@claude-flow/plugin-financial-risk
v3.0.0-alpha.1
Published
High-performance financial risk analysis plugin with portfolio risk scoring, anomaly detection, market regime classification, and compliance reporting using RuVector WASM packages.
Maintainers
Readme
@claude-flow/plugin-financial-risk
A high-performance financial risk analysis plugin combining sparse inference for efficient market signal processing with graph neural networks for transaction network analysis. The plugin enables real-time anomaly detection, portfolio risk scoring, and automated compliance reporting while maintaining the explainability required by financial regulators (SEC, FINRA, Basel III).
Features
- Portfolio Risk Analysis: Calculate VaR, CVaR, Sharpe ratio, Sortino ratio, and max drawdown
- Anomaly Detection: Detect fraud, AML violations, and market manipulation in transactions
- Market Regime Classification: Identify current market regime through historical pattern matching
- Compliance Checking: Automated verification against Basel III, MiFID II, Dodd-Frank, AML, and KYC
- Stress Testing: Run historical and hypothetical stress scenarios on portfolios
Installation
npm
npm install @claude-flow/plugin-financial-riskCLI
npx claude-flow plugins install --name @claude-flow/plugin-financial-riskQuick Start
import { FinancialRiskPlugin } from '@claude-flow/plugin-financial-risk';
// Initialize the plugin
const finance = new FinancialRiskPlugin({
marketDataPath: './data/market',
modelPath: './data/models',
auditLogPath: './logs/audit'
});
// Analyze portfolio risk
const risk = await finance.portfolioRisk({
holdings: [
{ symbol: 'AAPL', quantity: 100, assetClass: 'equity' },
{ symbol: 'GOOGL', quantity: 50, assetClass: 'equity' },
{ symbol: 'TLT', quantity: 200, assetClass: 'bond' }
],
riskMetrics: ['var', 'cvar', 'sharpe'],
confidenceLevel: 0.95,
horizon: '1d'
});
// Detect anomalies in transactions
const anomalies = await finance.anomalyDetect({
transactions: [
{ id: 'tx-001', amount: 50000, timestamp: '2024-01-15T10:30:00Z', parties: ['ACC-123', 'ACC-456'] },
{ id: 'tx-002', amount: 1000000, timestamp: '2024-01-15T10:31:00Z', parties: ['ACC-123', 'ACC-789'] }
],
sensitivity: 0.8,
context: 'fraud'
});
// Check regulatory compliance
const compliance = await finance.complianceCheck({
entity: 'FUND-001',
regulations: ['basel3', 'mifid2'],
scope: 'capital',
asOfDate: '2024-01-15'
});MCP Tools
1. finance/portfolio-risk
Calculate comprehensive portfolio risk metrics.
const result = await mcp.invoke('finance/portfolio-risk', {
holdings: [
{ symbol: 'SPY', quantity: 1000, assetClass: 'equity' },
{ symbol: 'BND', quantity: 500, assetClass: 'bond' },
{ symbol: 'GLD', quantity: 200, assetClass: 'commodity' }
],
riskMetrics: ['var', 'cvar', 'sharpe', 'max_drawdown'],
confidenceLevel: 0.99,
horizon: '1w'
});
// Returns:
// {
// var: { value: 0.023, confidenceLevel: 0.99 },
// cvar: { value: 0.031 },
// sharpe: { value: 1.45 },
// maxDrawdown: { value: 0.082, period: '2023-10-01 to 2023-10-15' }
// }2. finance/anomaly-detect
Detect anomalies in financial transactions and market data.
const result = await mcp.invoke('finance/anomaly-detect', {
transactions: [
{
id: 'tx-12345',
amount: 250000,
timestamp: '2024-01-15T14:30:00Z',
parties: ['CORP-A', 'CORP-B'],
metadata: { type: 'wire', currency: 'USD' }
}
],
sensitivity: 0.9,
context: 'aml'
});
// Returns:
// {
// anomalies: [{
// transactionId: 'tx-12345',
// score: 0.87,
// reasons: ['unusual_amount', 'first_time_counterparty', 'velocity_spike'],
// recommendation: 'review'
// }]
// }3. finance/market-regime
Identify current market regime through pattern matching.
const result = await mcp.invoke('finance/market-regime', {
marketData: {
prices: [100, 102, 99, 101, 103, 105, 104],
volumes: [1000000, 1200000, 900000, 1100000, 1300000, 1500000, 1400000],
volatility: [0.15, 0.16, 0.18, 0.17, 0.14, 0.13, 0.14]
},
lookbackPeriod: 252,
regimeTypes: ['bull', 'bear', 'high_vol', 'crisis']
});
// Returns:
// {
// currentRegime: 'bull',
// confidence: 0.82,
// similarHistoricalPeriods: ['2017-Q3', '2019-Q4'],
// transitionProbabilities: { bull: 0.75, bear: 0.15, high_vol: 0.10 }
// }4. finance/compliance-check
Automated regulatory compliance verification.
const result = await mcp.invoke('finance/compliance-check', {
entity: 'BANK-001',
regulations: ['basel3', 'dodd_frank'],
scope: 'capital',
asOfDate: '2024-01-15'
});
// Returns:
// {
// compliant: true,
// metrics: {
// tier1Capital: { value: 0.125, requirement: 0.06, status: 'pass' },
// leverageRatio: { value: 0.05, requirement: 0.03, status: 'pass' }
// },
// warnings: ['tier1 buffer approaching minimum']
// }5. finance/stress-test
Run stress testing scenarios on portfolios.
const result = await mcp.invoke('finance/stress-test', {
portfolio: {
holdings: [
{ symbol: 'SPY', quantity: 1000 },
{ symbol: 'QQQ', quantity: 500 }
]
},
scenarios: [
{ name: '2008 Financial Crisis', type: 'historical', shocks: {} },
{ name: 'Interest Rate +300bp', type: 'hypothetical', shocks: { rateShock: 0.03 } }
],
metrics: ['pnl', 'var_change', 'margin_call_risk']
});Configuration Options
interface FinancialRiskConfig {
// Data paths
marketDataPath: string; // Path to market data cache
modelPath: string; // Path to trained models
// Compliance
auditLogPath: string; // Path for SOX-compliant audit logs
regulatoryReporting: boolean; // Enable auto-generated reports
// Performance
maxMemoryMB: number; // WASM memory limit (default: 1024)
maxCpuTimeSeconds: number; // Operation timeout (default: 60)
// Security
encryptionEnabled: boolean; // AES-256 encryption (default: true)
hsmKeyId?: string; // HSM key identifier for production
// Rate limiting
rateLimits: {
requestsPerMinute: number;
maxConcurrent: number;
};
}Security Considerations
Regulatory Compliance
This plugin is designed for financial regulatory compliance:
| Requirement | Implementation | |-------------|----------------| | PCI-DSS Compliance | No storage of PAN/CVV in plugin memory | | SOX Compliance | Immutable audit logs for all risk calculations | | Data Encryption | AES-256 for data at rest, TLS 1.3 in transit | | Key Management | HSM or secure enclave for cryptographic keys | | Segregation of Duties | Separate roles for trading, risk, and compliance |
Role-Based Access Control
const roles = {
TRADER: ['portfolio-risk', 'market-regime'],
RISK_MANAGER: ['portfolio-risk', 'anomaly-detect', 'stress-test', 'market-regime'],
COMPLIANCE_OFFICER: ['compliance-check', 'anomaly-detect'],
AUDITOR: ['compliance-check'], // Read-only with full audit access
QUANT: ['portfolio-risk', 'market-regime', 'stress-test']
};Audit Logging (SOX/MiFID II)
All calculations are logged with:
- Timestamp with microsecond precision
- User ID and role
- Transaction IDs affected
- Portfolio state hash
- Model version used
- Input/output hashes for reproducibility
Logs are retained for 7 years per MiFID II requirements and available for regulatory inspection within 72 hours.
WASM Security Constraints
| Constraint | Value | Rationale | |------------|-------|-----------| | Memory Limit | 1GB max | Handle large transaction datasets | | CPU Time Limit | 60 seconds | Allow complex risk calculations | | No Network Access | Enforced | Prevent data exfiltration | | No File System Write | Enforced | Analysis-only mode | | Sandboxed Data Access | Enforced | No direct database queries |
Input Validation
All inputs are validated using Zod schemas:
- Stock symbols:
/^[A-Z0-9.]{1,10}$/ - Position quantities: between -1 billion and 1 billion
- Transaction amounts: between -1 trillion and 1 trillion
- Batch limits: maximum 100,000 transactions per request
- Timestamps: ISO 8601 format, within reasonable date range
- Entity identifiers: Alphanumeric with limited special characters
Rate Limiting
const rateLimits = {
'portfolio-risk': { requestsPerMinute: 60, maxConcurrent: 5 },
'anomaly-detect': { requestsPerMinute: 100, maxConcurrent: 10 },
'stress-test': { requestsPerMinute: 10, maxConcurrent: 2 },
'market-regime': { requestsPerMinute: 120, maxConcurrent: 10 },
'compliance-check': { requestsPerMinute: 30, maxConcurrent: 3 }
};Performance
| Metric | Target | |--------|--------| | Portfolio VaR calculation | <100ms for 10K positions | | Transaction anomaly scoring | <5ms per transaction | | Market regime classification | <50ms for 1-year history | | Compliance check | <1s for full entity scan |
Dependencies
micro-hnsw-wasm: Fast similarity search for historical pattern matchingruvector-sparse-inference-wasm: Efficient processing of sparse financial featuresruvector-gnn-wasm: Transaction network analysis for fraud detectionruvector-economy-wasm: Token economics and market microstructure modelingruvector-learning-wasm: Reinforcement learning for adaptive risk thresholds
Related Plugins
| Plugin | Description | Use Case | |--------|-------------|----------| | @claude-flow/plugin-legal-contracts | Contract analysis | Financial agreements, derivatives documentation | | @claude-flow/plugin-healthcare-clinical | Clinical decision support | Healthcare portfolio analysis | | @claude-flow/plugin-perf-optimizer | Performance optimization | High-frequency trading latency optimization |
License
MIT License
Copyright (c) 2026 Claude Flow
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
