Complete Python and Machine Learning in Financial Analysis

Course Curriculum

Chapter 1: Financial Data and Preprocessing

Lecture 1: Introduction of Python Programming in Financial Analysis

Lecture 2: Introduction of Financial Analysis

Lecture 3: Introduction

Lecture 4: Getting data from Yahoo Finance

Lecture 5: Getting data from Quandl

Lecture 6: Converting prices to returns

Lecture 7: Changing frequency

Lecture 8: Visualizing time series data

Lecture 9: Identifying outliers

Lecture 10: Investigating stylized facts of asset returns

Lecture 11: Codes of Chapter 1

Chapter 2: Technical Analysis in Python

Lecture 1: Introduction

Lecture 2: requirements of chapter 2

Lecture 3: Creating a candlestick chart

Lecture 4: Backtesting a strategy based on simple moving average

Lecture 5: Calculating Bollinger Bands and testing a buy/sell strategy

Lecture 6: Calculating the relative strength index and testing a long/short strategy

Lecture 7: Building an interactive dashboard for TA

Lecture 8: Codes of Chapter 2

Chapter 3: Time Series Modeling

Lecture 1: Introduction

Lecture 2: requirements of chapter 3

Lecture 3: Decomposing time series

Lecture 4: Testing for stationarity in time series

Lecture 5: Correcting for stationarity in time series

Lecture 6: Modeling time series with exponential smoothing methods

Lecture 7: Modeling time series with ARIMA class models

Lecture 8: Forecasting using ARIMA class models

Lecture 9: Codes of Chapter 3

Chapter 4: Multi-Factor Models

Lecture 1: Introduction

Lecture 2: requirements of chapter 4

Lecture 3: Implementing the CAPM in Python

Lecture 4: Implementing the Fama-French three-factor model in Python

Lecture 5: Implementing the rolling three-factor model on a portfolio of assets

Lecture 6: Implementing the four- and five-factor models in Python

Lecture 7: Codes of Chapter 4

Chapter 5: Modeling Volatility with GARCH Class Models

Lecture 1: Introduction

Lecture 2: requirements of chapter 5

Lecture 3: Explaining stock returns' volatility with ARCH models

Lecture 4: Explaining stock returns' volatility with GARCH models

Lecture 5: Implementing a CCC-GARCH model for multivariate volatility forecasting

Lecture 6: Forecasting a conditional covariance matrix using DCC-GARCH

Lecture 7: Codes of Chapter 5

Chapter 6: Monte Carlo Simulations in Finance

Lecture 1: Introduction

Lecture 2: requirements of chapter 6

Lecture 3: Simulating stock price dynamics using Geometric Brownian Motion

Lecture 4: Pricing European options using simulations

Lecture 5: Pricing American options with Least Squares Monte Carlo

Lecture 6: Pricing American options using Quantlib

Lecture 7: Estimating value-at-risk using Monte Carlo

Lecture 8: Codes of Chapter 6

Chapter 7: Asset Allocation in Python

Lecture 1: Introduction

Lecture 2: Evaluating the performance of a basic 1/n portfolio

Lecture 3: Finding the Efficient Frontier using Monte Carlo simulations

Lecture 4: Finding the Efficient Frontier using optimization with scipy

Lecture 5: Codes of Chapter 7

Chapter 8: Identifying Credit Default with Machine Learning

Lecture 1: Introduction

Lecture 2: requirements of chapter 8

Lecture 3: Loading data and managing data types

Lecture 4: Exploratory data analysis

Lecture 5: Splitting data into training and test sets

Lecture 6: Dealing with missing values

Lecture 7: Encoding categorical variables

Lecture 8: Fitting a decision tree classifier

Lecture 9: Implementing scikit-learn's pipelines

Lecture 10: Tuning hyperparameters using grid search and cross-validation

Lecture 11: Codes of Chapter 8

Chapter 9: Advanced Machine Learning Models in Finance

Lecture 1: Introduction

Lecture 2: requirements of chapter 9

Lecture 3: Investigating advanced classifiers

Lecture 4: Theres more about use advanced classifiers to achieve better results

Lecture 5: Using stacking for improved performance

Lecture 6: Investigating the feature importance

Lecture 7: Investigating different approaches to handling imbalanced data

Lecture 8: Bayesian hyperparameter optimization

Lecture 9: Codes of Chapter 9

Chapter 10: Deep Learning in Finance

Lecture 1: Introduction

Lecture 2: requirements of chapter 10

Lecture 3: Deep learning for tabular data

Lecture 4: Multilayer perceptrons for time series forecasting

Lecture 5: Convolutional neural networks for time series forecasting

Lecture 6: Recurrent neural networks for time series forecasting

Lecture 7: Codes of Chapter 10

Lecture 8: The End