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VWAP (Volume Weighted Average Price) Indicator

Overview​

VWAP is a critical indicator that combines price and volume to show the true average price of an asset. This implementation will include standard deviation bands and volume analysis for enhanced trading signals.

Status​

  • Domain Model Design
  • Core Implementation
  • API Integration
  • Testing
  • Documentation

Components​

1. Domain Model​

interface VWAPData {
  // Core Values
  vwap: number; // Current VWAP value
  vwap_upper_band: number; // Upper standard deviation band
  vwap_lower_band: number; // Lower standard deviation band

  // Analysis
  price_to_vwap_ratio: number; // Price relative to VWAP
  position: VWAPPosition; // Position enum
  volume_trend: VWAPVolumeTrend; // Volume trend analysis
  volume_strength: StrengthLevel; // Volume strength

  // Signals
  signal: SignalType; // Trading signal
  signal_strength: number; // Signal confidence (0-1)
}

enum VWAPPosition {
  ABOVE_VWAP = 'above_vwap',
  BELOW_VWAP = 'below_vwap',
  AT_VWAP = 'at_vwap',
}

enum VWAPVolumeTrend {
  ACCUMULATION = 'accumulation',
  DISTRIBUTION = 'distribution',
  NEUTRAL = 'neutral',
}

2. Configuration​

interface VWAPSettings {
  std_dev_multiplier: number; // Band calculation (default: 2.0)
  reset_on_timeframe: boolean; // Reset on new period (default: true)
  band_threshold: number; // AT_VWAP threshold (default: 0.02)
  volume_threshold: number; // Volume trend detection (default: 1.5)
}

3. API Endpoints​

REST API​

  • GET /v1/vwap
    • Parameters:
      • symbol: Trading pair
      • timeframe: Candle timeframe
      • std_dev_multiplier: Optional band multiplier
      • reset_on_timeframe: Optional reset flag

WebSocket​

  • Channel: vwap
    • Updates: Real-time VWAP and band values
    • Frequency: On each trade/tick

Technical Implementation​

1. Core Calculation​

def compute_vwap(df: pd.DataFrame) -> pd.DataFrame:
    """
    Calculate VWAP and standard deviation bands.

    Args:
        df: DataFrame with OHLCV data

    Returns:
        DataFrame with VWAP calculations
    """
    # Typical price * volume
    df['typical_price'] = (df['high'] + df['low'] + df['close']) / 3
    df['tp_volume'] = df['typical_price'] * df['volume']

    # Cumulative values
    df['cum_tp_volume'] = df['tp_volume'].cumsum()
    df['cum_volume'] = df['volume'].cumsum()

    # VWAP
    df['vwap'] = df['cum_tp_volume'] / df['cum_volume']

    # Standard deviation bands
    df['variance'] = (...)  # Implementation details
    df['std_dev'] = np.sqrt(df['variance'])
    df['vwap_upper'] = df['vwap'] + (df['std_dev'] * settings.std_dev_multiplier)
    df['vwap_lower'] = df['vwap'] - (df['std_dev'] * settings.std_dev_multiplier)

    return df

2. Analysis Functions​

def analyze_volume_trend(df: pd.DataFrame, row_idx: int) -> VWAPVolumeTrend:
    """Analyze volume distribution around VWAP."""
    # Implementation details

def analyze_position(price: float, vwap: float, threshold: float) -> VWAPPosition:
    """Determine price position relative to VWAP."""
    # Implementation details

def generate_signal(
    position: VWAPPosition,
    volume_trend: VWAPVolumeTrend,
    price_ratio: float
) -> Tuple[SignalType, float]:
    """Generate trading signal and strength."""
    # Implementation details

3. Integration Points​

  • Volume analysis integration with OBV
  • Signal combination with other indicators
  • Real-time calculation optimization
  • WebSocket stream management

Configuration​

TIMEFRAME_SETTINGS: Dict[str, VWAPSettings] = {
    "1m": VWAPSettings(
        std_dev_multiplier=2.0,
        reset_on_timeframe=True,
        band_threshold=0.02,
        volume_threshold=1.5
    ),
    "5m": VWAPSettings(...),
    "15m": VWAPSettings(...),
    "1h": VWAPSettings(...),
    "4h": VWAPSettings(...),
    "1d": VWAPSettings(...)
}

Development Guidelines​

1. Performance Optimization​

  • Use vectorized operations for calculations
  • Implement efficient cumulative sum updates
  • Cache intermediate results where possible
  • Optimize memory usage for real-time updates

2. Testing Strategy​

def test_vwap_calculation():
    """Test basic VWAP calculation accuracy."""

def test_band_calculation():
    """Test standard deviation bands."""

def test_volume_analysis():
    """Test volume trend detection."""

def test_signal_generation():
    """Test trading signal generation."""

def test_reset_behavior():
    """Test timeframe reset functionality."""

3. Error Handling​

  • Handle missing/invalid data
  • Validate configuration parameters
  • Manage timeframe transitions
  • Handle real-time calculation edge cases

Dependencies​

  • NumPy: Efficient numerical operations
  • Pandas: Data manipulation
  • FastAPI: REST API endpoints
  • WebSocket: Real-time updates

Future Enhancements​

  1. Multi-timeframe VWAP analysis
  2. Anchored VWAP calculations
  3. Volume profile integration
  4. Machine learning signal enhancement

References​

  1. VWAP Trading Strategies
  2. Standard Deviation Bands
  3. Volume Analysis