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Complete course on using Python, Machine learning and Deep learning in Finance with complete coding (step-by-step guide)

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.

 

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

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