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---
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**.
## WhitePaper
1. [WhitePaper Preview](https://pt.scribd.com/document/811362676/CloudQi-Innovating-Crypto-Trading-with-Artificial-Intelligence)
---
## **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.
---
## **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.
---
## **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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