Module 1: Introduction to Algorithmic Trading

• What is Algorithmic Trading?: Definition, history, and significance.
• Benefits and Risks: Advantages and potential pitfalls of algorithmic trading.
• No-Code Platforms Overview: Introduction to popular no-code platforms for algorithmic trading (e.g., QuantConnect, AlgoTrader, TradeStation).

Module 2: Basics of Trading Strategies

• Types of Trading Strategies: Trend following, mean reversion, arbitrage, momentum.
• Key Concepts: Indicators, signals, entries, exits, stop losses, and take profits.
• Backtesting and Forward Testing: Importance and methodologies.

Module 3: Setting Up Your No-Code Platform

• Choosing a Platform: Overview and comparison of no-code platforms.
• Account Setup: Creating and configuring accounts.
• Platform Navigation: Overview of the interface and key features.

Module 4: Creating Your First Algorithmic Strategy

• Strategy Design: Defining goals, selecting assets, and setting parameters.
• Building the Strategy: Step-by-step guide to creating a basic strategy using the platform's tools.
• Backtesting the Strategy: Running historical data simulations and interpreting results.

Module 5: Advanced Strategy Development

• Using Technical Indicators: Applying moving averages, RSI, MACD, Bollinger Bands, etc.
• Combining Indicators: Creating multi-indicator strategies.
• Risk Management Techniques: Position sizing, stop-loss, and take-profit strategies.

Module 6: Optimization and Fine-Tuning

• Parameter Optimization: Methods to optimize strategy parameters for better performance.
• Avoiding Overfitting: Techniques to ensure robustness and avoid over-optimization.
• Stress Testing: Simulating extreme market conditions to test strategy resilience.

Module 7: Live Trading and Execution

• Transitioning from Backtest to Live: Key considerations and steps.
• Paper Trading: Simulating live trading without real money.
• Live Execution: Setting up and monitoring live trades.

Module 8: Monitoring and Maintaining Strategies

• Performance Tracking: Using dashboards and reports to track performance.
• Adjustments and Tweaks: Making ongoing improvements based on performance data.
• Troubleshooting: Identifying and resolving common issues.

Module 9: Case Studies and Real-World Applications

• Successful Strategies: Analysis of successful algorithmic strategies.
• Failures and Lessons Learned: Case studies of failed strategies and key takeaways.
• Industry Applications: How institutions and retail traders use algorithmic trading.

Module 10: Ethics and Regulatory Considerations

• Ethical Trading Practices: Ensuring responsible and ethical use of algorithmic trading.
• Regulatory Environment: Understanding the legal landscape and compliance requirements.
• Risk Disclosure: Communicating risks to stakeholders.

Module 11: Capstone Project

• Project Introduction: Outline and expectations.
• Strategy Development: Participants create and develop their own trading strategies.
• Backtesting and Optimization: Refining the strategy using historical data.
• Final Presentation: Presenting the strategy, results, and learnings to the class.

Course Features

1. Lectures: Comprehensive explanations of key concepts and strategies.
2. Hands-On Workshops: Interactive sessions to build and test strategies.
3. Readings and Resources: Articles, guides, and videos for further learning.
4. Quizzes and Assessments: Regular assessments to gauge understanding.
5. Discussion Forums: Collaborative space for participants to share insights and ask questions.
6. Final Project: A capstone project to demonstrate mastery of course content.