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Key benefits

Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data

Explore unique recipes for financial data analysis and processing with Python

Estimate popular financial models such as CAPM and GARCH using a problem-solution approach

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.

Who is this book 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.

What you will learn

Download and preprocess financial data from different sources

Backtest the performance of automatic trading strategies in a real-world setting

Estimate financial econometrics models in Python and interpret their results

Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment

Improve the performance of financial models with the latest Python libraries

Apply machine learning and deep learning techniques to solve different financial problems

Understand the different approaches used to model financial time series data

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

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Amazon verified reviews

FieryHarrison Jan 28, 2021

I could not put this book down. I read every page multiple times and have run every line of code. I have been a SAS programmer for 20+ years and have been slowly migrating to Python for my financial analysis. A handful of books over the years have stood the test of time for their usefulness in analytics. This is one of those books. Don't expect to be a chef coder of Michelin status with this cookbook but at least you will learn by doing and be well established to take your skills to another level. I loved that the book assumes analytical knowledge and focuses on the recipes. The where to go to find more information is extremely helpful. Thank you so much Eryk for your contribution. I look forward to buying all of your books!

Amazon Verified review

Jeweler24 Sep 28, 2020

I work for a financial technology company and I was looking for a book to help me understand some of the work the teams were doing. I personally learn better by getting my hands dirty and writing code.This book covers a lot of topics and it does it in a way that lets you skip around and do it in any order that works for you. You don’t need to read it end to end, you can pick it up, read the chapter that you care about, and put it back on the shelf for when you need it again. Since the chapters are well written, and it is extremely easy and fun, and you probably won’t want to put it back on the shelf.I love the fact that all of the code is in python, and it gives lots of examples to get you started. Each one of the chapters gives you a starting point and you can easily customize the code to fit your given situation.While going through the book, I didn’t just learn how to solve some common financial problems, I also learned a lot about the different python libraries that there out there and what they are good for. Since there are a ton of libraries out there, having a resource like this that shows you which ones are good along with example code, saved me a lot of time and research doing that on my own.I highly recommend this book even if you don’t know python, it shouldn’t stop you, and by the end of the book you might just learn enough python to be dangerous.

Robin T. Wernick Jun 03, 2021

I liked the complete display of trading from trading centers. It certainly showed a quick way to make connections to two automated brokers.However, the calculations that supported the trading were completely hidden and if you want to make improvements, you are on your own.Some other book holds the secrets to tweaking the backtesting or holding the access to the complete list of instruments. So I took off a star for withholding critical data access from me.

William J. Brown Feb 08, 2021

I confess I often avoid Packt books because the quality is so variable, but Eryk Lewinson has provided us with an insightful and useful book. I also have the Hilpisch book and found it spent a lot of time teaching Python and didn't have a clear source for financial data, which was one of the reasons I bought the book.

Matthew Sep 03, 2020

fastai 2.0 API changed lot. Some of classes and methods are gone.What's fastai version the book using?Thanks.

Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis

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