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portfoliomanager

event studies by event identifying and market simulating using QSTK

precise

online covariance and precision matrix estimation

pycop

Python library for multivariate dependence modelling with Copulas

real-time-stock-market-prediction

In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

riskfolio-lib

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

scala-training-code

Contains code necessary participate in the Scala training course.

scala-training-slides

Contains slides for the Scala training course.

sentiment-analysis-with-bert

https://towardsdatascience.com/sentiment-analysis-in-10-minutes-with-bert-and-hugging-face-294e8a04b671

stat111

Sammenlikning av to utvalg, variansanalyse og forsøksplanlegging. Bivariat normalfordeling, korrelasjon og enkel regresjon. Innføring i ikke parametriske og Bayesianske metoder. Eksempler fra ulike anvendelsesområder blir gitt. Utvalgte emner fra sannsynlighetsregning blir også dekket, transformasjon av tilfeldige variable, momentgenerende funksjon og ordningsobservatoren. Bruk av statistikkpakken R

stat201_glm

Generaliser linear models -The theory for linear normal models is looked at and applied to regression and analysis of variance. Furthermore the topics of binary variables logistic regression, log-linear models, contingency tables and life time analysis are treated.

statistics-and-data-analysis-for-financial-engineering-copulas

stochasticprocessstat220

The course will consider Markov processes in discrete and continuous time. The theory is illustrated with examples from operation research, biology and economy.

suite

OpenGeo Suite Git Repository

time-series-momentum

🚂💨 Deep Momentum Networks for Time Series Strategies

time_series_stat211

This course gives an introduction to linear time series models, such as autoregressive, moving average and ARMA models. Moreover, it is shown how the empirical autocorrelation and partial correlation can be used to identify the model. The Durbin- Levinson, the innovation algorithm and the theory for optimal forecasts are explained. The last part of the course gives an introduction to methods of estimation. Empirical modelling using the AIC and FPE criteria is mentioned as is ARCH and GARCH models.

torchts

Time series forecasting with PyTorch

tspdb

tspdb: Time Series Predict DB

tuneta

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

verifying-methodsofbell-ringing-with-fdr

yahoo-ticker-symbol-downloader

A web scraper for ticker symbols from yahoo finance