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ardupilot

ArduPlane, ArduCopter, ArduRover, ArduSub source

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

btcpredictionwithtweets

Masterthesis repository - Bitcoin Price Prediction using Sentiment Analysis of Twitter Data

combined_news_djia

根据日常新闻预测股市动向

deep-learning-financial-news-stock-movement-prediction

Deep Time Series Code (Stock Sentiment Prediction)

deeplob-deep-convolutional-neural-networks-for-limit-order-books

This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.

deeptimeseries

Repo for 294-136 Project

digits

Deep Learning GPU Training System

dow_jones_industrial_average_prediction

GROUP PROJECT Context: Dow Jones Industrial Average (DJIA) prediction We will be predicting the DJIA closing value by using the top 25 headlines for the day. The Dow Jones Industrial Average, Dow Jones, or simply the Dow, is a stock market index that measures the stock performance of 30 large companies listed on stock exchanges in the United States. Content: There are two channels of datasets used: News data which is crawled historical news headlines from Reddit WorldNews Channel (Links to an external site.) (/r/worldnews). They are ranked by reddit users' votes, and only the top 25 headlines are considered for a single date. (Range: 2008-06-08 to 2016-07-01) Stock data: Dow Jones Industrial Average (DJIA) is used to "prove the concept". (Range: 2008-08-08 to 2016-07-01) three data files in .csv format used: RedditNews.csv: two columns The first column is the "date", and the second column is the "news headlines". All news are ranked from top to bottom based on how hot they are. Hence, there are 25 lines for each date. DJIA_table.csv: Downloaded directly from Yahoo Finance (Links to an external site.): webpage. CombinedNewsDJIA.csv: The first column is "Date", the second is "Label", and the following ones are news headlines ranging from "Top1" to "Top25". Projected ML techniques: We are going to use different models such as Ridge regression, Xg boost, Random forest to predict DJIA value and then compare the results of each of the models. Group members: Akhil Teja Balabadruni, Amaan, Varun Iyer

edgyr

R on the Edge: NVIDIAⓇ Jetson™ tools for R developers

event-driven-stock-prediction

My presentations for the event-driven stock prediction research group

event-driven-stock-prediction-using-deep-learning

A deep learning method for event driven stock market prediction. Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price movements

event-stock

finrl

A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020. 🔥

jetson-install

news-crawler

A news crawler for BBC News, Reuters and New York Times.

okuna-api

🤖 The Okuna Social Network API

open-jobboard

🚀 Open source React.js Headless CMS to easily build customisable job board

practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

predicting-stock-price-movements-using-nlp-and-dl

Using NLP to predict stock price movements

predicting_stock_with_twitter_sentiment

You have been watching the Telsa stock and are deciding if you should buy some stock before close because you think it will jump up tomorrow, but you want to be more certain about your decision. This project aims to help make that decision. Vader sentiment analysis was implemented on tweets to compute a daily sentiment score. From historical stock data the difference between Tesla opening price and the prior day’s closing price was computed and used as the endogenous variable in an ARIMAX time series model with daily sentiment as an exogenous variable. This final model was able to predict that the Tesla stock will open the next day at a higher price than today’s closing price with 58.8% precision.

pricing-prediction

Udacity project deliverable: Predicting the value of a given house in the Boston real estate market using various statistical analysis tools.

react-redux-firebase-gatsby

Don't use it... It's a demo.

rlquant

Applying Reinforcement Learning in Quantitative Trading

sentiment-analysis-in-event-driven-stock-price-movement-prediction

Use NLP to predict stock price movement associated with news

sentiment-analysis-of-twitter-data-for-predicting-apple-stock-price

smart-trader

ST system gives stock price prediction based on results from a training phase, it searches through a large space of models and hyper-parameters, and present testing and validation results for any user selected symbol.

smartcab-trainer

Udacity project deliverable: Applying reinforcement learning to build a simulated vehicle navigation agent.

statistical-arbitrage

Using Particle Markov Chain Monte Carlo

stock-analyzer

Using Technical, Fundamental, and Sentimental Analysis with Machine Learning