Machine Learning for Algorithmic Trading, 2/e (Paperback)

Stefan Jansen

Machine Learning for Algorithmic Trading, 2/e (Paperback)

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商品描述

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.

Key Features

  • Design, train, and evaluate machine learning algorithms that underpin automated trading strategies
  • Create a research and strategy development process to apply predictive modeling to trading decisions
  • Leverage NLP and deep learning to extract tradeable signals from market and alternative data

Book Description

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.

What you will learn

  • Leverage market, fundamental, and alternative text and image data
  • Research and evaluate alpha factors using statistics, Alphalens, and SHAP values
  • Implement machine learning techniques to solve investment and trading problems
  • Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader
  • Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio
  • Create a pairs trading strategy based on cointegration for US equities and ETFs
  • Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data

Who this book is for

If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.

Some understanding of Python and machine learning techniques is required.

商品描述(中文翻譯)

運用pandas、TA-Lib、scikit-learn、LightGBM、SpaCy、Gensim、TensorFlow 2、Zipline、backtrader、Alphalens和pyfolio，利用機器學習來設計和回測真實市場的自動交易策略。

主要特點：

- 設計、訓練和評估機器學習算法，支持自動交易策略
- 創建研究和策略開發流程，將預測建模應用於交易決策
- 利用自然語言處理（NLP）和深度學習從市場和替代數據中提取可交易信號

書籍描述：

數據數字的爆炸性增長推動了對使用機器學習（ML）的交易策略專業知識的需求。這本修訂和擴展的第二版使您能夠構建和評估複雜的監督、非監督和強化學習模型。

本書介紹了從想法和特徵工程到模型優化、策略設計和回測的交易工作流程的端到端機器學習。通過使用從線性模型和基於樹的集成到最新研究的深度學習技術的示例來說明這一點。

本版展示了如何處理市場、基本和替代數據，例如tick數據、分鐘和日K線、SEC文件、盈利電話轉錄、財經新聞或衛星圖像，以生成可交易的信號。它演示了如何通過工程化金融特徵或alpha因子，使ML模型能夠預測美國和國際股票和ETF的價格數據回報。它還展示了如何使用Alphalens和SHAP值評估新特徵的信號內容，並包含一個新的附錄，其中包含一百多個alpha因子示例。

最終，您將能夠將ML模型預測轉化為在日內或日內時間段內運作的交易策略，並評估其表現。

學到的內容：

- 利用市場、基本和替代文本和圖像數據
- 使用統計學、Alphalens和SHAP值研究和評估alpha因子
- 實施機器學習技術解決投資和交易問題
- 使用Zipline和Backtrader回測和評估基於機器學習的交易策略
- 使用pandas、NumPy和pyfolio優化投資組合風險和績效分析
- 基於共整合為美國股票和ETF創建配對交易策略
- 使用AlgoSeek的高質量交易和報價數據訓練梯度提升模型預測日內回報

適合閱讀對象：

如果您是數據分析師、數據科學家、Python開發人員、投資分析師或投資組合經理，並且有興趣獲得實踐機器學習知識以進行交易，那麼本書適合您。如果您想學習如何利用機器學習從多樣的數據源中提取價值，設計自己的系統交易策略，那麼本書也適合您。

需要具備一定的Python和機器學習技術的理解。

作者簡介

Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems.

Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank.

He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.

作者簡介(中文翻譯)

Stefan是Applied AI的創始人兼首席執行官。他為財富500強公司、投資公司和初創企業提供數據和人工智能策略、建立數據科學團隊以及為各種業務問題開發端到端的機器學習解決方案的建議。

在他現在的創業之前，他曾是一家國際投資公司的合夥人和董事總經理，負責建立預測分析和投資研究業務。他還曾是一家在15個市場運營的全球金融科技公司的高級執行官，為新興市場的中央銀行提供諮詢，並為世界銀行提供顧問服務。

他擁有喬治亞理工學院和哈佛大學以及柏林自由大學的計算機科學和經濟學碩士學位，並持有CFA證書。他在歐洲、亞洲和美洲的六種語言中工作過，並在Datacamp和General Assembly教授數據科學。

目錄大綱

(N.B. Please use the Look Inside option to see further chapters)

  1. Machine Learning for Trading – From Idea to Execution
  2. Market and Fundamental Data – Sources and Techniques
  3. Alternative Data for Finance – Categories and Use Cases
  4. Financial Feature Engineering – How to Research Alpha Factors
  5. Portfolio Optimization and Performance Evaluation
  6. The Machine Learning Process
  7. Linear Models – From Risk Factors to Return Forecasts
  8. The ML4T Workflow – From Model to Strategy Backtesting
  9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage
  10. Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading

目錄大綱(中文翻譯)

(N.B. 請使用「Look Inside」選項查看更多章節)

1. 交易機器學習 - 從構想到執行
2. 市場和基本數據 -來源和技術
3. 金融的替代數據 - 分類和應用案例
4. 金融特徵工程 - 如何研究 Alpha 因子
5. 投資組合優化和績效評估
6. 機器學習流程
7. 線性模型 - 從風險因素到回報預測
8. ML4T 工作流程 - 從模型到策略回測
9. 時間序列模型用於波動率預測和統計套利
10. 貝葉斯機器學習 - 動態夏普比率和配對交易

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