Python for Finance Cookbook – Second Edition: Over 80 powerful recipes for effective financial data analysis , Second Edition

Eryk Lewinson

$49.99

4.9 (37 Ratings)

Paperback Dec 2022 740 pages 2nd Edition

Eryk Lewinson

$49.99

4.9 (37 Ratings)

Paperback Dec 2022 740 pages 2nd Edition

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Python for Finance Cookbook – Second Edition

Getting data from CoinGecko

The last data source we cover is dedicated purely to cryptocurrencies. CoinGecko is a popular data vendor and crypto tracking website, on which you can find real-time exchange rates, historical data, information about exchanges, upcoming events, trading volumes, and much more.

We can list a few of the advantages of CoinGecko:

completely free, no need to register for an API key
aside from prices, they also provide updates and news about crypto
it covers many coins, not only the most popular ones

In this recipe, we download Bitcoin's OHLC from the last 14 days.

How to do it…

Execute the following steps to download data from CoinGecko.

Import the libraries:

from pycoingecko import CoinGeckoAPI
from datetime import datetime

Instantiate the CoinGecko API:

cg = CoinGeckoAPI()

Get Bitcoin's OHLC prices from the last 14 days:

ohlc = cg.get_coin_ohlc_by_id(id="bitcoin", vs_currency="usd", days="14")
ohlc_df...

Converting currencies

Another quite common preprocessing step you might encounter while working on financial tasks is converting currencies. Imagine you have a portfolio of multiple assets, priced in different currencies and you would like to arrive at a total portfolio’s worth. The simplest example might be American and European stocks.

In this recipe, we show how to easily convert stock prices from USD to EUR. However, the very same steps can be used to convert any pair of currencies.

How to do it…

Execute the following steps to convert stock prices from USD to EUR:

Import the libraries:

import pandas as pd
import yfinance as yf
from forex_python.converter import CurrencyRates

Download Apple’s OHLC prices from January 2020:

df = yf.download("AAPL",
                 start="2020-01-01",
                 end="2020-01-31",
                 progress=False)
df = df.drop(columns=[&quot...

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Key benefits

Explore unique recipes for financial data processing and analysis with Python
Apply classical and machine learning approaches to financial time series analysis
Calculate various technical analysis indicators and backtest trading strategies

Description

Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions. You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses. Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.

Who is this book for?

This book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You’ll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems. Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.

What you will learn

Preprocess, analyze, and visualize financial data
Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models
Uncover advanced time series forecasting algorithms such as Meta's Prophet
Use Monte Carlo simulations for derivatives valuation and risk assessment
Explore volatility modeling using univariate and multivariate GARCH models
Investigate various approaches to asset allocation
Learn how to approach ML-projects using an example of default prediction
Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet

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Frequently bought together

Machine Learning for Algorithmic Trading

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Modern Time Series Forecasting with Python

$52.99

Total $ 160.97

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bin Sep 11, 2023

This book is very comprehensive, with useful knowledge points. It is really a highly recommended book!

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Rubens C. Machado Jun 07, 2024

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David Zhang May 01, 2023

Die 2. Version ist nochmal viel kompakter als die erste von vor 3 Jahren. Grundlegende als auch tiefgreifende Prozesse der Statistik und Programmierung werden gut erklärt dargestellt. Die fast 800 Seiten des Buches decken theoretisch mehr als nur einen ganzen Semester ab.

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Ram Seshadri Feb 08, 2023

I was recently given the Python for Finance cookbook to review by Packt based on my experience with Finance and ML. I have to say that this is one hell of a book!! It is one of the most comprehensive and sweeping write-ups of Python in Finance I have read. Just for starters: it’s 720 pages long.Second, it has over 15 chapters covering everything from downloading and processing Time series data to EDA to modeling and finally explaining and evaluating results.The book provides over 80 recipes for everything from ARIMA to Garch to ML to Monte Carlo. The subjects range from derivatives evaluation to asset management and Bitcoin forecasting.The book has tons and tons of code. Every page is filled with step by step instructions with code and charts and graphs. I can go and on. If there is only one book that you plan to buy for learning to apply Python to financial problems, this is probably the book to buy. Highly recommended!

Asha Jan 19, 2023

Having just started as a Junior Data Scientist this book was really helpful for time series analysis and forecasting. It's not for beginners you need to have some basic understanding of Python and data analysis to get the most out of this book. I don't work in the Finance industry but it was nice to learn more about financial data.

Python for Finance Cookbook – Second Edition: Over 80 powerful recipes for effective financial data analysis , Second Edition

What do you get with Print?

Python for Finance Cookbook – Second Edition

Getting data from CoinGecko

How to do it…

Summary

Adjusting the returns for inflation

How to do it…

Changing the frequency of time series data

Different ways of imputing missing data

Converting currencies

How to do it…

Different ways of aggregating trade data

Summary

Page 1 of 8

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Python for Finance Cookbook – Second Edition: Over 80 powerful recipes for effective financial data analysis , Second Edition

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

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Table of Contents

Recommendations for you

Customer reviews

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About the author

FAQs