Displaying 1 to 7 from 7 results

Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

  •    Jupyter

Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. I code LSTM Recurrent Neural Network and Simple signal rolling agent inside Tensorflow JS, you can try it here, huseinhouse.com/stock-forecasting-js, you can download any historical CSV and upload dynamically.

Personae - 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading

  •    Python

Personae is a repo that implements papers proposed methods in Deep Reinforcement Learning & Supervised Learning and applies them to Financial Market. It will start from 2018-08-24 to 2018-09-01 a timestamp that I successfully found a job.

Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools.

  •    Javascript

Gekko Trading Bot. Repository of strategies which I found at Git and Google, orginal source is in README or .js file. Strategies was backtested, results are in backtest_database.csv file. I used ForksScraper and Gekko BacktestTool to create content of this repository.

dbg-pds-tensorflow-demo - Making predictions on prices in the Deutsche Börse Public Dataset using neural networks

  •    Jupyter

We use neural networks applied to stock market data from the Deutsche Börse Public Dataset (PDS) to make predictions about future price movements for each stock. Specifically, we make a prediction on the direction of the next minute's price change using information from the previous ten minutes. We use this to power a simplified trading strategy to show potential returns.




stocksight - Stock analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

  •    Python

Stock analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis. How much do emotions on Twitter and news headlines affect a stock's price? Let's find out ... Edit config.py and modify NLTK tokens required/ignored and twitter feeds you want to mine. NLTK tokens required are keywords which must be in tweet before adding it to Elasticsearch (whitelist). NLTK tokens ignored are keywords which if are found in tweet, it will not be added to Elasticsearch (blacklist).






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