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Hands-On Agentic Ai Trading System Using Mcp & Langchain
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 9m | Size: 3.34 GB[/center]
Build MCP-powered trading agents using LangChain, LangGraph, LangSmith, and real-time financial data
What you'll learn
Fetch and analyze real-time financial data using yfinance and combine it with LLM intelligence to build practical trading and analysis tools.
Build MCP clients and servers and integrate them with LangChain to create tool-driven, reasoning-capable AI workflows.
Use LangGraph to design multi-step, stateful workflows, enabling agents to orchestrate tools, handle branches, and manage complex execution paths.
Leverage LangSmith for monitoring, debugging, and tracing, gaining full visibility into agent behavior, tool calls, and performance metrics.
Apply production-ready AI engineering best practices, including environment setup, API handling, prompt design, and secure configuration with .env files.
Deploy, test, and validate AI agents locally or in cloud environments, ensuring reliability, scalability, and observability in real-world use cases.
Understand the future of agentic systems, including open standards, modular architectures, multi-agent collaboration, and workflow automation.
Requirements
Basic programming knowledge Familiarity with Python fundamentals (functions, classes, virtual environments). No advanced machine learning or AI experience required.
A working development environment Any OS is fine: macOS, Windows, or Linux. Python 3.10+ installed.
A willingness to learn modern AI tooling Curiosity to experiment with LangChain, LangGraph, LangSmith, MCP, and real financial data using yfinance.
Description
Agentic AI is rapidly transforming how intelligent systems are built - moving beyond simple chatbots to autonomous agents that can reason, act, and interact with real-world systems. In this hands-on course, you'll learn how to design, build, orchestrate, and monitor production-grade agentic AI trading systems using Model Context Protocol (MCP), LangChain, LangGraph, and LangSmith.This course goes beyond theory. You'll build a complete end-to-end trading agent that can interpret natural language queries, fetch real-time market data, compute technical indicators like RSI and Bollinger Bands, execute multi-step workflows, and generate actionable trading insights - all while being fully observable and auditable.We start by laying a strong foundation in MCP and agentic LLM concepts, explaining how modern agents communicate with tools, APIs, and workflows in a standardized and cloud-agnostic way. You'll then set up your development environment, configure LLM endpoints (including local models via Ollama), and connect real-time financial data sources.As the course progresses, you'll design multiple real-world systems - including a trading analysis agent, an enterprise assistant, and an IoT monitoring system - to understand how MCP-powered architectures scale across domains. You'll implement an MCP server with financial tools, orchestrate workflows using LangGraph, and integrate intelligent clients using LangChain agents with tool discovery and intent detection.A key focus of this course is observability and compliance. You'll integrate LangSmith to trace agent reasoning, monitor tool usage, debug workflows, and ensure transparency - an essential requirement for regulated domains like finance.Finally, everything comes together in a mini project, where you'll combine MCP server, LangChain client, LangGraph workflows, and LangSmith monitoring into a single cohesive trading system. You'll also learn the path forward - how to extend these ideas into multi-agent systems, production deployments, and emerging AI standards.By the end of this course, you won't just understand agentic AI - you'll know how to build, monitor, and evolve intelligent agents that work in the real world.
Who this course is for
This course is designed for learners who want to build real, production-ready agentic AI systems using modern open-source frameworks. You'll find this course valuable if you are: • AI/ML Engineers & Developers Looking to move beyond basic prompt engineering and learn how to build tool-using agents, workflows, and monitored AI systems with MCP, LangChain, LangGraph, and LangSmith. • Software Engineers exploring LLM integration If you want to connect LLMs with real tools, APIs, financial data, or automation workflows, this course gives you a complete hands-on foundation. • Data Analysts & Quant Learners Anyone working with market data who wants to automate analysis, compute indicators, and build intelligent trading assistants. • Students & Career Switchers entering AI If you're new to AI engineering but eager to learn practical, industry-relevant skills for building agentic systems, this course is perfect. • Tech Enthusiasts & Hobbyists Curious learners who want to experiment with MCP servers, LangChain tools, LangGraph workflows, and yfinance-powered market analysis.
Hands-On Agentic Ai Trading System Using Mcp & Langchain
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 9m | Size: 3.34 GB[/center]
Build MCP-powered trading agents using LangChain, LangGraph, LangSmith, and real-time financial data
What you'll learn
Fetch and analyze real-time financial data using yfinance and combine it with LLM intelligence to build practical trading and analysis tools.
Build MCP clients and servers and integrate them with LangChain to create tool-driven, reasoning-capable AI workflows.
Use LangGraph to design multi-step, stateful workflows, enabling agents to orchestrate tools, handle branches, and manage complex execution paths.
Leverage LangSmith for monitoring, debugging, and tracing, gaining full visibility into agent behavior, tool calls, and performance metrics.
Apply production-ready AI engineering best practices, including environment setup, API handling, prompt design, and secure configuration with .env files.
Deploy, test, and validate AI agents locally or in cloud environments, ensuring reliability, scalability, and observability in real-world use cases.
Understand the future of agentic systems, including open standards, modular architectures, multi-agent collaboration, and workflow automation.
Requirements
Basic programming knowledge Familiarity with Python fundamentals (functions, classes, virtual environments). No advanced machine learning or AI experience required.
A working development environment Any OS is fine: macOS, Windows, or Linux. Python 3.10+ installed.
A willingness to learn modern AI tooling Curiosity to experiment with LangChain, LangGraph, LangSmith, MCP, and real financial data using yfinance.
Description
Agentic AI is rapidly transforming how intelligent systems are built - moving beyond simple chatbots to autonomous agents that can reason, act, and interact with real-world systems. In this hands-on course, you'll learn how to design, build, orchestrate, and monitor production-grade agentic AI trading systems using Model Context Protocol (MCP), LangChain, LangGraph, and LangSmith.This course goes beyond theory. You'll build a complete end-to-end trading agent that can interpret natural language queries, fetch real-time market data, compute technical indicators like RSI and Bollinger Bands, execute multi-step workflows, and generate actionable trading insights - all while being fully observable and auditable.We start by laying a strong foundation in MCP and agentic LLM concepts, explaining how modern agents communicate with tools, APIs, and workflows in a standardized and cloud-agnostic way. You'll then set up your development environment, configure LLM endpoints (including local models via Ollama), and connect real-time financial data sources.As the course progresses, you'll design multiple real-world systems - including a trading analysis agent, an enterprise assistant, and an IoT monitoring system - to understand how MCP-powered architectures scale across domains. You'll implement an MCP server with financial tools, orchestrate workflows using LangGraph, and integrate intelligent clients using LangChain agents with tool discovery and intent detection.A key focus of this course is observability and compliance. You'll integrate LangSmith to trace agent reasoning, monitor tool usage, debug workflows, and ensure transparency - an essential requirement for regulated domains like finance.Finally, everything comes together in a mini project, where you'll combine MCP server, LangChain client, LangGraph workflows, and LangSmith monitoring into a single cohesive trading system. You'll also learn the path forward - how to extend these ideas into multi-agent systems, production deployments, and emerging AI standards.By the end of this course, you won't just understand agentic AI - you'll know how to build, monitor, and evolve intelligent agents that work in the real world.
Who this course is for
This course is designed for learners who want to build real, production-ready agentic AI systems using modern open-source frameworks. You'll find this course valuable if you are: • AI/ML Engineers & Developers Looking to move beyond basic prompt engineering and learn how to build tool-using agents, workflows, and monitored AI systems with MCP, LangChain, LangGraph, and LangSmith. • Software Engineers exploring LLM integration If you want to connect LLMs with real tools, APIs, financial data, or automation workflows, this course gives you a complete hands-on foundation. • Data Analysts & Quant Learners Anyone working with market data who wants to automate analysis, compute indicators, and build intelligent trading assistants. • Students & Career Switchers entering AI If you're new to AI engineering but eager to learn practical, industry-relevant skills for building agentic systems, this course is perfect. • Tech Enthusiasts & Hobbyists Curious learners who want to experiment with MCP servers, LangChain tools, LangGraph workflows, and yfinance-powered market analysis.
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