Python for Finance (第二版) 项目教程

Python for Finance (第二版) 项目教程

py4fi2nd Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch. py4fi2nd 项目地址: https://gitcode.com/gh_mirrors/py/py4fi2nd

1. 项目介绍

本项目是基于Yves Hilpisch所著的《Python for Finance -- Mastering Data-Driven Finance》第二版的代码和Jupyter笔记本。这本书深入探讨了使用Python进行金融数据分析的方法,适合希望在金融领域应用Python进行数据分析的专业人士和学生。

2. 项目快速启动

为了快速启动本项目,你需要安装Miniconda或Anaconda。以下是启动项目的步骤:

# 克隆项目到本地
git clone https://github.com/yhilpisch/py4fi2nd.git

# 进入项目目录
cd py4fi2nd

# 创建conda环境并安装所需的Python包
conda env create -f py4fi2nd.yml

# 激活conda环境
source activate py4fi2nd

# 启动Jupyter Notebook
jupyter notebook

启动Jupyter Notebook后,你可以浏览到项目中的Jupyter笔记本文件,开始学习和实践。

3. 应用案例和最佳实践

在项目中,你可以找到以下应用案例和最佳实践:

  • 使用Pandas进行金融数据分析
  • 利用NumPy进行数学计算
  • 使用Matplotlib和Seaborn进行数据可视化
  • 利用Statsmodels进行统计分析
  • 应用机器学习模型于金融预测

每个案例都配有相应的Jupyter笔记本,其中包含了代码和解释。

4. 典型生态项目

本项目典型的生态项目包括:

  • TensorFlow:用于构建和训练复杂的机器学习模型。
  • Pandas:强大的数据分析工具,用于处理和清洗结构化数据。
  • Matplotlib和Seaborn:数据可视化库,用于创建高质量的图形。
  • Jupyter Notebook:交互式计算平台,可以创建包含代码、文本和图形的文档。

以上这些项目都是Python数据科学和金融分析中常用的工具,它们共同构建了一个强大的生态系统,支持金融数据的分析、可视化和模型构建。

py4fi2nd Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch. py4fi2nd 项目地址: https://gitcode.com/gh_mirrors/py/py4fi2nd

创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考

eBook Description: Hands-On Python for Finance: Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Python is one of the most popular languages used for quantitative finance. With this book, you’ll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you’ll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you’ll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You’ll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this Hands-On Python for Finance book, you will be equipped with the skills you need to perform different financial analysis tasks using Python.
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