Python for Finance Cookbook (Paperback)

Eryk Lewinson

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商品描述

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries.

 

In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks.

 

By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.

  • Download and preprocess financial data from different sources
  • Backtest the performance of automatic trading strategies in a real-world setting
  • Estimate financial econometrics models in Python and interpret their results
  • Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment
  • Improve the performance of financial models with the latest Python libraries
  • Apply machine learning and deep learning techniques to solve different financial problems
  • Understand the different approaches used to model financial time series data
  • Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data
  • Explore unique recipes for financial data analysis and processing with Python
  • Estimate popular financial models such as CAPM and GARCH using a problem-solution approach

商品描述(中文翻譯)

Python是金融業中最受歡迎的程式語言之一,擁有大量相關的函式庫。

在這本書中,您將學習不同的方法來下載金融數據並準備進行建模。您將計算技術分析中常用的指標,如布林帶、MACD、RSI,並回測自動交易策略。接下來,您將學習時間序列分析和模型,如指數平滑、ARIMA和GARCH(包括多變量規格),然後探索流行的CAPM和Fama-French三因子模型。您還將了解如何優化資產配置,並使用蒙特卡羅模擬來計算美式期權價格和估計風險價值(VaR)。在後面的章節中,您將在金融領域中完成一個完整的數據科學項目。您還將學習如何使用隨機森林、XGBoost、LightGBM和堆疊模型等高級分類器來解決信用卡詐騙和違約問題。然後,您將能夠調整模型的超參數並處理類別不平衡。最後,您將專注於學習如何使用深度學習(PyTorch)來處理金融任務。

通過閱讀本書,您將學會如何使用基於配方的方法有效地分析金融數據。

- 從不同來源下載和預處理金融數據
- 在實際環境中回測自動交易策略的表現
- 在Python中估計金融計量模型並解釋其結果
- 使用蒙特卡羅模擬進行各種任務,如衍生品估值和風險評估
- 使用最新的Python函式庫提高金融模型的性能
- 應用機器學習和深度學習技術解決不同的金融問題
- 了解用於建模金融時間序列數據的不同方法
- 使用強大的Python函式庫,如pandas、NumPy和SciPy分析您的金融數據
- 使用Python進行金融數據分析和處理的獨特方法
- 使用問題解決方法估計流行的金融模型,如CAPM和GARCH

作者簡介

Eryk Lewinson received his Master's degree in Quantitative Finance from Erasmus University Rotterdam. In his professional career, he gained experience in the practical application of data science methods while working for two "Big 4" companies and a Dutch FinTech scale-up. In his work, he focuses on using machine learning for providing business value to the company. In his free time, he enjoys writing about topics related to data science, playing video games, and traveling with his girlfriend.

作者簡介(中文翻譯)

Eryk Lewinson 在 Erasmus University Rotterdam 獲得了量化金融碩士學位。在他的職業生涯中,他在兩家「Big 4」公司和一家荷蘭金融科技初創公司工作期間,獲得了實際應用數據科學方法的經驗。在他的工作中,他專注於使用機器學習為公司提供商業價值。在空閒時間,他喜歡寫有關數據科學相關主題的文章,玩視頻遊戲,和他的女朋友一起旅行。

目錄大綱

Table of Contents

  1. Financial Data and Preprocessing
  2. Technical Analysis in Python
  3. Time Series Modelling
  4. Multi-factor Models
  5. Modeling Volatility with GARCH class models
  6. Monte Carlo Simulations in Finance
  7. Asset Allocation in Python
  8. Identifying Credit Default with Machine Learning
  9. Advanced Machine Learning Models in Finance
  10. Deep Learning in Finance

目錄大綱(中文翻譯)

目錄

1. 財務數據和預處理
2. Python中的技術分析
3. 時間序列建模
4. 多因子模型
5. 使用GARCH類模型建模波動性
6. 金融中的蒙特卡洛模擬
7. Python中的資產配置
8. 使用機器學習識別信用違約
9. 金融中的高級機器學習模型
10. 金融中的深度學習