Option Chain Data Analysis

Overview

This project involves creating Python functions to retrieve and analyze option chain data for financial instruments using the Upstox API. The goal is to determine the highest bid or ask price for each strike price and calculate the margin required and premium earned for selling options. This work was completed as part of an assessment for the BreakoutAI internship.

Code Structure

Function 1 - get_option_chain

• Purpose: Retrieves the option chain data for a specified instrument and expiry date. It then identifies the highest bid price for put options (PE) or the highest ask price for call options (CE) for each strike price.
• Key Columns:
  • instrument_name: Name of the instrument (e.g., NIFTY).
  • strike_price: The strike price of the option.
  • side: "PE" for put options or "CE" for call options.
  • bid_or_ask: Highest bid price for PE or highest ask price for CE.
  • instrument_key: Unique identifier used to fetch margin details.
• Output: A DataFrame row containing the required columns.

Function 2 - calculate_margin_premium

• Purpose: This function calculates the margin_required and premium_earned based on the retrieved data. Using instrument_key from Function 1, it calls the margin API to get the necessary margin for option selling.
• Calculation:
  • Margin Required: Uses the Upstox margin API, passing instrument_key and quantity (lot size) to determine the margin.
  • Premium Earned: Calculated by multiplying bid_or_ask price by the lot size.
• Output: Adds margin_required and premium_earned to the DataFrame.

Approach

Unique Aspects

• An additional instrument_key column was included to seamlessly use this data in margin calculations.
• Used two API calls effectively by breaking down the code into two separate functions for simplicity and clarity.

Challenges

• Understanding and extracting only specific data parts from the API response (e.g., highest bid/ask prices and instrument keys) required careful handling of JSON data.
• Integrating multiple API calls to retrieve margin requirements in real-time and ensuring error-free data handling.

Usage

1. Define client_id, secret_key, and redirect_uri for authentication.
2. Use get_option_chain to retrieve the required option data.
3. Pass the resulting DataFrame row to calculate_margin_premium with the correct lot size.

AI Assistance

• Used AI tools like ChatGPT to clarify financial terms (e.g., options, premiums) and provide initial code structure for API data retrieval and JSON handling.