cloudqi/crypto_trading_insights

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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

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Table of Contents

1. Introduction
2. Features
3. Requirements
4. Model Training
5. Real-Time Execution
6. File Structure
7. Example Data
8. Future Enhancements
9. Disclaimer

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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:

pip install -r requirements.txt

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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:

python train_model.py

• The trained model is saved as web3_trade_specialist_v1.0.0.h5.

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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:

python real_time_prediction.py

• The model provides real-time recommendations based on live market data.

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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

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Example Data

Download a sample CSV file with simulated cryptocurrency data for training:
Download Simulated Crypto Data

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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.

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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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