Complete Python and Machine Learning in Financial Analysis

Complete Python and Machine Learning in Financial Analysis, available at $69.99, has an average rating of 4.08, with 83 lectures, based on 456 reviews, and has 56397 subscribers.

You will learn about 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. This course is ideal for individuals who are Developers or Financial Analysts or Data Analysts or Data Scientists or Stock and cryptocurrency traders or Students or Teachers or Researchers It is particularly useful for Developers or Financial Analysts or Data Analysts or Data Scientists or Stock and cryptocurrency traders or Students or Teachers or Researchers.

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Summary

Title: Complete Python and Machine Learning in Financial Analysis

Price: $69.99

Average Rating: 4.08

Number of Lectures: 83

Number of Published Lectures: 83

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 83

Original Price: $119.99

Quality Status: approved

Status: Live

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.

Who Should Attend

Developers

Financial Analysts

Data Analysts

Data Scientists

Stock and cryptocurrency traders

Students

Teachers

Researchers

Target Audiences

Developers

Financial Analysts

Data Analysts

Data Scientists

Stock and cryptocurrency traders

Students

Teachers

Researchers

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 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.

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-learns 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

Instructors

S. Emadedin Hashemi
AI Expert and Data Scientist

Rating Distribution

1 stars: 11 votes

2 stars: 14 votes

3 stars: 40 votes

4 stars: 101 votes

5 stars: 290 votes

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