trendmaster 0.2.3
Stock Price Prediction using Transformer Deep Learning Architecture
Requires Python: >=3.7

# TrendMaster: Advanced Stock Price Prediction using Transformer Deep Learning

[![Python Version](https://img.shields.io/badge/python-3.7%2B-blue)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-green)](https://opensource.org/licenses/MIT)
[![GitHub Stars](https://img.shields.io/github/stars/hemangjoshi37a/TrendMaster?style=social)](https://github.com/hemangjoshi37a/TrendMaster/stargazers)

TrendMaster leverages cutting-edge Transformer deep learning architecture to deliver highly accurate stock price predictions, empowering you to make informed investment decisions.

![TrendMaster Demo](https://user-images.githubusercontent.com/12392345/125791380-341cecb7-a605-4147-9310-e5055f30b220.gif)

## 🚀 Features

- Advanced Transformer-based prediction model
- High accuracy with mean average error of just a few percentage points
- Real-time data visualization
- User-friendly interface
- Customizable model parameters
- Support for multiple stock symbols

## 📊 Why TrendMaster?

TrendMaster stands out as a top-tier tool for financial forecasting by:

- Utilizing a wealth of historical stock data
- Employing sophisticated deep learning algorithms
- Identifying patterns and trends beyond human perception
- Providing actionable insights for smarter investment strategies

## 🛠️ Installation

Get started with TrendMaster in just one command:

```bash
pip install TrendMaster
```

## 📈 Quick Start

Here's how to integrate TrendMaster into your Python projects:

```python
from trendmaster import TrendMaster

# Initialize TrendMaster
test_symbol = 'SBIN'
tm = TrendMaster(symbol_name_stk=test_symbol)

# Load data
data = tm.load_data(symbol=test_symbol)

# Train the model
tm.train(test_symbol, transformer_params={'epochs': 1})

# Perform inference
predictions = tm.inferencer.predict_future(val_data=data, future_steps=100, symbol=test_symbol)
print(predictions)
```

## 📊 Sample Results

Our Transformer-based prediction model demonstrates impressive accuracy:

![Transformer-Future200](https://user-images.githubusercontent.com/12392345/125791397-a344831b-b28c-4660-b295-924cb7123872.png)

## 🖥️ User Interface

TrendMaster comes with a sleek, user-friendly interface for easy data visualization and analysis:

![TrendMaster UI](https://user-images.githubusercontent.com/12392345/125791827-a4597af0-1292-42d0-9eb1-118d7ef64cbc.png)

## 📘 Documentation

For detailed documentation, including API reference and advanced usage, please visit our [Wiki](https://github.com/hemangjoshi37a/TrendMaster/wiki).

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for more details.

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🌟 Show Your Support

If you find TrendMaster helpful, please consider giving it a star on GitHub. It helps others discover the project and motivates us to keep improving!

[![GitHub Star History](https://api.star-history.com/svg?repos=hemangjoshi37a/TrendMaster&type=Date)](https://star-history.com/#hemangjoshi37a/TrendMaster&Date)

## 📫 Contact

For questions, suggestions, or collaboration opportunities, please reach out:

- Website: [hjlabs.in](https://hjlabs.in/)
- Email: [hemangjoshi37a@gmail.com](mailto:hemangjoshi37a@gmail.com)
- LinkedIn: [Hemang Joshi](https://www.linkedin.com/in/hemang-joshi-046746aa)

## 🔗 More from HJ Labs

Check out our other exciting projects:
- [pyPortMan](https://github.com/hemangjoshi37a/pyPortMan)
- [AutoCut](https://github.com/hemangjoshi37a/AutoCut)
- [TelegramTradeMsgBacktestML](https://github.com/hemangjoshi37a/TelegramTradeMsgBacktestML)

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Created with ❤️ by [Hemang Joshi](https://github.com/hemangjoshi37a)