Python & Machine Learning in Financial Analysis 2021
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- Curriculum
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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.
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1
Introducing Python and its advantages -
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What is financial analysis and what will you learn in this course?
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Introduction
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Getting data from Yahoo Finance
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Getting data from Quandl
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Converting prices to returns
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Changing frequency
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Visualizing time series data
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Identifying outliers
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Investigating stylized facts of asset returns
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Source codes of section 1
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Introduction
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Creating a candlestick chart
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Simple Moving Average (SMA) and Exponential Moving Average (EMA)
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15
Bollinger Bands
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Backtesting a strategy based on simple moving average
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17
Calculating Bollinger Bands and testing a buy/sell strategy
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TA-Lib library installation tutorial
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Calculating the relative strength index and testing a long/short strategy
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Building an interactive dashboard for TA
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21
Source codes of section 2
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Introduction
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Decomposing time series
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Testing for stationarity in time series
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Download Library
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Correcting for stationarity in time series
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27
Article (1) about Exponential Smoothing Methods (ESM)
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Article (2) about Time series exponential smoothing
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Modeling time series with exponential smoothing methods
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30
Modeling time series with ARIMA class models
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Forecasting using ARIMA class models
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Source codes of Section 3
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Introduction
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Implementing the CAPM in Python
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Download csv file
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Implementing the Fama-French three-factor model in Python
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Implementing the rolling three-factor model on a portfolio of assets
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Implementing the four and five-factor models in Python
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More about Multi-factor models
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Source codes of section 4
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Introduction
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Explaining stock returns' volatility with ARCH models
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43
There's more about Explaining stock returns' volatility with ARCH models
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Explaining stock returns' volatility with GARCH models
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There's more about Explaining stock returns' volatility with GARCH models
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Implementing a CCC-GARCH model for multivariate volatility forecasting
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How to program with Python and R in the same Jupyter notebook
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48
Download Dataset (csv file)
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Forecasting the conditional covariance matrix using DCC-GARCH (Python and R)
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There's more about Forecasting the conditional covariance matrix using DCC-GARCH
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Source codes of Section 5
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Introduction
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Simulating stock price dynamics using Geometric Brownian Motion
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54
Download chapter_6_utils
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Pricing European options using simulations
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Pricing American Options with Least Squares Monte Carlo
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Pricing American Options using Quantlib
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Estimating Value-at-risk using Monte Carlo
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Source codes of Section 6
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Introduction
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Download credit card default.csv
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Loading the data and managing data types
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Exploratory Data Analysis
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Splitting the data into training and test sets
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70
Dealing with missing values
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Encoding categorical variables
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72
Download chapter_8_utils
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Fitting a decision tree classifier
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Implementing scikit-learns pipelines
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75
Tuning hyperparameters using grid search and cross-validation
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Source codes of Section 8
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Introduction
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Download chapter_9_utils and csv files
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Investigating advanced classifiers
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There's more about use advanced classifiers to achieve better results
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Codes of There's more about use advanced classifiers to achieve better results
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Using stacking for improved performance
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Investigating the feature importance
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Investigating different approaches to handling imbalanced data
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There's more about Investigating different approaches to handling imbalanced dat
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Download Trials.p files
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Bayesian Hyperparameter Optimization
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Source codes of section 9
