What you'll learn
Financial Data and Preprocessing: explores how financial data is different from other types of data commonly used in machine learning tasks. You will be able to use the functions provided to download financial data from a number of sources (such as Yahoo Finance and Quandl) and preprocess it for further analysis. Finally, you will learn how to investigate whether the data follows the stylized facts of asset returns.
Technical Analysis in Python: demonstrates some fundamental basics of technical analysis as well as how to quickly create elegant dashboards in Python. 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).
Time Series Modeling: Time Series Modeling, introduces the basics of time series modeling (including time series decomposition and statistical stationarity). Then, we look at two of the most widely used approaches of time series modeling-exponential smoothing methods and ARIMA class models. Lastly, we present a novel approach to modeling a time series using the additive model from Facebook's Prophet library.
Multi-Factor Models: shows you how to estimate various factor models in Python. We start with the simplest one-factor model and then explain how to estimate more advanced three-, four-, and five-factor models.
Modeling Volatility with GARCH Class 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.
Monte Carlo Simulations in Finance: introduces you to the concept of Monte Carlo simulations and how to use them for simulating stock prices, the valuation of European/American options, and for calculating the VaR.
Asset Allocation in Python: introduces the concept of Modern Portfolio Theory and shows you how to obtain the Efficient Frontier in Python. Then, we look at how to identify specific portfolios, such as minimum variance or the maximum Sharpe ratio. We also show you how to evaluate the performance of such portfolios.
Identifying Credit Default with Machine Learning: presents a case of using machine learning for predicting credit default. You will get to know the state-of-the-art classification algorithms, learn how to tune the hyperparameters of the models, and handle problems with imbalanced data.
Advanced Machine Learning Models in Finance: introduces you to a selection of advanced classifiers (including stacking multiple models). Additionally, we look at how to deal with class imbalance, use Bayesian optimization for hyperparameter tuning, and retrieve feature importance from a model.
Deep Learning in Finance: demonstrates how to use deep learning techniques for working with time series and tabular data. The

Requirements
Basic knowledge of Python and statistics
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

Screenshots

Citación :

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