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Portfolio Risk

The Portfolio Risk module provides comprehensive position analysis, risk metrics, and portfolio optimization for connected brokerage accounts. This enterprise feature enables portfolio managers to understand and manage their portfolio risk using institutional-grade analytics.

Overview​

Portfolio Risk connects to your brokerage accounts to import positions and run sophisticated risk analysis. The module calculates key risk metrics, identifies concentration risks, and provides AI-powered insights about your portfolio.

What This Feature Provides​

  • Brokerage Integration — Connect to 100+ supported brokerages
  • Position Import — Automatic synchronization of holdings
  • Risk Metrics — VaR, CVaR, correlation analysis, drawdown
  • Portfolio Optimization — Efficient frontier and risk parity approaches
  • Thematic Classification — Group positions by sector, strategy, or custom themes
  • AI Portfolio Chat — Natural language Q&A about your portfolio

Why It Matters​

Understanding portfolio risk helps:

  • Identify concentration — Overweight positions or sectors
  • Manage drawdown — Understand potential loss scenarios
  • Optimize allocation — Improve risk-adjusted returns
  • Monitor correlation — Identify hidden dependencies
  • Make informed decisions — Data-driven position adjustments

Brokerage Integration​

Connecting Your Account​

The module uses secure OAuth-based brokerage connections:

  1. Navigate to Portfolio Risk > Accounts
  2. Click "Connect Brokerage"
  3. Select your brokerage from the list
  4. Complete authentication on your brokerage's site
  5. Authorize read-only position access

Supported Brokerages​

Over 100 brokerages are supported including:

  • Charles Schwab
  • Fidelity
  • TD Ameritrade
  • Interactive Brokers
  • E*TRADE
  • Robinhood
  • Vanguard
  • Merrill Edge
  • And many more

Data Privacy​

  • Read-only access — We cannot execute trades
  • Encrypted storage — Credentials securely stored
  • On-demand sync — Positions updated when you request
  • Disconnect anytime — Remove access instantly

Risk Metrics​

Value at Risk (VaR)​

The maximum expected loss over a specified time horizon at a given confidence level.

Example: 95% 1-day VaR of $5,000
Interpretation: 95% confident loss won't exceed $5,000 in one day

TradeAnon calculates VaR using historical simulation with configurable parameters:

  • Confidence levels: 90%, 95%, 99%
  • Time horizons: 1-day, 1-week, 1-month

Conditional Value at Risk (CVaR)​

Also called Expected Shortfall. The average loss in the worst-case scenarios beyond VaR.

Example: 95% CVaR of $8,000
Interpretation: In the worst 5% of scenarios, average loss is $8,000

CVaR is a more conservative risk measure than VaR.

Portfolio Beta​

Sensitivity of portfolio returns to market (SPY) returns:

Beta = Covariance(Portfolio, Market) / Variance(Market)

Interpretation:

  • Beta = 1.0: Portfolio moves with market
  • Beta > 1.0: Portfolio is more volatile than market
  • Beta < 1.0: Portfolio is less volatile than market

Maximum Drawdown​

The largest peak-to-trough decline in portfolio value:

Max Drawdown = (Peak - Trough) / Peak × 100

Historical and potential forward-looking drawdown estimates provided.

Sharpe Ratio​

Risk-adjusted return measure:

Sharpe = (Portfolio Return - Risk-Free Rate) / Portfolio Volatility

Interpretation:

  • Above 1.0: Good risk-adjusted returns
  • Above 2.0: Excellent risk-adjusted returns
  • Below 0.5: May not justify the risk taken

Correlation Analysis​

Position Correlations​

The module calculates correlations between all positions:

  • High correlation (>0.7) — Positions move together, less diversification
  • Low correlation (<0.3) — Positions move independently, good diversification
  • Negative correlation (<0) — Positions move opposite, hedging effect

Sector Exposure​

Aggregated exposure by GICS sector:

SectorWeightContribution to Risk
Technology35%45%
Healthcare20%15%
Financials15%18%
.........

Risk contribution can exceed weight if sector is highly volatile.

Factor Exposure​

Exposure to common risk factors:

  • Market (Beta) — Overall equity market exposure
  • Size — Small vs large cap tilt
  • Value — Value vs growth orientation
  • Momentum — Exposure to momentum factor
  • Volatility — Low vs high volatility tilt

Portfolio Optimization​

Efficient Frontier​

Calculate optimal portfolios across risk-return spectrum:

  • Minimum Variance — Lowest risk portfolio
  • Maximum Sharpe — Best risk-adjusted return
  • Risk Parity — Equal risk contribution from each position
  • Target Return — Minimum risk for specified return

Optimization Constraints​

Customizable constraints:

  • Minimum/maximum position weights
  • Sector exposure limits
  • Turnover constraints
  • Long-only vs long-short

Suggested Adjustments​

Based on optimization, the module suggests:

  • Positions to reduce (overweight, high risk contribution)
  • Positions to increase (underweight, good diversification)
  • Estimated impact on risk metrics

AI Portfolio Chat​

Natural Language Analysis​

Ask questions about your portfolio in plain language:

Example Questions:

  • "What's my biggest concentration risk?"
  • "How would a 10% market drop affect my portfolio?"
  • "Which positions are most correlated with each other?"
  • "What sectors am I overweight in?"
  • "How has my portfolio performed vs SPY this year?"

Context-Aware Responses​

The AI understands:

  • Your current positions and weights
  • Historical performance
  • Risk metrics and correlations
  • Market conditions

Responses are personalized to your specific portfolio.

How to Use This Feature​

Initial Setup​

  1. Connect brokerage accounts you want to analyze
  2. Sync positions to import current holdings
  3. Review portfolio summary for high-level metrics
  4. Check risk metrics for current risk profile

Regular Monitoring​

  1. Resync positions after trades
  2. Review VaR and CVaR for risk exposure
  3. Check correlations for concentration
  4. Monitor sector weights for imbalances

Optimization Workflow​

  1. Run optimization with your constraints
  2. Review suggested adjustments
  3. Evaluate trade-offs between risk and return
  4. Make informed rebalancing decisions

Practical Examples​

Example 1: Concentration Risk​

Portfolio analysis reveals:

  • 40% in technology sector
  • Top 3 positions = 55% of portfolio
  • Tech positions highly correlated (0.75+)

Insight: Significant concentration risk. A tech selloff would disproportionately impact portfolio. Consider diversification.

Example 2: Hidden Correlation​

Two positions in different sectors show:

  • 0.82 correlation over past year
  • Both are high-growth stocks
  • Factor exposure similar (growth, momentum)

Insight: Despite sector diversification, these positions provide less risk reduction than expected. They share common factor exposures.

Example 3: Optimization Suggestion​

Current portfolio: 12% volatility, 0.8 Sharpe Optimized portfolio: 10% volatility, 1.1 Sharpe

Suggested changes:

  • Reduce Position A from 15% to 8%
  • Increase Position B from 5% to 10%
  • Add uncorrelated asset class

Insight: Modest changes could meaningfully improve risk-adjusted returns.

Limitations​

  • Historical-based — Risk metrics based on historical data may not predict future
  • Assumes normality — Extreme events may exceed model expectations
  • Position delays — Broker sync has some lag
  • No trading capability — Analysis only, no execution
  • Equity-focused — Options and complex instruments have limited support

Data Security​

  • Bank-level encryption — All data encrypted in transit and at rest
  • No credential storage — OAuth tokens, not passwords
  • Audit logging — All access logged
  • SOC 2 compliance — Third-party security validation

Subscription Access​

TierAccess Level
Free—
Pro—
EnterpriseFull access