Python for Finance Cookbook : Over 80 powerful recipes for effective financial data analysis, 2/e (Paperback)

Lewinson, Eryk

Python for Finance Cookbook : Over 80 powerful recipes for effective financial data analysis, 2/e (Paperback)
  • 出版商: Packt Publishing
  • 出版日期: 2022-12-30
  • 售價: $1,930
  • 貴賓價: 9.5 折 $1,834
  • 語言: 英文
  • 頁數: 740
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1803243198
  • ISBN-13: 9781803243191
  • 相關分類: Python、程式語言、Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problems

Key Features

- Explore unique recipes for financial data processing and analysis with Python
- Apply classical and machine learning approaches to financial time series analysis
- Calculate various technical analysis indicators and backtest trading strategies

Book Description

Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.

You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.

Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.

What you will learn

- Preprocess, analyze, and visualize financial data
- Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models
- Uncover advanced time series forecasting algorithms such as Meta's Prophet
- Use Monte Carlo simulations for derivatives valuation and risk assessment
- Explore volatility modeling using univariate and multivariate GARCH models
- Investigate various approaches to asset allocation
- Learn how to approach ML-projects using an example of default prediction
- Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet

Who this book is for

This book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.

Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.

商品描述(中文翻譯)

使用現代Python庫，如pandas、NumPy和scikit-learn，以及流行的機器學習和深度學習方法來解決金融建模問題。

主要特點：

- 使用Python進行金融數據處理和分析的獨特方法
- 應用經典和機器學習方法進行金融時間序列分析
- 計算各種技術分析指標並回測交易策略

書籍描述：

Python是金融行業中最受歡迎的編程語言之一，擁有大量相應的庫。在這本《Python金融烹飪書》的新版本中，您將探索傳統的量化金融數據建模方法，如GARCH、CAPM、因子模型，以及現代的機器學習和深度學習解決方案。

您將使用流行的Python庫，在幾行代碼中快速處理、分析和得出金融數據的結論。在這個新版本中，更加強調探索性數據分析，以幫助您可視化和更好地理解金融數據。在此過程中，您還將學習如何使用Streamlit創建優雅、交互式的Web應用程序，以展示技術分析的結果。

通過本書中的示例，您將熟練掌握金融數據分析，無論是個人還是專業項目。您還將了解進行此類分析時可能遇到的潛在問題，更重要的是，如何克服這些問題。

您將學到什麼：

- 預處理、分析和可視化金融數據
- 使用統計（指數平滑、ARIMA）和機器學習模型探索時間序列建模
- 揭示Meta's Prophet等高級時間序列預測算法
- 使用蒙特卡羅模擬進行衍生品估值和風險評估
- 使用單變量和多變量GARCH模型探索波動性建模
- 探索各種資產配置方法
- 學習如何使用預設預測示例來進行機器學習項目
- 探索Google的TabNet、Amazon的DeepAR和NeuralProphet等現代深度學習模型

本書適合對金融概念有一定了解的金融分析師、數據分析師和科學家，以及熟悉Python開發的開發人員。您將學習如何正確使用高級方法進行分析，避免潛在的陷阱和常見錯誤，並對各種金融問題得出正確結論。

需要具備Python編程語言的工作知識（尤其是pandas和NumPy等庫）。

目錄大綱

1. Acquiring Financial Data
2. Data Preprocessing
3. Visualizing Financial Time Series
4. Exploring Financial Time Series Data
5. Technical Analysis and Building Interactive Dashboards
6. Time Series Analysis and Forecasting
7. Machine Learning-Based Approaches to Time Series Forecasting
8. Multi-Factor Models
9. Modelling Volatility with GARCH Class Models
10. Monte Carlo Simulations in Finance
11. Asset Allocation
12. Backtesting Trading Strategies
13. Applied Machine Learning: Identifying Credit Default
14. Advanced Concepts for Machine Learning Projects
15. Deep Learning in Finance

目錄大綱(中文翻譯)

1. 獲取金融數據
2. 數據預處理
3. 金融時間序列可視化
4. 探索金融時間序列數據
5. 技術分析和構建互動式儀表板
6. 時間序列分析和預測
7. 基於機器學習的時間序列預測方法
8. 多因子模型
9. 使用GARCH類模型建模波動性
10. 金融中的蒙特卡洛模擬
11. 資產配置
12. 測試交易策略
13. 應用機器學習：識別信用違約
14. 機器學習項目的高級概念
15. 金融中的深度學習

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