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amazon-sagemaker-stock-prediction-archived

Workshop to demonstrate how to apply NN based algorithms to stock market data and forecast price movements.

amazon-sagemaker-time-series-prediction-using-gluonts

ccp

Codes for replicating results in the research paper "Risk-based versus target-based portfolio strategies in the cryptocurrency market"

conformalized_quantile_regression-cqr

Conformalized Quantile Regression

courseraml_matlab

MATLAB assignments in Coursera's Machine Learning course

courseraml_python

I took Andrew Ng's Machine Learning course on Coursera and did the homework assigments... but, on my own in python because I love jupyter notebooks!

courserml-fall2013-4thed-andrewng

Coursera-Machine-Learning-Fall2013-4thEd-AndrewNg

darts

A python library for easy manipulation and forecasting of time series.

deepar

Implement Amazon's DeepAR algorithm

deepjmqr

Deep joint mean and quantile regression for spatio-temporal problems

demand_forecast

end-to-end-time-series

This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

es_reg

The goal of esreg is to simultaneously model the quantile and the expected shortfall of a response variable given a set of covariates.

gar-replication

Backtesting Global Growth-at-Risk Replication Files

gluon-ts

Probabilistic time series modeling in Python

hatalis_spnn

Tutorial code for a smooth pinball neural network.

inet

Quantlets for Project INET

keras_quantile_model

Quantile Regression using Deep Learning. An alternative to Bayesian models to get uncertainty.

liquidsvm

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

matlab_copula

Matlab Copula Toolbox

mcqrnn-tf2

Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.

mictools

A practical tool for Maximal Information Coefficient (MIC) analysis

midas_pro

Python version of Mixed Data Sampling (MIDAS) regression (allow for multivariate MIDAS) :golf:

ml-research

A repository for machine learning based investment strategies

mqrnn

Multi-Quantile Recurrent Neural Network for Quantile Regression

multiple_support_vector_machine

MSVR (Multiple Support Vector Regression) python module

nnqr

Master Thesis "Modelling Systemic Risk using Neural Network Quantile Regression"

optimalportfolio

An open source library for portfolio optimisation

portfolio-optimization-using-genetic-algorithm

Portfolio optimization using Genetic algorithm.

portfolio_sortino_ratio

This function optimizes portfolio weights based on a user-specified weighted linear combination of the Sortino ratio, Sharpe ratio, average total return, average downside risk, average standard deviation of returns, and max drawdown.