AXE Capital

Project Title

AXE Capital

Project Area of Specialization

Artificial Intelligence

Project Summary

Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. Our project focuses on the use of LSTM (Long Short-Term Memory) based Machine learning to predict stock values. Factors considered are open, close, low, high and volume and making this prediction visble to general public throught a web application.

The uderlying advantages will be news and watchllist give the users an all in one platform for making martket decisions.

Project Objectives

Giving easy to user to make a watch list of interseted companies stock.
Show the prediction of stock prices of companies.
Aims to reduce the risk of losing money of our users (potentional investors).
Giving latest news of the companies with postitive and negative indicators.
generate buy and sell signals.

Project Implementation Method

Stock market prediction seems a complex problem because there are many factors that have yet to be addressed and it doesn’t seem statistical at first. But by proper use of machine learning techniques, one can relate previous data to the current data and train the machine to learn from it and make appropriate assumptions. Machine learning as such has many models but we focuses on LSTM and made the predictions using it.

LSTM (Long Short-Term Memory) is the advanced version of Recurrent-NeuralNetworks (RNN) where the information belonging to previous state persists. These are different from RNNs as they involve long term dependencies and RNNs works on finding the relationship between the recent and the current information. This indicates that the interval of information is relatively smaller than that to LSTM. The main purpose behind using this model in stock market prediction is that the predictions depends on large amounts of data and are generally dependent on the long term history of the market. So LSTM regulates error by giving an aid to the RNNs through retaining information for older stages making the prediction more accurate. Since stock market involves processing of huge data, the gradients with respect to the weight matrix may become very small and may degrade the learning rate of the system. This corresponds to the problem of Vanishing Gradient. LSTM prevents this from happening. The LSTM consists of a remembering cell, input gate, output gate and a forget gate. The cell remembers the value for long term propagation and the gates regulate them.

we will implemnet the model in the backend and make a webapplication for the users. the news data is beeing colleted throught a news API (Application Programming Interface) and the trding signals are generated on the bases of the news and model predition. The users can make watchlist of interesed companies to filter out the users interstered signal, prediction and newses.

Benefits of the Project

Axe-Capital is a big opportunity for new investors to trade easily by seeing the predicted graphs of companies. There is a demand for these types of platforms by which people can invest by seeing predicted graphs, but due to fewer resources, people cannot write these algorithms easily. There are some platforms that are used by quants but those aren't affordable for a common investor. So, we are providing a platform for investors that would be highly practical and applicable to potential trading, forecast, and generate signals to users by focusing on the application of AI and machine learning and analyzing big data.

The uderlying advantages will be news and watchllist give the users an all in one platform for making martket decisions.