Hands On Python For Finance

Download Hands On Python For Finance full books in PDF, epub, and Kindle. Read online Hands On Python For Finance ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Hands-On Python for Finance

Author : Krish Naik
Publisher :
ISBN 13 : 9781789346374
Total Pages : 378 pages
Book Rating : 4.3/5 (463 download)

DOWNLOAD NOW!

Book Synopsis Hands-On Python for Finance by : Krish Naik

Download or read book Hands-On Python for Finance written by Krish Naik and published by . This book was released on 2019-03-29 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Key Features Understand Python data structure fundamentals and work with time series data Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance Explore various Python programs and learn finance paradigms Book Description Python is one of the most popular languages used for quantitative finance. With this book, you'll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you'll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You'll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this book, you will be equipped with the skills you need to perform different financial analysis tasks using Python What you will learn Clean financial data with data preprocessing Visualize financial data using histograms, color plots, and graphs Perform time series analysis with pandas for forecasting Estimate covariance and the correlation between securities and stocks Optimize your portfolio to understand risks when there is a possibility of higher returns Calculate expected returns of a stock to measure the performance of a portfolio manager Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow Who this book is for This book is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you're looking to pursue a career as a financial analyst or a data analyst. Working knowledge of Python programming language is necessary.

Python for Finance Cookbook

Author : Eryk Lewinson
Publisher : Packt Publishing Ltd
ISBN 13 : 1789617324
Total Pages : 426 pages
Book Rating : 4.7/5 (896 download)

DOWNLOAD NOW!

Book Synopsis Python for Finance Cookbook by : Eryk Lewinson

Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Hands-On Financial Trading with Python

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

DOWNLOAD NOW!

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

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

Python for Finance

Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492024295
Total Pages : 720 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Python for Finance by : Yves Hilpisch

Download or read book Python for Finance written by Yves Hilpisch and published by "O'Reilly Media, Inc.". This book was released on 2018-12-05 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python for Finance

Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491945389
Total Pages : 989 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Python for Finance by : Yves Hilpisch

Download or read book Python for Finance written by Yves Hilpisch and published by "O'Reilly Media, Inc.". This book was released on 2014-12-11 with total page 989 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Applied Quantitative Finance

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

DOWNLOAD NOW!

Book Synopsis Applied Quantitative Finance by : Mauricio Garita

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

Python for Finance

Author : Yuxing Yan
Publisher : Packt Publishing Ltd
ISBN 13 : 1783284382
Total Pages : 653 pages
Book Rating : 4.7/5 (832 download)

DOWNLOAD NOW!

Book Synopsis Python for Finance by : Yuxing Yan

Download or read book Python for Finance written by Yuxing Yan and published by Packt Publishing Ltd. This book was released on 2014-04-25 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.

Machine Learning and Data Science Blueprints for Finance

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

DOWNLOAD NOW!

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

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

Mastering Python for Finance

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

DOWNLOAD NOW!

Book Synopsis Mastering Python for Finance by : James Ma Weiming

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

Mastering Python for Finance

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

DOWNLOAD NOW!

Book Synopsis Mastering Python for Finance by : James Ma Weiming

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

Hands-On Artificial Intelligence for Banking

Author : Jeffrey Ng
Publisher : Packt Publishing Ltd
ISBN 13 : 1788833961
Total Pages : 232 pages
Book Rating : 4.7/5 (888 download)

DOWNLOAD NOW!

Book Synopsis Hands-On Artificial Intelligence for Banking by : Jeffrey Ng

Download or read book Hands-On Artificial Intelligence for Banking written by Jeffrey Ng and published by Packt Publishing Ltd. This book was released on 2020-07-10 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers in KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via Open Bank APISense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.

Personal Finance with Python

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

DOWNLOAD NOW!

Book Synopsis Personal Finance with Python by : Max Humber

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

Quantitative Finance with Python

Author : Chris Kelliher
Publisher : CRC Press
ISBN 13 : 100058237X
Total Pages : 801 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!

Book Synopsis Quantitative Finance with Python by : Chris Kelliher

Download or read book Quantitative Finance with Python written by Chris Kelliher and published by CRC Press. This book was released on 2022-05-19 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https://github.com/lingyixu/Quant-Finance-With-Python-Code.

Hands-On Machine Learning for Algorithmic Trading

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
ISBN 13 : 1789342716
Total Pages : 668 pages
Book Rating : 4.7/5 (893 download)

DOWNLOAD NOW!

Book Synopsis Hands-On Machine Learning for Algorithmic Trading by : Stefan Jansen

Download or read book Hands-On Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook Description The 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 learnImplement machine learning techniques to solve investment and trading problemsLeverage market, fundamental, and alternative data to research alpha factorsDesign and fine-tune supervised, unsupervised, and reinforcement learning modelsOptimize portfolio risk and performance using pandas, NumPy, and scikit-learnIntegrate machine learning models into a live trading strategy on QuantopianEvaluate strategies using reliable backtesting methodologies for time seriesDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlowWork with reinforcement learning for trading strategies in the OpenAI GymWho this book is for Hands-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.

Hands-On Python Natural Language Processing

Author : Aman Kedia
Publisher : Packt Publishing Ltd
ISBN 13 : 1838982582
Total Pages : 304 pages
Book Rating : 4.8/5 (389 download)

DOWNLOAD NOW!

Book Synopsis Hands-On Python Natural Language Processing by : Aman Kedia

Download or read book Hands-On Python Natural Language Processing written by Aman Kedia and published by Packt Publishing Ltd. This book was released on 2020-06-26 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get well-versed with traditional as well as modern natural language processing concepts and techniques Key FeaturesPerform various NLP tasks to build linguistic applications using Python librariesUnderstand, analyze, and generate text to provide accurate resultsInterpret human language using various NLP concepts, methodologies, and toolsBook Description Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP. What you will learnUnderstand how NLP powers modern applicationsExplore key NLP techniques to build your natural language vocabularyTransform text data into mathematical data structures and learn how to improve text mining modelsDiscover how various neural network architectures work with natural language dataGet the hang of building sophisticated text processing models using machine learning and deep learningCheck out state-of-the-art architectures that have revolutionized research in the NLP domainWho this book is for This NLP Python book is for anyone looking to learn NLP’s theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.

Python for Algorithmic Trading

Author : Yves Hilpisch
Publisher : O'Reilly Media
ISBN 13 : 1492053325
Total Pages : 380 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Python for Algorithmic Trading by : Yves Hilpisch

Download or read book Python for Algorithmic Trading written by Yves Hilpisch and published by O'Reilly Media. This book was released on 2020-11-12 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

Derivatives Analytics with Python

Author : Yves Hilpisch
Publisher : John Wiley & Sons
ISBN 13 : 1119037999
Total Pages : 390 pages
Book Rating : 4.1/5 (19 download)

DOWNLOAD NOW!

Book Synopsis Derivatives Analytics with Python by : Yves Hilpisch

Download or read book Derivatives Analytics with Python written by Yves Hilpisch and published by John Wiley & Sons. This book was released on 2015-08-03 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.