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Financial Engineering and Artificial Intelligence in Python

Financial Engineering and Artificial Intelligence in Python

Financial Engineering and Artificial Intelligence in Python

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Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE! Get Udemy Course
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What you'll learn

  • Forecasting stock prices and stock returns
  • Time series analysis
  • Holt-Winters exponential smoothing model
  • ARIMA
  • Efficient Market Hypothesis
  • Random Walk Hypothesis
  • Exploratory data analysis
  • Alpha and Beta
  • Distributions and correlations of stock returns
  • Modern portfolio theory
  • Mean-Variance Optimization
  • Efficient frontier, Sharpe ratio, Tangency portfolio
  • CAPM (Capital Asset Pricing Model)
  • Q-Learning for Algorithmic Trading

Description

Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?
Today, you can stop imagining, and start doing.
This course will teach you the core fundamentals of financial engineering, with a machine learning twist.
We will cover must-know topics in financial engineering, such as:
  • Exploratory data analysis, significance testing, correlations, alpha and beta
  • Time series analysis, simple moving average, exponentially-weighted moving average
  • Holt-Winters exponential smoothing model
  • ARIMA and SARIMA
  • Efficient Market Hypothesis
  • Random Walk Hypothesis
  • Time series forecasting ("stock price prediction")
  • Modern portfolio theory
  • Efficient frontier / Markowitz bullet
  • Mean-variance optimization
  • Maximizing the Sharpe ratio
  • Convex optimization with Linear Programming and Quadratic Programming
  • Capital Asset Pricing Model (CAPM)
  • Algorithmic trading
In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

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