Python for Finance Course

Price
$1,895.00 USD

Duration
3 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Python for Finance 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.

What Is Included

  • Expert-Led Instruction – Learn from instructors with real-world experience in financial markets and Python programming.

  • 6-Month Access to Course Materials – Continue learning with on-demand access after course completion.

  • Private Group Training Available – Tailored sessions designed for your organization’s financial and trading needs.

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.

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.

  • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
  • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
  • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
  • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Learning Credits: Learning Credits can be purchased well in advance of your training date to avoid having to commit to specific courses or dates. Learning Credits allow you to secure your training budget for an entire year while eliminating the administrative headache of paying for individual classes. They can also be redeemed for a full year from the date of purchase. If you have previously purchased a Learning Credit agreement with New Horizons, you may use a portion of your agreement to pay for this class.

If you have questions about Learning Credits, please contact your Account Manager.

Course Prerequisites

Introduction to Python

Python for Finance Agenda

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.



 

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