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Using Python and machine learning in financial analysis with step-by-step coding (with all codes)

What you will learn

You will be able to use the functions provided to download financial data from a number of sources and preprocess it for further analysis

You will be able to draw some insights into patterns emerging from a selection of the most commonly used metrics (such as MACD and RSI)

Introduces the basics of time series modeling. Then, we look at exponential smoothing methods and ARIMA class models.

shows you how to estimate various factor models in Python. one ,three-, four-, and five-factor models.

Introduces you to the concept of volatility forecasting using (G)ARCH class models, how to choose the best-fitting model, and how to interpret your results.

Introduces concept of Monte Carlo simulations and use them for simulating stock prices, the valuation of European/American options and calculating the VaR.

Introduces the Modern Portfolio Theory and shows you how to obtain the Efficient Frontier in Python. how to evaluate the performance of such portfolios.

Presents a case of using machine learning for predicting credit default. You will get to know tune the hyperparameters of the models and handle imbalances

Introduces you to a selection of advanced classifiers (including stacking multiple models)and how to deal with class imbalance, use Bayesian optimization.

Demonstrates how to use deep learning techniques for working with time series and tabular data. The networks will be trained using PyTorch.


In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise.

This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis.

The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR).

In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems.




Financial Data and Preprocessing

Introduction of Python Programming in Financial Analysis

Introduction of Financial Analysis


Getting data from Yahoo Finance

Getting data from Quandl

Converting prices to returns

Changing frequency

Visualizing time series data

Identifying outliers

Investigating stylized facts of asset returns

Codes of Chapter 1

Technical Analysis in Python


requirements of chapter 2

Creating a candlestick chart

Backtesting a strategy based on simple moving average

Calculating Bollinger Bands and testing a buy/sell strategy

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

Building an interactive dashboard for TA

Codes of Chapter 2

Time Series Modeling


requirements of chapter 3

Decomposing time series

Testing for stationarity in time series

Correcting for stationarity in time series

Modeling time series with exponential smoothing methods

Modeling time series with ARIMA class models

Forecasting using ARIMA class models

Codes of Chapter 3

Multi-Factor Models


requirements of chapter 4

Implementing the CAPM in Python

Implementing the Fama-French three-factor model in Python

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

Implementing the four- and five-factor models in Python

Codes of Chapter 4

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Modeling Volatility with GARCH Class Models


requirements of chapter 5

Explaining stock returns’ volatility with ARCH models

Explaining stock returns’ volatility with GARCH models

Implementing a CCC-GARCH model for multivariate volatility forecasting

Forecasting a conditional covariance matrix using DCC-GARCH

Codes of Chapter 5

Monte Carlo Simulations in Finance


requirements of chapter 6

Simulating stock price dynamics using Geometric Brownian Motion

Pricing European options using simulations

Pricing American options with Least Squares Monte Carlo

Pricing American options using Quantlib

Estimating value-at-risk using Monte Carlo

Codes of Chapter 6

Asset Allocation in Python


Evaluating the performance of a basic 1/n portfolio

Finding the Efficient Frontier using Monte Carlo simulations

Finding the Efficient Frontier using optimization with scipy

Codes of Chapter 7

Identifying Credit Default with Machine Learning


requirements of chapter 8

Loading data and managing data types

Exploratory data analysis

Splitting data into training and test sets

Dealing with missing values

Encoding categorical variables

Fitting a decision tree classifier

Implementing scikit-learn’s pipelines

Tuning hyperparameters using grid search and cross-validation

Codes of Chapter 8

Advanced Machine Learning Models in Finance


requirements of chapter 9

Investigating advanced classifiers

Theres more about use advanced classifiers to achieve better results

Using stacking for improved performance

Investigating the feature importance

Investigating different approaches to handling imbalanced data

Bayesian hyperparameter optimization

Codes of Chapter 9

Deep Learning in Finance


requirements of chapter 10

Deep learning for tabular data

Multilayer perceptrons for time series forecasting

Convolutional neural networks for time series forecasting

Recurrent neural networks for time series forecasting

Codes of Chapter 10