This is the code repository for Hands-On Python for Finance, published by Packt.
A practical guide to implementing financial analysis strategies using Python
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
In [12]:
#Example of a list
list_1 = [1,2,3]
#show
list_1
Following is what you need for this book: 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..
With the following software and hardware list you can run all code files present in the book (Chapter 2-11).
| Chapter | Software required | OS required |
|---|---|---|
| 2-11 | Jupyter Notebook, Anaconda Command Prompt NumPy, pandas, matplotlib, SciPy, TensorFlow, Keras libraries | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Krish Naik Krish Naik works as a lead data scientist, pioneering in machine learning, deep learning, and computer vision, and is an artificial intelligence practitioner, an educator, and a mentor, with over 7 years' experience in the industry. He also runs a YouTube channel where he explains various topics on machine learning, deep learning, and AI with many real-world problem scenarios. He has implemented various complex projects involving complex financial data with predictive modeling, machine learning, text mining, and sentiment analysis in the healthcare, retail, and e-commerce domains. He has delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums.
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