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Introduction to Python for Finance: Why It's the Quant's Language

A first course in Python built entirely around Indian market data, for engineering graduates, working coders and traders who want to systematise what they do. Explains why Python beat Excel, R and C++ as the working language of quant desks, sets up a proper workstation with Jupyter and free NSE data, then teaches core Python, NumPy and pandas by computing returns, moving averages, volatility and drawdowns on Nifty 50 stocks. Ends with a simple, honest backtest that includes STT and slippage, a look at Zerodha Kite Connect and SEBI's algo rules, and a realistic map of quant careers in India.

MODULES

7

DURATION

4 Hours

TRACK

Python for Finance

Access Level

LEARNER

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Everything included

Full Text Playbooks

Actionable Exercises

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Curriculum Breakdown

Chapter 1: Why Python Became the Quant's Language

4 Lessons

What a Quant Actually Does All Day, and Where Code Fits In9 min read

Python vs Excel vs R vs C++: Choosing the Right Tool for Each Job10 min read

The Python Finance Ecosystem: pandas, NumPy, Matplotlib and Friends10 min read

What You Can Realistically Build in Your First 30 Days8 min read

Chapter 2: Setting Up Your Quant Workstation

4 Lessons

Installing Python the Right Way: Anaconda, Virtual Environments and Jupyter10 min read

Your First Notebook: Nifty 50 Closing Prices in Ten Lines10 min read

Free Data Sources for Indian Markets: NSE Bhavcopy, yfinance and screener.in11 min read

Reproducibility and Version Control: Why Quants Use Git9 min read

Chapter 3: Python Fundamentals Through Market Data

4 Lessons

Variables, Types and Arithmetic: Computing a Stock Return by Hand10 min read

Lists, Dictionaries and Loops: Managing a Nifty Watchlist11 min read

Functions: Writing a Reusable CAGR Calculator10 min read

Conditionals and Errors: Handling Market Holidays and Missing Data10 min read

Chapter 4: NumPy and pandas: The Quant's Core Tools

4 Lessons

NumPy Arrays: Why Vectorisation Beats Loops10 min read

pandas DataFrames: Loading and Cleaning an NSE Bhavcopy11 min read

Time Series in pandas: Resampling Daily Prices to Weekly and Monthly10 min read

Rolling Windows: Building the 50 and 200 Day Moving Averages on Reliance11 min read

Chapter 5: From Data to Insight: Analysis and Visualisation

4 Lessons

Returns, Volatility and Correlation Across Nifty Sector Indices11 min read

Plotting with Matplotlib: Charts a Fund Manager Would Actually Read10 min read

Sharpe Ratio, Maximum Drawdown and Beta in Code11 min read

Case Study: Nifty 50 vs Nifty Midcap 150 Over Ten Years12 min read

Chapter 6: Your First Quant Project: An Honest Backtest

4 Lessons

What a Backtest Is and Why Most of Them Lie10 min read

Coding a Moving Average Crossover Strategy on Nifty 5012 min read

Adding Brokerage, STT and Slippage: The Cost of Trading in India11 min read

Reading the Results: Look Ahead Bias, Overfitting and Survivorship11 min read

Chapter 7: Where This Leads: APIs, Careers and Next Steps

3 Lessons

Broker APIs: Zerodha Kite Connect and SEBI's Retail Algo Trading Rules11 min read

Quant Career Paths in India: Roles, Skills and What Interviews Test10 min read

Your 90 Day Learning Plan and What to Study Next8 min read