Python & Machine Learning in Financial Analysis 2021

Python & Machine Learning in Financial Analysis 2021, Complete course on using Python, Machine learning and Deep learning in Finance with complete coding (step-by-step guide).

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.

Enroll Now

Add Comment