Indicat.co

Front Page

Indicat analyses unorthodox alternative data sources in order to predict whether the price of the Dow Jones Index will increase or decrease using machine learning.

In the future, we plan to include a multitude of uncorrelated factors and variables into the model including sentiment analysis and Google search trends. What you see here is a proof of concept: we have analysed the top news stories from r/worldnews on Reddit.

The Reddit news stories are run through our ML models to come up with a binary price movement (rise/fall) prediction that approaches 60% accuracy.

Our goal was to get as far over the 50% mark as possible. In aggregate even 51% has the potential for serious profit, if enough volume is used, so the fact that we managed to reach almost 60% has some serious potential.

We built a tool to make stock predictions using Reddit and Twitter data. We believe that using non conventional data (such as Twitter and Reddit) can give traders an edge when it comes to predicting the stock market. The two tools can be easily integrated with the traders terminal or used through our web app.

Reddit stock prediction:
Many traders used Reddit in their spare time. So we thought of analysing Reddit’s world news data to predict the outcome of DJIA (Dow Jones Index Average). We try two methods of prediction: news to predict whether open > close using news on the day and using news from the previous days to predict the outcome (open > close) on the next day.

To accomplish this we build a neural network model using the state of the art LSTM (Long Short Term Memory). Recent research shows that LSTM works best with textual and sequential data. We trained our LSTM based on dataset by Sun, J, 2016, which contains Reddit world news data over a period of 8 years starting in 2008 and DJIA score. The score is 0 if opening is lower than closing, 1 if it is equal to or higher. Our best model predicts the DJIA score using the data from the day before to predict the outcome of DJIA in the following day.

Twitter Sentiment analysis stock prediction:
Many traders are avid Twitter users. Therefore it might be useful to find out what people are thinking about certain topics in real time. With this, traders could find recent sentiments on a certain topic, which may influence stock prices. To build this tool, we used the Twitter API along with Google Cloud sentiment analysis under their NLP service.

Built With

askln
beautiful-soup
chart.js
css
flask
google-cloud
google-cloud-language
heroku
html5
javascript
keras
numpy
python
react
sklearn
tweepy

Submitted to

//Slash Hackathon 2019

Created by

I designed the layout of the site, translating sketch wireframes to the React Framework code alongside Peter. We were both new to working with React and front-end development and gained a lot of invaluable experience as well as some insight into the powerful and complex world of machine learning.

Nicoló Stewen
I worked with Tom on stock prediction (LSTM) and twitter sentiment analysis.

momobi :3
I created most of the front-end using a React framework, working with Nicolo to get from his mock-up design to a functioning site. Nicolo and I were both new to React and learned a lot.

plwallich
My role was mainly in the backend with Muhammad, specifically, I built the time series models for our Reddit news stock price predictions. I helped manage the architecture to make sure all the components would fit together properly, which included the development of a custom Flask API for our "r/worldnews" section. Later on, I helped with the React client.

Tom Flatters