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#

bash

pip install pandas numpy scipy plotly dash

Usage#

Initialize Dashboard#

python

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#

python

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#

python

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#

python

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#

python

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

API Reference#

Core Methods#

MethodDescriptionadd_position(symbol, shares, entry, current)Add positionremove_position(symbol)Close positionupdate_price(symbol, price)Update market priceget_positions()Get all positionsget_risk_metrics()Calculate risk metrics

Risk Analysis#

MethodDescriptioncalculate_var(method='historical')Calculate VaRcalculate_cvar()Calculate CVaRstress_test(scenarios)Run stress testsfactor_exposure()Calculate factor exposuresector_allocation()Get sector breakdown

Alerts#

MethodDescriptionadd_alert(condition, message)Create alertget_alerts()Get active alertsclear_alerts()Clear alerts

Reports#

MethodDescriptiongenerate_report(format='pdf')Generate reportget_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#

python

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#

python

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#

python

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#

python

# 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#

python

# 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#

python

# 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