Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Alphapy | 1,003 | 3 months ago | 25 | August 29, 2020 | 13 | apache-2.0 | Python | |||
Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost | ||||||||||
Pyastrotrader | 69 | a year ago | 17 | Jupyter Notebook | ||||||
Machine Learning + Financial Markets + Astrology = A Match made in Heaven | ||||||||||
Automatic Stock Trading | 39 | 5 years ago | apache-2.0 | Python | ||||||
Trading Algorithm by XGBoost | ||||||||||
Stock Market Prediction Via Google Trends | 38 | 2 years ago | 1 | mit | Python | |||||
Attempt to predict future stock prices based on Google Trends data. | ||||||||||
Analysis Of Stock High Frequent Data With Lstm | 11 | 5 years ago | mit | Python | ||||||
A simply framework of researching stock data through LSTM by Tensorflow | ||||||||||
Simple Stock Predictor Xgboost Knn | 11 | 5 years ago | mit | R | ||||||
Stock prediction using xgboost and knn classification done in R | ||||||||||
Xgboost_index Enhancement Strategy | 10 | a year ago | mit | Python | ||||||
【Framework】A Multi Factor Strategy based on XGboost, its my homework project in Tsinghua, the Introduction to Quantitative Finance, 2019 Spring. | ||||||||||
Dash Stock Price Prediction | 7 | 2 years ago | other | Python | ||||||
Visualising stock price predictions for several Machine Learning models using DASH, Python | ||||||||||
Stock Prediction Portfolio Optimization | 5 | 4 years ago | Jupyter Notebook | |||||||
A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WIP and I will keep on adding new ideas to this in future. | ||||||||||
Ml Finance | 5 | 5 years ago | Jupyter Notebook | |||||||
Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable. |