Financial Engineering and Artificial Intelligence in Python

Financial Engineering and Artificial Intelligence in Python, available at $74.99, has an average rating of 4.82, with 154 lectures, based on 2075 reviews, and has 9684 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who loves or wants to learn about financial engineering or Students and professionals who want to advance their career in finance or artificial intelligence and machine learning It is particularly useful for Anyone who loves or wants to learn about financial engineering or Students and professionals who want to advance their career in finance or artificial intelligence and machine learning.

Enroll now: Financial Engineering and Artificial Intelligence in Python

Summary

Title: Financial Engineering and Artificial Intelligence in Python

Price: $74.99

Average Rating: 4.82

Number of Lectures: 154

Number of Published Lectures: 150

Number of Curriculum Items: 154

Number of Published Curriculum Objects: 150

Original Price: $74.99

Quality Status: approved

Status: Live

What 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

Who Should Attend

Anyone who loves or wants to learn about financial engineering

Students and professionals who want to advance their career in finance or artificial intelligence and machine learning

Target Audiences

Anyone who loves or wants to learn about financial engineering

Students and professionals who want to advance their career in finance or artificial intelligence and machine learning

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 (VIP only)

Statistical Factor Models (VIP only)

Regime Detection with Hidden Markov Models (VIP only)

In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

Regression models

Classification models

Unsupervised learning

Reinforcement learning and Q-learning

***VIP-only sections (get it while it lasts!) ***

Algorithmic trading (trend-following, machine learning, and Q-learning-based strategies)

Statistical factor models

Regime detection and modeling volatility clustering with HMMs

We will learn about the greatest flub made in the past decade by marketers posing as “machine learning experts” who promise to teach unsuspecting students how to “predict stock prices with LSTMs“. You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.

As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn’t help but wander into the vast and complex world of financial engineering.

This course is for anyone who loves finance or artificial intelligence, and especially if you love both!

Whether you are a student, a professional, or someone who wants to advance their career – this course is for you.

Thanks for reading, I will see you in class!

Suggested Prerequisites:

Matrix arithmetic

Probability

Decent Python coding skills

Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!)

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail – email me any time if you disagree

No wasted time “typing” on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch

Not afraid of university-level math – get important details about algorithms that other courses leave out

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction and Outline

Lecture 2: Scope of the course

Lecture 3: How to Practice

Lecture 4: Warmup (Optional)

Chapter 2: Getting Set Up

Lecture 1: Where to get the code, notebooks, and data

Lecture 2: How to Succeed in This Course

Lecture 3: Temporary 403 Errors

Chapter 3: Financial Basics

Lecture 1: Financial Basics Section Introduction

Lecture 2: Getting Financial Data

Lecture 3: Getting Financial Data (Code)

Lecture 4: Understanding Financial Data

Lecture 5: Understanding Financial Data (Code)

Lecture 6: Dealing with Missing Data

Lecture 7: Dealing with Missing Data (Code)

Lecture 8: Returns

Lecture 9: Adjusted Close, Stock Splits, and Dividends

Lecture 10: Adjusted Close (Code)

Lecture 11: Back to Returns (Code)

Lecture 12: QQ-Plots

Lecture 13: QQ-Plots (Code)

Lecture 14: The t-Distribution

Lecture 15: The t-Distribution (Code)

Lecture 16: Skewness and Kurtosis

Lecture 17: Confidence Intervals

Lecture 18: Confidence Intervals (Code)

Lecture 19: Statistical Testing

Lecture 20: Statistical Testing (Code)

Lecture 21: Covariance and Correlation

Lecture 22: Covariance and Correlation (Code)

Lecture 23: Alpha and Beta

Lecture 24: Alpha and Beta (Code)

Lecture 25: Mixture of Gaussians

Lecture 26: Mixture of Gaussians (Code)

Lecture 27: Volatility Clustering

Lecture 28: Price Simulation

Lecture 29: Price Simulation (Code)

Lecture 30: Financial Basics Section Summary

Lecture 31: Suggestion Box

Chapter 4: Time Series Analysis

Lecture 1: Time Series Analysis Section Introduction

Lecture 2: Efficient Market Hypothesis

Lecture 3: Random Walk Hypothesis

Lecture 4: The Naive Forecast

Lecture 5: Simple Moving Average (Theory)

Lecture 6: Simple Moving Average (Code)

Lecture 7: Exponentially-Weighted Moving Average (Theory)

Lecture 8: Exponentially-Weighted Moving Average (Code)

Lecture 9: Simple Exponential Smoothing for Forecasting (Theory)

Lecture 10: Simple Exponential Smoothing for Forecasting (Code)

Lecture 11: Holts Linear Trend Model (Theory)

Lecture 12: Holts Linear Trend Model (Code)

Lecture 13: Holt-Winters (Theory)

Lecture 14: Holt-Winters (Code)

Lecture 15: Autoregressive Models – AR(p)

Lecture 16: Moving Average Models – MA(q)

Lecture 17: ARIMA

Lecture 18: ARIMA in Code (pt 1)

Lecture 19: Stationarity

Lecture 20: Stationarity Code

Lecture 21: ACF (Autocorrelation Function)

Lecture 22: PACF (Partial Autocorrelation Function)

Lecture 23: ACF and PACF in Code (pt 1)

Lecture 24: ACF and PACF in Code (pt 2)

Lecture 25: Auto ARIMA and SARIMAX

Lecture 26: Model Selection, AIC and BIC

Lecture 27: ARIMA in Code (pt 2)

Lecture 28: ARIMA in Code (pt 3)

Lecture 29: ACF and PACF for Stock Returns

Lecture 30: Forecasting

Lecture 31: Time Series Analysis Section Conclusion

Chapter 5: Portfolio Optimization and CAPM

Lecture 1: Portfolio Optimization Section Introduction

Lecture 2: The S&P500

Lecture 3: What is Risk?

Lecture 4: Why Diversify?

Lecture 5: Describing a Portfolio (pt 1)

Lecture 6: Describing a Portfolio (pt 2)

Lecture 7: Visualizing Random Portfolios and Monte Carlo Simulation (pt 1)

Lecture 8: Visualizing Random Portfolios and Monte Carlo Simulation (pt 2)

Lecture 9: Maximum and Minimum Portfolio Return

Lecture 10: Maximum and Minimum Portfolio Return in Code

Lecture 11: Mean-Variance Optimization

Lecture 12: The Efficient Frontier

Lecture 13: Mean-Variance Optimization And The Efficient Frontier in Code

Lecture 14: Global Minimum Variance (GMV) Portfolio

Lecture 15: Global Minimum Variance (GMV) Portfolio in Code

Lecture 16: Sharpe Ratio

Lecture 17: Maximum Sharpe Ratio in Code

Lecture 18: Portfolio with a Risk-Free Asset and Tangency Portfolio

Lecture 19: Risk-Free Asset and Tangency Portfolio in Code

Lecture 20: Capital Asset Pricing Model (CAPM)

Lecture 21: Problems with Markowitz Portfolio Theory and Robust Estimation

Lecture 22: Portfolio Optimization Section Conclusion

Chapter 6: VIP: Algorithmic Trading

Lecture 1: Algorithmic Trading Section Introduction

Lecture 2: Trend-Following Strategy

Lecture 3: Trend-Following Strategy in Code (pt 1)

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 10 votes

2 stars: 10 votes

3 stars: 34 votes

4 stars: 440 votes

5 stars: 1581 votes

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