Skip to content

TECHNICAL_DETAILS.md

Latest commit

 

History

History
129 lines (104 loc) · 3.03 KB

TECHNICAL_DETAILS.md

File metadata and controls

129 lines (104 loc) · 3.03 KB

Technical Details: Prediction System & Learning Mechanism

Prediction System Architecture

1. Core Components

LSTM Neural Network

  • Input shape: (30, 5) - 30 days of historical data with 5 features
  • Architecture:
    • LSTM Layer 1: 50 units with dropout (0.2)
    • LSTM Layer 2: 30 units with dropout (0.2)
    • Dense output layer: 1 unit
  • Optimization: Adam optimizer with MSE loss

Technical Indicators

  • Moving Averages (20, 50, 200 days)
  • RSI (14-period)
  • MACD (12, 26, 9)
  • Bollinger Bands (20, 2σ)

2. Signal Generation

Feature Engineering

Features used:
- Price data (OHLCV)
- Technical indicators
- Price momentum
- Volatility metrics
- Volume analysis

Signal Strength Calculation

  • Combines multiple indicators
  • Weighted scoring system:
    • MA crossovers
    • RSI extremes
    • MACD signals
    • Volume confirmation
  • Confidence calculation based on signal agreement

3. Incremental Learning System

Background Learning Process

  • Runs in a separate thread
  • Continuously updates model weights
  • Adapts to new market conditions
  • Maintains a rolling window of recent data

Learning Algorithm

  1. Data Collection:

    • Stores last 1000 price movements
    • Features: price, indicators, volume
    • Labels: actual price changes

  2. Training Process:

    • Batch size: 32 samples
    • Updates every 5 minutes
    • Uses most recent data points
    • Applies gradient updates

  3. Adaptation Mechanism:

    • Adjusts to market volatility
    • Updates signal thresholds
    • Modifies confidence levels
    • Fine-tunes prediction weights

4. Signal Validation

Entry Points

  • Strong buy signals require:
    • RSI < 30 (oversold)
    • Positive MACD crossover
    • Price above key moving averages
    • High volume confirmation

Exit Points

  • Sell signals triggered by:
    • RSI > 70 (overbought)
    • MACD bearish crossover
    • Break below support levels
    • Divergence patterns

5. Performance Metrics

Backtesting Engine

  • Simulates trading strategy
  • Calculates key metrics:
    • Total return
    • Win rate
    • Maximum drawdown
    • Risk-adjusted returns

Real-time Validation

  • Monitors prediction accuracy
  • Adjusts confidence levels
  • Updates signal thresholds
  • Maintains performance stats

System Limitations

  1. Market Conditions

    • Works best in trending markets
    • May struggle in highly volatile periods
    • Requires sufficient volume for accuracy

  2. Technical Constraints

    • API rate limits affect data freshness
    • Processing delays in high-volume periods
    • Memory constraints for historical data

  3. Model Adaptation

    • Takes time to adapt to new patterns
    • May overfit to recent market conditions
    • Requires regular performance monitoring

Future Improvements

  1. Planned Enhancements

    • Advanced pattern recognition
    • Multiple timeframe analysis
    • Sentiment analysis integration
    • Enhanced risk management

  2. Optimization Opportunities

    • Feature selection refinement
    • Model architecture improvements
    • Performance optimization
    • Enhanced error handling