Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python (Paperback)

Stefan Jansen

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商品描述

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras

Key Features

  • Implement machine learning algorithms to build, train, and validate algorithmic models
  • Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions
  • Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics

Book Description

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.

This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies.

Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.

What you will learn

  • Implement machine learning techniques to solve investment and trading problems
  • Leverage market, fundamental, and alternative data to research alpha factors
  • Design and fine-tune supervised, unsupervised, and reinforcement learning models
  • Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn
  • Integrate machine learning models into a live trading strategy on Quantopian
  • Evaluate strategies using reliable backtesting methodologies for time series
  • Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow
  • Work with reinforcement learning for trading strategies in the OpenAI Gym

Who this book is for

Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

Table of Contents

  1. Machine Learning for Trading
  2. Market and Fundamental Data
  3. Alternative Data for Finance
  4. Alpha Factor Research
  5. Strategy Evaluation
  6. The Machine Learning Process
  7. Linear Models
  8. Time Series Models
  9. Bayesian Machine Learning
  10. Decision Trees and Random Forests
  11. Gradient Boosting Machines
  12. Unsupervised Learning
  13. Working with Text Data
  14. Topic Modeling
  15. Word Embeddings
  16. Next Steps

商品描述(中文翻譯)

使用NumPy、spaCy、pandas、scikit-learn和Keras在現實世界市場中探索有效的交易策略

主要特點


  • 實施機器學習算法來構建、訓練和驗證算法模型

  • 創建自己的算法設計流程,應用概率機器學習方法進行交易決策

  • 開發用於算法交易的神經網絡,執行時間序列預測和智能分析

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。本書使您能夠使用各種監督和非監督算法從各種數據源中提取信號,並創建強大的投資策略。

本書展示了如何通過API或網絡爬蟲訪問市場、基本和替代數據,並提供了評估替代數據的框架。您將在時間序列上下文中實踐ML工作流程,從模型設計、損失度量定義和參數調整到性能評估。您將了解ML算法,如貝葉斯和集成方法以及流形學習,並且將知道如何使用pandas、statsmodels、sklearn、PyMC3、xgboost、lightgbm和catboost訓練和調整這些模型。本書還教您如何使用spaCy從文本數據中提取特徵,對新聞進行分類並分配情感分數,並使用gensim對主題進行建模和從財務報告中學習詞嵌入。您還將使用Keras和PyTorch構建和評估神經網絡,包括RNN和CNN,以利用非結構化數據進行複雜的策略。

最後,您將應用轉移學習到衛星圖像中,以預測經濟活動,並使用強化學習構建學習在OpenAI Gym中進行交易的代理。

您將學到什麼


  • 實施機器學習技術來解決投資和交易問題

  • 利用市場、基本和替代數據研究Alpha因子

  • 設計和微調監督、非監督和強化學習模型

  • 使用pandas、NumPy和scikit-learn優化投資組合風險和績效

  • 將機器學習模型整合到Quantopian的實時交易策略中

  • 使用可靠的時間序列回測方法評估策略

  • 使用Keras、PyTorch和TensorFlow設計和評估深度神經網絡

  • 在OpenAI Gym中使用強化學習進行交易策略

本書適合對象

《算法交易的實踐機器學習》適用於數據分析師、數據科學家和Python開發人員,以及金融和投資行業的投資分析師和投資組合經理。如果您想通過開發智能調查策略使用機器學習算法進行高效的算法交易,那麼這本書就是為您而寫的。需要具備一定的Python和機器學習技術的理解。

目錄


  1. 交易的機器學習

  2. 市場和基本數據

  3. 金融替代數據

  4. Alpha因子研究

  5. 策略評估

  6. 機器學習過程

  7. 線性模型

  8. 時間序列模型

  9. 貝葉斯機器學習

  10. 決策樹和隨機森林

  11. 梯度提升機

  12. 非監督學習

  13. 處理文本數據

  14. 主題建模

  15. 詞嵌入

  16. 下一步