If you are a trader looking to automate your trading edge, or a developer eager to get into algorithmic trading, this 4-part webinar series is designed to give you a clear, step-by-step approach to building trading strategies in Python.
Using OpenAlgo, a robust open-source trading automation framework, we will take you from concept to execution, covering practical intraday and options strategies along the way.
Webinar Outline
Module 1: How to Build a Trading Strategy in Python
- Key skills required to build trading strategies
- Setting up OpenAlgo on your machine
- Writing your first trading strategy in Python
- Understanding OpenAlgo Python SDK functions
Module 2: Intraday Options Strategy and Testing
- How to Host OpenAlgo in Server and Run your python strategies
- Building an intraday options trading strategy
- Using the API Analyzer to test and debug your strategies
- How to structure and execute multi-leg options strategies
- Using OpenAlgo GPT to generate strategy code
Module 3: Multi-Leg Options and GPT Strategy Builder
- Building Portfolio Option Greeks using Agentic AI Coding tools like windsurf
- Prompt guidelines for building trading dashboards.
Module 4: Understanding Model Context Protocol (MCP)
- A quick introduction to MCP – the open standard for connecting AI models to external tools and data
- Step-by-step guide to setting up MCP with OpenAlgo for live trading interaction
- Real-world use cases: placing orders, checking funds, and querying positions via LLM chat
- Best practices for building secure, AI-accessible trading tools using MCP
What you will gain
- Confidence to build, test, and deploy your own trading strategies
- Hands-on familiarity with OpenAlgo, a leading open-source toolkit for Indian markets
- Clear understanding of how to align your strategies with SEBI’s latest norms
- The ability to craft better prompts for generating strategy logic.
Prerequisites for Crash Course – Python for Traders
- Basic Knowledge of Trading: Familiarity with stock market fundamentals, including types of orders, trading instruments, market terminology, and the structure of markets.
- Technical Analysis Fundamentals: Some background in technical analysis, including chart reading and interpretation of common indicators, would be beneficial.
- Software Installation: Capability to install software and set up a working environment, as the course will require setting up Python and associated libraries.
- Access to a Computer: A reliable computer with internet access capable of running Python and processing data.
- Time Commitment: Willingness to dedicate time outside of course hours for practice, exploration, and implementation of the concepts learned.
Who Should Attend the Course
Students of Finance and Computer Science: University students seeking practical skills that combine finance and programming for a career in fintech or trading.
Aspiring Traders: Individuals looking to enter the trading world and seeking to leverage Python’s power to analyze and trade markets.
Financial Analysts: Professionals in finance who want to enhance their data analysis skills and automate trading strategies.
Quantitative Analysts: Quants who need to strengthen their programming skills in Python for quantitative analysis and model development.
Data Scientists: Data experts interested in branching out into financial data analysis and algorithmic trading.
Algorithmic Developers: Programmers who aim to build or improve algorithmic trading models using Python’s extensive libraries and frameworks.
Python developers interested in real-world finance applications
