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Master advanced options volatility and delta-neutral strategies using Python and Machine Learning to turn market uncertainty into a measurable edge.
Learn Options – Advanced Option Course
Live Trading
- Analyse volatility skew & surfaces; compute delta-neutral skew and thresholds.
- Construct delta-neutral options portfolios; hedge with Greeks (Δ/Γ/ν).
- Trade event-driven volatility (e.g., FOMC meetings) while avoiding IV crush.
- Apply portfolio hedging frameworks and stress testing.
- Build Python backtests for skew/event strategies on real options data.
REAL-WORLD CASE STUDIES
Case Study 1: Delta-Neutral Skew Trading
The case study demonstrates trading volatility skew using a delta-neutral options strategy by simultaneously buying and selling options at different strikes. This exploits mispricing in implied volatilities while hedging directional exposure. Results show how traders can capture edge from volatility differences rather than predicting market direction.
Case Study 2: Event-Driven Long Straddle
The case study applies a long straddle around scheduled events like FOMC meetings, buying both ATM call and put options ~14 days before, then exiting the day before the announcement. Backtests suggest this approach can benefit from the rise in implied volatility while avoiding post-event “volatility crush,” though outcomes vary across events. Try the python notebook in the course to backtest across historical FOMC meetings; visualize IV and P&L.
Hands-On Labs in Python
- Compute Greeks; plot vol skew graphs; estimate IV rank/skew rank.
- Run GARCH & Monte Carlo simulations (for comparison to skew/event methods).
- Compute Implied Volatility using LSTM Network.
- No setup: Start instantly with a pre-configured browser environment
Course Features
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Faculty Support on Community
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Interactive Coding Practice
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Capstone Project Using Real Market Data
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Trade and Learn Together
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Get Certified
Prerequisites
This course is ideal for options traders, quant/algo developers, portfolio managers, and risk professionals. It is recommended to complete the courses “Volatility Trading Strategies for Beginners“ and “Options Volatility Trading: Concepts and Strategies” beforehand, as these courses provide valuable background knowledge.
Syllabus
| Module 1: Introduction & Core Concepts | |
| Introduction: Course overview, structure, and interactive learning tools. | |
| Options Volatility: Fundamentals of volatility behaviour and trading opportunities. | |
| Sourcing Data: Importance of data, sourcing/storing in pickle files, and available sources. | |
| Options Pricing: Black-Scholes-Merton model, its assumptions, and alternative pricing models. | |
| Deliverable: Starter notebook with data sourcing and BSM model examples. | |
| Module 2: Volatility Skew & Surfaces | |
| Volatility Skew: OTM puts vs calls, IV differences, plotting skew. | |
| Kinks in Volatility Surface: Why surfaces aren’t always smooth, irregularities, and trading limitations. | |
| Calculation of Volatility Skew: Objective methods and interpretation. | |
| Trading Volatility Skew: Hypothesis formation, backtesting skew-based strategies, performance analysis. | |
| Deliverable: Volatility skew analysis notebook. | |
| Module 3: Delta-Neutral & Volatility Strategies | |
| Delta Neutral Skew Analysis: Using delta vs average OTM IVs, trading delta-neutral skew. | |
| Volatility and Mean Reversion: Mean-reversion behaviour of volatility, why it doesn’t trend like prices, and trading strategies. | |
| Trading Strategy on VIXY Using VIX: VIX index insights, VIXY ETF, and applying mean reversion to short VIXY strategies. | |
| Deliverable: Delta-neutral and VIXY trading strategy backtest. | |
| Module 4: Ranks & Advanced Forecasting | |
| IV Rank: Intuition and calculation, context of IV values. | |
| IV Rank in Trading: Strategy creation, backtest, and performance. | |
| Skew Rank: Intuition, calculation, and combining IV & Skew Rank. | |
| Skew Rank in Trading: Short Straddle Strategy – IV Rank and Skew Bank. | |
| Forecasting IV Using Machine Learning: Using multiple variables, ML-driven predictions. | |
| LSTM’s Role in Forecasting IV: LSTM for IV forecasting, trading strategies on predicted IV. | |
| Deliverable: Forecasting and rank-based strategy generator. | |
| Module 5: Event-Driven Volatility & Options Pricing | |
| Volatility Around Events: Historical patterns, pre-event volatility surges, and event-driven strategies. | |
| Volatility and Options Pricing: Impact of volatility on option pricing and trading. | |
| Relative View on Volatility: Long calendar spread strategy, backtest, and performance analysis. | |
| Deliverable: Event-driven volatility strategy backtest notebook. | |
| Module 6: Risk Management & Hedging | |
| Risk Management of a Volatility Position: Dollar-based risk management. | |
| Risk Management Using Option Greeks: Managing risk through Greeks in volatility positions. | |
| Hedging the Option Greeks: Example of hedging a short straddle with Greeks. | |
| Risk Management Using Delta Hedging: Concept and application in short straddle. | |
| Threshold for Delta Hedging: Selective hedging to reduce transaction costs. | |
| Implementation of Delta Hedging: Selective delta hedging in Python. | |
| Hedging an Options Portfolio Using Greeks: Portfolio-level hedging across multiple assets. | |
| Summary: Recap of learnings, with downloadable code and data. | |
| Deliverable: Risk management and hedging implementation notebook. | |
| Capstone Project 1: Strategy Deployment and Analysis | |
| In this project, you will apply all the concepts you have learned to design, test, and evaluate a trading strategy. You will: | |
| Deploy a short strangle strategy. | |
| Backtest it on historical data. | |
| Analyse the strategy’s performance using key metrics. | |
| Deliverable: A complete backtest notebook with performance analysis. | |
| Capstone Project 2: Risk Management of an Options Volatility Position | |
| This project challenges you to tackle risk management in a practical setting. You will be presented with a problem statement related to managing the risks of an options volatility position. Along with this, you will receive a solution template to guide your work and a model solution for reference. | |
| Deliverable: A risk management notebook and a completed template with your solution. |
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About author
Course Features
- Lecture 0
- Quiz 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 45
- Assessments Yes