Python for Finance Cookbook (Paperback)
暫譯: Python 財務食譜

Eryk Lewinson

Python for Finance Cookbook (Paperback)

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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，擁有大量的相關函式庫。

在這本書中，您將學習不同的方式來下載金融數據並為建模做準備。您將計算技術分析中常用的指標，例如布林帶（Bollinger Bands）、移動平均收斂發散指標（MACD）、相對強弱指標（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，例如指數平滑（exponential smoothing）、自回歸整合移動平均模型（ARIMA）和廣義自回歸條件異方差模型（GARCH，包括多變量規範），然後探索流行的資本資產定價模型（CAPM）和法馬-法蘭奇三因子模型（Fama-French three-factor model）。接著，您將發現如何優化資產配置並使用蒙地卡羅模擬（Monte Carlo simulations）來執行任務，例如計算美式期權的價格和估算風險價值（Value at Risk, VaR）。在後面的章節中，您將完成一個整個金融領域的數據科學專案。您還將學習如何使用先進的分類器，例如隨機森林（random forest）、XGBoost、LightGBM 和堆疊模型（stacked models）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問題。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。

在這本書結束時，您將學會如何有效地使用基於食譜的方法分析金融數據。

- 從不同來源下載並預處理金融數據
- 在實際環境中回測自動交易策略的表現
- 在 Python 中估算金融計量經濟學模型並解釋其結果
- 使用蒙地卡羅模擬執行各種任務，例如衍生品估值和風險評估
- 使用最新的 Python 函式庫改善金融模型的表現
- 應用機器學習和深度學習技術解決不同的金融問題
- 理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 來分析您的金融數據
- 探索使用 Python 進行金融數據分析和處理的獨特食譜
- 使用問題解決方法估算流行的金融模型，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 獲得鹿特丹伊拉斯謨斯大學的量化金融碩士學位。在他的職業生涯中，他在兩家「四大」會計師事務所和一家荷蘭金融科技新創公司獲得了數據科學方法的實際應用經驗。在工作中，他專注於利用機器學習為公司提供商業價值。在空閒時間，他喜歡撰寫與數據科學相關的主題、玩電子遊戲，以及與女友旅行。

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)

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

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