Machine Learning and Big Data with KDB+/Q (Hardcover)

Jan Novotny, Paul A. Bilokon, Aris Galiotos, Frederic Deleze

  • 出版商: Wiley
  • 出版日期: 2019-12-31
  • 定價: $2,500
  • 售價: 9.0$2,250
  • 語言: 英文
  • 頁數: 640
  • ISBN: 1119404754
  • ISBN-13: 9781119404750
  • 相關分類: 大數據 Big-dataMachine Learning
  • 立即出貨 (庫存=1)

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商品描述

Upgrade your programming language to more effectively handle high-frequency data

Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading.

The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing “bible”-type reference, this book is designed with a focus on real-world practicality ­to help you quickly get up to speed and become productive with the language.

  • Understand why kdb+/q is the ideal solution for high-frequency data
  • Delve into “meat” of q programming to solve practical economic problems
  • Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more
  • Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks

The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data ­– more variables, more metrics, more responsiveness and altogether more “moving parts.”

Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.   

商品描述(中文翻譯)

升級您的程式語言,更有效地處理高頻數據

《機器學習與大數據:使用KDB+/Q》為量化交易員、程式設計師和演算法交易員提供了一個實用的入門指南,介紹了強大但非直觀的kdb+數據庫和q程式語言。這些工具被理想地設計來處理買賣雙方機構的高頻金融數據的速度和量,已成為事實上的標準;本書提供了從業人員需要的基礎知識,以有效地應用這種快速發展的分析交易方法。

本書的討論遵循著工作策略開發的自然進程,讓讀者在熟悉的領域中進行實踐學習,展示了q語言和其他程式設計方法之間的效率和能力對比。本書不是一本全面的「聖經」式參考書,而是專注於實際應用,幫助您快速上手並提高語言的生產力。


  • 了解為什麼kdb+/q是處理高頻數據的理想解決方案

  • 深入研究q程式設計的「精華」,解決實際的經濟問題

  • 執行包括基本回歸、協整、波動率估計、建模等日常操作


  • 從市場影響和微觀結構分析到機器學習技術,包括神經網絡,學習高級技巧

kdb+數據庫及其底層程式語言q提供了前所未有的速度和能力。隨著交易算法和金融模型對它們所預測的市場變得越來越複雜,它們涵蓋了越來越多的數據範圍-更多的變量、更多的指標、更高的反應速度和更多的「移動部分」。

傳統的程式設計語言越來越難以應對日益增長的數據速度和量,並且缺乏尖端金融建模所需的靈活性。《機器學習與大數據:使用KDB+/Q》打開了這項技術,降低了學習曲線,幫助您快速採用更有效的工具組。