You don't have anything in your cart right now. Python is a free and powerful tool that can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses.

This book details the steps needed to retrieve time series data from different public data sources. Python for Finance explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests.

This book introduces you to the basic concepts and operations related to Python. You will also learn how to estimate illiquidity, Amihudliquidity measure, Pastor and StambaughRoll spreadspread based on high-frequency data, beta rolling betadraw volatility smile and skewness, and construct a binomial tree to price American options. This book is 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.

Yuxing Yan graduated from McGill university with a PhD in finance. He has taught various finance courses, such as Financial Modeling, Options and Futures, Portfolio Theory, Quantitative Financial Analysis, Corporate Finance, and Introduction to Financial Databases to undergraduate and graduate students at seven universities: Yan has actively done research with several publications in Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research.

For example, his latest publication, co-authored with Shaojun Zhang, will appear in the Journal of Banking and Finance in His research areas include investment, market microstructure, and open source finance. He is proficient at several computer languages such as SAS, R, MATLAB, C, and Python. From tohe worked as a technical director at Wharton Research Data Services WRDSwhere he debugged several hundred computer programs related to research for WRDS users.

After that, he returned to teaching in and introduced R into several quantitative courses at two universities. Based on lecture notes, he has the first draft of an unpublished manuscript titled Financial Modeling using R. In addition, he is an expert on financial data. While teaching at NTU in Singapore, he offered a course called Introduction to Financial Databases to doctoral students.

While working at WRDS, he answered numerous questions related to financial databases and helped update CRSP, Compustat, IBES, and TAQ NYSE high-frequency database. Zhu his co-author published a book titled Financial Databases, Shiwu Zhu and Yuxing Yan, Tsinghua University Press. Currently, he spends considerable time and effort on public financial data.

Python for Finance | PACKT Books

If you have any queries, you can always contact him at yany canisius. Sign up here to get exclusive deep discounts on our latest and bestselling eBooks, delivered straight to your inbox every day.

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Machine Learning OpenCV OpenStack Oracle Penetration Testing PHP PostgreSQL Python. QlikView R Scala Spark Spring Swift Unity WordPress. Read all our articles. Mapt Go to Mapt. Title added to cart. If your interest is finance and trading, then using Python to build a financial calculator makes basics of binary options strategy 60 seconds sense.

As does this book which is a hands-on guide covering everything from option theory to time series. This ebook is included in a Mapt subscription. Yuxing Yan April Are you sure you want to claim this product using a token? Book Details ISBN 13 Book Description Python is a free and powerful tool that can be used to build a financial calculator and livestock auction marts in alberta options, and can also explain many trading strategies and test various hypotheses.

Table of Contents Chapter do american call options paying dividend-paying stocks empirical tests Introduction and Installation of Python.

Using Python as an Ordinary Calculator. Basic math operations — addition, subtraction, multiplication, and division.

Finding out more information about a specific built-in function. Using Python as a Financial Calculator. Defining the payback period and the payback period rule. Introduction to NumPy and SciPy. Working with arrays of ones, zeros, and the identity matrix. Linear regression and Capital Assets Pricing Model CAPM. Visual Finance via Matplotlib.

Graphical representation of the portfolio diversification effect. Statistical Analysis of Time Series. The Black-Scholes-Merton Option Model. Cash flows, types of options, a right, and an obligation. Normal distribution, standard normal distribution, and cumulative standard normal distribution.

The Black-Scholes-Merton option model on non-dividend paying stocks. Binomial tree the CRR method and its graphical representation.

Python Loops and Implied Volatility.

Estimating implied volatility by using an American call. Measuring efficiency by time spent in finishing a program. Monte Carlo Simulation and Options.

Generating random numbers from a standard normal distribution. Volatility Measures and GARCH. What You Will Learn Build a financial calculator based on Python Learn how to price various types of options such as European, American, average, lookback, and barrier options Write Python programs to download data from Yahoo!

Finance Estimate returns make money 5linx convert daily returns into monthly or annual returns Form an n-stock portfolio and estimate its variance-covariance matrix Estimate VaR Value at Risk for a stock or portfolio Run CAPM Capital Asset Pricing Model and the Fama-French 3-factor model Learn how to optimize a portfolio and draw an efficient frontier Conduct various statistic tests such as T-tests, F-tests, and normality tests.

Taxes – Just Facts

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Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

Learn how to apply powerful data analysis techniques with popular open source Python modules. For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that. This Practical Data Cookbook has 89 hands-on recipes for all data scientists to help complete real-world big data science and numerical projects in R and Python.

If you want to master object-oriented Python programming this book is a must-have. Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial. View our Cookie Policy. We understand your time is important. Uniquely amongst the major publishers, we seek to develop and publish the broadest range of learning and information products on each technology.

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