Preface

This second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the finance industry, using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn how to manage risks using advanced examples.

You will start by setting up a Jupyter notebook to implement the tasks throughout the book. You will learn how to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, NumPy, SciPy, scikit-learn, and so on. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn how to apply statistical analysis on time series data and understand how to harness high-frequency data to devise trading strategies in building an algorithmic trading platform. You will learn to validate your trading strategies by implementing an event-driven backtesting system and measure its performance. Finally, you will explore machine learning and deep learning techniques that are applied in finance.

By the end of this book, you will have learned how to apply Python to different paradigms in the financial industry and perform efficient data analysis.

Who this book is for

If you are a financial or data analyst, or a software developer in the financial industry, who is interested in using advanced Python techniques for quantitative methods, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications using smart machine learning techniques.

What this book covers

Chapter 1, Overview of Financial Analysis with Python, goes briefly through setting up a Python environment, including a Jupyter Notebook, so that you can proceed with the rest of the chapters in this book. Within Jupyter, we will perform some time series analysis with pandas, using plots for analysis.

Chapter 2, The Importance of Linearity in Finance, uses Python to solve systems of linear equations, perform integer programming, and apply matrix algebra to the linear optimization of portfolio allocation.

Chapter 3, Nonlinearity in Finance, explores some methods that will help us extract information from nonlinear models. You will learn root-finding methods in nonlinear volatility modeling. The optimize module of SciPy contains the root and fsolve functions, which can also help us to perform root finding on non-linear models.

Chapter 4, Numerical Methods for Pricing Options, explores trees, lattices, and finite differencing schemes for the valuation of options.

Chapter 5, Modeling Interest Rates and Derivatives, discusses the bootstrapping process of the yield curve and covers some short-rate models for pricing interest rate derivatives with Python.

Chapter 6, Statistical Analysis of Time Series Data, introduces principal component analysis for identifying principal components. The Dicker-Fuller test is used for testing whether a time series is stationary.

Chapter 7, Interactive Financial Analytics with VIX, discusses volatility indexes. We will perform analytics on a US stock index and VIX data, and replicate the main index using the options prices of the sub-indexes.

Chapter 8, Building an Algorithmic Trading Platform, takes a step-by-step approach to developing a mean-reverting and trend-following live trading infrastructure using a broker API.

Chapter 9, Implementing a Backtesting System, discusses how to design and implement an event-driven backtesting system and helps you to visualize the performance of our simulated trading strategy.

Chapter 10, Machine Learning for Finance, introduces us to machine learning, allowing us to study its concepts and applications in finance. We will also look at some practical examples for applying machine learning to assist in trading decisions.

Chapter 11, Deep Learning for Finance, encourages us to take a hands-on approach to learning TensorFlow and Keras by building deep learning prediction models using neural networks.

To get the most out of this book

Prior experience in Python is required.

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In [ ]:
     %matplotlib inline
     import quandl

     quandl.ApiConfig.api_key = QUANDL_API_KEY
     df = quandl.get('EURONEXT/ABN.4')
     daily_changes = df.pct_change(periods=1)
     daily_changes.plot();

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2015-02-26 TICK WIKI/AAPL open: 128.785 close: 130.415
2015-02-26 FILLED BUY 1 WIKI/AAPL at 128.785
2015-02-26 POSITION value:-128.785 upnl:1.630 rpnl:0.000
2015-02-27 TICK WIKI/AAPL open: 130.0 close: 128.46

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