Algorithmic Options Trading in the Indian Market

Extract live option instruments from Zerodha Kite Connect by filtering the instrument list to option contracts for a given underlying, using trading symbol, expiry, and strike to build option chains.

Extract all live option instruments for a given underlying using the angel one smartapi; filter by option index or stock like banknifty, identify calls and puts, and sort the data.

Develop a program to fetch the Banknifty option chain across all expiries with Zerodha Kiteconnect, compute time-to-expiry, and identify at-the-money call contracts by strike proximity to the underlying price.

Extract the Banknifty option chain with the Angel One API, mirror the Kite Connect approach, convert strikes from passes to rupees, and fetch the four most recent contracts.

This lecture builds intuition for valuing option contracts on Amazon stock, showing how the Black-Scholes framework uses current price, strike, time to expiry, volatility, and interest rate.

Learn to implement the Black-Scholes pricing model in Python using Kite Connect data, compute historical volatility from Banknifty data, and update the option chain with Black-Scholes prices.

Adapt Black-Scholes pricing for the Angel One Smartapi by mirroring the Kite Connect approach, extracting at-the-money option chains, and computing volatility and prices with a 7% rate.

Understand implied volatility as the value that aligns Black-Scholes prices with market option prices, and apply Brent root finding via SciPy optimize to solve for it.

Compute implied volatility for an option using the Black-Scholes model and Brent optimization with Zerodha Kite Connect, analyzing market price vs. historical volatility to explain pricing dynamics.

Implement implied volatility with Angel One Smartapi, mirroring the Kite Connect API, using market prices in the option chain and an optimization function to produce IV estimates.

Explore Monte Carlo simulation for valuing options, especially exotic options, by simulating geometric Brownian motion paths via a stochastic differential equation and averaging discounted payoffs.

Explore how option greeks like delta, theta, vega, rho, and gamma measure price sensitivity to changes in price, time, volatility, and rates, with intuition and risk management insights.

Apply Monte Carlo simulation to price options by generating multiple price paths using Brownian motion, leveraging historical volatility, and computing discounted payoffs for call and put options.

Stream real-time Bank Nifty at-the-money call option data with Zerodha Kite Connect, subscribing to the instrument token and printing live price, market depth, volume, and open interest.

Stream real-time option data from Angel One Smart API using the WebSocket V2, subscribing to ATM contracts and receiving ticks, market depth, and last traded metrics.

Discover how Python threads work, memory management for Python processes, and the global interpreter lock as you learn to implement multi-threading with the threading module.

Learn how daemon threads run in the background, terminate with the main program, and how to set the daemon flag in Python's threading module to control background tasks.

Learn to implement multithreading with event objects and flags, coordinating concurrent threads and using event.set to stop one thread when another completes.

Master multi-threaded streaming for option data using the Angel One smart API. Learn to implement threading, daemon threads, and real-time last traded price updates in Python.

Stream real-time option data and compute implied volatility and Greeks using the Mbean library, then store the multi-contract option chain and Greeks in a structured data dictionary.

Learn to stream and store options data with Zerodha Kite Connect, iterate ticks, map tokens to symbols, and compute greeks using Black-Scholes and implied volatility, updating in real time.

Stream real-time market data and calculate option Greeks on the fly using Angel One Smartapi, storing updates in a structured data format with a multithreaded websocket architecture.

Fetch option greeks with the angel one smartapi option greek endpoint, retrieving delta, gamma, theta, vega, and implied volatility via expiry-based rest queries (not streaming).

Explore span, exposure, and delivery margins for selling options, how margins block liquidity, and how hedging and index option settlement affect capital needs in India.

Learn to calculate margin and place option orders with Zerodha Kite Connect and Angel One, covering order parameters, product types, and basket orders for margin benefits.

Explore how to place simple option orders, including limit and market orders, using Zerodha Kite Connect in Python by building order params from the option chain and trading symbol.

Place simple limit option orders on the Angel One platform by selecting an instrument from the option chain and submitting order parameters via the Smart API to obtain order ID.

Learn to modify pending option orders with Zerodha Kiteconnect, including updating price and quantity via the modify order API and tracking the new order ID.

Learn to modify an option order on Angel One smart api by using the order parameter, adding the order id, and changing the price via the modify order api.

Learn how to retrieve your orders and cancel them using Zerodha Kiteconnect APIs, including calling kite.orders, filtering by order IDs and variety, and handling open, rejected, and executed statuses.

Cancel an option order with the Angel One Smart API by supplying the order id and variety, then fetch the order book to review active and past orders.

Compute order-specific margins with the Kite Connect API using the order margins function and a list of order parameter dictionaries, then compare buy versus sell margins and netted basket margins.

Calculate the basket order margin using Kite Connect to reveal the final margin, initial margin, and hedge benefits; learn why placing the buy leg first unlocks margin efficiency.

Explore angel one smart api's funds and margins endpoint, which provides only account-level margin, and the limit call reveals available cash and margin impact on a Bank Nifty call option.

Place basket orders in Zerodha Kiteconnect while ensuring the hedge leg executes first to secure margin benefit, with an order status check and assured execution.

Mimic the basket order logic from the Kite Connect API using Angel One SmartAPI, implementing hedge and non-hedge option legs with two limit orders.

Explain the bear spread strategy by selling at-the-money options and buying higher strike options with the same expiration, offering known maximum profit and risk-limited maximum loss for risk managers.

Implement a bear spread on banknifty by selling the at-the-money call and buying the two-strikes-away call for the closest expiry, using Zerodha Kiteconnect.

Implement a bear spread strategy on Zerodha Kiteconnect by validating no existing positions, using option dataframes, applying Bollinger bands, and enforcing risk, reward, and margin checks with automated five-minute execution.

Implement a bear spread strategy on Zerodha Kiteconnect by building Bollinger band, ema, and rolling standard deviation, computing risk reward, checking margin, and placing basket orders.