Indian Stock Market
AI in the Indian Stock Market – Full Course Track
Welcome to QuantML’s Indian Stock Market AI track — where we stop playing with outdated Excel sheets and start building tools that think, forecast, and explain.
If you’ve ever wanted to analyze NSE/BSE stocks, automate stock picks, or even build your own AI stock research assistant, this course is your golden ticket.
This isn’t theory. It’s real code, real market data, and real projects you’ll actually use.
🧠 What You’ll Learn
You'll go from:
“How do I get stock data from NSE?”
to
“I built a GPT-powered screener that explains why it picked ITC.”
You’ll learn how to:
Pull stock data from NSE/BSE and Yahoo Finance
Analyze trends, volumes, and indicators
Run machine learning models on price, volume, and sentiment
Generate AI commentary and explanations for your stock picks
Deploy dashboards and backtest trading strategies
🧱 How This Track Works
Just like the Python course, this is hands-on and modular. Each lesson includes:
✅ Working Python notebooks
📊 Live NSE/BSE data from APIs and scrapers
🧪 Real market use cases
🧰 Deployable tools you can actually show off
🎥 Walkthroughs + templates
🔥 Complete Module Breakdown
✅ Module 0 – Setup & Market Data 101
Goal: Pull real-time and historical stock data from Indian exchanges
Outcome: You’ll create your own NSE data pipeline.
Lessons:
Introduction to Indian market structure (NSE/BSE, indices, sectors)
Using
nsetools,yfinance, and scraping withBeautifulSoupFetching real-time quotes, OHLC, FII/DII activity
Getting fundamentals (P/E, market cap, ROCE) from sources like Screener.in
Saving and organizing stock data locally
✅ Module 1 – Technical Analysis with Python
Goal: Analyze price and volume like a pro
Outcome: Build your own indicator-rich charts and signal detectors.
Lessons:
Calculating moving averages (SMA, EMA)
Bollinger Bands, RSI, MACD, Supertrend
Price action strategies with Python
Building signal generators
Visualizing technical indicators with
matplotlib
✅ Module 2 – Quant Strategies & Backtesting
Goal: Simulate trades and evaluate strategies
Outcome: Know if your “gut feeling” actually works.
Lessons:
Introduction to backtesting basics
Strategy design: crossover, breakout, volatility-based
Building backtests with
bt,backtrader, and pandasPerformance metrics: Sharpe, CAGR, Max Drawdown
Portfolio simulation: multiple tickers + capital allocation
✅ Module 3 – Sentiment Analysis & News
Goal: Use NLP to detect market mood
Outcome: Feed your models with human emotion. What could go wrong?
Lessons:
Scraping news headlines and earnings summaries
Preprocessing text for sentiment analysis
Using
VADERandTextBlobfor sentiment scoringBasic topic modeling (LDA) for market chatter
Reddit/Twitter scraping for trend detection (optional, chaotic)
✅ Module 4 – Forecasting Stock Prices with ML
Goal: Predict trends, not just follow them
Outcome: You’ll build models that try to forecast prices and trends.
Lessons:
Feature engineering: price, volume, indicators
Train/test split for time series data
Linear regression, decision trees, and ensemble models
Classification: “Buy / Hold / Sell” predictions
Model evaluation and live prediction setup
✅ Module 5 – GPT for Stock Commentary & Automation
Goal: Use GPT to summarize, explain, and automate insights
Outcome: You’ll have a financial co-pilot that doesn’t need coffee breaks.
Lessons:
Connecting OpenAI API to your Python stock analysis
Generating earnings summaries and analysis explanations
GPT as a commentary writer for dashboards
Building a chat assistant for Indian stocks
Prompt engineering for financial use cases
✅ Module 6 – Building a Screener & Dashboard
Goal: Replace Moneycontrol with your own app
Outcome: You’ll screen stocks based on your logic, your filters, and your brand.
Lessons:
Creating a stock screener with filters (P/E, volume, RSI, etc.)
Streamlit UI with input controls
Integrating GPT summaries into your dashboard
Exporting stock picks to Excel and PDF
Deploying the screener online (Streamlit Cloud / HuggingFace)
✅ Module 7 – Portfolio Analysis & Risk Management
Goal: Build tools for real investors, not gamblers
Outcome: Track your portfolio like a PMS, not like a gambling diary.
Lessons:
Portfolio performance tracker with equity curve
Tracking P&L, win rate, average return
Risk metrics: Value at Risk, Beta, volatility
Diversification tools and sector exposure analysis
Alerts for stop-loss and price targets
✅ Module 8 – Final Capstone Project
Goal: Combine all modules into one polished product
Outcome: You’ll ship your own AI-powered finance product.
Project Ideas:
AI Stock Research Assistant – Input a stock, get charts, analysis, GPT commentary
Smart Screener – Custom filters + ML signal + GPT explanation
FP&A Market Tracker – Automated macros + GPT updates + visual dashboard
🎯 Who This Is For
This is made for:
Retail traders and investors
Quants and analysts interested in Indian markets
Finance pros who want to apply AI locally
Students looking to build job-ready projects
Anyone who says, “Indian data is too messy to automate” (wrong)
🧠 Bonus Tracks Coming Soon
NSE FO Data Analysis (Options, OI, PCR)
Intraday Strategy Automation
Zerodha Kite / Upstox API Bot Integration
Machine Learning with NSE Derivatives
👇 Get Started
You now have an entire AI toolkit for the Indian stock market. Go wild.
👉 Subscribe for all future lessons
Or, keep scrolling NSE Top Gainers manually like a caveman. Your call.
📜 Disclaimer & Declaration
This course and all related content on QuantML — including code, tutorials, tools, dashboards, and commentary — is intended strictly for educational and informational purposes only.
QuantML is not a SEBI-registered advisor, and no part of this course constitutes financial advice, investment recommendations, or stock tips. This content is designed to help you learn Python, machine learning, and data science using real-world finance examples — including Indian stock market data — but it should not be used to make trading or investment decisions.
All tools and examples are shared as learning resources, not for live trading or portfolio management.
Please consult with a SEBI-registered financial advisor before making any investment decisions. We do not guarantee the accuracy, completeness, or performance of any tool, model, or data source used in this course.
Use of market data, APIs, and public web scraping is subject to the terms and policies of the respective websites and data providers. Users are responsible for complying with applicable laws, including those of SEBI, RBI, MCA, and Exchange Regulations.
By using this course or website, you agree to hold QuantML and its creators harmless from any liabilities or losses that may result from the use of content provided herein.