Introduction
The AI for Trading course is designed for those who want a core understanding of AI and its real-world applications in trading, rather than just using tools like ChatGPT. This is a practice-oriented course that focuses on real-world data, AI techniques, and market insights.
Participants will learn how AI models analyze financial data, predict market trends, and automate trading decisions. The course also covers geopolitical, economic, and policy-driven factors that influence financial markets and AI-based strategies.
This course is ideal for beginners and intermediate learners. While basic Python and trading knowledge is preferable, it is not mandatory. By the end, learners will be confident in using AI beyond just trading—applying it across various industries.
1. AI, Machine Learning & Trading Fundamentals
Introduction to AI, ML, and financial trading. Understanding how AI analyzes trends, improves trading efficiency, and automates decisions.
2. Data Collection, Preprocessing &Handling
Fetching live financial data from APIs (Yahoo Finance, Alpha Vantage). Cleaning and transforming data, handling missing values, and feature engineering for AI models.
3. Python for Trading & AI
Introduction to Python programming for financial analysis. Working with NumPy, Pandas for stock data and Matplotlib, Seaborn for visualization. Implementing moving averages, volatility, and daily returns.
4. Mathematics for AI in Trading
Understanding linear algebra, probability, statistics, and optimization for AI applications.
Applying these concepts for profit maximization and trading constraints.
5. Machine Learning for Trading
Building AI models for financial markets. Training supervised models (Linear Regression, Decision Trees, K-NN) for stock price prediction and buy/sell decisions. Exploring unsupervised models (K-Means, PCA) for market segmentation and pattern recognition.
6. Market & Trading Biases, Geopolitical & Economic Factors
Understanding cognitive biases in AI trading. Analyzing political, geopolitical, and economic events (inflation, trade policies, global crises) that affect financial markets and AI-driven strategies.
7. Natural Language Processing (NLP) & Sentiment Analysis
Using AI to analyze financial news sentiment and track market trends. Implementing TF-IDF, NLP basics, and Naive Bayes for sentiment classification.
8. API Integration & Real-Time Trading Bots
Fetching real-time stock data via APIs. Implementing automated trading logic with AI models. Understanding algorithmic trading bots and their applications in finance.