If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help.Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders.By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem.Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice.

Financial trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of...(展开全部)

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. Completely updated for Python 3, the recipes in this book include: Data structures and algorithms Strings and text Dates and times Metaprogramming Testing With scores of practical examples and pertinen...(展开全部)

The Python Cookbook is a collection of problems, solutions, and practical examples for Python programmers, written by Python programmers. It contains over two hundred recipes for text manipulation, object oriented programming, XML processing, system administration, and much more. This book is a treasure trove of useful code for both novices and advanced practitioners, with cont...(展开全部)

本书介绍了Python应用在各个领域中的一些使用技巧和方法，从最基本的字符、文件序列、字典和排序，到进阶的面向对象编程、数据库和数据持久化、 XML处理和Web编程，再到比较高级和抽象的描述符、装饰器、元类、迭代器和生成器，均有涉及。书中还介绍了一些第三方包和库的使用，包括 Twisted、GIL、PyWin32等。本书覆盖了Python应用中的很多常见问题，并提出了通用的解决方案。书中的代码和方法具有很强的实用性，可以方便地应用到实际的项目中，并产生立竿见影的效果。尤为难得的是，本书的各位作者都具有丰富的业界实践经验，因此，本书不仅给出了对各种问题的解决方案，同时还体现了很多专家的思维方式和良好的编程习惯，与具体的细节性知识相比，这部分内容无疑是本书的精华。 本书适合具有一定Python基础的读者阅读参考。

Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. It is now being used by an increasing number of major organizations, including NASA and Google. Updated for Python 2.4, The Python Cookbook, 2nd Edition offers a wealth of useful code for all Python progra...(展开全部)

本书介绍了Python应用在各个领域中的一些使用技巧和方法，其主题涵盖了数据结构和算法，字符串和文本，数字、日期和时间，迭代器和生成器，文件和I/O，数据编码与处理，函数，类与对象，元编程，模块和包，网络和Web编程，并发，实用脚本和系统管理，测试、调试以及异常，C语言扩展等。 本书覆盖了Python应用中的很多常见问题，并提出了通用的解决方案。书中包含了大量实用的编程技巧和示例代码，并在Python 3.3环境下进行了测试，可以很方便地应用到实际项目中去。此外，本书还详细讲解了解决方案是如何工作的，以及为什么能够工作。 本书非常适合具有一定编程基础的Python程序员阅读参考。

本书介绍了Python应用在各个领域中的一些使用技巧和方法，其主题涵盖了数据结构和算法，字符串和文本，数字、日期和时间，迭代器和生成器，文件和I/O，数据编码与处理，函数，类与对象，元编程，模块和包，网络和Web编程，并发，实用脚本和系统管理，测试、调试以及异常，C语言扩展等。 本书覆盖了Python应用中的很多常见问题，并提出了通用的解决方案。书中包含了大量实用的编程技巧和示例代码，并在Python 3.3环境下进行了测试，可以很方便地应用到实际项目中去。此外，本书还详细讲解了解决方案是如何工作的，以及为什么能够工作。 本书非常适合具有一定编程基础的Python程序员阅读参考。