Hands-On Deep Learning for Finance
暫譯: 金融深度學習實戰

Troiano, Luigi, Kriplani, Pravesh, Mejuto Villa, Elena

Hands-On Deep Learning for Finance
  • 出版商: Packt Publishing
  • 出版日期: 2020-02-28
  • 售價: $1,880
  • 貴賓價: 9.5 折 $1,786
  • 語言: 英文
  • 頁數: 442
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789613175
  • ISBN-13: 9781789613179
  • 相關分類: DeepLearning

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商品描述

Quantitative methods are the vanguard of the investment management industry. This book shows how to enhance trading strategies and investments in financial markets using deep learning algorithms.

This book is an excellent reference to understand how deep learning models can be leveraged to capture insights from financial data. You will implement deep learning models using Python libraries such as TensorFlow and Keras. You will learn various deep learning algorithms to build models for understanding financial market dynamics and exploiting them in a systematic manner. This book takes a pragmatic approach to address various aspects of asset management. The information content in non-structured data like news flow is crystalized using BLSTM. Autoencoders for efficient index replication is discussed in detail. You will use CNN to develop a trading signal with simple technical indicators, and improvements offered by more complex techniques such as CapsNets. Volatility is given due emphasis by demonstrating the superiority of forecasts employing LSTM, and Monte Carlo simulations using GAN for value at risk computations. These are then brought together by implementing deep reinforcement learning for automated trading.

This book will serve as a continuing reference for implementing deep learning models to build investment strategies.

商品描述(中文翻譯)

量化方法是投資管理行業的先鋒。本書展示了如何利用深度學習算法來增強金融市場的交易策略和投資。

本書是理解如何利用深度學習模型從金融數據中獲取洞察的絕佳參考。您將使用 Python 庫如 TensorFlow 和 Keras 實現深度學習模型。您將學習各種深度學習算法，以建立理解金融市場動態並以系統化方式利用它們的模型。本書採取務實的方法來解決資產管理的各個方面。非結構化數據（如新聞流）的信息內容通過 BLSTM 進行提煉。自編碼器用於高效的指數複製，並詳細討論。您將使用 CNN 開發一個基於簡單技術指標的交易信號，並探討更複雜技術（如 CapsNets）所提供的改進。通過展示使用 LSTM 的預測優越性以及使用 GAN 進行風險價值計算的蒙地卡羅模擬，給予波動性應有的重視。這些內容最終通過實施深度強化學習來實現自動交易。

本書將作為持續參考，幫助實施深度學習模型以建立投資策略。

作者簡介

Luigi Troiano

Luigi Troiano, Ph.D., is an Associate Professor of Artificial Intelligence, Data Science, and Machine Learning at the University of Salerno (Italy), Dept. of Management and Innovation Systems. He is a coordinator of Computational and Intelligent System Engineering Lab at the University of Sannio and an NVIDIA Deep Learning Institute University Ambassador. He is also the chairman of the ISO/JTC 1/SC 42, AI and Big Data, Italian section.

Arjun Bhandari

Arjun Bhandari is Chief Investment Officer of a family office. His previous positions have been Head of Quantitative Strategies at ADIA ( largest sovereign wealth fund in the middle east ) and APG Investments ( largest pension plan in Europe ). He has been deploying quantitative techniques for multi-asset class investments for over 20 years, bringing this experience to bear on his most recent focus on machine learning applied to fund management.

Elena Mejuto Villa

Elena Mejuto Villa, Ph.D., is a data scientist in the Advanced Analytics team for Technology Services Consulting in a multinational firm in Milan. She completed her Master's Degree in Telecommunication Engineering at the University of Oviedo (Spain), and she received her Ph.D. in Information Technologies for Engineering from the University of Sannio (Italy). During her Ph.D., she researched the application of machine learning and signal processing techniques to time-varying signals/data in the fields of finance and gravitational wave data analysis.

作者簡介(中文翻譯)

路易吉·特羅亞諾

路易吉·特羅亞諾（Luigi Troiano）博士是義大利薩萊諾大學（University of Salerno）管理與創新系統系的人工智慧、數據科學和機器學習副教授。他是薩尼奧大學（University of Sannio）計算與智能系統工程實驗室的協調員，並且是NVIDIA深度學習學院的校園大使。他也是ISO/JTC 1/SC 42（人工智慧與大數據）義大利分會的主席。

阿爾君·班達里

阿爾君·班達里（Arjun Bhandari）是某家族辦公室的首席投資官。他之前擔任阿布達比投資局（ADIA，中東最大的主權財富基金）和APG投資（APG Investments，歐洲最大的退休金計畫）的量化策略主管。他在多資產類別投資方面運用量化技術已有超過20年的經驗，並將這些經驗應用於他最近專注於機器學習在基金管理中的應用。

艾蓮娜·梅胡托·維拉

艾蓮娜·梅胡托·維拉（Elena Mejuto Villa）博士是米蘭一家跨國公司的技術服務諮詢部門的高級分析團隊數據科學家。她在西班牙奧維耶多大學（University of Oviedo）獲得電信工程碩士學位，並在義大利薩尼奧大學（University of Sannio）獲得工程資訊技術博士學位。在她的博士研究期間，她研究了機器學習和信號處理技術在金融和重力波數據分析領域中對時間變化信號/數據的應用。

目錄大綱

  1. Deep learning for finance 101
  2. Designing neural network architectures
  3. Construction, testing and validation of financial models
  4. Index replication by auto-encoders
  5. Volatility forecasting by LSTM
  6. Trading rule identification by CNN
  7. Asset allocation by LSTM over CNN
  8. Digesting news by NLP with BLSTM
  9. Risk Measurement Using GAN
  10. Chart visual analysis by transfer learning
  11. Better chart analysis using CapsNet
  12. Training trader robots by deep reinforcement learning
  13. What’s next ?

目錄大綱(中文翻譯)


  1. Deep learning for finance 101

  2. Designing neural network architectures

  3. Construction, testing and validation of financial models

  4. Index replication by auto-encoders

  5. Volatility forecasting by LSTM

  6. Trading rule identification by CNN

  7. Asset allocation by LSTM over CNN

  8. Digesting news by NLP with BLSTM

  9. Risk Measurement Using GAN

  10. Chart visual analysis by transfer learning

  11. Better chart analysis using CapsNet

  12. Training trader robots by deep reinforcement learning

  13. What’s next ?

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