Machine Learning for Finance: Principles and practice for financial insiders Author: Jannes Klaas Pub Date: 2019 ISBN: 978-1789136364 Pages: 456 Language: English Format: EPUB A guide to advances in machine learning for financial professionals, with working Python code Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learn Apply machine learning to structured data, natural language, photographs, and written text How machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Dig deep into neural networks, examine uses of GANs and reinforcement learning Debug machine learning applications and prepare them for launch Address bias and privacy concerns in machine learning

For financial math learners

投资，分享-无法交付（FTD） 我对SEC的无法交付（FTD）数据的分析。 请参阅以下视频： 无法交付（FTD）-我们是否应该担心-是否即将出现另一种“金融危机”？

3/12最新题库 V17

C++设计模式和金融衍生品定价 原版 非扫描

These days, Python is undoubtedly one of the major strategic technology platforms in the financial industry. When I started writing the first edition of this book in 2013, I still had many conversations and presentations in which I argued relentlessly for Python’s competitive advantages in finance over other languages and platforms. Toward the end of 2018, this is not a question anymore: financial institutions around the world now simply try to make the best use of Python and its powerful ecosystem of data analysis, visualization, and machine learning packages.

Python for Finance(2nd) 英文无水印pdf 第2版 pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

开源金融科技标准和数据 关于 使命：加速开发并帮助FinTech服务以一种语言进行交流。 提供：供应商，公司，组织，货币，银行，数字交换器，付款提供商（PSP），付款方式等的开放数据。 创建用于：以“一种语言”进行交叉集成的微服务的通信。 目标是：标准化用于在不同Web服务之间交换信息的实体标识符。 总览 国际标准产生技术，经济和社会优势。 对社区的好处： 发展动力。 标准加快了新应用程序的开发速度，并简化了服务之间的通信过程。 免费！ 数据和服务可根据MIT许可获得。 合作。 这是一个开放的标准和开放的数据，金融科技市场的每个参与者都可以为开发和增强做出贡献。 易于集成。

quantmod：量化金融建模框架

财务数据库 作为私人投资者，可以在互联网上找到的大量信息令人生畏。 随着市场上有成千上万的公司和衍生产品，试图了解可用的公司类型或ETF是非常困难的。 当然，仅因为公众知道（例如，Microsoft，Tesla，S＆P500 ETF或All-World ETF），即可很快找到交易量最大的公司和ETF。 但是，外面还不知道什么。 这个数据库试图解决这个问题。 它具有超过180.000个交易品种，包含股票，ETF，基金，指数，期货，期权，货币，加密货币和货币市场。 因此，它使您可以广泛了解行业，行业，投资类型等等。 该数据库的目的明确地是不提供最新的基础数据或库存数据，因为可以通过使用或轻松获