Intraday Short Straddle Strategy Backtest in Python | Bank Nifty Options Strategy

Why It Matters

It demonstrates a concrete, data‑driven method to capture theta decay in Indian index options, highlighting both the profit potential and the hidden tail risk that institutional and retail traders must manage.

Key Takeaways

• Short ATM straddle profits from intraday theta decay.
• Strategy is delta‑neutral, short gamma, short vega, long theta.
• Entry at 9:20 am, exit at 3:15 pm, no stop‑loss.
• Backtest on 60 days shows ~869 points profit, 40‑point max drawdown.
• Results exclude transaction costs; risk lies in occasional large losses.

Summary

The video walks through a Python back‑test of an intraday short straddle on Bank Nifty options, illustrating how to build, execute, and evaluate the trade from data ingestion to equity‑curve visualization.

The author explains that selling at‑the‑money calls and puts creates a delta‑neutral, short‑gamma, short‑vega, long‑theta position. Entry is fixed at 9:20 am after the first five minutes of price discovery, exit at 3:15 pm, with no stop‑loss. Using one‑minute option data for the first quarter of 2022, the back‑test on 60 trading days generated roughly 869 points of profit and a maximum drawdown of about 40 points.

Key code steps include converting date strings to datetime, rounding the underlying price to the nearest 100‑point strike, isolating the ATM strike, and computing minute‑by‑minute P&L by subtracting the entry premium from the prevailing premium. The plotted equity curve shows frequent small gains and occasional sharp drops, confirming the characteristic negative‑skew return profile of short‑volatility strategies.

The results suggest the short straddle can be profitable in low‑volatility, range‑bound sessions, but the tail risk from volatility spikes and the omission of transaction costs mean traders must apply strict risk controls before deploying the approach live.

Original Description

In this session, we test and analyze an intraday short straddle options strategy using Python and minute-level Bank Nifty options data.

A short straddle involves selling an at-the-money call option and put option simultaneously, aiming to profit from option premium decay when markets remain range-bound. However, while this strategy can generate frequent small gains, it can also face large losses during strong directional moves or volatility expansion.

In this video, we walk through the complete process of building and testing this strategy:

• Understanding how the short straddle strategy works

• Why traders sell at-the-money options

• Relationship between implied volatility and realized volatility

• Implementing the strategy in Python using Jupyter Notebook

• Using minute-level options data for backtesting

• Evaluating P&L, drawdowns, win rate, and Sharpe ratio

The results show an important insight about short volatility strategies: they often produce frequent small profits but occasional large losses, creating a negatively skewed return distribution.

If you want to explore the concepts discussed in this video further, the following resources will help.

➡️ Download the codes from the link below: https://bit.ly/3PoV3TH

Learn Options Trading & Python-Based Strategies

Straddle Options Trading Strategy in Python: https://bit.ly/3PoV3TH

Options Trading Strategies in Python (Quantra Course): https://bit.ly/40AadYQ

Day Trading Strategies Course: https://bit.ly/471DnDL

Learn Backtesting for Trading Strategies: Backtesting helps traders evaluate whether a strategy works before risking capital.

Guide to Backtesting Trading Strategies: https://bit.ly/4sUkeMB

Backtesting Trading Strategies Course: https://bit.ly/4uFxJBx

Explore AI, Machine Learning & Agentic AI for Trading

AI for Trading Guide: https://bit.ly/4bKcDKC

Introduction to Machine Learning for Trading: https://bit.ly/4bJ6Jtf

Machine Learning & Deep Learning for Trading Track: https://bit.ly/4uyCooA

AI Portfolio Management with LSTM Networks: https://bit.ly/4doQjaz

Agentic AI for Trading: https://bit.ly/4sm6f2i

🎓 About the Speaker:

Mohak Pachisia is a Senior Quantitative Researcher at QuantInsti, specializing in trading strategy development, financial modeling, and quantitative research. Before joining QuantInsti, he worked with Evalueserve, where he led the learning and development function for the the Risk and Quant Solutions division.

Mohak is an alumnus of EPAT and has cleared all levels of the Chartered Market Technician (CMT) program and two levels of the CFA program. He is currently pursuing the Certificate in Quantitative Finance (CQF). He has also consulted for organisations such as Upstox, Motilal Oswal, Spider Software, and TradeSmart. His expertise lies in simplifying complex quant problems and turning them into structured, executable strategies.

About EPAT

The EPAT program by QuantInsti is a structured learning track focused on algorithmic and quantitative trading. It emphasizes Python-based strategy development, backtesting, risk management, and applied projects guided by mentors.

Join EPAT - Executive Programme in Algorithmic Trading: https://bit.ly/3P3i8vh

Perfect For

This video is ideal for:

• Options traders learning short volatility strategies

• Traders interested in intraday options strategies

• Beginners learning algorithmic trading with Python

• Quant traders testing systematic trading strategies

• Anyone interested in backtesting trading strategies with data

Timestamps (Chapters)

00:00 Understanding the drawdown and return distribution

00:32 Introduction to the strategy

01:04 Understanding market conditions for short straddles

01:44 What is a short straddle strategy

02:17 Why trade at-the-money options

03:02 Delta neutrality and option greeks

03:53 Relationship between volatility and strategy profitability

04:57 Setting up the Python environment

05:28 Loading one-minute options data

06:47 Cleaning and preparing the dataset

07:39 Defining strategy rules

08:03 Entry rule for the short straddle

09:06 Exit rule for the strategy

10:02 Strategy parameters and strike selection

11:07 Identifying the at-the-money strike

12:25 Constructing the short straddle trade

13:29 Implementing exit logic

16:04 Building the intraday P&L time series

17:32 Plotting the equity curve

18:44 Analyzing drawdowns

19:07 Performance metrics of the strategy

20:07 Worst drawdown day analysis

21:40 Morning gamma risk explained

22:04 Average intraday P&L profile

23:20 Return distribution and negative skew

Hashtags

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