This project focuses on constructing a Recurrent Neural Network (RNN) model for analyzing monthly stock market datasets. The dataset contains information such as the stock symbol, date, closing price, high price, low price, opening price, trading volume, adjusted closing price, adjusted high price, adjusted low price, adjusted opening price, adjusted trading volume, dividend cash, and split factor.
The dataset used in this project consists of monthly stock market data. Each entry in the dataset includes information about a specific stock on a particular date, such as the closing price, high price, low price, opening price, trading volume, adjusted closing price, adjusted high price, adjusted low price, adjusted opening price, adjusted trading volume, dividend cash, and split factor.
The dataset can be accessed in CSV format, with the following columns:
symbol: The stock symbol.
- date: The date of the stock market data.
- close: The closing price of the stock.
- high: The highest price reached by the stock.
- low: The lowest price reached by the stock.
- open: The opening price of the stock.
- volume: The trading volume of the stock.
- adjClose: The adjusted closing price of the stock.
- adjHigh: The adjusted highest price reached by the stock.
- adjLow: The adjusted lowest price reached by the stock.
- adjOpen: The adjusted opening price of the stock.
- adjVolume: The adjusted trading volume of the stock.
- divCash: The dividend cash. -splitFactor: The split factor.
The objective of this project is to construct a Recurrent Neural Network (RNN) model that can analyze and predict stock market trends based on the provided monthly stock market dataset. The RNN model will be trained using historical stock market data and will aim to predict future stock prices or identify patterns and trends in the stock market.
The project will utilize Python and popular deep learning frameworks such as TensorFlow or PyTorch to build and train the RNN model. The dataset will be preprocessed to prepare it for training, and appropriate data normalization techniques will be applied. The RNN model architecture will be designed to capture temporal dependencies and patterns in the stock market data. The model will be trained using a suitable loss function and optimized using an appropriate optimization algorithm.
To get started with this project, follow these steps:
Clone the project repository. Install the required dependencies and libraries. Preprocess and prepare the dataset. Design and train the RNN model. Evaluate the model's performance and make predictions. Contributions Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License.