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Platform for NYSE & NASDAQ stocks to display RSI, P/E, P/B, EPS, CAP and candlestick chart with fibonacci + 6 latest news. This also calculates sector averages for RSI, P/E and P/B for displaying BUY | HOLD | SELL images on tables.
A repository for predicting stock prices using machine learning techniques. Includes data preprocessing, model training, evaluation, and visualization.
Conducted research in the fusion of machine learning models to improve stock market index prediction accuracy. Evaluated individual models (LSTM, RF, LR, GRU) and compared their performance to fusion prediction models (RF-LSTM, RF-LR, RF-GRU).
A reinforcement learning model specialized in stock prediction utilizing deep learning techniques, incorporating reward mechanisms, compatible with any machine equipped with Python.
The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.
An implementation of the scikit-learn Random Forest Classifier, used to predict the direction of AAPL stock over a time horizon of 90 days, loosely following the paper at https://arxiv.org/abs/1605.00003 .
Created a machine learning model using Pycaret's logistic regression classification module to predict the direction of the next day's Microsoft closing stock price.