SLATE – Stock Learning And Trend Estimation

Project Duration: Mar 2017 – Dec 2017

Project description:

SLATE is a Stock Market Investment Simulator and Trend forecasting system using Machine Learning. It consists of 2 modules, A web based stock Market Simulator and a ML and Neural networks based Stock Prediction System.

We created a very easy to use Graphical user interface for the user that is used for Stock simulation and estimation. Also stock prediction results show good accuracy when using Support vector machine with RNN (Recurrent neural networks). In future we are soon expecting to complete our simulation work and then focus more to include various regressors for stock prediction like Gradient boosting, bagging regressor etc.


  • To make a user friendly portal where user would be able to learn about stocks and invest virtually in real life stocks.
  • Each user will be given specific amount of virtual currency and they can trade in stocks.
  • To provide a portal where users will not only enable to trade and create portfolio but also watch prediction of various stocks prices.

Technical Aspects:

  • Classification Methods: This phase would involve supervised classification methods like Support Vector Machines, Neural Networks, Naive Bayes classifiers etc.
  • Regression Methods: These models would be used to get the expected numerical value of the interested stocks. This phase would involve supervised regressions methods like Linear Regressions, Support Vector Regressions, and Usage of Kernel Methods etc
  • Social Media Sentiment Analysis: Analysing the current market situation from the latest news headlines and social media platform such as Twitter to gain insights into the future of stock prices.
  • Analysis of Different Models: Comparison between the various methods and models implemented over the stock datasets of each company.

Sahil Gupta

A budding Entrepreneur, Thinker, Writer and most importantly a Seeker... Read More