This project aims to predict stock prices using Generative Adversarial Networks (GANs). GANs are a type of neural network architecture that consists of two networks, a generator and a discriminator, trained simultaneously to produce realistic synthetic data.
Stock price prediction is a challenging task due to the inherent volatility and complexity of financial markets. Traditional time-series forecasting methods often struggle to capture the non-linear and dynamic nature of stock price movements. In this project, we explore the use of GANs to generate synthetic stock price data and evaluate its performance against real-world data.
The project consists of the following main components:
- Data Collection: We collect historical stock price data and corresponding Twitter sentiment analysis data.
- Preprocessing: The collected data is preprocessed to extract relevant features and prepare it for model training.
- Model Architecture: We implement a GAN architecture consisting of a generator and a discriminator using TensorFlow and Keras.
- Training: The GAN model is trained on the preprocessed data to learn the underlying patterns and relationships.
- Evaluation: The trained model is evaluated on test data to assess its performance in predicting stock prices.
- Results Visualization: We visualize the predicted stock prices and compare them with actual prices to gauge the model's effectiveness.
- Python 3.x
- TensorFlow
- Pandas
- NumPy
- Matplotlib
- NLTK
- Statsmodels
- tqdm
Install the required packages using the following command:
pip install -r requirements.txt- Clone this repository:
git clone https://github.com/your-username/stock-price-prediction.git- Navigate
cd stock-price-prediction- Run Main script
python SentimentalAnalysis.py