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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.


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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