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Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
A game theoretic approach to explain the output of any machine learning model.
A unified framework for machine learning with time series
sktime companion package for deep learning based on TensorFlow
In this work an application of the Triple-Barrier Method and Meta-Labeling techniques is explored with XGBoost for the creation of a sentiment-based trading signal on the S&P 500 stock market index. The results confirm that sentiment data have predictive power, but a lot of work is to be carried out prior to implementing a strategy.
Improve S&P 500 stock price prediction (random forest and gradient boosting trees) with time series similarity measurements: DTW, SAX, co-integration, Euclidean and Pearson.
Does the l1-norm learn a sparse graphical model under Laplacian constraints? It doesn't.
scikit-learn compatible implementation of stability selection.
Code and documentation to train Stanford's Alpaca models, and generate the data.
Code relating to the paper - Stock Embeddings: Learning Distributed Representations for Financial Assets
A novel stock market trend forecasting system using fuzzy logic
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
R scripts to analyze relationship between stock prices and periods of high volatility
Stock Market Prediction Using Unsupervised Features
Official code implementation of AAAI 2024 paper "StockMixer: A Simple yet Strong MLP-based Architecture for Stock Price Forecasting".
A comprehensive dataset for stock movement prediction from tweets and historical stock prices.
Intra day Stock Prediction 10 minutes into the future
Implementation of (Re-)Imag(in)ing Price Trends
This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"
Stock Price Predictor with Deep Learning
机器学习算法在策略上的应用
The official implementation of AAAI'24 paper: Self-Interpretable Graph Learning with Sufficient and Necessary Explanations.
A python script that estimates the support and resistance lines of a stock's prices or a period
Original implementation and experiment results of T2G-FORMER
Implementation of TabTransformer, attention network for tabular data, in Pytorch
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Official implementation of the TabPFN paper (https://arxiv.org/abs/2207.01848) and the tabpfn package.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.