Smart Market Surveillance Engine¶

38.1 System Overview¶

The Smart Market Surveillance Engine provides real-time monitoring of market microstructure changes, detecting volatility spikes, volume anomalies, order book changes, and price-volume divergences. It enables proactive market surveillance and automatic defensive mechanisms to protect portfolios during market stress events.

38.1.1 Core Objectives¶

• Volatility Monitoring: Detect sudden volatility spikes and market stress
• Volume Anomaly Detection: Monitor unusual volume spikes and bursts
• Order Book Surveillance: Track bid/ask depth and order flow anomalies
• Price-Volume Analysis: Detect price-volume divergences and market inefficiencies
• Multi-Market Support: Unified monitoring across US, HK, A-shares, and crypto markets
• Automatic Defense: Trigger strategy defense mechanisms and position adjustments

38.2 Architecture Design¶

38.2.1 Microservice Architecture¶

Market Surveillance Center Service:

services/market-surveillance-center/
├── src/
│   ├── main.py
│   ├── monitor/
│   │   ├── volatility_monitor.py
│   │   ├── volume_monitor.py
│   │   ├── orderbook_monitor.py
│   ├── analyzer/
│   │   ├── price_volume_analyzer.py
│   ├── notifier/
│   │   ├── alert_notifier.py
│   ├── risklink/
│   │   ├── defensive_trigger.py
│   ├── api/
│   │   ├── market_alert_api.py
│   ├── config.py
│   ├── requirements.txt
├── Dockerfile

38.2.2 Core Components¶

• Volatility Monitor: Detects market volatility spikes and stress events
• Volume Monitor: Monitors unusual volume spikes and bursts
• Order Book Monitor: Tracks bid/ask depth and order flow changes
• Price-Volume Analyzer: Detects price-volume divergences
• Alert Notifier: Sends market alerts via multiple channels
• Defensive Trigger: Automatically triggers risk reduction measures
• API Interface: Query current market alerts and surveillance data
• Frontend Dashboard: Market anomaly radar and alert center

38.3 Module Design¶

38.3.1 Volatility Monitor (volatility_monitor.py)¶

• Detects sudden volatility spikes using rolling calculations

import numpy as np

class VolatilityMonitor:
    def detect_volatility_spike(self, price_series):
        returns = np.diff(np.log(price_series))
        volatility = np.std(returns)
        if volatility > 0.02:  # Custom threshold
            return True
        return False

38.3.2 Volume Monitor (volume_monitor.py)¶

• Monitors volume spikes and unusual trading activity

class VolumeMonitor:
    def detect_volume_spike(self, volume_series):
        avg_volume = np.mean(volume_series[-20:])
        current_volume = volume_series[-1]
        if current_volume > avg_volume * 3:
            return True
        return False

38.3.3 Order Book Monitor (orderbook_monitor.py)¶

• Monitors bid/ask depth and order flow anomalies

class OrderBookMonitor:
    def detect_orderbook_anomaly(self, best_bid_qty, best_ask_qty, history):
        avg_bid_qty = np.mean([h["bid_qty"] for h in history])
        avg_ask_qty = np.mean([h["ask_qty"] for h in history])
        if best_bid_qty < avg_bid_qty * 0.5 or best_ask_qty < avg_ask_qty * 0.5:
            return True
        return False

38.3.4 Price-Volume Analyzer (price_volume_analyzer.py)¶

• Detects price-volume divergences and market inefficiencies

class PriceVolumeAnalyzer:
    def detect_price_volume_divergence(self, price_change, volume_change):
        if volume_change > 2.0 and price_change < 0:
            return True
        return False

38.3.5 Alert Notifier (alert_notifier.py)¶

• Sends alerts via Telegram, Slack, and other channels

import requests

class AlertNotifier:
    def send_alert(self, message):
        url = f"https://api.telegram.org/bot{TOKEN}/sendMessage"
        data = {"chat_id": CHAT_ID, "text": message}
        requests.post(url, data=data)

38.3.6 Defensive Trigger (defensive_trigger.py)¶

• Automatically triggers risk reduction measures

class DefensiveTrigger:
    async def trigger_defensive_mode(self):
        await portfolio_service.reduce_positions(percentage=0.3)  # Reduce by 30%

38.3.7 API Interface (market_alert_api.py)¶

• FastAPI endpoints for market surveillance queries

from fastapi import APIRouter
router = APIRouter()

@router.get("/market_alerts")
async def get_market_alerts():
    return market_surveillance_center.recent_alerts

38.3.8 Frontend Dashboard¶

• Real-time anomaly radar (market sector heatmap)
• Alert log list (event type, trigger time)
• Event severity levels (normal/severe/extreme)

38.4 Surveillance Flow Example¶

1. Market data streams in → volatility/volume/orderbook/price_volume monitors analyze
2. Anomaly detected → alert notifier immediately sends warnings
3. Defensive trigger activates dynamic risk control
4. Dashboard displays comprehensive market anomaly overview

38.5 Technology Stack¶

• Python (FastAPI, numpy, pandas): Service implementation and analysis
• Redis: Real-time market data caching
• Docker: Containerization
• React/TypeScript: Frontend dashboard
• Telegram/Slack APIs: Alert notification

38.6 API Design¶

• GET /market_alerts: Get current market alerts
• GET /market_surveillance/status: Get surveillance system status
• GET /market_surveillance/anomalies: Get detected anomalies
• POST /market_surveillance/defensive_mode: Trigger defensive mode

38.7 Frontend Integration¶

• Real-time market anomaly visualization
• Alert management and notification center
• Defensive mode controls and monitoring

38.8 Implementation Roadmap¶

• Phase 1: Basic volatility and volume monitoring
• Phase 2: Order book monitoring and price-volume analysis
• Phase 3: Advanced anomaly detection and defensive triggers

38.9 Integration with Existing System¶

• Integrates with market data feeds and portfolio management
• Provides surveillance capabilities for all trading strategies

38.10 Business Value¶