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SignalMasters is a cutting-edge platform dedicated to providing cryptocurrency investors and traders with invaluable signals in the ever-evolving crypto market. Success in cryptocurrency trading hinges on accurate information and timely signals, and SignalMasters is engineered to meet this critical need.
Signal Masters, Our Cryptocurrency Signal Service is a cutting-edge platform that leverages advanced algorithms and market analysis tools to deliver real-time trading signals to our users. These signals are designed to assist traders in identifying potential buy and sell opportunities in the cryptocurrency market.
The project is aimed at developing an intelligent trading for automated trading cryptocurrencies using state-of-the-art machine learning (ML) algorithms and feature engineering. The project provides the following major functionalities:
Features:
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Analyzing historic data and training machine learning models in batch off-line mode
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Analyzing the predicted scores and choosing best signal parameters
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Signaling service which is regularly requests new data from the exchange and generates buy-sell signals by applying the previously trained models in on-line mode
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Trading service which does real trading by buying or selling the assets according to the generated signals
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Reliable Signals: SignalMasters employs a team of seasoned analysts and advanced algorithms to continuously monitor the cryptocurrency markets. It delivers users with real-time, trustworthy signals to support their buy and sell decisions.
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Personalized Notifications: Users can receive customized notifications for specific cryptocurrencies or trading strategies. This ensures they are promptly alerted to any significant market changes that align with their interests.
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Education and Analysis Tools: SignalMasters offers a wide range of resources to help users better understand cryptocurrency trading. Whether you're a novice or experienced trader, you'll find educational materials and advanced analysis tools to enhance your skills.
Every minute, the signaler performs the following steps to make a prediction about whether the price is likely to increase or decrease:
- Retrieve the latest data from the server and update the current data window which includes some history (the history length is defined by a configuration parameter)
- Compute derived features based on the nearest history collected (which now includes the latest data). The features to be computed are described in the configuration file and are exactly the same as used in batch mode during model training
- Apply several (previously trained) ML models by forecasting some future values (not necessarily prices) which are also treated as (more complex) derived features. We apply several forecasting models (currently, Gradient Boosting, Neural network, and Linear regression) to several target variables (labels)
- Aggregate the results of forecasting produced by different ML models and compute the final signal score which reflects the strength of the upward or downward trend. Here we use many previously computed scores as inputs and derive one output score. Currently, it is implemented as an aggregation procedure but it could be based on a dedicated ML model trained on previously collected scores and the target variable. Positive score means growth and negative score means fall
- Use the final score for notifications
We welcome contributions from the community. To contribute to BlockchainBridge, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Commit your changes.
- Submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
