AI Market Predictor

K.I.T.'s brain for price predictions - Machine Learning that WORKS!

Features

🔮 LSTM Neural Networks

• Time Series Prediction mit Deep Learning
• Multi-Step Forecasting (1h, 4h, 24h, 7d)
• Attention Mechanisms für wichtige Patterns

📊 Feature Engineering

• Technical Indicators (RSI, MACD, Bollinger, 50+ mehr)
• Volume Profile Analysis
• Order Flow Imbalance
• Funding Rates (Perps)
• Open Interest Changes

🎯 Confidence Scoring

• Monte Carlo Dropout für Uncertainty Estimation
• Ensemble Models für robustere Predictions
• Dynamische Confidence basierend auf Volatilität

🏆 Model Performance

• Rolling Backtests
• Walk-Forward Optimization
• Real-time Model Retraining

Usage

from ai_predictor import MarketPredictor

predictor = MarketPredictor()

# Single prediction
prediction = await predictor.predict(
    symbol="BTC/USDT",
    timeframe="1h",
    horizon=24  # hours ahead
)

print(f"Price: ${prediction.price:.2f}")
print(f"Direction: {prediction.direction}")  # UP/DOWN/NEUTRAL
print(f"Confidence: {prediction.confidence:.1%}")
print(f"Range: ${prediction.low:.2f} - ${prediction.high:.2f}")

# Batch predictions
predictions = await predictor.predict_batch(
    symbols=["BTC/USDT", "ETH/USDT", "SOL/USDT"],
    timeframe="4h",
    horizon=168  # 1 week
)

Models

Model	Use Case	Accuracy
LSTM-Attention	Short-term (1-24h)	~65% direction
Transformer	Medium-term (1-7d)	~58% direction
XGBoost Ensemble	Volatility Prediction	MAE < 2%
CNN-LSTM	Pattern Recognition	~62% breakouts

Configuration

ai_predictor:
  models:
    lstm:
      layers: [128, 64, 32]
      dropout: 0.2
      attention: true
    ensemble_size: 5

  features:
    technical: true
    orderflow: true
    sentiment: true  # requires sentiment-analyzer

  training:
    lookback: 168  # hours
    retrain_interval: 24h
    min_samples: 1000

Dependencies

• tensorflow>=2.15.0
• scikit-learn>=1.3.0
• ta-lib (technical analysis)
• numpy, pandas