Packages Dane Hillard, 2023-02-28 Create masterful, maintainable Python packages! This book includes pro tips for design, automation, testing, deployment, and even release as an open source project! In Publishing Python Packages you will learn how to: Build extensions and console script commands Use tox to automate packaging, installing, and testing Build a continuous integration pipeline using GitHub Actions Improve code quality and reduce manual review using black, mypy, and flake8 Create published documentation for your packages Keep packages up to date with pyupgrade and Dependabot Foster an open source community using GitHub features Publishing Python Packages teaches you how to easily share your Python code with your team and the outside world. Learn a repeatable and highly automated process for package maintenance that’s based on the best practices, tools, and standards of Python packaging. This book walks you through creating a complete package, including a C extension, and guides you all the way to publishing on the Python Package Index. Whether you’re entirely new to Python packaging or looking for optimal ways to maintain and scale your packages, this fast-paced and engaging guide is for you. Foreword by David Beazley. About the technology Successful Python packages install easily, run flawlessly, and stay reliably up to date. Publishing perfect Python packages requires a rigorous process that supports systematic testing and review, along with excellent documentation. Fortunately, the Python ecosystem includes tools and techniques to automate package creation and publishing. About the book Publishing Python Packages presents a practical process for sharing Python code in an automated and scalable way. Get hands-on experience with the latest packaging tools, and learn the ins and outs of package testing and continuous integration. You’ll even get pro tips for setting up a maintainable open source project, including licensing, documentation, and nurturing a community of contributors. What's inside Build extensions and console script commands Improve code quality with automated review and testing Create excellent documentation Keep packages up to date with pyupgrade and Dependabot About the reader For intermediate Python programmers. About the author Dane Hillard has spent the majority of his development career using Python to build web applications. Table of Contents PART 1 FOUNDATIONS 1 The what and why of Python packages 2 Preparing for package development 3 The anatomy of a minimal Python package PART 2 CREATING A VIABLE PACKAGE 4 Handling package dependencies, entry points, and extensions 5 Building and maintaining a test suite 6 Automating code quality tooling PART 3 GOING PUBLIC 7 Automating work through continuous integration 8 Authoring and maintaining documentation 9 Making a package evergreen 10 Scaling and solidifying your practices 11 Building a community |
| best python library for technical analysis: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| best python library for technical analysis: Mastering pandas for Finance Michael Heydt, 2015-05-25 If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected. |
| best python library for technical analysis: Professional Automated Trading Eugene A. Durenard, 2013-10-04 An insider's view of how to develop and operate an automated proprietary trading network Reflecting author Eugene Durenard's extensive experience in this field, Professional Automated Trading offers valuable insights you won't find anywhere else. It reveals how a series of concepts and techniques coming from current research in artificial life and modern control theory can be applied to the design of effective trading systems that outperform the majority of published trading systems. It also skillfully provides you with essential information on the practical coding and implementation of a scalable systematic trading architecture. Based on years of practical experience in building successful research and infrastructure processes for purpose of trading at several frequencies, this book is designed to be a comprehensive guide for understanding the theory of design and the practice of implementation of an automated systematic trading process at an institutional scale. Discusses several classical strategies and covers the design of efficient simulation engines for back and forward testing Provides insights on effectively implementing a series of distributed processes that should form the core of a robust and fault-tolerant automated systematic trading architecture Addresses trade execution optimization by studying market-pressure models and minimization of costs via applications of execution algorithms Introduces a series of novel concepts from artificial life and modern control theory that enhance robustness of the systematic decision making—focusing on various aspects of adaptation and dynamic optimal model choice Engaging and informative, Proprietary Automated Trading covers the most important aspects of this endeavor and will put you in a better position to excel at it. |
| best python library for technical analysis: Comparative Approaches to Using R and Python for Statistical Data Analysis Sarmento, Rui, Costa, Vera, 2017-01-06 The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to Using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis. |
| best python library for technical analysis: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| best python library for technical analysis: The Book of Trading Strategies Sofien Kaabar, 2021-07-06 Trading strategies come in different shapes and colors, and having a detailed view on their structure and functioning is very useful towards the path of creating a robust and profitable trading system. The book presents various technical strategies and the way to back-test them in Python. You can think of the book as a mix between introductory Python and an Encyclopedia of trading strategies with a touch of reality. |
| best python library for technical analysis: Learning Geospatial Analysis with Python Joel Lawhead, 2013 This is a tutorial-style book that helps you to perform Geospatial and GIS analysis with Python and its tools/libraries. This book will first introduce various Python-related tools/packages in the initial chapters before moving towards practical usage, examples, and implementation in specialized kinds of Geospatial data analysis.This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually.This book primarily targets Python developers, researchers, and analysts who want to perform Geospatial, modeling, and GIS analysis with Python. |
| best python library for technical analysis: New Technical Indicators in Python Sofien Kaabar, 2021-02-18 What is this book all about?This book is a modest attempt at presenting a more modern version of Technical Analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around technical indicators which is exactly the purpose of this book. I believe it is time to be creative and invent our own indicators that fit our profiles. Having had more success with custom indicators than conventional ones, I have decided to share my findings. The following chapters present new indicators that are the fruit of my research as well as indicators created by brilliant people. I also include the functions to create the indicators in Python and provide how to best use them as well as back-testing results. What am I going to gain?You will gain exposure to many new indicators and concepts that will change the way you think about trading and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized?The order of chapters is not important, although reading the introductory technical chapter is helpful. The book is divided into three parts: part 1 deals with trend-following indicators, part 2 deals with contrarian indicators, part 3 deals with market timing indicators, and finally, part 4 deals with risk and performance indicators.What do you mean when you say this book is dynamic and not static?This means that everything inside gets updated regularly with new material on my Medium profile. I always publish new findings and strategies. Make sure to follow me.What level of knowledge do I need to follow this book?Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python. |
| best python library for technical analysis: Python for Beginners Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, B Balamurugan, 2023-03-17 Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily, there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources, it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python. Provides concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools Offers practical advice for each major area of development with both Python 3.x and Python 2.x Based on the latest research in cognitive science and learning theory Helps the reader learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features This book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools, development of web applications, handling big data, complex calculations, workflow creation, rapid prototyping, and other software development purposes. It also targets graduates, postgraduates in computer science, information technology, academicians, practitioners, and research scholars. |
| best python library for technical analysis: Advances in Best-Worst Method Jafar Rezaei, Matteo Brunelli, Majid Mohammadi, 2023-01-31 This book presents recent advances in the theory and application of the Best-Worst Method (BWM). It includes selected papers from the Third International Workshop on Best-Worst Method (BWM2022), held in Delft, the Netherlands, from 9 to 10 June 2022. The book provides valuable insights on why and how to use BWM in a diverse range of applications including health, energy, supply chain management, and engineering. Moreover, it highlights the use of BWM in different settings including individual decision-making vs group decision-making, and with complete information vs incomplete and uncertain information. Academics and practitioners whose work involves multi-criteria decision-making and decision analysis will particularly benefit from the papers gathered here. |
| best python library for technical analysis: ADVANCED AI TRADING: TECHNIQUES FOR MAXIMUM PROFIT SHIKHAR SINGH (THE ZENITH), 🤖 Unleash the Power of AI: Discover cutting-edge artificial intelligence and machine learning techniques specifically tailored for financial markets. 📈 Algorithmic Strategy Mastery: Develop and refine sophisticated trading algorithms using neural networks, reinforcement learning, and other advanced AI models. 📊 Data-Driven Decision Making: Learn how to effectively collect, process, and analyze vast datasets to identify profitable trading opportunities. 🛡️ Risk Management Optimization: Implement robust risk management strategies powered by AI to minimize losses and protect your capital. ⚙️ Automated Trading Systems: Build and deploy fully automated trading systems that can execute trades with speed and precision, 24/7. 🧠 Advanced Model Training: Master the art of training and optimizing AI models for maximum performance, avoiding common pitfalls and biases. 💰 Maximize Profit Potential: Transform your trading strategies and achieve significant profit gains by leveraging the power of AI-driven insights and automation. |
| best python library for technical analysis: Learning the Pandas Library Matt Harrison, 2016-06 Python is one of the top 3 tools that Data Scientists use. One of the tools in their arsenal is the Pandas library. This tool is popular because it gives you so much functionality out of the box. In addition, you can use all the power of Python to make the hard stuff easy! Learning the Pandas Library is designed to bring developers and aspiring data scientists who are anxious to learn Pandas up to speed quickly. It starts with the fundamentals of the data structures. Then, it covers the essential functionality. It includes many examples, graphics, code samples, and plots from real world examples. The Content Covers: Installation Data Structures Series CRUD Series Indexing Series Methods Series Plotting Series Examples DataFrame Methods DataFrame Statistics Grouping, Pivoting, and Reshaping Dealing with Missing Data Joining DataFrames DataFrame Examples Preliminary Reviews This is an excellent introduction benefitting from clear writing and simple examples. The pandas documentation itself is large and sometimes assumes too much knowledge, in my opinion. Learning the Pandas Library bridges this gap for new users and even for those with some pandas experience such as me. -Garry C. I have finished reading Learning the Pandas Library and I liked it... very useful and helpful tips even for people who use pandas regularly. -Tom Z. |
| best python library for technical analysis: Algorithmic Trading with Python Chris Conlan, 2020-04-09 Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline. This book maintains a high standard of reprocibility. All code and data is self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities. Algorithmic Trading with Python (2020) is the spiritual successor to Automated Trading with R (2016). This book covers more content in less time than its predecessor due to advances in open-source technologies for quantitative analysis. |
| best python library for technical analysis: Technical Services in the 21st Century Samantha Schmehl Hines, 2021-01-08 By showcasing the work of technical services, and the ground-breaking changes they have encountered, this edited collection provides readers with an opportunity to re-assess the opportunities and challenges for library administration, and to understand how libraries should be managed in the future. |
| best python library for technical analysis: Intelligent Systems Design and Applications Ajith Abraham, Anu Bajaj, Thomas Hanne, Patrick Siarry, 2024-07-24 This book highlights recent research on intelligent systems and nature-inspired computing. It presents 45 selected papers focused on Natural Language Processing from the 23rd International Conference on Intelligent Systems Design and Applications (ISDA 2023), which was held in 5 different cities namely Olten, Switzerland; Porto, Portugal; Kaunas, Lithuania; Greater Noida, India; Kochi, India, and in online mode. The ISDA is a premier conference in the field of artificial intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. ISDA 2023 had contributions by authors from 64 countries. This book offers a valuable reference guide for all specialists, scientists, academicians, researchers, students, and practitioners in the field of artificial intelligence and Natural Language Processing. |
difference - "What was best" vs "what was the best"? - English …
Oct 18, 2018 · On the linked page, best is used as an adverb, modifying the verb knew. In that context, the phrase the best can also be used as if it were an adverb. The meaning is …
adverbs - About "best" , "the best" , and "most" - English …
Oct 20, 2016 · I like you best. I like chocolate best, better than anything else. can be used when what one is choosing from is not specified. I like you the best. Between chocolate, vanilla, and …
articles - "it is best" vs. "it is the best" - English Language ...
Jan 2, 2016 · This is the best car in the garage. We use articles like the and a before nouns, like car. The word "best" is an adjective, and adjectives do not take articles by themselves. …
expressions - "it's best" - how should it be used? - English …
Dec 8, 2020 · 3 "It's best (if) he (not) buy it tomorrow." is not a subjunctive form, and some options do not work well. 3A It's best he buy it tomorrow. the verb tense is wrong with 3A. Better would …
word choice - "his best-seller book" or "his best-selling book ...
Jun 12, 2016 · @J.R. If something is a New York Times Best Seller, the whole five word string is the adjective in use to modify book, although why book is specified is beyond me; perhaps to …
Word choice - Way of / to / for - Way of / to / for - English …
Jun 16, 2020 · The best way to use "the best way" is to follow it with an infinitive. However, this is not the only way to use the phrase; "the best way" can also be followed by of with a gerund: …
plural forms - It's/I'm acting in your best interest/interests ...
Dec 17, 2014 · have someone's (best) interests at heart (=want to help them): He claims he has only my best interests at heart. be in someone's/something's (best) interest(s) (=bring an …
"Best regards" vs. "Best Regards" - English Language Learners …
Dec 28, 2013 · The rule for formal letters is that only the first word should be capitalized (i.e. "Best regards"). Emails are less formal, so some of the rules are relaxed. That's why you're seeing …
Would be or will be - English Language Learners Stack Exchange
Oct 1, 2019 · It indicates items that (with the best understanding) are going to happen. Would is a conditional verb form. It states that something happens based on something else. Sometimes …
What is the correct usage of "deems fit" phrase?
Nov 15, 2016 · This plan of creating an electoral college to select the president was expected to secure the choice by the best citizens of each state, in a tranquil and deliberate way, of the …
difference - "What was best" vs "what was t…
Oct 18, 2018 · On the linked page, best is used as an adverb, modifying the verb knew. In that context, the phrase the best can also …
adverbs - About "best" , "the best" , and "m…
Oct 20, 2016 · I like you best. I like chocolate best, better than anything else. can be used when what one is choosing from is not …
articles - "it is best" vs. "it is the best" - Engli…
Jan 2, 2016 · This is the best car in the garage. We use articles like the and a before nouns, like car. The word "best" is an adjective, and …
expressions - "it's best" - how should it …
Dec 8, 2020 · 3 "It's best (if) he (not) buy it tomorrow." is not a subjunctive form, and some options do not work well. 3A It's best he buy it …
word choice - "his best-seller book" or …
Jun 12, 2016 · @J.R. If something is a New York Times Best Seller, the whole five word string is the adjective in use to modify …
