| | --- |
| | license: bsl-1.0 |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | --- |
| | --- |
| |
|
| | # **Web3 Trade Specialist Model** |
| | ## Revolutionizing Crypto Trading with AI-Powered Predictions |
| |
|
| | This repository soon has contains the code and documentation for the **Web3 Trade Specialist**, an AI-powered model designed to predict cryptocurrency market trends with recommendation scores ranging from **-10 (strong sell)** to **+10 (strong buy)**, with **0 indicating neutral market conditions**. |
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| |
|
| | ## WhitePaper |
| |
|
| | 1. [WhitePaper Preview](https://pt.scribd.com/document/811362676/CloudQi-Innovating-Crypto-Trading-with-Artificial-Intelligence) |
| |
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| |
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| | --- |
| |
|
| | ## **Table of Contents** |
| | 1. [Introduction](#introduction) |
| | 2. [Features](#features) |
| | 3. [Requirements](#requirements) |
| | 4. [Model Training](#model-training) |
| | 5. [Real-Time Execution](#real-time-execution) |
| | 6. [File Structure](#file-structure) |
| | 7. [Example Data](#example-data) |
| | 8. [Future Enhancements](#future-enhancements) |
| | 9. [Disclaimer](#disclaimer) |
| |
|
| | --- |
| |
|
| | ## **Introduction** |
| | The **Web3 Trade Specialist Model** leverages **Long Short-Term Memory (LSTM)** networks for time-series analysis of cryptocurrency data. It processes historical data to extract features, predict market trends, and provide actionable insights for traders. The real-time capabilities of this model enable near-instantaneous decision-making in dynamic markets. |
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| | --- |
| |
|
| | ## **Features** |
| | - **Predictive Recommendations**: Generates buy/sell/hold signals with scores ranging from -10 to +10. |
| | - **Historical Data Processing**: Aggregates and analyzes data such as prices, volumes, market caps, and liquidity. |
| | - **Real-Time Execution**: Processes live market data to make predictions. |
| | - **GPU Acceleration**: Utilizes GPU for faster model training and prediction. |
| |
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| | --- |
| |
|
| | ## **Requirements** |
| | ### **Hardware** |
| | - GPU-enabled system for efficient training and execution. |
| |
|
| | ### **Software** |
| | 1. Python (>= 3.8) |
| | 2. TensorFlow (>= 2.9) |
| | 3. Pandas, NumPy, Scikit-learn |
| | 4. Requests (for live data fetching) |
| | 5. Any CSV editor (for preparing historical data) |
| |
|
| | Install dependencies using: |
| | ```bash |
| | pip install -r requirements.txt |
| | ``` |
| |
|
| | --- |
| |
|
| | ## **Model Training** |
| | ### **Steps to Train the Model** |
| | 1. **Prepare Historical Data**: Organize data with fields for `timestamp`, `price`, `volume`, `market_cap`, and `liquidity`. |
| | 2. **Create Indicators**: Use the training script to process data and generate features such as moving averages and targets. |
| | 3. **Train the Model**: Execute the training script to train an LSTM-based model with historical data. |
| |
|
| | ### **Command** |
| | Run the training script: |
| | ```bash |
| | python train_model.py |
| | ``` |
| |
|
| | - The trained model is saved as `web3_trade_specialist_v1.0.0.h5`. |
| |
|
| | --- |
| |
|
| | ## **Real-Time Execution** |
| | ### **Steps to Execute in Real-Time** |
| | 1. **Set API Credentials**: Configure the API endpoint (e.g., Binance) for live data. |
| | 2. **Run the Real-Time Script**: Continuously fetch live market data, preprocess it, and make predictions. |
| |
|
| | ### **Command** |
| | Run the real-time script: |
| | ```bash |
| | python real_time_prediction.py |
| | ``` |
| |
|
| | - The model provides real-time recommendations based on live market data. |
| |
|
| | --- |
| |
|
| | ## **File Structure** |
| | ``` |
| | root/ |
| | │ |
| | ├── train_model.py # Script for model training |
| | ├── real_time_prediction.py # Script for real-time execution |
| | ├── historical_data/ # Directory for historical data CSV files |
| | ├── web3_trade_specialist_v1.0.0.h5 # Trained model |
| | ├── requirements.txt # Dependencies list |
| | └── README.md # Documentation |
| | ``` |
| |
|
| | --- |
| |
|
| | ## **Example Data** |
| | Download a sample CSV file with simulated cryptocurrency data for training: |
| | [Download Simulated Crypto Data](sandbox:/mnt/data/simulated_crypto_data.csv) |
| |
|
| | --- |
| |
|
| | ## **Future Enhancements** |
| | 1. **Integration with Popular Trading Platforms**: Automate trade execution. |
| | 2. **Advanced Risk Management**: Implement dynamic stop-loss and risk assessment. |
| | 3. **Improved Accuracy**: Enhance predictive performance by integrating new data sources. |
| | 4. **User-Friendly API**: Develop an API for easier integration with trading systems. |
| |
|
| | --- |
| |
|
| | ## **Disclaimer** |
| | 1. The model's predictions are based on historical data and may not guarantee future performance. |
| | 2. Cryptocurrency trading carries significant financial risk. Use the model with caution and trade responsibly. |
| |
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| | --- |
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