Python and Statistics for Financial Analysis
Python and Statistics for Financial Analysis course focuses on using Python for financial data analysis, as it's becoming the leading programming language for data science due to its simplicity and readability. It covers both Python coding and statistical concepts and shows their application in analyzing stock data. The course is set up with a Jupyter Notebook environment, allowing for hands-on coding practice without the need for additional installations.
per person
Level
Duration
Training Delivery Format
Face-to-face / Virtual Class
per person
Level
Duration
Training Delivery Format
Face-to-face (F2F) / Virtual Class
Class types
Public Class
Private Class
In-House Training
Bespoke
About this course
Python is now becoming the number 1 programming language for data science. Due to Python’s simplicity and high readability, it is gaining importance in the financial industry. The course combines both Python coding and statistical concepts and applies them to analyzing financial data, such as stock data.
Unlock the potential of Python and Statistics for Financial Analysis across four comprehensive modules. Learn to import, manipulate, and visualize stock data using Python while building the Trend Following strategy. Explore probability, random variables, and distributions to gauge investment risks. Utilize statistical inference, confidence intervals, and hypothesis testing to estimate real mean returns based on historical data.
Finally, delve into predictive modeling with linear regression for stock prices. Acquire data-driven skills to make informed investment decisions and design effective trading strategies
Who should attend?
The Python and Statistics for Financial Analysis course is designed for a diverse range of individuals interested in enhancing their skills and knowledge in the field of finance. The course is particularly well-suited for:
- Finance Professionals
- Quantitative Analysts
- Data Analysts and Data Scientists
- Finance Students
- Professionals from Related Fields
Learning Outcome
By the end of the Python and Statistics for Financial Analysis course, you can achieve the following using Python:
- Import, pre-process, save, and visualize financial data into pandas Data frame.
- 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 a multiple linear regression model
- Evaluate the performance of the trading model using different investment indicators
Course Objectives
Skills You Will Gain
- Statistical Analysis
- Financial Analysis
- Financial Data Analysis
- Python Programming
- Data Visualization (DataViz)
Prerequisites
You will get the most out of the Python and Statistics for Financial Analysis course if you have basic knowledge of probability.
Course Content
Module 1: 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!
- Packages for Data Analysis
- Importing data
- Basics of Dataframe
- Generate new variables in Dataframe
- Trading Strategy
Module 2: 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.
- Outcomes and Random Variables
- Frequency and Distributions
- Models of Distribution
Module 3: 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.
- Population and Sample
- Variation of Sample
- Confidence Interval
- Hypothesis Testing
Module 4: 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 we will also show you how to evaluate them!
- Association of random variables
- Simple linear regression model
- Diagnostic of linear regression model
- Multiple linear regression model
- Evaluate the strategy.
Certification
This is a non-certification course.
If you are looking for a certification, Pyhton Institute offers the best options for you to choose from.
You can start with PCEP, Certified Entry Level Python Programmer.
Then you can pursue PCAP, Certified Associate in Python Programming, and then PCPP, Certified Professional in Python Programming 1.
FAQs
Q: Why do investment banks and consumer banks use Python and Statistics for Financial Analysis?
A: Python is widely used in financial analysis due to its versatility, readability, and simplicity. It allows banks to build quantitative models to predict returns and evaluate risks efficiently. Additionally, Python has a robust ecosystem of data analysis libraries, making it one of the most popular tools for handling and visualizing stock data.
Q: What will I learn in this Python and statistics module?
A: In this Python and statistics module, you will learn the basics of Python, including importing, manipulating, and visualizing stock data using data analysis packages. By the end of the module, you will be able to build a popular trading model known as the Trend Following strategy.
Q: How are random variables relevant to financial analysis?
A: Random variables play a crucial role in financial analysis, especially in building trading strategies. In this module, we explore the concepts of random variables, their frequency, and distributions. Understanding these concepts helps us make informed decisions and assess the probability of certain outcomes in stock market investments.
Q: What prediction method will be explored in this Python and Statistics module?
A: In this Pyhton and Statistics module, we will delve into linear regression, one of the most frequently used prediction methods. You will learn how to establish associations between random variables and create simple and multiple linear regression models. The module’s highlight includes building a model using multiple indices from global markets to predict the price change of an ETF of the S&P500.
At this time, this course is available for private class and in-house training only. Please contact us for any inquiries.