Created by: S. Emadedin Hashemi
Rate: 0.0/ 0 ratings
Enroll: 15,101 students
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 networks will be trained using PyTorch.
Basic knowledge of Python and statistics
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
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