In this module, we will explore the key strategies used in pairs trading, starting with statistical arbitrage and cointegration to identify profitable trading pairs. You will learn how to apply the Z-Score for entry and exit points, calculate the hedge ratio to manage market risk, and understand the concept of half-life in mean-reverting strategies. Additionally, we’ll cover position sizing and risk management, ensuring you’re equipped to manage risk effectively while maximizing potential returns.

In this module, we will take you through the technical setup needed to interact with the DYDX platform. From installing MetaMask and setting up the Alchemy HTTP provider, to securely accessing your DYDX credentials, you’ll be ready to programmatically trade on the platform. We’ll walk you through connecting to DYDX using Python and Colab, interpreting the API documentation, and retrieving crucial market data. By the end of this module, you'll know how to place and manage orders on DYDX using the platform’s private API.

In this module, we will guide you through setting up your development environment for building the trading bot. You’ll learn how to configure VS Code and Python virtual environments, ensuring a clean and manageable workspace. We’ll also walk you through creating a GitHub repository for version control, and show you how to securely store sensitive information, like API keys, as environment variables to protect your data.

In this module, we will guide you through the initial steps of building your trading bot. You'll begin by configuring the bot constants, followed by an overview of the plan for Stage 1. We will walk you through securely connecting your bot to the DYDX platform and then dive into developing the close all positions function, split across two parts. By the end of this stage, your bot will be able to connect to DYDX and manage open positions.

In this module, we will dive into Stage 2 of building the trading bot. You'll begin by understanding the plan of attack for gathering and processing key data, such as ISO times and market prices. We’ll walk you through constructing market prices, followed by implementing cointegration functions to identify asset pairs that move together. Finally, we’ll guide you on how to store and manage cointegrated pairs data, ensuring your bot can make informed decisions based on this critical information.

In this module, we’ll focus on the final stages of building your trading bot. You’ll start by initializing and completing the BotAgent class, which will allow your bot to interact with the trading platform and execute trades. We’ll guide you through setting up open trades, implementing trade triggers based on your trading strategy, and automating the execution process. Additionally, we’ll cover managing trade exits with various strategies to ensure effective risk management. Finally, you’ll build a test run to evaluate your bot’s performance before deploying it using Git for live trading.

In this module, we’ll integrate Telegram messaging into your trading bot for real-time notifications. You’ll start by setting up a Telegram bot and learning how to send messages via URL for basic communication. We’ll then guide you through sending messages programmatically using Python for more flexibility. Finally, we’ll discuss the strategic placement of messages within your code and how to use GitHub for version control, ensuring smooth integration and collaboration for your project.

In this module, we’ll walk you through the process of deploying your trading bot on AWS, starting with an overview of AWS and signing up for an account. You will learn how to launch an EC2 instance, create a security group for secure communication, and install Python. Once your bot is running on AWS, we’ll show you how to test, update, and iterate on your code. Finally, we’ll introduce CRON for automating your trading bot, ensuring that it runs continuously and autonomously in the cloud.

In this final module, we will explore the next steps you can take to improve your trading bot. You’ll gain insights on enhancing its strategies, adding new features, and adjusting to dynamic market conditions. This video will serve as a guide for ongoing development, ensuring that your trading bot remains effective and optimized as you continue to evolve its performance over time.