TradingView图表数据提取器 影片教学 如何进行屏幕录制： ： 上面教程中的结果文件： : 发布图表之前，请确保缩放/平移，以便在TradingView上可以看到所需的最早日期。 指标太多或时间分辨率太低都会增加数据点，并可能使空闲服务器超载。 为避免这种情况，请在本地计算机上托管/运行脚本，或者使用较少的指示符多次抓取脚本，然后手动组合CSV。 用法 只需将在TradingView上发布的图表/想法的URL添加到下面的链接。 这不是证券图表的URL，而是用户发布的图表的URL： : url 即对于此图表： : 您将使用： : : 安装 pip3 ins

Statistical Arbitrage Algorithmic Trading Insights and Techniques.pdf

Written in an easily accessible style, this book provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python.

This book is a practical guide to algorithmic trading strategies that can be readily implemented by both retail and institutional traders. It is not an academic treatise on financial theory. Rather, I hope to make accessible to the reader some of the most useful financial research done in the past few decades, mixing them with insights I gained from actually exploiting some of those theories in live trading.

成功的算法交易，算法交易的入门教程，适合想入门量化交易的同学学习

The Science of Algorithmic Trading and Portfolio Management - Kissell, Robert，量化交易的经典！

The Science of Algorithmic Trading and Portfolio Management Algorithm Trading book

基于随机逼近理论，我们在限价单中提出了一个做市商的优化框架。 在最佳清算策略的背景下，我们考虑了Lavelelle，Lehalle和Pagès的文章中类似于Avellaneda-Stoikov模型的离散时间变体。 想法是利用更新出价和要价的过程的迭代性质，以使算法在反复试验的基础上（即在线学习）优化其策略。 这种方法的优点是，通过算法对系统的探索是在运行时执行的，因此不需要像随机控制方法那样对价格动态进行明确的说明。 正如将要讨论的那样，我们的方法的原理可以扩展到除做市商以外的更广泛的算法交易战术问题类别。

This book is for anyone who wants to learn computer programming and knows absolutely nothing about it. If you are wondering whether this book is going to teach you how to create amazing applets or incredible desktop or mobile applications, the answer is “no”—that is a job for other books. So many books out there can teach you those skills in Java, C++, or C#. Many of them even claim that they can teach you in 24 hours! Don’t laugh! They probably can do that, but all of them take one thing for granted—that the reader knows some basics about computer programming. None of those books, unfortunately, bothers to teach you the first thing that a novice programmer needs to learn, which is “Algorithmic Thinking.” Algorithmic Thinking involves more than just learning code. It is a problem solving process that involves learning how to code. With more than 200 solved and about 400 unsolved exercises, 450 true/false, 150 multiple choice, and 160 review questions (the solutions and the answers to which can be found on the Internet), this book is ideal for students, teachers, professors, novices or average programmers, or for anyone who wants to start learning or teaching computer programming using the proper conventions and techniques.

It is not the main objective of this book to present you with the theorems and proofs on data structures and algorithms. I have followed a pattern of improving the problem solutions with different complexities (for each problem, you will find multiple solutions with different, and reduced, complexities). Basically, it’s an enumeration of possible solutions. With this approach, even if you get a new question, it will show you a way to think about the possible solutions. You will find this book useful for interview preparation, competitive exams preparation, and campus interview preparations.