🧠 AI Crypto Future Trading Bot - Daily PNL :8~27%

Overview

An enterprise-grade AI-powered cryptocurrency futures trading bot achieving 8-27% daily returns with machine learning-driven predictions, real-time signal generation, and fully automated execution across multiple exchanges. Main Platform: Bitget, Blofin, Binance

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Performance Highlights

Metric	Value
Average Daily PNL	8-27%
Maximum Drawdown	<9%
Supported Exchanges	Bitget, Blofin, Binance
Trading Pairs	SOL/USDT, ETH/USDT, DOGE/USDT, XRP/USDT
Automation Level	100% Fully Automated
Signal Generation	Real-time AI Predictions

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What Makes This Bot Different?

Unlike traditional trading bots that rely on simple technical indicators, this system combines High accuracy AI models with real-time market analysis to make intelligent trading decisions:

Advanced AI Forecasting

• Multiple AI models working in ensemble (Chronos T5, Chronos BOLT, TimeGPT)
• Pattern detection and recognition
• Sentiment analysis integration
• Fine-tuned models specifically for crypto markets

Real-Time Intelligence

• Live data streaming from multiple exchanges
• Millisecond-level signal generation
• Momentum calculation and regime detection
• Dynamic risk management with ATR + Trailing Stop loss

Professional Infrastructure

• GPU-accelerated processing
• Parallel training and prediction pipelines
• Cloud-ready with Ubuntu server management
• Automated position sizing and risk control

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System Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    AI CRYPTO TRADING SYSTEM                      │
└─────────────────────────────────────────────────────────────────┘

┌──────────────────┐    ┌──────────────────┐    ┌──────────────────┐
│ Data Collection │───▶│  AI Forecasting  │───▶│  Signal Generator│
│                  │    │                  │    │                  │
│ • Bitget API     │    │ • Chronos Models │    │ • Buy/Sell       │
│ • Blofin API     │    │ • TimeGPT        │    │ • Position Size  │
│ • Binance API    │    │ • Pattern AI     │    │ • Risk Level     │
│ • Real-time OHLCV│    │ • Sentiment AI   │    │                  │
└──────────────────┘    └──────────────────┘    └──────────────────┘
         │                      │                        │
         ▼                      ▼                        ▼
┌──────────────────┐    ┌──────────────────┐    ┌─────────────────────────┐
│  Data Processing │    │  Model Training  │    │ Order Execution With API│
│                  │    │                  │    │                         │
│ • Validation     │    │ • GPU Training   │    │ • Auto Trading          │
│ • Cleaning       │    │ • Fine-tuning    │    │ • Risk Control          │
│ • Indicators     │    │ • Optimization   │    │ • P&L Tracking          │
└──────────────────┘    └──────────────────┘    └─────────────────────────┘

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Core Components

1. Data Collection System

• Real-time OHLCV data from Bitget, Blofin, and Binance
• Historical data collection (supports up to 1000+ days)
• Multi-symbol tracking (SOL, ETH, DOGE, XRP)
• Automatic data validation and quality checks
• Multiple timeframes (1m, 5m, 15m, 1h, 4h, 1d)

2. AI Forecasting System

• Chronos T5 - Time series foundation model
• Chronos BOLT - Fast inference optimized model
• TimeGPT - Advanced forecasting with GPT architecture
• Pattern Detection AI - Identifies market patterns and formations
• Sentiment Analysis - News and social media sentiment integration

Location: AI_fine-turning_system_forecasting_system/

3. Real-Time Prediction Engine

• Momentum Calculator - Real-time momentum tracking
• Regime Detection (LGMM) - Identifies market conditions
• Ensemble Predictions - Combines multiple model outputs
• Signal Validation - Filters false signals
• Confidence Scoring - Assigns probability to each prediction

Location: AI_momentum_real_time_predicting_system/

4. Training System

• Advanced AI Trainers - Automated model training
• LLM + RL Hybrid - Combines language models with reinforcement learning
• Modern Architecture - Transformers, LSTMs, CNNs
• Model Registry - Tracks and versions all models
• Performance Metrics - Comprehensive evaluation

Location: AI_training_system(models_trainers)/

5. Technical Analysis System

• 8+ Technical Indicators (RSI, MACD, Bollinger Bands, etc.)
• Custom Momentum Indicators
• Support/Resistance Detection
• Volume Analysis
• Real-time Indicator Updates

Location: Analyzing_indicators_comparing_momentum_system/

6. GPU & Cloud Infrastructure

• GPU Manager - Efficient GPU allocation and monitoring
• Parallel Processing - Train multiple models simultaneously
• Resource Optimization - Automatic resource allocation
• Cloud Deployment - Ubuntu server management

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## Usage for running

### Collecting Historical Data
```python
from AI_Data_collecting_system_bitget.bitget_client import BitgetClient, DataCollector

# Initialize
collector = DataCollector(symbol="SOLUSDT", timeframe="15m")

# Collect 100 days of data
data = await collector.collect_historical_data(days=100)

# Data is automatically saved to database and JSON

Running AI Forecasting

from AI_fine-turning_system_forecasting_system.forecasting import ChronosForecaster

# Initialize forecaster
forecaster = ChronosForecaster(model_name="chronos-t5-large")

# Make predictions
predictions = forecaster.predict(
    historical_data=data,
    horizon=96  # Predict next 24 hours (15m intervals)
)

print(f"Predicted price movement: {predictions['direction']}")
print(f"Confidence: {predictions['confidence']}%")

Real-time Trading Signals

from AI_momentum_real_time_predicting_system.momentum_predictor import MomentumPredictor

# Initialize predictor
predictor = MomentumPredictor(symbol="SOLUSDT")

# Get real-time signal
signal = predictor.get_signal()

if signal['action'] == 'BUY':
    print(f"🟢 BUY Signal - Confidence: {signal['confidence']}%")
    print(f"   Entry: ${signal['entry_price']}")
    print(f"   Take Profit: ${signal['take_profit']}")
    print(f"   Stop Loss: ${signal['stop_loss']}")

Training Custom Models

from AI_training_system.advanced_ai_trainer import AdvancedAITrainer

# Initialize trainer
trainer = AdvancedAITrainer(
    model_type="transformer",
    use_gpu=True
)

# Train on historical data
trainer.train(
    data=historical_data,
    epochs=100,
    batch_size=32
)

# Evaluate
metrics = trainer.evaluate()
print(f"Model Accuracy: {metrics['accuracy']}%")
print(f"Sharpe Ratio: {metrics['sharpe_ratio']}")

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🔧 Configuration

Configure API Keys

# Create .env file
cp .env.example .env

# Add your API keys
BITGET_API_KEY=bg_025caa13ea1938260ed6ae47968d4bfb
BITGET_API_SECRET=a8f58ef61c44d1435187181070306d842cdc1a592ebe464cac60c38fb46a6289
BITGET_PASSPHRASE=bg2025!@#

BLOFIN_API_KEY=your_api_key
BLOFIN_API_SECRET=your_api_secret

BINANCE_API_KEY=your_api_key
BINANCE_API_SECRET=your_api_secret

### Trading Configuration
Edit `config.py` in your chosen trading system:

```python
# Trading Parameters
TRADING_CONFIG = {
    'symbols': ['SOLUSDT', 'ETHUSDT', 'DOGEUSDT', 'XRPUSDT'],
    'leverage': 10,
    'risk_per_trade': 0.02,  # 2% risk per trade
    'max_positions': 4,
    'stop_loss_pct': 0.03,   # 3% stop loss
    'take_profit_pct': 0.06, # 6% take profit
}

# AI Configuration
AI_CONFIG = {
    'model_ensemble': ['chronos-t5', 'chronos-bolt', 'timegpt'],
    'prediction_horizon': 96,  # 24 hours at 15m intervals
    'min_confidence': 0.65,    # 65% minimum confidence
    'use_sentiment': True,
    'use_patterns': True,
}

# Risk Management
RISK_CONFIG = {
    'max_daily_loss': 0.10,    # Stop trading if 10% daily loss
    'max_drawdown': 0.15,      # 15% maximum drawdown
    'position_sizing': 'dynamic',
    'use_martingale': True,
}

Supported Trading Pairs

Symbol	Exchange	Leverage	Status
SOL/USDT	Bitget, Blofin, Binance	1-30x	✅ Active
ETH/USDT	Bitget, Blofin, Binance	1-30x	✅ Active
DOGE/USDT	Bitget, Blofin, Binance	1-20x	✅ Active
XRP/USDT	Bitget, Blofin, Binance	1-20x	✅ Active

More trading pairs can be added easily

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