Skip to Scheduled Dates
Course Overview
Python is one of the most powerful tools for financial data analysis and algorithmic trading, providing flexibility, scalability, and efficiency for developing trading strategies. This Python for Finance course is designed for students familiar with Python who want to apply their programming skills to financial markets. Participants will explore core financial concepts and strategies, such as Modern Portfolio Theory, and pair them with clean Python code to implement trading algorithms on QuantConnect’s Lean engine.
Private classes on this topic are available. We can address your organization’s issues, time constraints, and save you money, too. Contact us to find out how.
Who Should Attend
This course is designed for intermediate to experienced Python developers who want to work with financial time series data. A basic understanding of financial concepts is recommended. If you are new to Python programming, we recommend starting with Introduction to Python.
Course Objectives
By the end of this course, participants will be able to use Python libraries like NumPy, pandas, and Matplotlib for financial data analysis, apply Modern Portfolio Theory to optimize investment strategies, and implement Monte Carlo simulations for portfolio allocation. They will learn to apply SciPy minimization algorithms for portfolio optimization, analyze stock fundamentals such as Cash Flow, Revenue, and EPS, and calculate risk-adjusted returns using the Sharpe and Sortino Ratios.
The course also covers QuantConnect’s LEAN engine for automated trading, technical analysis techniques like Bollinger Bands, and the Capital Asset Pricing Model (CAPM). Participants will gain experience in rules-based algorithmic trading, backtesting strategies, and conducting full universe stock selection screening to refine financial models and trading strategies.
Course Outline
1. Introduction to Python for Finance
- Overview of Python’s role in finance and algorithmic trading.
- Introduction to QuantConnect and the Lean engine.
2. Python Libraries for Financial Analysis
- NumPy – Formatting and structuring financial data.
- pandas – Data manipulation and time-series analysis.
- Matplotlib – Data visualization for financial trends.
3. Core Financial Concepts with Python
- Modern Portfolio Theory (MPT) – Diversification and efficient frontier modeling.
- Capital Asset Pricing Model (CAPM) – Valuing securities based on risk and return.
- Sharpe Ratio & Sortino Ratio – Measuring risk-adjusted returns.
- Effective Market Hypothesis (EMH) – Understanding market efficiency in trading.
4. Algorithmic Trading with QuantConnect
- How to use QuantConnect for strategy development and execution.
- Buying and selling shares using automated trading strategies.
- Implementing custom algorithms for systematic trading.
- Applying Bollinger Bands and other classic technical analysis techniques.
5. Futures & Options Trading Strategies
- Using algorithmic trading to trade derivative futures contracts.
- Understanding risk management techniques for leveraged trading.
6. Backtesting and Strategy Evaluation
- How to read and interpret backtest results.
- Understanding Probabilistic Sharpe Ratios for performance analysis.
- Conducting full universe stock selection screening in QuantConnect.