Quant Risk Dashboard

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name: quant-risk-dashboard description: Professional quantitative trading risk management dashboard. Real-time VaR/CVaR calculation, stress testing, position limits, exposure monitoring, drawdown alerts, and comprehensive risk metrics visualization. tags: - quant - trading - risk - dashboard - var - monitoring version: 1.0.0 author: chenq

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Quant Risk Dashboard

Professional risk management system for quantitative trading.

Features

1. Risk Metrics

VaR (Value at Risk): Historical, Parametric, Monte Carlo

CVaR (Conditional VaR): Expected shortfall

Max Drawdown: Current and historical

Volatility: Realized and implied

Beta: Market sensitivity

Sharpe/Sortino/Calar: Risk-adjusted returns

2. Position Management

Real-time Positions: Current holdings with P&L

Position Limits: Per-stock and total limits

Concentration Risk: Single position max%

Sector Exposure: Industry allocation

3. Exposure Monitoring

Long/Short Ratio: Net exposure

Sector Allocation: Industry breakdown

Factor Exposure: Style factors (value, growth, momentum)

Geographic Exposure: Market cap breakdown

4. Stress Testing

Historical Scenarios: 2008 crash, 2020 covid, etc.

Custom Scenarios: User-defined shocks

Scenario Comparison: Side-by-side analysis

Recovery Time: Estimated recovery from scenarios

5. Alerts & Notifications

Drawdown Alerts: Threshold-based warnings

Position Breach: Limit violation alerts

Volatility Spikes: Unusual market moves

Custom Rules: User-defined triggers

6. Reporting

Daily Risk Report: Automated PDF/HTML reports

Risk Attribution: P&L explained by factors

Compliance Reports: Regulatory compliance

Custom Reports: Flexible report builder

Installation

pip install pandas numpy scipy plotly dash

Usage

Initialize Dashboard

from quant_risk import RiskDashboard
dashboard = RiskDashboard(
    initial_capital=1000000,
    var_confidence=0.95,
    max_position_pct=0.15,
    max_drawdown_pct=0.20
)

Add Positions

dashboard.add_position(
    symbol='600519',
    shares=1000,
    entry_price=1800.0,
    current_price=1850.0
)
dashboard.add_position(
    symbol='000858',
    shares=5000,
    entry_price=45.0,
    current_price=48.0
)

Get Risk Metrics

metrics = dashboard.get_risk_metrics()
print(f"VaR (95%): {metrics['var_95']:,.2f}")
print(f"CVaR (95%): {metrics['cvar_95']:,.2f}")
print(f"Sharpe Ratio: {metrics['sharpe_ratio']:.2f}")
print(f"Max Drawdown: {metrics['max_drawdown']:.2%}")
print(f"Total Exposure: {metrics['total_exposure']:,.0f}")

Stress Test

scenarios = {
    '2008 Crash': -0.50,
    '2020 Covid': -0.30,
    'Rate Hike': -0.15,
    'Custom': -0.25
}
results = dashboard.stress_test(scenarios)
for name, result in results.items():
    print(f"{name}: P&L = {result['pnl']:,.2f}")

Start Web Dashboard

dashboard.start_dashboard(port=8050)
Open http://localhost:8050

API Reference

Core Methods

| Method | Description | |--------|-------------| | add_position(symbol, shares, entry, current) | Add position | | remove_position(symbol) | Close position | | update_price(symbol, price) | Update market price | | get_positions() | Get all positions | | get_risk_metrics() | Calculate risk metrics |

Risk Analysis

| Method | Description | |--------|-------------| | calculate_var(method='historical') | Calculate VaR | | calculate_cvar() | Calculate CVaR | | stress_test(scenarios) | Run stress tests | | factor_exposure() | Calculate factor exposure | | sector_allocation() | Get sector breakdown |

Alerts

| Method | Description | |--------|-------------| | add_alert(condition, message) | Create alert | | get_alerts() | Get active alerts | | clear_alerts() | Clear alerts |

Reports

| Method | Description | |--------|-------------| | generate_report(format='pdf') | Generate report | | get_daily_summary() | Daily summary |

Risk Metrics Explained

VaR (Value at Risk)

Definition: Maximum expected loss at given confidence level

Interpretation: "95% VaR = 50,000" means 95% chance loss < 50,000

CVaR (Conditional VaR)

Definition: Average loss beyond VaR threshold

Interpretation: More conservative than VaR

Sharpe Ratio

Definition: Risk-adjusted return

Interpretation: >1.0 good, >2.0 excellent

Max Drawdown

Definition: Largest peak-to-trough decline

Interpretation: Lower is better

Sortino Ratio

Definition: Downside risk-adjusted return

Interpretation: Only considers downside risk

Configuration

Risk Limits

limits = {
    'max_position_pct': 0.15,    # 15% per position
    'max_sector_pct': 0.30,       # 30% per sector
    'max_leverage': 1.5,          # 1.5x leverage
    'max_drawdown': 0.20,         # 20% stop loss
    'max_var_pct': 0.05,          # 5% VaR limit
}

Alert Thresholds

alerts = {
    'drawdown_warning': 0.10,     # 10% drawdown warning
    'drawdown_critical': 0.15,    # 15% critical
    'var_warning': 0.03,          # 3% VaR warning
    'volatility_spike': 2.0,      # 2x normal volatility
}

Visualization

Web Dashboard

dashboard.start_dashboard()
Features:
- Real-time position table
- P&L charts
- Risk metrics gauges
- Sector pie chart
- Drawdown curve
- Factor exposure bar chart

Generate Charts

# P&L Chart
chart = dashboard.plot_pnl_history()
Risk Decomposition
chart = dashboard.plot_risk_attribution()
Scenario Comparison
chart = dashboard.plot_scenarios()

Integration

Connect to Trading System

# From trading system
import asyncio
async def update_positions():
    while True:
        positions = await trading_system.get_positions()
        
        for pos in positions:
            dashboard.update_price(pos.symbol, pos.current_price)
        
        await asyncio.sleep(60)  # Update every minute
asyncio.run(update_positions())

Webhook Alerts

# Send alerts to Slack/WeChat
def on_alert(alert):
    send_webhook(
        url=os.getenv('ALERT_WEBHOOK'),
        message=f"Risk Alert: {alert['message']}"
    )
dashboard.set_alert_callback(on_alert)

Use Cases

Live Trading: Real-time risk monitoring

Backtesting: Post-trade risk analysis

Portfolio Management: Multi-strategy risk

Compliance: Regulatory risk reports

Risk Research: Strategy risk profiling

Links

RiskMetrics VaR

quantlib

Portfolio Visualizer

Initialize Dashboard

from quant_risk import RiskDashboard
dashboard = RiskDashboard(
    initial_capital=1000000,
    var_confidence=0.95,
    max_position_pct=0.15,
    max_drawdown_pct=0.20
)

Add Positions

dashboard.add_position(
    symbol='600519',
    shares=1000,
    entry_price=1800.0,
    current_price=1850.0
)
dashboard.add_position(
    symbol='000858',
    shares=5000,
    entry_price=45.0,
    current_price=48.0
)

Get Risk Metrics

metrics = dashboard.get_risk_metrics()
print(f"VaR (95%): {metrics['var_95']:,.2f}")
print(f"CVaR (95%): {metrics['cvar_95']:,.2f}")
print(f"Sharpe Ratio: {metrics['sharpe_ratio']:.2f}")
print(f"Max Drawdown: {metrics['max_drawdown']:.2%}")
print(f"Total Exposure: {metrics['total_exposure']:,.0f}")

Stress Test

scenarios = {
    '2008 Crash': -0.50,
    '2020 Covid': -0.30,
    'Rate Hike': -0.15,
    'Custom': -0.25
}
results = dashboard.stress_test(scenarios)
for name, result in results.items():
    print(f"{name}: P&L = {result['pnl']:,.2f}")

Start Web Dashboard

dashboard.start_dashboard(port=8050)
Open http://localhost:8050

Risk Limits

limits = {
    'max_position_pct': 0.15,    # 15% per position
    'max_sector_pct': 0.30,       # 30% per sector
    'max_leverage': 1.5,          # 1.5x leverage
    'max_drawdown': 0.20,         # 20% stop loss
    'max_var_pct': 0.05,          # 5% VaR limit
}

Alert Thresholds

alerts = {
    'drawdown_warning': 0.10,     # 10% drawdown warning
    'drawdown_critical': 0.15,    # 15% critical
    'var_warning': 0.03,          # 3% VaR warning
    'volatility_spike': 2.0,      # 2x normal volatility
}