Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

Tatsat, Hariom, Puri, Sahil, Lookabaugh, Brad

Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

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商品描述

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).

Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.

This book covers:

  • Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
  • Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
  • Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
  • Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
  • Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
  • NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

商品描述(中文翻譯)

在未來幾十年中，機器學習和數據科學將改變金融行業。這本實用書籍將教導分析師、交易員、研究人員和開發人員如何構建對該行業至關重要的機器學習算法。您將學習監督式、非監督式和強化學習的機器學習概念和超過20個案例研究，以及自然語言處理（NLP）。

這本書非常適合在對沖基金、投資和零售銀行以及金融科技公司工作的專業人士。它深入探討了投資組合管理、算法交易、衍生品定價、欺詐檢測、資產價格預測、情感分析和聊天機器人開發等領域。您將探索從業人員面臨的實際問題，並學習到科學上支持的代碼和示例。

本書涵蓋了以下內容：
- 監督學習回歸模型，用於交易策略、衍生品定價和投資組合管理
- 監督學習分類模型，用於信用違約風險預測、欺詐檢測和交易策略
- 降維技術，並提供投資組合管理、交易策略和收益率曲線構建的案例研究
- 用於尋找相似對象的算法和聚類技術，並提供交易策略和投資組合管理的案例研究
- 強化學習模型和技術，用於構建交易策略、衍生品對沖和投資組合管理
- 使用Python庫（如NLTK和scikit-learn）的NLP技術，將文本轉換為有意義的表示形式

作者簡介

Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York. Hariom has extensive experience as a Quant in the areas of predictive modelling, financial instrument pricing, and risk management in several global investment banks and financial organizations. He completed his MS at UC Berkeley and his BE at IIT Kharagpur (India). Hariom has also completed FRM (Financial Risk Manager), CQF (Certificate in Quantitative Finance) and is a candidate for CFA Level 3.

Sahil Puri works as a Quantitative Researcher in the Analytics Division at P.I.M.C.O. His work involves testing model assumptions and finding strategies for multiple asset classes. Sahil has applied multiple statistical and machine learning based techniques to a wide variety of problems; examples include: generating text features, labeling curve anomalies, non-linear risk factor detection, and time series prediction. He completed his MS at UC Berkeley and his BE at Delhi College of Engineering (India).

作者簡介(中文翻譯)

Hariom Tatsat目前在紐約的一家投資銀行的量化分析部門擔任副總裁。Hariom在多家全球投資銀行和金融機構擁有豐富的量化分析經驗，專注於預測建模、金融工具定價和風險管理等領域。他在加州大學伯克利分校獲得了碩士學位，並在印度的卡拉格普爾工學院獲得了學士學位。Hariom還完成了金融風險管理師（FRM）、量化金融證書（CQF）的課程，並正在CFA第三級的考試中。

Sahil Puri在P.I.M.C.O的分析部門擔任量化研究員。他的工作涉及測試模型假設並為多個資產類別尋找策略。Sahil應用了多種統計和機器學習技術解決了各種問題，例如生成文本特徵、標記曲線異常、非線性風險因子檢測和時間序列預測。他在加州大學伯克利分校獲得了碩士學位，並在印度的德里工程學院獲得了學士學位。

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