This module guides learners through the essential steps of setting up a domain, configuring a VPS, installing necessary software, and securing a server for hosting the Arrissa Market Data API. It provides hands-on instruction for preparing a reliable environment for market data integration.

This module guides learners through the installation, configuration, and deployment of n8n, a no-code workflow automation tool. It covers setting up the environment, configuring the tool for trading workflows, and understanding best practices for reliability and updates.

This module guides learners through building an AI-driven market analysis workflow using n8n. It covers setting up LLM credentials, crafting effective prompts, and parsing structured data outputs for actionable insights. By the end, learners will be able to create and execute automated market analysis processes.

This module guides learners through the setup and configuration of the Arrissa Market Data API, focusing on practical skills like acquiring a Windows VPS, integrating trading terminals, and configuring various APIs for market analysis and trading execution.

This module explores the fundamentals of constructing AI-driven market analysis workflows using n8n. Learners will gain hands-on experience with JSON data structures, AI trading models, and vision-based chart analysis. The module also covers building a chat agent to interact with and provide insights from market data.

This module explores the development of an autonomous AI trading agent that interacts with the MT5 platform. Learners will gain hands-on experience in integrating APIs, managing trades, and implementing safety protocols. The content emphasizes practical application and secure AI-driven decision-making in financial trading.

This module provides a comprehensive overview of the Tool Matching Protocol (TMP) in n8n, covering its role in automating tool configuration and improving AI agent performance. Learners will explore how to build and test a TMP chat agent, including setting up categories, prompts, and actions. By the end, they will have practical skills in integrating TMP into AI workflows.

This module explores the Model Context Protocol (MCP) and its application in AI-driven trading. Learners will gain hands-on experience setting up MCP servers, building chat agents, and integrating them with n8n for automated workflows. The content emphasizes real-time decision-making and system automation in modern trading environments.

This module focuses on the process of backtesting AI models to improve prediction accuracy. Learners will explore techniques for evaluating model performance using real-world data and develop skills in integrating financial data sources effectively.

This module provides a reflective overview of the course journey, highlighting key achievements and offering a moment to celebrate progress. Learners will gain a sense of accomplishment and clarity on their learning outcomes.