Jundalnabi jundalnabi

PyQuotex - AI-Powered Trading Bot with Neural Network

An advanced AI-powered trading bot for Quotex platform featuring real-time neural network learning, pattern recognition, and comprehensive monitoring.

🚀 Features

🧠 Advanced AI & Machine Learning

Deep Neural Network: 5-layer architecture with 20,000+ parameters
Real-time Learning: Learns from every trade instantly
Pattern Recognition: Identifies market patterns and trends
Confidence Scoring: Knows how confident each prediction is
Adaptive Training: Automatically retrains based on performance
Incremental Learning: Continuous learning without forgetting

📊 Comprehensive Monitoring

Rolling Accuracy: Tracks success rate of last 50 trades
Profit Tracking: Real-time profit/loss monitoring
Streak Analysis: Win/loss streak tracking
CSV Export: All data saved for analysis
Performance Insights: Detailed statistics and recommendations

🔄 Smart Trading Features

Auto-trading: Automated trade execution
Risk Management: Martingale strategy with smart recovery
Multiple Assets: Support for various trading pairs
Technical Indicators: RSI, MACD, Stochastic, SMA, EMA
Strategy Adaptation: Aggressive/Balanced/Conservative modes

📋 Requirements

Python 3.8 or higher
Windows 10/11 (PowerShell)
Quotex account
Internet connection
VPN for bot and website

🛠️ Installation

1. Clone the Repository

git clone https://github.com/yourusername/pyquotex.git
cd pyquotex

2. Create Virtual Environment

# Windows PowerShell
python -m venv myenv
myenv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Credentials

Edit pyquotex/config.py and add your Quotex credentials:

def credentials():
    return "your_email@example.com", "your_password"

🚀 Quick Start

Basic Trading

# Start auto-trading
python app.py auto-trade --amount 50 --asset EURUSD_otc --duration 60 --interval 60

# Check account balance
python app.py balance

# View real-time prices
python app.py realtime-price --asset EURJPY_otc

AI & Machine Learning

# Check AI status
python app.py ml-status

# View learned patterns
python app.py ml-patterns

# Analyze performance
python app.py ml-analyze

# Neural network insights
python app.py nn-insights

# Monitor trading statistics
python app.py monitor-stats

📖 Commands Reference

Trading Commands

Command	Description	Example
`auto-trade`	Start automated trading	`python app.py auto-trade --amount 50`
`balance`	Check account balance	`python app.py balance`
`realtime-price`	View live prices	`python app.py realtime-price --asset EURUSD_otc`
`signals`	Get trading signals	`python app.py signals`

AI & ML Commands

Command	Description	Example
`ml-status`	Show AI learning status	`python app.py ml-status`
`ml-analyze`	Analyze trading performance	`python app.py ml-analyze`
`ml-patterns`	View learned patterns	`python app.py ml-patterns`
`ml-save`	Save AI model	`python app.py ml-save --name "my_model"`
`ml-save-all`	Save all learning data	`python app.py ml-save-all`
`nn-insights`	Neural network analysis	`python app.py nn-insights`
`monitor-stats`	Trading statistics	`python app.py monitor-stats`

Advanced ML Commands

Command	Description	Example
`ml-incremental`	Manage incremental learning	`python app.py ml-incremental --replay`
`ml-incremental --buffer-size 2000`	Set memory buffer size	`python app.py ml-incremental --buffer-size 2000`

🧠 Neural Network Architecture

Model Structure

Input Layer (36 features)
    ↓
Batch Normalization
    ↓
Dense Layer (128 units) + Dropout(0.3) + BatchNorm
    ↓
Dense Layer (64 units) + Dropout(0.2) + BatchNorm
    ↓
Dense Layer (32 units) + Dropout(0.2) + BatchNorm
    ↓
Dense Layer (16 units) + Dropout(0.1)
    ↓
Output Layer (1 unit, sigmoid)

Features (36 total)

Candle Data: Open, Close, High, Low, Volume (5 candles × 5 = 25)
Technical Indicators: SMA, EMA, RSI (3 each = 9)
MACD: Signal, Histogram (2)
Previous Trade: Result, Profit (2)
Time: Hour of day (1)

Advanced Features

Batch Normalization: Prevents internal covariate shift
Dropout Regularization: Prevents overfitting
Learning Rate Decay: Adaptive learning rate
Early Stopping: Prevents overtraining
Model Checkpointing: Saves best model

📊 Monitoring & Analytics

Real-time Display

[Neural Network] Prediction: CALL (confidence: 85%)
📊 [2024-09-14] Rolling Accuracy (last 5 trades): 80.00%
💰 [2024-09-14] Last Trade Profit: R$ 5.00, Total Profit: R$ 25.00
📈 [2024-09-14] Trade #5 | Win Streak: 3 | Loss Streak: 0
🧠 [2024-09-14] Strategy: AGGRESSIVE | Learning Confidence: 75%

CSV Export

All trading data is automatically saved to trade_metrics.csv:

Timestamp
Rolling accuracy
Total profit
Last trade profit
Trade count
Win/loss streaks
Strategy
Learning confidence

⚙️ Configuration

Trading Parameters

# In auto_trade method
amount = 50          # Trade amount
asset = "EURUSD_otc" # Trading pair
duration = 60        # Trade duration (seconds)
interval = 60        # Time between trades (seconds)

AI Parameters

# Neural network settings
feature_size = 36    # Input features
memory_buffer_size = 1000  # Experience replay buffer
incremental_batch_size = 32  # Mini-batch size
learning_rate = 0.001  # Initial learning rate

🔧 Advanced Usage

Custom Model Training

# The bot automatically:
# 1. Learns from every trade
# 2. Retrains when performance drops
# 3. Saves models automatically
# 4. Adapts strategy based on results

Pattern Learning

The bot learns and recognizes:

Candle Patterns: Doji, Hammer, Engulfing
Indicator Patterns: RSI divergence, MACD crossovers
Time Patterns: Best trading hours
Market Conditions: Volatility, trends

Strategy Adaptation

Aggressive: High confidence, larger positions
Balanced: Moderate risk, steady growth
Conservative: Low risk, small positions

📁 Project Structure

pyquotex/
├── app.py                 # Main application
├── requirements.txt       # Dependencies
├── README.md             # This file
├── LICENSE               # MIT License
├── pyquotex/            # Core library
│   ├── __init__.py
│   ├── api.py
│   ├── config.py
│   └── ...
├── examples/            # Example scripts
├── docs/               # Documentation
└── models/             # Saved AI models (auto-created)

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

⚠️ Disclaimer

This software is for educational purposes only. Trading involves risk and you may lose money. Use at your own risk. The authors are not responsible for any financial losses.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Support

Email: alet8319@gmail.com
Phone: +92-3478042183
Issues: GitHub Issues

🙏 Acknowledgments

Team Jund Al Nabi for the original PyQuotex library
TensorFlow team for the machine learning framework
The open-source community for various dependencies

⭐ If you find this project helpful, please give it a star!