Summary of "Advanced Algo Trading Series | Episode 3: Connecting Your Algo With Excel | Part 2"

Summary of “Advanced Algo Trading Series | Episode 3: Connecting Your Algo With Excel | Part 2”

Technological Concepts & Product Features Covered

Live Option Chain Integration with Excel
- Demonstrated how to connect a tick-by-tick option chain feed to Excel using Python.
- Used the [Excel Wings](https://www.amazon.com/dp/1492081000?tag=dtdgstoreid08-20) (xlwings) library to send data frames (entire option chains) to Excel and keep them auto-updating every few seconds.
- Implemented formatting in Excel (e.g., bold headers, blue coloring) and froze the top row for better visibility.
- Limited the option chain display to a range around the at-the-money strikes (e.g., 15 strikes above and below) to optimize performance and relevance.
- Incorporated rate limiting by adding a 2-second sleep delay between requests to avoid API restrictions.
- Provided tips on managing Excel sheets, such as clearing data ranges automatically to avoid manual cleanup.
Order Placement via Excel
- Discussed the possibility of embedding buy/sell buttons or order placement logic directly within Excel sheets.
- Highlighted that Excel can be used not only for viewing option chains but also for sending orders programmatically.
- Encouraged users to ask implementation questions on the “Mate for Trade” website.
Telegram Integration for Algo Alerts
- Explained how to integrate Telegram messaging with the algo to receive real-time alerts for order placement, modification, cancellations, entries, exits, and position updates.
- Detailed the process to create a Telegram Bot (referred to as “BOT”):
  - Using Telegram’s BotFather to create a bot and obtain a Bot API token.
  - Obtaining the receiver chat ID by interacting with a Telegram user info bot.
- Showed how to send messages programmatically from Python using the Telegram Bot API by passing the message, chat ID, and bot token.
- Demonstrated formatting messages using Python f-strings for dynamic content such as traded stock names, RSI values, quantities, stop loss, and order IDs.
- Emphasized the importance of keeping API tokens and chat IDs confidential.
Automation and Code Debugging
- Walked through debugging issues like code not stopping on buy signals due to unsaved files or logic errors.
- Automated clearing of Excel ranges before updating new data to avoid manual cleanup.
- Explained how the algo avoids firing back-to-back orders for the same signals by managing state internally.
Algo Deployment and Architecture
- Described the final deployment setup:
  - Algo runs on a remote server located in Mumbai (close to the exchange’s data center).
  - Telegram alerts are received on the trader’s mobile device anywhere (e.g., Pondicherry).
  - Order placement happens on the server, while the trader monitors alerts remotely.
- Highlighted that the foundational structure of the algo (Excel integration, Telegram alerts, order management) remains consistent across different strategies; only entry/exit conditions vary.
- Encouraged viewers that after completing the series, their algo will be fully automated, eliminating manual trading.

Key Guides / Tutorials Included

Step-by-step tutorial on connecting live option chain data to Excel with continuous updates.
Guide on creating and integrating Telegram bots for alert messaging.
Instructions on formatting and managing Excel sheets programmatically.
Debugging and improving algo code with practical examples.
Overview of deploying algos on remote servers with live monitoring via Telegram.

Main Speaker / Source

The primary speaker is Prashaas, who provides a detailed walkthrough of the coding, integration, and deployment process for algorithmic trading using Python, Excel, and Telegram.