Predictive Analytics in Stock Market Trends Using Machine Learning in R Overview This project leverages machine learning techniques in R to analyze and predict stock market trends. It employs several R scripts that utilize different predictive models and trading strategies to forecast stock prices and simulate trading scenarios.

Dependencies The project requires the following R packages:

dplyr ggplot2 tidyr corrplot maps randomForest neuralnet shiny quantmod TTR (Technical Trading Rules) forecast caret tseries Make sure to install these packages before running the scripts.

Project Components

1. Simple Trade Setup File: simple trade setup.R Description: Establishes a basic trading strategy by identifying potential buy and sell signals based on technical indicators.
2. Trade Simulation File: Trade Simulation.R Description: Simulates trading activities based on the predictions from the models and the trade setup strategies.
3. Random Forest Model File: Random Forest Code.R Description: Implements a random forest model to predict stock prices based on historical data.
4. Neural Network Model File: Neural Network.R Description: Utilizes a neural network to analyze stock market data and predict future trends.
5. Simple Dashboard File: Simple dashboard.R Description: Creates an interactive dashboard using Shiny for real-time visualization and analysis of the stock data and forecasts. Running the Scripts To run any script, open your R environment and set the working directory to the location of the script and associated data files. Execute the script commands sequentially to reproduce the analysis and simulations.

How to Contribute Contributions to the project are welcome. You can contribute by:

Enhancing the existing predictive models. Adding new models or trading strategies. Improving the visualization dashboard.