لایب سان

Author(s): Yves Hilpisch

Publisher: O'Reilly Media, Year: 2020

ISBN: 149205335X,9781492053354

Contents and Structure Here’s a quick overview of the topics and contents presented in each chapter. Chapter 1, Python and Algorithmic Trading The first chapter is an introduction to the topic of algorithmic trading—that is, the automated trading of financial instruments based on computer algorithms. It discusses fundamental notions in this context and also addresses, among other things, what the expected prerequisites for reading the book are. Chapter 2, Python Infrastructure This chapter lays the technical foundations for all subsequent chapters in that it shows how to set up a proper Python environment. This chapter mainly uses conda as a package and environment manager. It illustrates Python deployment via Docker containers and in the cloud. Chapter 3, Working with Financial Data Financial time series data is central to every algorithmic trading project. This chapter shows you how to retrieve financial data from different public data and proprietary data sources. It also demonstrates how to store financial time series data efficiently with Python. Chapter 4, Mastering Vectorized Backtesting Vectorization is a powerful approach in numerical computation in general and for financial analytics in particular. This chapter introduces vectorization with NumPy and pandas and applies that approach to the backtesting of SMA-based, momentum, and mean-reversion strategies. Preface | xi Chapter 5, Predicting Market Movements with Machine Learning This chapter is dedicated to generating market predictions by the use of machine learning and deep learning approaches. By mainly relying on past return obser‐ vations as features, approaches are presented for predicting tomorrow’s market direction by using such Python packages as Keras in combination with Tensor Flow and scikit-learn. Chapter 6, Building Classes for Event-Based Backtesting While vectorized backtesting has advantages when it comes to conciseness of code and performance, it’s limited with regard to the representation of certain market features of trading strategies. On the other hand, event-based backtesting, technically implemented by the use of object oriented programming, allows for a rather granular and more realistic modeling of such features. This chapter presents and explains in detail a base class as well as two classes for the backtest‐ ing of long-only and long-short trading strategies. Chapter 7, Working with Real-Time Data and Sockets Needing to cope with real-time or streaming data is a reality even for the ambi‐ tious individual algorithmic trader. The tool of choice is socket programming, for which this chapter introduces ZeroMQ as a lightweight and scalable technology. The chapter also illustrates how to make use of Plotly to create nice looking, interactive streaming plots. Chapter 8, CFD Trading with Oanda Oanda is a foreign exchange (forex, FX) and Contracts for Difference (CFD) trading platform offering a broad set of tradable instruments, such as those based on foreign exchange pairs, stock indices, commodities, or rates instruments (benchmark bonds). This chapter provides guidance on how to implement auto‐ mated algorithmic trading strategies with Oanda, making use of the Python wrapper package tpqoa. Chapter 9, FX Trading with FXCM FXCM is another forex and CFD trading platform that has recently released a modern RESTful API for algorithmic trading. Available instruments span multi‐ ple asset classes, such as forex, stock indices, or commodities. A Python wrapper package that makes algorithmic trading based on Python code rather convenient and efficient is available (http://fxcmpy.tpq.io). Chapter 10, Automating Trading Operations This chapter deals with capital management, risk analysis and management, as well as with typical tasks in the technical automation of algorithmic trading oper‐ ations. It covers, for instance, the Kelly criterion for capital allocation and leverage in detail

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