Financial Engineering and Artificial Intelligence in Python

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

We have added plenty of supplemental resources to our lectures (Excel sheets, course notes) in an effort to enhance your learning experience throughout the course. You can click on link below to go to Download Page for Resources :

Note : Download Link Only Available for Enrolled Students

Course Content

Welcome 5 Lessons

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Introduction and Outline

Where to get the code

Scope of the course

How to Practice

Warmup (Optional)

Financial Basics 31 Lessons

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Financial Basics Section Introduction

Getting Financial Data

Getting Financial Data (Code)

Understanding Financial Data

Understanding Financial Data (Code)

Dealing with Missing Data

Dealing with Missing Data (Code)

Returns

Adjusted Close, Stock Splits, and Dividends

Adjusted Close (Code)

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Time Series Analysis 31 Lessons

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Time Series Analysis Section Introduction

Efficient Market Hypothesis

Random Walk Hypothesis

The Naive Forecast

Simple Moving Average (Theory)

Simple Moving Average (Code)

Exponentially-Weighted Moving Average (Theory)

Exponentially-Weighted Moving Average (Code)

Simple Exponential Smoothing for Forecasting (Theory)

Simple Exponential Smoothing for Forecasting (Code)

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Portfolio Optimization and CAPM 22 Lessons

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Portfolio Optimization Section Introduction

The S&P500

What is Risk?

Why Diversify?

Describing a Portfolio (pt 1)

Describing a Portfolio (pt 2)

Visualizing Random Portfolios and Monte Carlo Simulation (pt 1)

Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)

Maximum and Minimum Portfolio Return

Maximum and Minimum Portfolio Return in Code

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Algorithmic Trading 9 Lessons

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Algorithmic Trading Section Introduction

Trend-Following Strategy

Trend-Following Strategy in Code (pt 1)

Trend-Following Strategy in Code (pt 2)

Machine Learning-Based Trading Strategy

Machine Learning-Based Trading Strategy in Code

Classification-Based Trading Strategy in Code

Using a Random Forest Classifier for Machine Learning-Based Trading

Algorithmic Trading Section Summary

The Basics of Reinforcement Learning 12 Lessons

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Q-Learning

How to Learn Reinforcement Learning

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Reinforcement Learning for Algorithmic Trading 5 Lessons

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Trend-Following Strategy with Reinforcement Learning API

Trend-Following Strategy Revisited (Code)

Q-Learning in an Algorithmic Trading Context

Representing States

Q-Learning for Algorithmic Trading in Code

Statistical Factor Models and Unsupervised Machine Learning 4 Lessons

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Statistical Factor Models (Beginner)

Statistical Factor Models (Intermediate)

Statistical Factor Models (Advanced)

Statistical Factor Models (Code)

Regime Detection and Sequence Modeling with Hidden Markov Models 5 Lessons

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Why Sequence Models? (pt 1)

Why Sequence Models? (pt 2)

HMM Parameters

HMM Tasks and the Viterbi Algorithm

HMM for Modeling Volatility Clustering in Code

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Course Includes

9 Sections
124 Lessons
Course Certificate