Volume Analysis¶

Advanced volume analysis capabilities for cryptocurrency trading and analytics.

Overview¶

The volume analysis system provides comprehensive tools for analyzing trading volume patterns, identifying trends, and generating actionable insights for trading strategies.

Features¶

Real-time Volume Monitoring¶

• Live Volume Tracking: Real-time monitoring of trading volumes across exchanges
• Volume Alerts: Configurable alerts for unusual volume spikes
• Cross-Exchange Analysis: Volume comparison across multiple exchanges
• Historical Volume Data: Access to historical volume patterns

Volume Indicators¶

On-Balance Volume (OBV)¶

Tracks cumulative volume flow to predict price movements:

pub fn calculate_obv(prices: &[f64], volumes: &[f64]) -> Vec<f64> {
    let mut obv = vec![0.0; prices.len()];
    obv[0] = volumes[0];

    for i in 1..prices.len() {
        if prices[i] > prices[i-1] {
            obv[i] = obv[i-1] + volumes[i];
        } else if prices[i] < prices[i-1] {
            obv[i] = obv[i-1] - volumes[i];
        } else {
            obv[i] = obv[i-1];
        }
    }

    obv
}

Volume Moving Average¶

Smoothed volume trends over time periods:

pub fn volume_moving_average(volumes: &[f64], period: usize) -> Vec<f64> {
    volumes.windows(period)
        .map(|window| window.iter().sum::<f64>() / period as f64)
        .collect()
}

Volume Pattern Recognition¶

Volume Spike Detection¶

Identifies unusual volume activity:

• Statistical Analysis: Z-score based spike detection
• Threshold Alerts: Customizable volume threshold alerts
• Pattern Classification: Classification of volume spike types

Volume Profile Analysis¶

Price-volume distribution analysis:

• Volume at Price: Distribution of volume across price levels
• Point of Control: Price level with highest volume
• Value Area: Price range containing majority of volume

Analytics Dashboard¶

Volume Metrics¶

• Average Daily Volume: Rolling average calculations
• Volume Volatility: Measure of volume consistency
• Volume Trend: Directional volume analysis
• Relative Volume: Current vs. historical volume comparison

Visualization Components¶

interface VolumeChartProps {
  data: VolumeData[];
  timeframe: TimeFrame;
  indicators: VolumeIndicator[];
}

export const VolumeChart: React.FC<VolumeChartProps> = ({
  data,
  timeframe,
  indicators
}) => {
  return (
    <div className="volume-chart">
      <CandlestickChart data={data} />
      <VolumeHistogram data={data} />
      {indicators.map(indicator => 
        <IndicatorOverlay key={indicator.id} indicator={indicator} />
      )}
    </div>
  );
};

API Integration¶

REST API Endpoints¶

# Get volume data
GET /api/v1/analytics/volume/{symbol}?period=1d&limit=100

# Get volume indicators
GET /api/v1/analytics/volume/{symbol}/indicators?type=obv

# Get volume alerts
GET /api/v1/analytics/volume/alerts

WebSocket Streams¶

// Subscribe to real-time volume updates
ws.send(JSON.stringify({
  method: 'subscribe',
  params: ['volume@1m', 'volume@5m']
}));

Configuration¶

Volume Analysis Settings¶

volume_analysis:
  enabled: true
  update_interval: 1000  # milliseconds
  history_depth: 7200    # data points

  indicators:
    obv:
      enabled: true
      smoothing: 14

    volume_ma:
      enabled: true
      periods: [20, 50, 200]

  alerts:
    volume_spike:
      threshold: 2.0  # standard deviations
      min_volume: 1000000

Machine Learning Integration¶

Volume Prediction Models¶

• LSTM Networks: Sequential volume prediction
• Random Forest: Feature-based volume forecasting
• XGBoost: Gradient boosting for volume analysis

Feature Engineering¶

def extract_volume_features(data):
    features = {
        'volume_ma_ratio': data['volume'] / data['volume_ma_20'],
        'volume_std_ratio': data['volume'] / data['volume_std'],
        'price_volume_correlation': correlation(data['price'], data['volume']),
        'volume_momentum': data['volume'].pct_change(5),
    }
    return features

Performance Optimization¶

Caching Strategy¶

• Redis Cache: Hot volume data caching
• Memory Cache: Frequently accessed indicators
• Database Indexing: Optimized volume data queries

Parallel Processing¶

use rayon::prelude::*;

pub fn parallel_volume_analysis(symbols: &[String]) -> Vec<VolumeAnalysis> {
    symbols.par_iter()
        .map(|symbol| analyze_volume(symbol))
        .collect()
}

Monitoring and Alerts¶

Volume Anomaly Detection¶

• Statistical Thresholds: Z-score based detection
• Machine Learning: Anomaly detection models
• Rule-based Alerts: Custom rule engine

Alert Configuration¶

{
  "alerts": [
    {
      "name": "High Volume Alert",
      "condition": "volume > 2 * avg_volume_7d",
      "channels": ["email", "slack", "webhook"]
    }
  ]
}

See Also¶

• Advanced Analytics
• Pattern Recognition
• Market Analysis
• API Documentation

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This documentation is part of the Anya Enterprise Analytics suite.