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Mastering Financial Engineering: Quantitative Analysis and Risk Management

Categories: AI and ML, Data Science
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About Course

Have you ever thought, about what would happen if you combine the principles of Financial Engineering with the power of Artificial Intelligence and Machine Learning? Well, you can stop imagining and start working towards your goal.

This course combines the principles of Financial Engineering with Artificial Intelligence and Machine Learning, providing a comprehensive understanding and innovative approaches for navigating the evolving financial landscape

  • Regression models.
  • Classification models.
  • Unsupervised learning.
  • Q-learning and reinforcement learning.

*VIP SECTION*

  • Trading using algorithms (including methods based on Machine Learning, Trend-Following, and Q-learning).
  • Models for statistical factors.
  • Utilising HMMs to detect regimes and model volatility clustering.

We’ll find out about the greatest mistake made in the last decade by marketing representatives acting as “Machine Learning Specialists” who promised to instruct naive students on how to “predict stock prices with LSTMs.” You will discover the precise reasons behind their methodology’s basic flaws and the absurdity of their findings. It serves as a cautionary tale on how not to use AI in finance.

If you enjoy finance or artificial intelligence, or both—should take this course, whether you’re a professional, a student, or someone looking to better your career.

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What Will You Learn?

  • Predicting stock returns and price fluctuations.
  • Time Series Analysis for Analysing Data Point Sequences
  • Holt-Winters exponential smoothing model for making predictions.
  • ARIMA.
  • Efficient market hypothesis.
  • Random walk theory.
  • Analysing exploratory data.
  • Beta and Alpha testing.
  • Stock return distributions and correlations.
  • Current portfolio theory.
  • Maximising mean-variance.
  • Sharpe ratio, Tangency portfolio, and Efficient frontier.
  • Capital asset pricing model or CAPM.
  • Q-Learning for Automated Trading via algorithms.

Course Content

Getting Started

  • Introduction and course overview.
    08:53
  • Data links and practical coding experience.
    00:00
  • Learn to use Github + additional coding tips (Optional).
    00:00
  • Where to download the data, code, and notebooks.
    00:00
  • Strategies for successfully completing this course.
    00:00

Environment Setup

Python Coding For Beginners (Extra Help)

Basics of Finance

Time Series Analysis for Analysing Data Point Sequences

Optimisation of Portfolio and CAPM

Automated Trading via Algorithms

Reinforcement Learning: The Basics

Reinforcement Learning for Automated Trading

Unsupervised Learning and Statistical Factor Models

Sequence Model with Hidden Markov Model and Regime Detection

Summary of the Course

Conclusion

Course Completion Quiz

Student Ratings & Reviews

5.0
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2 years ago
Enrolling in this course was one of the best decisions I've made.
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