ChatGPT & Python for Stock Market Analysis

Name: ChatGPT & Python for Stock Market Analysis
Rating: 4.4 (23 reviews)

Mastering Technical Analysis and Algorithmic Trading in Python using ChatGPT

Created byJothi Prakash A

Last updated 1/2024

English

English [Auto],

What you'll learn

Use ChatGPT to create technical analysis using Python
Understand various technical indicators and their applications in trading.
Backtest trading strategies and evaluate performance using Python programming language.
Gain practical experience by building and testing trading algorithms in real market conditions.

Course content

5 sections • 21 lectures • 3h 36m total length

Introduction2:26
Develop stock market data analysis and quantitative data analysis skills with Chatgpt and Python, prerequisites in Python, market basics, math, Excel, and IDE setup with Anaconda and Visual Studio Code.
What is the stock market & why data analysis is important for trading3:55
Identify what the stock market is and how data analysis guides trading by blending technical, fundamental, and quantitative methods to spot quality companies and opportunities.
What is ChatGPT and how can it be used for stock market data analysis6:13
Explore how ChatGPT analyzes stock market data, answers questions, and generates Python code for indicators like RSI. See how human‑machine collaboration speeds analysis while noting its limitations.
Overview of the course curriculum1:24
Discover stock market analysis with ChatGPT and Python, sourcing data, storing it in a database, applying technical indicators, backtesting, and performance KPIs, with instructor-led, interactive guidance to build trading strategy.

Overview of different sources of stock market data5:06
Explore market data sources—from exchanges, news outlets, data vendors, and regulators to social media sentiment and web scraping—and learn how APIs like Alpha Vantage provide intraday, daily, and fundamentals data.
Retrieving data from different sources (e.g. APIs, CSV files, websites)1:17
Learn to fetch stock data from APIs, CSV files, and websites using code written with ChatGPT, including Yahoo Finance, Alpha Vantage, and Quandl, noting that web scraping requires technical knowledge.
Fetching data from Yahoo Finance13:38
Use ChatGPT to generate Python code that fetches data from Yahoo Finance for ten Indian stocks with the finance library, consolidating into a single dataframe with a trade date column.
Fetching data from Alpha Vantage10:12
Fetch five years of Indian stock data with the Alpha Vantage API using ChatGPT, generate and debug Python code in Visual Studio Code, and handle API keys and errors.
Fetching data from Quandl5:29
Fetch ten stocks for five years using the Quandl API, manage the API key, install the quandl package, and run a ChatGPT-generated Python script to retrieve data.
Scraping data from NSE (Educational purpose only)18:38
Learn how to scrape stock intraday data from NSE using web scraping, fetch json data, convert epoch timestamps to readable dates, and organize it in a pandas DataFrame with Python.
Scraping data from Nasdaq (Educational purpose only)20:59
Discover how to scrape Nasdaq data for educational purposes by locating the api request, extracting json, and loading it into a Python dataframe with ChatGPT guidance.
Storing data in a MySQL database (optional) using ChatGPT8:56
Learn to store Nasdaq data in a MySQL database using ChatGPT-generated Python code with SQLAlchemy, including creating a schema in workbench and enabling append or create workflows.

Understanding the basics of technical indicators and how they are used8:09
Explore how technical indicators like moving averages, RSI, Bollinger bands, and Ichimoku cloud reveal trends, volatility, and overbought or oversold conditions.
Moving Average using ChatGPT12:06
Learn how ChatGPT can serve as a mentor to grasp moving averages, compute the indicator with step-by-step examples, and evaluate its pros and cons for stock analysis.
RSI using ChatGPT17:58
discover how the relative strength index, a momentum oscillator with a 14-day basis and 70/30 overbought/oversold thresholds, is explained step-by-step using chatgpt.
Bollinger Bands using ChatGPT8:17
Explore Bollinger bands: measure volatility with standard deviation, identify trends, and signal reversals using a middle SMA and upper and lower bands at two standard deviations over 20 days.

How a trading strategy is created6:07
Learn to create a data analysis based trading strategy, backtest it, and analyze performance to ensure robustness using a six-step framework of indicators, money and risk management, and strategy evaluation.
Creating a Trading Strategy Plan12:46
Create a pullback trading strategy using RSI-10 and the 200-day SMA to time long entries, with a 5% stop loss and exits on RSI-10 above 40 or after ten days.
Creating the Trading Strategy29:38
Combine ChatGPT and Python to build a swing trading strategy, fetch ten years of data from Yahoo! Finance, compute RSI and moving averages, and backtest entry and exit rules.
Performance Measurement19:20
Learn to measure backtested stock strategies by calculating profit and loss and key metrics like total trades, profitable trades, loss trades, and average profit or loss per trade using ChatGPT.

Requirements

Basic Python knowledge
Basic Market Knowledge
Basic Math Concepts
If you know what is ChatGPT, that's enough

Description

The course "Quantitative Analysis using ChatGPT & Python" is designed for individuals interested in learning how to analyze their trading strategies and backtest their trading ideas using Python & ChatGPT. This course provides a practical introduction to algorithmic trading, including the basics of quantitative analysis and technical analysis.

Through a step-by-step approach, this course teaches students how to develop and implement trading strategies using Python. The course starts by introducing the basics of Python programming, data analysis, and visualization, and then moves on to more advanced topics such as statistical modeling, time series analysis, and machine learning.
The course also covers popular trading indicators, such as moving averages, Bollinger Bands, and Relative Strength Index (RSI), and shows students how to implement these indicators in Python to generate trading signals.
In addition, students will learn how to use Python libraries such as pandas, NumPy, and Talib for data analysis and how to backtest their trading strategies.
By the end of the course, students will have a solid understanding of algorithmic trading, backtesting, and the Python programming language, enabling them to develop and implement their own automated trading strategies.

This course is ideal for individuals who have a basic understanding of financial markets and trading concepts and are looking to learn how to automate their trading strategies. Some prior programming experience, preferably in Python, would be helpful but is not required.

Who this course is for:

Python programmers
Traders, Investors
Students of any level
Stock Market Enthusiasts

ChatGPT & Python for Stock Market Analysis

What you'll learn

Explore related topics

Course content

Introduction to Stock Market Data Analysis4 lectures • 14min

Data Collection, Storage and Preparation using ChatGPT8 lectures • 1hr 24min

Technical Indicators & ChatGPT4 lectures • 47min

Strategy Creation & Backtesting & Performance Measurement using ChatGPT4 lectures • 1hr 8min

Next Steps & Conclusion1 lecture • 4min

Requirements

Description

Who this course is for: