281 Open source projects that are alternatives of or similar to Stock Market Sentiment Analysis

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利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

Stocktwits market sentiment analysis in Python with Keras and TensorFlow.

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Trade stocks and ETFs with free brokerage Robinhood and Perl

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(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset

Tuchart is a visualization interface for the Chinese stock market. Tuchart supports candlestick charts, price charts, tick data, high-frequency data and distribution of top shareholders for individual stocks. Tuchart是一个基于pyqt和echarts的股票视觉化应用。Tuchart 支持日／月线，分笔，高频数据，前十股东分笔的视觉化

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…

Python wrapper for an unofficial Yahoo Finance API

Framework for creating and running trading strategies. Blatantly stolen copy of qtpylib to make it work for Indian markets.

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Machine Learning Lectures at the European Space Agency (ESA) in 2018

A simple Python API for Investopedia's stock simulator games. This programmatically logs into Investopedia and can retrieve portfolio summary, get stock quotes & option chain lookups, execute trades - buy & sell shares, puts, calls, sell short, etc.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

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Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.

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