How to build a Stock-Predicting Telegram Bot with Python & Telegram for Algorithmic Trading ?

🔹 Open a Free Trading Account within 15mins with @aliceblue_online https://app.aliceblueonline.com/OpenAccount.aspx?C=SCHE679 🔹 Join the Blog & WhatsApp group for engaging conversations about Tech, AI, and Trading! https://chat.whatsapp.com/LwBoSJJMtZ32HsH53HgEBz https://medium.com/thequantlab https://twitter.com/akarshverma 🔹 Capture, research, and manage your Second Brain 🧠 with Heptabase, a visual note-taking tool for learning complex topics: https://get.heptabase.com/19xq4hkjtene 🔹 Book an Algorithmic Trading Discovery call today: https://topmate.io/akarsh/626435 ---------------------------------------------------------------------------------------- In this comprehensive Quant Lab tutorial, the speaker explains in detail how to create a Telegram bot with Python and use it for stock price prediction. The bot operates in real-time, drawing on two years' worth of historical market data and employing machine learning algorithms to forecast future stock prices. The speaker clearly demonstrates the steps for setting up the bot, importing necessary modules, implementing functionalities, and testing it with different stock symbols on Telegram. This serves as a primer for learning about algorithmic trading, understanding handle commands, data preparation and model training, as well as the speed and precision of process automation in the context of trading. Note: This tutorial intends to demonstrate the bot-building process and should not be taken as financial advice. Future tutorials will further explore advanced functionalities and practical applications in real-time trading. 00:00 Introduction to Quant Lab and Bot Creation 00:41 The Importance of Automation in Algorithmic Trading 01:04 Setting Up the Telegram Bot 02:08 Creating a Bot with Botfather 04:09 Building the Core Functionality of the Bot 05:04 Implementing Machine Learning for Stock Forecasting 06:14 Initializing and Testing the Bot 09:48 Demonstration of Bot Functionality 12:15 Understanding the Technical Architecture of the Bot 13:08 Conclusion and Future Plans