Python & Machine Learning in Financial Analysis 2021
Description
In this course, you will learn financial analysis using the Python programming language. Use libraries related to financial issues and learn how to install and set them up.
You will know various things in the field of finance, such as:
Getting data from Yahoo Finance and Quandl
Changing frequency
Visualizing time series data
Creating a candlestick chart
Calculating Bollinger Bands and testing a buy/sell strategy
Building an interactive dashboard for TA
Modeling time series with exponential smoothing methods and ARIMA class models
Forecasting using ARIMA class models
Implementing the Capital Asset Pricing Model in Python
Implementing the Fama-French three-factor model, rolling three-factor model on a portfolio of assets, and four- and five-factor models in Python
Explaining stock returns' volatility with ARCH and GARCH models
Implementing a CCC-GARCH model for multivariate volatility forecasting
Forecasting a conditional covariance matrix using DCC-GARCH
Simulating stock price dynamics using Geometric Brownian Motion
Pricing European options using simulations
Pricing American options with Least Squares Monte Carlo and Pricing it using Quantlib
Estimating value-at-risk using Monte Carlo
Evaluating the performance of a basic 1/n portfolio
Finding the Efficient Frontier using Monte Carlo simulations and optimization with scipy
Identifying Credit Default with Machine Learning
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
Investigating advanced classifiers
Using stacking for improved performance
Investigating the feature importance
Investigating different approaches to handling imbalanced data
Bayesian hyperparameter optimization
Tuning hyperparameters using grid search and cross-validation
Deep Learning in Finance
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
And many other cases ...
And you will be able to implement all of these issues in Python.
All the steps of coding are taught step by step and all the codes will be provided to you to use in your projects and articles.
In this course, you will learn financial analysis using the Python programming language. Use libraries related to financial issues and learn how to install and set them up.
You will know various things in the field of finance, such as:
Getting data from Yahoo Finance and Quandl
Changing frequency
Visualizing time series data
Creating a candlestick chart
Calculating Bollinger Bands and testing a buy/sell strategy
Building an interactive dashboard for TA
Modeling time series with exponential smoothing methods and ARIMA class models
Forecasting using ARIMA class models
Implementing the Capital Asset Pricing Model in Python
Implementing the Fama-French three-factor model, rolling three-factor model on a portfolio of assets, and four- and five-factor models in Python
Explaining stock returns' volatility with ARCH and GARCH models
Implementing a CCC-GARCH model for multivariate volatility forecasting
Forecasting a conditional covariance matrix using DCC-GARCH
Simulating stock price dynamics using Geometric Brownian Motion
Pricing European options using simulations
Pricing American options with Least Squares Monte Carlo and Pricing it using Quantlib
Estimating value-at-risk using Monte Carlo
Evaluating the performance of a basic 1/n portfolio
Finding the Efficient Frontier using Monte Carlo simulations and optimization with scipy
Identifying Credit Default with Machine Learning
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
Investigating advanced classifiers
Using stacking for improved performance
Investigating the feature importance
Investigating different approaches to handling imbalanced data
Bayesian hyperparameter optimization
Tuning hyperparameters using grid search and cross-validation
Deep Learning in Finance
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
And many other cases ...
And you will be able to implement all of these issues in Python.
All the steps of coding are taught step by step and all the codes will be provided to you to use in your projects and articles.
Who this course is for:
- Financial analysts
- Stock market and cryptocurrencies traders
- Data analysts
- Data scientists
- Python developers
- Students and researchers in the field of finance
