Hands-On AI Trading with Python, Quantconnect and AWS
暫譯: 使用 Python、Quantconnect 和 AWS 的實戰 AI 交易

Pik, Jiri, Chan, Ernest P., Broad, Jared

Hands-On AI Trading with Python, Quantconnect and AWS
  • 出版商: Wiley
  • 出版日期: 2025-01-29
  • 售價: $2,080
  • 貴賓價: 9.5 折 $1,976
  • 語言: 英文
  • 頁數: 416
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1394268432
  • ISBN-13: 9781394268436
  • 相關分類: Amazon Web Services、Python、程式語言、人工智慧

    • 無法訂購

相關主題

商品描述

Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance

Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt.

Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks.

The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:

  • Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.
  • Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.
  • Predict market volatility regimes and allocate funds accordingly.
  • Predict daily returns of tech stocks using classifiers.
  • Forecast Forex pairs' future prices using Support Vector Machines and wavelets.
  • Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.
  • Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.
  • Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.
  • Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.
  • AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.

Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.

商品描述(中文翻譯)

掌握透過實作範例、深入見解和逐步指導的 AI 驅動算法交易策略的藝術

《Hands-On AI Trading with Python, QuantConnect, and AWS》探討了 AI 技術在算法交易中的實際應用。它提供了完整的程式碼實作範例，讓讀者能夠理解並擴展他們的 AI 工具箱。

與其他書籍不同，本書專注於設計實際的交易策略，而非建立回測基礎設施。它利用 QuantConnect，提供來自 Algoseek 等公司的關鍵市場數據。範例可在本書的 GitHub 倉庫中找到，使用 Python 編寫，並包括績效報告或研究 Jupyter 筆記本。

本書首先概述了金融交易和 QuantConnect 平台，並根據使用的 AI 技術進行組織：
- 範例包括使用回歸模型構建投資組合、預測股息收益率，以及利用機器學習套件（如 SKLearn 和 MLFinLab）來防範市場波動。
- 使用主成分分析來減少模型特徵、識別交易對，並使用 LightGBM 等套件進行統計套利。
- 預測市場波動狀態並相應地分配資金。
- 使用分類器預測科技股的每日回報。
- 使用支持向量機和小波預測外匯對的未來價格。
- 使用 TensorFlow 和時間卷積神經網絡預測交易日的動量或回歸風險。
- 應用大型語言模型（LLMs）進行股票研究分析，包括提示工程和構建 RAG 應用。
- 對即時新聞源進行情感分析，並訓練時間序列預測模型以優化投資組合。
- 透過強化學習和 AI 進行更好的對沖：實施強化學習模型以對沖期權和衍生品，使用 PyTorch。
- 風險管理和優化的 AI：使用修正 AI 和條件投資組合優化技術進行風險管理和資本配置。

本書由領域專家撰寫，包括 Jiri Pik、Ernest Chan、Philip Sun、Vivek Singh 和 Jared Broad，對對沖基金專業人士、交易員、資產管理者和金融學生至關重要。將 AI 整合到您的下一個算法交易策略中，請參考《Hands-On AI Trading with Python, QuantConnect, and AWS》。

作者簡介

JIRI PIK: Founder and CEO of RocketEdge.com. A software architect and cloud computing expert, Jiri Pik specializes in designing high-performance trading systems. He has decades of experience in financial technologies and has worked with some of the world's leading financial institutions, including Goldman Sachs and JPMorgan Chase.

ERNEST P. CHAN: A pioneer in applying machine learning to quantitative trading, Ernest P. Chan founded Predictnow.ai and QTS Capital Management. He is author of books such as Quantitative Trading and Machine Trading.

JARED BROAD: Founder and CEO of QuantConnect(TM), Jared Broad has empowered over 300,000 algorithmic traders worldwide with a platform that simplifies strategy design, backtesting, and live deployment.

PHILIP SUN: CEO and Co-founder of Adaptive Investment Solutions, LLC, and a seasoned quantitative fund manager, Philip Sun and his team focus on building state-of-the-art AI-driven risk management platform for wealth advisors and institutional investors.

VIVEK SINGH: A product leader at Amazon Web Services (AWS), Vivek Singh spearheads the development of large language models (LLMs) and Generative AI applications, bringing cutting-edge AI technologies to the trading domain.

作者簡介(中文翻譯)

**JIRI PIK:** RocketEdge.com 的創辦人兼執行長。Jiri Pik 是一位軟體架構師和雲端運算專家，專注於設計高效能的交易系統。他在金融科技領域擁有數十年的經驗，曾與一些全球領先的金融機構合作，包括高盛（Goldman Sachs）和摩根大通（JPMorgan Chase）。

**ERNEST P. CHAN:** 在將機器學習應用於量化交易方面的先驅，Ernest P. Chan 創立了 Predictnow.ai 和 QTS Capital Management。他是《量化交易》（Quantitative Trading）和《機器交易》（Machine Trading）等書籍的作者。

**JARED BROAD:** QuantConnect™ 的創辦人兼執行長，Jared Broad 為全球超過 300,000 名算法交易者提供了一個簡化策略設計、回測和實時部署的平台。

**PHILIP SUN:** Adaptive Investment Solutions, LLC 的執行長及共同創辦人，Philip Sun 是一位經驗豐富的量化基金經理，他和他的團隊專注於為財富顧問和機構投資者建立最先進的 AI 驅動風險管理平台。

**VIVEK SINGH:** 亞馬遜網路服務（AWS）的產品領導者，Vivek Singh 主導大型語言模型（LLMs）和生成式 AI 應用的開發，將尖端的 AI 技術帶入交易領域。

類似商品