Financial Engineering and Artificial Intelligence in Python

Description

In this course, you will 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

Syllabus :

• Financial Basics
• Time Series Analysis
• Portfolio Optimization and CAPM
• VIP: Algorithmic Trading
• VIP: The Basics of Reinforcement Learning
• VIP: Reinforcement Learning for Algorithmic Trading
• VIP: Statistical Factor Models and Unsupervised Machine Learning
• VIP: Regime Detection and Sequence Modeling with Hidden Markov Models
• Course Summary and Common Questions
• Extras
• Setting Up Your Environment FAQ
• Extra Help With Python Coding for Beginners FAQ
• Effective Learning Strategies for Machine Learning FAQ