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.
Visualizing and Munging Stock Data
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!
Random variables and distribution
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.
Sampling and Inference
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.
Linear Regression Models for Financial Analysis
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!