IRL & Online Program
AI for Finance & Trading
Build predictive models, algorithmic trading bots, and risk assessment tools using quantitative machine learning.
Hands-on Highlights
- Develop time-series forecasting models using LSTMs
- Build an algorithmic trading bot with backtesting
- Implement credit risk scoring using XGBoost
- Extract financial sentiment from news feeds
Detailed Syllabus
Week 1-3
Quantitative Data & Feature Engineering
- Handling financial time-series data (Pandas/NumPy)
- Technical indicators and alpha generation
- Stationarity and cointegration
- Building a robust data pipeline from APIs (Yahoo Finance, Alpaca)
Week 4-6
Predictive Modeling & LSTMs
- Time-series forecasting with ARIMA/Prophet
- Recurrent Neural Networks (RNNs) and LSTMs
- Predicting asset price movements
- Handling imbalanced financial datasets
Week 7-9
Algorithmic Trading & Reinforcement Learning
- Backtesting frameworks (Backtrader/Vectorbt)
- Building a mean-reversion trading strategy
- Introduction to Deep RL for trading
- Risk management and position sizing
Week 10-12
Alternative Data & Sentiment
- Scraping financial news and SEC filings
- FinBERT for financial sentiment analysis
- Credit risk scoring with XGBoost
- Deploying a live trading bot on AWS
Target Roles & Career Paths
Quantitative Researcher
Algorithmic Trader
Financial Data Scientist
AI Risk Analyst
These are the primary roles you will be equipped to apply for upon successful completion of the course and portfolio projects.