|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-statistics-financial-analysis
课程评论：没有评论
课程名称：Python与统计学在金融分析中的应用 课程概述： Python正在成为数据科学领域的首选编程语言，由于其简洁性和可读性，逐渐在金融行业中崭露头角。本课程将Python编码和统计概念相结合，应用于分析金融数据，例如股票数据。 课程结束时，您将能够使用Python完成以下内容： - 导入、预处理、保存和可视化金融数据至pandas DataFrame。 - 通过生成新变量来处理现有金融数据。 - 在金融语境中回顾和应用重要的统计概念（随机变量、频率、分布、总体和样本、置信区间、线性回归等）。 - 使用多元线性回归模型构建交易模型。 - 使用不同的投资指标评估交易模型的表现。 本课程平台配置了Jupyter Notebook环境，方便您在无需安装客户端应用的情况下练习Python编码。 课程大纲： 1. **可视化与处理股票数据** - 描述：投资银行和消费银行为何使用Python构建量化模型以预测回报和评估风险？Python为何成为金融分析中最流行的工具之一？在本模块中，您将学习基本的Python知识，以导入、处理和可视化股票数据。通过高可读性和简单性，您将在本模块结束时构建一种流行的交易模型——趋势跟随策略！ 2. **随机变量与分布** - 描述：在上一个模块中，我们基于10和50日移动平均线构建了简单的交易策略，这些被视为统计学中的“随机变量”。本模块将探索随机变量的基本概念。通过理解随机变量的频率和分布，我们将进一步讨论概率。在模块后期，我们将利用Python应用概率概念来评估投资股票的风险。 3. **抽样与推断** - 描述：在金融分析中，我们常常基于几年的历史数据推断股票或股权基金的真实平均回报。这符合统计学的核心部分——统计推断。在本模块中，您将理解统计推断的基本概念，如总体、样本和随机抽样。我们还将使用置信区间估计股票的平均回报范围，并使用假设检验来验证投资回报的主张。 4. **金融分析的线性回归模型** - 描述：本模块将探索常用的预测方法——线性回归。从随机变量的关联性到简单和多元线性回归模型，我们将构建一个利用全球市场多个指数的模型，预测S&P500的ETF价格变化。除了建立股票交易模型，您还将学习如何评估自己模型的表现，这也将是本课程中有趣的一部分！
Name:Visualizing and Munging Stock Data
Description:Why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? What makes Python one of the most popular tools for financial analysis? You are going to learn basic python to import, manipulate and visualize stock data in this module. As Python is highly readable and simple enough, you can build one of the most popular trading models - Trend following strategy by the end of this module!
Name:Random variables and distribution
Description:In the previous module, we built a simple trading strategy base on Moving Average 10 and 50, which are "random variables" in statistics. In this module, we are going to explore basic concepts of random variables. By understanding the frequency and distribution of random variables, we extend further to the discussion of probability. In the later part of the module, we apply the probability concept in measuring the risk of investing a stock by looking at the distribution of log daily return using python. Learners are expected to have basic knowledge of probability before taking this module.
Name:Sampling and Inference
Description:In financial analysis, we always infer the real mean return of stocks, or equity funds, based on the historical data of a couple years. This situation is in line with a core part of statistics - Statistical Inference - which we also base on sample data to infer the population of a target variable.In this module, you are going to understand the basic concept of statistical inference such as population, samples and random sampling. In the second part of the module, we shall estimate the range of mean return of a stock using a concept called confidence interval, after we understand the distribution of sample mean.We will also testify the claim of investment return using another statistical concept - hypothesis testing.
Name:Linear Regression Models for Financial Analysis
Description:In this module, we will explore the most often used prediction method - linear regression. From learning the association of random variables to simple and multiple linear regression model, we finally come to the most interesting part of this course: we will build a model using multiple indices from the global markets and predict the price change of an ETF of S&P500. In addition to building a stock trading model, it is also great fun to test the performance of your own models, which I will also show you how to evaluate them!
Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data. By the end of the course, you can achieve the following using python: - Import, pre-process, save and visualize financial data into pandas Dataframe - Manipulate the existing financial data by generating new variables using multiple columns - Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts - Build a trading model using multiple linear regression model - Evaluate the performance of the trading model using different investment indicators Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.
