Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
GARCH and Multivariate LSTM forecasting models for realized volatility with potential applications in options trading, hedging, portfolio management, and risk management
Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
Open source software for image correlation, distance and analysis
Collection of notebooks about quantitative finance, with interactive python code.
The source code and data of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".
Framework for a information retrieval engine (QnA, knowledge base query, etc)
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
Python-based portfolio / stock widget , calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice
A Python package for time series classification
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
ruptures: change point detection in Python
Sentence Embeddings with BERT & XLNet
A python tool for evaluating the quality of sentence embeddings.
[SDM 2022] Towards Similarity-Aware Time-Series Classification
machine learning and deep learning models for Stock forecasting including trading bots and simulations
discover different patterns based on similarity measures in historical (financial) data
Find big moving stocks before they move using machine learning and anomaly detection
An intuitive library to extract features from time series, statistical, temporal and spectral domains.
Gets the last 5 months of volume history for every ticker, and alerts you when a stock's volume exceeds 10 standard deviations from the mean within the last 3 days
Python library for identifying the peaks and valleys of a time series.