Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets
RNN based Time-series Anomaly detector model implemented in Pytorch.
The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data
A simple flask application to collect annotations for the Turing Change Point Dataset, a benchmark dataset for change point detection algorithms
SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.
The Turing Change Point Dataset - A collection of time series for the evaluation and development of change point detection algorithms
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.
some strategy for stock selection, include machine learning, data mining and etc.
Stock Portfolio Performance by Weighted Stock Selection - The project quantitatively predicts the factors that must be given the most and least weights to obtain obtain the highest returns, risk and win-rates while developing a S&P500 portfolio
A python project to fetch stock financials/statistics and perform preliminary screens to aid in the stock selection process
This program focused on the core concepts and practice of quantitative investment (multi-factor combination analysis, technical analysis CTA strategy, real-time stock selection and timing strategy, etc.).
A multi-factor stock selection model based on random forest with an average annualized yield of 33.74% from March 2014 to June 2017 when the market index yield was 12.32%.
A stock selection and prediction tool for the next day using a variety of stacked LSTM neural networks
