Systemic Risk Monitoring Engine¶

35.1 System Overview¶

The Systemic Risk Monitoring Engine provides real-time monitoring of global macro market volatility, asset correlations, portfolio exposures, and anomalous trading activities. It implements a comprehensive early warning system that automatically triggers defensive actions when systemic risks are detected—protecting portfolios during market stress events.

35.1.1 Core Objectives¶

• Macro Market Monitoring: Real-time tracking of VIX, SPX, DXY, gold, and other systemic indicators
• Correlation Analysis: Monitor changes in asset class correlations and detect unusual relationships
• Portfolio Exposure Analysis: Track sector, currency, and asset class risk exposures across all accounts
• Anomaly Detection: Identify unusual trading activity (volume spikes, slippage anomalies, execution issues)
• Automatic Response: Trigger warnings, position reduction, and trading freezes when risks exceed thresholds

35.2 Architecture Design¶

35.2.1 Microservice Architecture¶

Systemic Risk Center Service:

services/systemic-risk-center/
├── src/
│   ├── main.py
│   ├── monitor/
│   │   ├── macro_monitor.py
│   │   ├── correlation_analyzer.py
│   ├── detector/
│   │   ├── anomaly_detector.py
│   ├── responder/
│   │   ├── system_responder.py
│   ├── api/
│   │   ├── risk_api.py
│   ├── config.py
│   ├── requirements.txt
├── Dockerfile

35.2.2 Core Components¶

• Macro Market Monitor: Tracks VIX, SPX, DXY, and other systemic indicators
• Correlation Analyzer: Real-time calculation of asset correlation matrices
• Portfolio Exposure Analyzer: Aggregates sector, currency, and asset class exposures
• Anomaly Detector: Captures volume, price, slippage, and execution anomalies
• System Responder: Automatically triggers defensive actions when thresholds are exceeded
• API Interface: Query risk indicators and anomaly events
• Frontend Dashboard: Global risk heatmap and anomaly event visualization

35.3 Module Design¶

35.3.1 Macro Market Monitor (macro_monitor.py)¶

• Real-time monitoring of systemic risk indicators

class MacroMonitor:
    def fetch_macro_indicators(self):
        return {
            "vix": fetch_vix_index(),
            "spx": fetch_sp500_index(),
            "dxy": fetch_usd_index()
        }

35.3.2 Correlation Analyzer (correlation_analyzer.py)¶

• Computes real-time asset correlation matrices

import numpy as np
import pandas as pd

class CorrelationAnalyzer:
    def compute_correlation_matrix(self, price_data: pd.DataFrame):
        return price_data.pct_change().corr()

35.3.3 Portfolio Exposure Analyzer (portfolio_exposure.py)¶

• Analyzes sector, currency, and asset class exposures

class PortfolioExposure:
    def analyze_exposure(self, holdings):
        exposure_by_sector = {}
        exposure_by_currency = {}
        for asset, details in holdings.items():
            exposure_by_sector[details["sector"]] += details["value"]
            exposure_by_currency[details["currency"]] += details["value"]
        return exposure_by_sector, exposure_by_currency

35.3.4 Anomaly Detector (anomaly_detector.py)¶

• Detects unusual trading activity and market anomalies

class AnomalyDetector:
    def detect_anomalies(self, trades, quotes):
        anomalies = []
        for t in trades:
            if t["slippage"] > 0.005:
                anomalies.append({"type": "High Slippage", "trade_id": t["id"]})
        return anomalies

35.3.5 System Responder (system_responder.py)¶

• Automatically triggers defensive actions when risks exceed thresholds

class SystemResponder:
    async def shrink_positions(self):
        await order_service.reduce_all_positions_by(0.5)  # Reduce by 50%

35.3.6 API Interface (risk_api.py)¶

• FastAPI endpoints for risk monitoring and anomaly queries

from fastapi import APIRouter
router = APIRouter()

@router.get("/risk/macro")
async def get_macro_indicators():
    return macro_monitor.fetch_macro_indicators()

@router.get("/risk/anomalies")
async def get_detected_anomalies():
    return anomaly_detector.recent_anomalies

35.3.7 Frontend Dashboard¶

• Macro indicators trend charts (VIX, SPX, DXY)
• Asset correlation matrix heatmap
• Portfolio exposure radar charts
• Anomaly event tables
• System risk level indicators (green/yellow/red)

35.4 Risk Monitoring Flow Example¶

1. Macro data updates → MacroMonitor analyzes systemic indicators
2. Asset correlations refresh → CorrelationAnalyzer monitors relationships
3. Portfolio exposures update → PortfolioExposure calculates concentrations
4. Risk or anomaly detected → SystemResponder triggers automatic defense
5. Frontend dashboard visualizes global and local risk status in real-time

35.5 Technology Stack¶

• Python (FastAPI, pandas, numpy): Service implementation and analysis
• Redis: Real-time data caching
• Docker: Containerization
• React/TypeScript: Frontend dashboard
• Prometheus: Risk monitoring metrics

35.6 API Design¶

• GET /risk/macro: Get current macro indicators
• GET /risk/correlations: Get asset correlation matrix
• GET /risk/exposures: Get portfolio exposure analysis
• GET /risk/anomalies: Get detected anomalies
• GET /risk/status: Get overall system risk status

35.7 Frontend Integration¶

• Real-time macro indicators and correlation visualization
• Portfolio exposure analysis and risk heatmaps
• Anomaly event monitoring and alerting

35.8 Implementation Roadmap¶

• Phase 1: Macro monitoring and basic correlation analysis
• Phase 2: Anomaly detection and portfolio exposure analysis
• Phase 3: System responder and advanced risk visualization

35.9 Integration with Existing System¶

• Integrates with portfolio service, risk center, and execution layer
• Provides systemic risk monitoring for all strategies and accounts

35.10 Business Value¶