Python in Finance

Name: Python in Finance
Rating: 4.5 (3 reviews)

Get ready for using Python in data gathering and analysis. Python, Data, Finance Market.

Created byAvetik Babayan

Last updated 2/2025

English

What you'll learn

Understand the basics of Python for financial analysis: set up a Python environment and use essential libraries like pandas, numpy, and matplotlib
Analyze stock market data using Python: gain skills in importing, cleaning, and processing stock market data from various sources, including APIs and CSV files.
Creat Python scripts for stock market: master the creation of Python scripts to calculate key financial metrics such as moving averages, RSI, Bollinger band etc
Automate stock market data analysis: learn to automate the analysis of stock trends, generate actionable insights

Course content

5 sections • 25 lectures • 6h 29m total length

Introduction2:24
This video is explaining the purpose of the course together with our targets during our journey
Financial Basics: Why they matter9:29
In this video, we explore why starting with the fundamentals is crucial for building a strong investment strategy. You'll learn: The importance of understanding financial basics before using advanced tools. How foundational knowledge helps in better decision-making. A preview of what's to come in this series: stocks, options, metrics, trading platforms, and more! We’ll also take a sneak peek at popular financial platforms like Yahoo Finance and Robinhood to show how these basics come to life.
Stock Market Basics15:13
In this video, we break down the basics of the stock market to help beginners understand its purpose, key participants, and common terms.
You’ll learn: ? What is the stock market, and how does it work? Who are the key players: retail investors, brokers, and market makers? ? Trading platforms by region
Stock metrics explained: Fundamental and Technical analysis for beginners22:33
You’ll discover:
Key stock metrics: P/E ratio, EPS, P/B ratio, and dividend yield.
What is fundamental analysis, and why is it crucial for long-term investing?
What is technical analysis, and how does it help in short-term trading?
The difference between fundamental and technical analysis and when to use each.
Understanding Stock Pricing and Market Mechanisms11:02
What determines a stock's price? Learn how supply and demand shape the market. Factors influencing stock prices: Company earnings, economic indicators, global events, and investor sentiment. Types of stock orders: Market orders, limit orders, and stop-loss orders explained. Bid-ask spread and liquidity: Why they matter for smooth trading.
ETFs for Beginners What are they and why you should invest ETF basics explained12:17
What are ETFs?
A beginner-friendly explanation of ETFs and how they work.
Types of ETFs: Index ETFs, sector ETFs, bond ETFs, and thematic ETFs.
Why invest in ETFs? Benefits of diversification, low costs, and passive investing.
How to choose an ETF: Key factors like expense ratio, trading volume, and index tracking.
Popular ETFs examples: SPY, QQQ, VTI, and more.
Top Stock metrics every investor should know detailed guide to evaluating stocks14:15
Metrics Covered (with thresholds):
P/E Ratio: Price-to-Earnings explained and what values to look for.
EPS: Earnings Per Share and how to interpret growth trends.
P/B Ratio: Price-to-Book and its significance for asset-heavy industries.
Dividend Yield: Finding reliable income-paying stocks.
D/E Ratio: Debt-to-Equity and its role in financial stability.
ROE: Return on Equity to measure profitability.
FCF: Free Cash Flow for assessing financial health.
Market Cap: Categorizing companies by size and risk.
Getting started with Python for finance11:10
In this video, we transition from theory to practice by introducing Python—one of the most powerful tools for financial analysis. We’ll also dive into yfinance, a popular Python library for stock market data, explore its features, and discuss alternative data sources for your investing journey.

What You’ll Learn:
- Why Python for Finance? Simplicity, versatility, and powerful libraries like Pandas and Matplotlib.
- What is yfinance? A detailed look at its features: stock prices, financials, dividends, and more.
- Exploring the yfinance GitHub Page: Installation steps and documentation walkthrough.
- Alternative Data Sources: Alpha Vantage, Quandl .
- What’s Next: A sneak peek into the first Python file for stock data retrieval.

First little code with Python9:44
In our first working script we are going to install Python library, talk about data gathering process. We are also going to write and execute a script to get Stock data information with the help of yfinance Python library. Source code is added as attachment
Get Stock historical price3:33
Fetching a script to get stock historical data with Python code
Streamlit for Python and yfinance14:39
In this video we are going over the data related to earnings calendar, major holders and also introducing Streamlit powerful framework for Python, which helps with providing great visualization for the data.
Company financial statements with Python and Streamlit9:01
In this lesson we will create another dashboard to properly display the financial statements, such as company balance sheet, income statements and overall company information. We use Python for coding and Streamlit framework for display of the data
Getting the list of Nasdaq stocks and stock information16:30
In this video we are navigating to the Nasdaq website to get the list of the stocks and writing a code to get and display the stock pricing information for all the stocks from Nasdaq list.
Building Stock historical data dashboard15:12
In this video we are building interactive web-dashboard for the Stock data display with Python and Streamlit and prepare for the next section

Streamlit financial data dashboard_ Part 1 - Structure14:58
In this video, we are going to start working on Multi-asset financial dashboard with Python and Streamlit.
Streamlit financial data dashboard_ Part 2 - Commodities27:01
In this video we are creating a code to set a list of commodities we are interested in, prepare a data set for acquiring data and prepare it to be displayed as a page in our main application. It includes import of streamlit, yfinance, pandas etc.
Streamlit financial data dashboard_Part 3_Cryptos.21:44
In this video we are created a Streamlit dashboard for crypto currencies using coingecko API in Python. We will set the parameter scope for request, get the data from the endpoint, transform it into the list for each cryptocurrency and display it in Streamlit app with a simple example of conditional formatting.
Streamlit financial data dashboard _Part 4_ ETFs17:13
In this video we are going to get the most important ETF information: we will provide a list of ETF's as a file, then get each ETF from it and save it as a list. Next we will fetch the data for each of elements in the list and apply our request to fetch the necessary information regarding the ETF. After data is received and stored as a list, we will transform it into data frame and from data frame it will become a Streamlit table to be displayed in our dashboard.
Streamlit financial data dashboard_ Part 5_ SP50027:36
In this video, last in series of pages for our Streamlit multi-asset dashboard, we will fetch the current list of the SP500 stocks from FMP, get their financial information from yfinance and try to understand if the the stock is underpriced (market price is lower than expected fair price, based on P/E and EPS metrics) or overpriced and transform the data into Pandas DataFrame. Later we will have pandas dataframe integrated as table of data into Streamlit dashboard.
Streamlit financial data dashboard_ Part 6_ Running as a system6:49
This video shows the results of our weekly work: we will run our code, see how the dashboard look and feel is, go over all 4 tabs: commodities, cryptos, ETF's and underpriced stocks.

EODHD as data source16:48
The reliability of data is paramount. Many investors and analysts rely on popular platforms like Yahoo Finance (via yfinance) or other commonly used APIs. While these sources are valuable, placing all your eggs in one basket could limit your access to diverse and reliable information, especially in dynamic markets. Having an alternative and robust data source ensures not only the accuracy but also the completeness of your analysis. Platforms like EOD Historical Data (EODHD) offer a comprehensive set of metrics, from fundamental analysis to technical indicators, providing the depth and breadth needed for informed decision-making. Diversifying your data sources minimizes the risks of dependency and enhances your ability to validate findings, making your investment strategies more resilient. In this video, we’ll explore how to fetch fundamental data from EODHD, understand its structure, and leverage it for better insights, adding a reliable alternative to your data toolbox.
FMP as Data Source22:26
Today we will get the data from FMP - it also offers a comprehensive set of metrics, from fundamental analysis to technical indicators, providing the depth and breadth needed for informed decision-making.

Stock Price Dashboard with python and Streamlit25:17
In this video we are going to build a dashboard to display daily stock price for multiple stocks (We'll go with Apple, Microsoft, Meta/Faceboook, Google and Amazon. We will check: - Open price - Closure price - Daily Highest - Daily Lowest. Understanding this range of pricing can be a powerful tool for investors to detect daily deviations or a bigger range of fluctuation of the stock price within a larger scale.
Python code for SMA5, SMA8 and SMA13 analysis23:24
Today we'll go over 3 other SMA indicators: 5, 8, 13: with those indicators we're going to analyze the price of Amazon stock and check if there were the moments when SMA indicators could provide to investor the buy or sell signals to operate on the market and whether those signals would be considered realistic - proven by market data
Python dashboard for SMA50 and SMA200 analysis18:42
In this article we are going to review the SMA50 and SMA200 indicators and why they are important for the investors. We will build a dashboard using Streamlit and check price, SMA50 and SMA200 for top-10 of stocks based on their capitalization and see, whether the SMA50 and SMA200 crosses are matching to the market interpretation

Requirements

Basic Python programming

Description

Embark on an exciting journey into the world of finance and stocks with this comprehensive course, designed to take you from the fundamentals to creating actionable insights. Whether you are a complete beginner or someone with a budding interest in financial markets, this course equips you with the skills to analyze and visualize stock market data effectively.

Start by familiarizing yourself with key financial terms like stocks, ETFs, and market sectors, and gain a solid foundation in understanding how the stock market operates. From there, dive into practical lessons on sourcing reliable financial data from APIs and public datasets. Learn how to process this data and display it in a meaningful way using tools like Python, Pandas, Plotly, Dash and Streamlit.

The course takes you step-by-step through building customized dashboards for tracking market performance, applying key criteria for stock filtering, and creating heatmaps to visualize stock trends. With hands-on examples, you will master the art of transforming raw data into actionable insights.

By the end of this course, you will have the confidence to analyze market trends, monitor stocks, and create professional-grade dashboards—empowering you to make informed investment decisions or share insights with others. Join now and turn your curiosity into expertise!

Who this course is for:

Beginner Python and Finance enthusiasts

Python in Finance

What you'll learn

Explore related topics

Course content

Introduction to the course and prerequisites8 lectures • 1hr 38min

Starting practical coding: one little step at a time6 lectures • 1hr 9min

Building dashboards6 lectures • 1hr 55min

Data Source review2 lectures • 39min

Advanced Dashboards3 lectures • 1hr 7min

Requirements

Description

Who this course is for: