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Intermediate

Building an Options Pricing Calculator Using Python

A hands-on build course for traders and aspiring quants who want to understand where the numbers on an NSE option chain come from. You will write a complete options pricing library in Python: the Black-Scholes formula term by term, all five Greeks in closed form and by finite differences, an implied volatility solver, a binomial tree and a Monte Carlo engine. Every model is tested against real Nifty 50 and Bank Nifty contracts, with Indian conventions handled correctly: the 91-day T-bill as the risk-free rate, lot sizes, weekly and monthly expiry timing, and India VIX as a cross-check on your implied volatility. You finish by packaging the code as a tested module with a Streamlit front end and a strategy payoff tool. Built for systematising traders, prop-desk applicants and quant analyst aspirants who can already read an option chain and want to rebuild it from first principles.

Black-Scholes ModelOption GreeksImplied VolatilityVolatility Smile and SkewBinomial TreesMonte Carlo SimulationNumPy and SciPyNSE Option Chain DataPut-Call ParityIndia VIXStreamlitUnit Testing

MODULES

6

DURATION

4 Hours

TRACK

Python for Finance

What You'll Master

Set up a clean Python environment for options work with NumPy, SciPy, pandas and pytest

Prepare the Black-Scholes inputs the Indian way: spot vs futures, T-bill risk-free rate, dividend yield and exact time to expiry

Code the Black-Scholes pricer and verify it with put-call parity and live Nifty 50 chain prices

Compute Delta, Gamma, Vega, Theta and Rho in closed form and confirm them with finite differences

Solve for implied volatility with bisection, Newton-Raphson and Brent, and plot the Nifty volatility smile

Reconcile your implied volatility surface with India VIX and understand why they differ

Price options with a binomial tree and a Monte Carlo engine and know when each beats Black-Scholes

Package everything as a tested module with a CLI, a strategy payoff tool and a Streamlit interface

Access Level

LEARNER

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Everything included

Full Text Playbooks

Actionable Exercises

Mobile Reading Mode

Lifetime Updates

Curriculum Breakdown

Chapter 1: Foundations for Builders

4 Lessons

Why Build Your Own Pricer When Sensibull and Opstra Exist10 min read

Setting Up Python for Options Work: NumPy, SciPy, pandas and Jupyter12 min read

Reading an NSE Option Chain as Data: Fields You Will Compute Yourself12 min read

Getting the Inputs Right: Spot, Risk-Free Rate, Dividends and Time to Expiry13 min read

Chapter 2: Black-Scholes in Code

4 Lessons

The Black-Scholes Formula, Term by Term14 min read

Coding the Pricer: d1, d2 and the Normal CDF With SciPy13 min read

Put-Call Parity as a Unit Test11 min read

Pricing a Nifty 50 Call and Put Against the Live Chain13 min read

Chapter 3: The Greeks

4 Lessons

Delta and Gamma: Closed Forms and Finite Differences13 min read

Vega, Theta and Rho: Coding and Sanity Checking13 min read

Vectorising the Greeks Across Strikes and Time With NumPy12 min read

Building a Greeks Table for the Whole Chain12 min read

Chapter 4: Implied Volatility

4 Lessons

What Implied Volatility Is and Why the Chain Quotes It11 min read

Solving for IV: Bisection, Newton-Raphson and Brent14 min read

Building the Volatility Smile and Skew From the Nifty Chain13 min read

Your IV vs India VIX: Reconciling the Two11 min read

Chapter 5: Beyond Black-Scholes

3 Lessons

Binomial Trees: A Second Model and the American-Style Question14 min read

Monte Carlo Pricing: Simulating Nifty Paths14 min read

Convergence, Variance Reduction and When Each Model Wins12 min read

Chapter 6: Shipping the Calculator

4 Lessons

Structuring the Project: Modules, pytest and a CLI12 min read

Strategy Payoffs and P&L Curves: Straddles, Strangles and Spreads13 min read

A Streamlit Front End for the Pricer12 min read

Feeding Live Data: NSE Chain, Kite Connect and Their Limits12 min read