Real-Time Stock Market Analysis using LSTM

Abstract

Introduction: For financial participants, the National Stock Exchange of India Limited (NSE) offers an unpredictable playground. Its benchmark index, the Nifty 50, displays intricate patterns and shifting market dynamics. Historically, price-action methods and historical data have helped traders make sense of this unpredictable market. Nevertheless, these techniques frequently struggle to capture and quickly adjust to changing market conditions.

Objectives: Using the capabilities of Long Short-Term Memory (LSTM) networks, this study suggests a unique method for predicting Nifty 50 stock values. Our project helps people who are just starting with trading a helping hand so they can make more data-driven decisions with accuracy.

Methods: LSTMs are highly advanced neural networks that can recall and use historical data to forecast future occurrences. LSTMs examine past pricing data, finding recurring patterns and minute details that would escape conventional approaches, just as human memory recovers and applies collected experiences for well-informed decision-making. In our Prediction system, we have used technologies like LSTM, and Yahoo Finance API. We first took real-time data on the stock market from Yahoo Finance (YFinance) and loaded it onto our machine learning system. We analyzed and trained that data to predict the future condition of the stock market.

Results: With the help of yfinance and Long short-term memory (LSTM) the projected values of our model is at an error rate of 2.7%. This research also dives in the cases of uncertainty where the error rate if not applicable like sharp selling/buying.

Conclusions: This research using LSTM model with an error rate of 2.7% is better when it is compared to the other research that has been conducted on the financial markets. We see a decrease in error rate because of the use of Real-time data curated from yfinance as well as option chain analysis.