Hands On Financial Trading With Python
Download Hands On Financial Trading With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Hands On Financial Trading With Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Hands-On Financial Trading with Python
Discover how to build and backtest algorithmic trading strategies with Zipline Key Features: Get to grips with market data and stock analysis and visualize data to gain quality insights Find out how to systematically approach quantitative research and strategy generation/backtesting in algorithmic trading Learn how to navigate the different features in Python's data analysis libraries Book Description: Algorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses. The book starts by introducing you to algorithmic trading and explaining why Python is 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. You'll also focus on 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. What You Will Learn: Discover how quantitative analysis works by covering financial statistics and ARIMA Use core Python libraries to perform quantitative research and strategy development using real datasets Understand how to access financial and economic data in Python Implement effective data visualization with Matplotlib Apply scientific computing and data visualization with popular Python libraries Build and deploy backtesting algorithmic trading strategies Who this book is for: This book is for data analysts and financial traders who want to explore how to design algorithmic trading strategies using Python's core libraries. If you are looking for a practical guide to backtesting algorithmic trading strategies and building your own strategies, then this book is for you. Beginner-level working knowledge of Python programming and statistics will be helpful.
Hands-On Financial Trading with Python
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 Cookbook
Author: Eryk Lewinson
language: en
Publisher: Packt Publishing Ltd
Release Date: 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.