This repository focuses on predicting Apple Inc.'s stock prices using time series analysis from 2013 to 2018. The project involves data exploration, stationarity analysis, time series decomposition, model development, and generating insights to assist investors.

This project is a basic look into using R Programming to work on stocks.

Developed a website using Python and Streamlit for Stock Price Prediction to assist in making buy or sell decisions during a college hackathon.

Stock price prediction using historical data from Yahoo Finance, implemented in R with an interactive RShiny dashboard.

StableStockPredictor is a robust deep learning model for predicting S&P 500 stock prices, built with TensorFlow and Keras. It leverages LSTM networks with gradient clipping, robust scaling, and stable feature engineering (e.g., RSI, moving averages, volatility) to ensure reliable performance in volatile markets.

Python script analyzes Tesla stock data, displaying candlestick chart, 3D scatter plot, & line chart (closing price, MA50, MA200). Generates buy/sell signals based on moving average crossovers.

A repository containing a Python script for implementing a simple trading strategy based on moving average crossover.