Endre Moen's repositories
Machine-Learning-for-Asset-Managers
Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
Automatic-interpretation-of-otoliths-using-deep-learning
Recent advances in machine learning have brought forth methods that have been remarkably successful in a variety of settings, most notably in image analysis. These methods are now being applied to data analysis in marine sciences, where they have the potential to automate analysis that previously required manual curation. Here we adapt a machine learning model intended for object recognition to the task of estimating age from otolith images. The model is trained and validated on a collection of otolith images from Greenland halibut. We show that the precision of the model's age estimates is comparable to and may even surpass that of human experts. Age reading from otoliths is an important element in the management of many marine stocks, and automating this analysis is an important step to ensure consistency, lower cost, and increase scale.
Deep-learning-for-regression-of-cod-otoliths
Using the EfficientNet family to predict cod-otolith age.
Deep-learning-for-salmon-scales
Fish scales constitute a valuable source of information about individual life histories, but correctly extracting this information requires a highly skilled expert. Here, we train a deep convolutional neural network architecture EfficientNet B4 on a set of about 9000 salmon scale images, and show that it attains good performance on predicting a set of variables used in stock management. Further, we see substantial benefits from user transfer learning with a network pre-trained on ImageNet, even if the salmon scale images are very different from those found in the data used for pre-training.
demo_Marchenko_Pastur_Analysis
Presentation held at IMR Machine Learning journal club 15. October 2020. Demo Marcenko Pasture distribution applied to eigenvalues of random matrix
Sentiment-Analysis-with-BERT
https://towardsdatascience.com/sentiment-analysis-in-10-minutes-with-bert-and-hugging-face-294e8a04b671
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.
bidask
Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices
DirtyQuant
Code that I show on my YouTube Channel
efficientnet
Implementation of EfficientNet model. Keras and TensorFlow Keras.
freqtrade
Free, open source crypto trading bot
Hierarchical-SigCWGAN
Implementation of the [Hierarchical (Sig-Wasserstein) GAN] algorithm for large dimensional Time Series Generation: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3942764
jib
🏗 Build container images for your Java applications.
nordnet
Uonfficial wrapper for financial data api from the Scandinavian broker Nordnet
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
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.
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
Yahoo-ticker-symbol-downloader
A web scraper for ticker symbols from yahoo finance