Quantitative Finance n8nQuant Workflows

A collection of production-ready n8nQuant workflows specifically designed for quantitative finance applications. Each workflow is fully documented, tested, and includes comprehensive error handling, monitoring, and integration capabilities.

📋 Workflow Catalog

Market Data

#	Workflow	Description	Documentation
1	Real-Time Market Data Pipeline	Fetches real-time market data from multiple sources, validates, and stores in database with error handling and monitoring	[View Documentation](Real-Time Market Data Pipeline/README.md)

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Other

#	Workflow	Description	Documentation
2	Automated Backtesting Engine	Comprehensive backtesting framework for quantitative strategies with performance metrics and walk-forward analysis	[View Documentation](Automated Backtesting Engine/README.md)
3	Best Execution Monitor	Monitors trade execution quality, calculates slippage, and ensures compliance with best execution obligations	[View Documentation](Best Execution Monitor/README.md)
4	Corporate Actions Processor	Automates processing of corporate actions including dividends, stock splits, mergers, and spin-offs with position updates and cash reconciliation	[View Documentation](Corporate Actions Processor/README.md)
5	Corporate Bond Pricing Engine	Real-time corporate bond pricing engine with credit risk adjustment and liquidity factors	[View Documentation](Corporate Bond Pricing Engine/README.md)
6	FX Exposure Hedger	Automates foreign exchange exposure monitoring, hedge ratio calculation, and execution of hedging strategies with real-time FX rate monitoring	[View Documentation](FX Exposure Hedger/README.md)
7	Factor Model Data Aggregator	Aggregates and processes multi-source data for quantitative factor models including fundamental, macroeconomic, and technical factors	[View Documentation](Factor Model Data Aggregator/README.md)
8	Margin Call Processor	Automates margin call processing, liquidation prioritization, and collateral management	[View Documentation](Margin Call Processor/README.md)
9	Performance Attribution System	Calculates performance attribution by factors, sectors, and other dimensions to understand portfolio returns	[View Documentation](Performance Attribution System/README.md)
10	Portfolio Reconciliation System	Automates daily portfolio reconciliation between internal systems and prime brokers, identifies breaks, and initiates resolution workflows	[View Documentation](Portfolio Reconciliation System/README.md)
11	Quant Strategy Deployment Pipeline	Automated CI/CD pipeline for quantitative strategies with testing, validation, and deployment	[View Documentation](Quant Strategy Deployment Pipeline/README.md)
12	Real-time P&L Calculator	Real-time P&L calculation engine that processes market price updates and calculates position valuations	[View Documentation](Real-time P&L Calculator/README.md)
13	Research Data Pipeline	ETL pipeline for financial research data including news, sentiment, and alternative data sources	[View Documentation](Research Data Pipeline/README.md)
14	Stress Testing Framework	Executes comprehensive stress tests using multiple historical and hypothetical scenarios to assess portfolio resilience	[View Documentation](Stress Testing Framework/README.md)
15	Volatility Surface Builder	Builds and maintains real-time volatility surfaces for options pricing, risk management, and trading strategies	[View Documentation](Volatility Surface Builder/README.md)

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Reporting

#	Workflow	Description	Documentation
16	Regulatory Reporting Automation	Automates MiFID II transaction reporting with validation, formatting, submission, and compliance monitoring	[View Documentation](Regulatory Reporting Automation/README.md)

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Risk Management

#	Workflow	Description	Documentation
17	Counterparty Risk Monitor	Monitors counterparty exposures, calculates CVA, and alerts on deteriorating credit conditions	[View Documentation](Counterparty Risk Monitor/README.md)
18	Liquidity Risk Dashboard	Monitors real-time liquidity metrics, bid-ask spreads, and market depth with alerts for deteriorating liquidity conditions	[View Documentation](Liquidity Risk Dashboard/README.md)
19	Portfolio Risk Monitor	Calculates Value at Risk (VaR) for portfolios using historical simulation, monitors breaches, and alerts risk team	[View Documentation](Portfolio Risk Monitor/README.md)

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Trading

#	Workflow	Description	Documentation
20	Algorithmic Trading Signal Generator	Generates real-time trading signals using multiple quantitative strategies and routes them to execution systems with risk checks	[View Documentation](Algorithmic Trading Signal Generator/README.md)

🚀 Quick Start Guide

Prerequisites

• n8n instance (v1.0+ recommended)
• PostgreSQL database
• Access to financial data APIs
• Notification services (Slack/Email) for alerts

Installation & Setup

1. Clone the repository:

   git clone <n8nQuant>
   cd n8nQuant

2. Environment Configuration: Create a .env file in the root directory with your configuration:

   # Database
   DB_HOST=localhost
   DB_PORT=5432
   DB_NAME=quant_finance
   DB_USER=your_username
   DB_PASSWORD=your_password
   
   # APIs
   MARKET_DATA_API_URL=https://api.marketdata.com  
   MARKET_DATA_API_KEY=your_api_key
   SLACK_WEBHOOK_URL=https://hooks.slack.com/your-webhook  
   
   # Email
   SMTP_HOST=smtp.yourcompany.com
   SMTP_PORT=587
   SMTP_USER=your_email@company.com
   SMTP_PASSWORD=your_password

3. Import workflows to n8n:

   • Navigate to your n8n instance
   • Go to Settings > Workflows
   • Click "Import from file" for each workflow JSON
   • Configure credentials for each service

📊 Workflow Categories

📊 Data Management

• Real-Time Market Data Pipeline - ETL pipeline for processing and validating real-time market data feeds
• Research Data Pipeline - Aggregates and processes alternative data sources for quantitative research
• Factor Model Data Aggregator - Collects and processes factor model data for quantitative analysis

⚖️ Risk Management

• Portfolio Risk Monitor - Real-time VaR calculation with breach alerts
• Liquidity Risk Dashboard - Monitors liquidity metrics and generates alerts
• Counterparty Risk Monitor - Tracks and analyzes counterparty exposure
• Margin Call Processor - Automated processing of margin calls and collateral management
• Stress Testing Framework - Performs scenario analysis and stress testing

🔄 Trading & Execution

• Algorithmic Trading Signal Generator - Generates trading signals using quantitative models
• Best Execution Monitor - Trades execution quality and best execution compliance
• FX Exposure Hedger - Manages and hedges foreign exchange exposure

📈 Performance & Analytics

• Real-time P&L Calculator - Calculates and aggregates real-time profit and loss
• Performance Attribution System - Attributes performance to various risk factors
• Portfolio Reconciliation System - Reconciles positions and transactions across systems

🔧 Operations & Compliance

• Corporate Actions Processor - Automates corporate action processing
• Regulatory Reporting Automation - Generates and submits regulatory reports
• Quant Strategy Deployment Pipeline - CI/CD pipeline for quantitative strategies

📊 Market Data & Analytics

• Corporate Bond Pricing Engine - Real-time pricing for corporate bonds
• Volatility Surface Builder - Constructs and analyzes volatility surfaces
• Automated Backtesting Engine - Backtests trading strategies with historical data

📈 Workflow Details

1. Real-Time Market Data Pipeline

File: real-time-market-data-pipeline/real-time-market-data-pipeline.json

Fetches, validates, and stores real-time market data with comprehensive error handling and quality monitoring.

Key Features:

• 5-minute interval data collection
• Data validation and quality checks
• Error logging and alerting
• Performance metrics calculation

Nodes Used:

• Cron Trigger
• HTTP Request (Market Data API)
• If (Data Validation)
• PostgreSQL (Data Storage)
• Code (Metrics Calculation)
• Slack (Alerts)

2. Portfolio Risk Monitor

File: portfolio-risk-monitor/portfolio-risk-monitor.json

Calculates Value at Risk (VaR) using historical simulation and monitors for risk limit breaches.

Key Features:

• Historical simulation VaR (95% confidence)
• Real-time breach detection
• Multi-channel alerts
• Daily risk reporting

Nodes Used:

• Cron Trigger
• PostgreSQL (Portfolio Data)
• Code (VaR Calculation)
• If (Breach Detection)
• Slack/Email (Alerts)

3. Automated Backtesting Engine

File: automated-backtesting-engine/automated-backtesting-engine.json

Comprehensive backtesting framework for quantitative strategies with performance analytics.

Key Features:

• Multiple strategy configurations
• Performance metrics (Sharpe, Max Drawdown)
• Walk-forward analysis
• Detailed reporting

Nodes Used:

• Manual Trigger
• PostgreSQL (Historical Data)
• Code (Backtest Engine)
• Email (Reports)

4. Regulatory Reporting Automation

File: regulatory-reporting-automation/regulatory-reporting-automation.json

Automates MiFID II transaction reporting with validation and compliance monitoring.

Key Features:

• Daily trade aggregation
• XML report generation
• Regulatory API submission
• Compliance status tracking

Nodes Used:

• Cron Trigger
• PostgreSQL (Trade Data)
• Code (XML Generation)
• HTTP Request (Regulatory API)
• Email (Compliance Summary)

5. Real-Time Liquidity Risk Dashboard

File: real-time-liquidity-risk-dashboard/real-time-liquidity-risk-dashboard.json

Monitors real-time liquidity metrics and alerts on deteriorating market conditions.

Key Features:

• 1-minute monitoring intervals
• Bid-ask spread analysis
• Market depth monitoring
• Portfolio-level liquidity scoring

Nodes Used:

• Cron Trigger
• PostgreSQL (Market Data)
• Code (Liquidity Metrics)
• If (Alert Conditions)
• Slack/Email (Alerts)

6. Volatility Surface Construction Engine

File: volatility-surface-construction-engine/volatility-surface-construction-engine.json

Builds and maintains real-time volatility surfaces for options pricing and risk management.

Key Features:

• SVI parameterization
• Arbitrage checks
• Surface quality monitoring
• Term structure analysis

Nodes Used:

• Cron Trigger
• PostgreSQL (Options Data)
• Code (Surface Construction)
• If (Quality Checks)
• Email (Analysis Reports)

7. Corporate Actions Automation Engine

File: corporate-actions-automation-engine/corporate-actions-automation-engine.json

Automates processing of corporate actions including dividends, splits, and mergers.

Key Features:

• Multi-source action monitoring
• Position adjustment automation
• Cash reconciliation
• Operations team alerts

Nodes Used:

• Cron Trigger
• HTTP Request (Corporate Actions API)
• PostgreSQL (Portfolio Data)
• Code (Action Processing)
• Slack/Email (Notifications)

8. Quantitative Factor Model Data Pipeline

File: quantitative-factor-model-data-pipeline/quantitative-factor-model-data-pipeline.json

Aggregates and processes multi-source data for quantitative factor models.

Key Features:

• Fundamental data collection
• Macroeconomic indicator processing
• Factor exposure calculation
• Data quality assessment

Nodes Used:

• Cron Trigger
• HTTP Request (Data APIs)
• PostgreSQL (Storage)
• Code (Factor Calculation)
• Email (Data Updates)

9. Algorithmic Trading Signal Generation Engine

File: algorithmic-trading-signal-generation-engine/algorithmic-trading-signal-generation-engine.json

Generates real-time trading signals using multiple quantitative strategies.

Key Features:

• Multi-strategy signal generation
• Risk parameter validation
• Kafka integration for execution
• Real-time performance analytics

Nodes Used:

• Cron Trigger
• PostgreSQL (Market Data)
• Code (Signal Generation)
• Kafka (Execution)
• Slack/Email (Alerts)

10. Automated Portfolio Reconciliation Engine

File: automated-portfolio-reconciliation-engine/automated-portfolio-reconciliation-engine.json

Automates daily portfolio reconciliation between internal systems and prime brokers.

Key Features:

• Break detection and classification
• Auto-resolution of simple breaks
• Operations team escalation
• Reconciliation reporting

Nodes Used:

• Cron Trigger
• PostgreSQL (Internal Positions)
• HTTP Request (Prime Broker API)
• Code (Reconciliation Logic)
• Email (Summary Reports)

11. FX Exposure Hedging Automation

File: fx-exposure-hedging-automation/fx-exposure-hedging-automation.json

Monitors FX exposures and automatically executes hedging strategies.

Key Features:

• Real-time exposure monitoring
• Optimal hedge ratio calculation
• Automated order execution
• Hedge effectiveness reporting

Nodes Used:

• Cron Trigger
• PostgreSQL (Portfolio Data)
• HTTP Request (FX Rates API)
• Code (Hedge Calculation)
• HTTP Request (Trading API)

12. Comprehensive Stress Testing Engine

File: comprehensive-stress-testing-engine/comprehensive-stress-testing-engine.json

Performs multi-scenario stress testing across portfolios with regulatory compliance.

Key Features:

• Historical and hypothetical scenarios
• Regulatory compliance checks
• Concentration risk analysis
• Executive reporting

Nodes Used:

• Cron Trigger
• PostgreSQL (Portfolio Data)
• Code (Scenario Application)
• If (Regulatory Breaches)
• Email (Comprehensive Reports)

13. Counterparty Credit Risk Monitoring System

File: counterparty-credit-risk-monitoring-system/counterparty-credit-risk-monitoring-system.json

Monitors counterparty credit risk with CVA calculation and collateral management.

Key Features:

• Real-time credit rating monitoring
• CVA and expected loss calculation
• Collateral requirement analysis
• Margin call automation

Nodes Used:

• Cron Trigger
• PostgreSQL (Counterparty Data)
• HTTP Request (Credit API)
• Code (Risk Metrics)
• HTTP Request (Collateral API)

14. Quantitative Research Data Pipeline

File: quantitative-research-data-pipeline/quantitative-research-data-pipeline.json

Aggregates alternative data sources for quantitative research and sentiment analysis.

Key Features:

• News sentiment analysis (NLP)
• Social media sentiment tracking
• Earnings call transcript processing
• Composite sentiment scoring

Nodes Used:

• Cron Trigger
• HTTP Request (Multiple APIs)
• Code (Sentiment Analysis)
• PostgreSQL (Research Data)
• Slack/Email (Insights)

15. Portfolio Performance Attribution Engine

File: portfolio-performance-attribution-engine/portfolio-performance-attribution-engine.json

Performs daily performance attribution using Brinson-Fachler models and factor analysis.

Key Features:

• Brinson-Fachler attribution
• Factor model integration
• Contribution analysis
• Attribution quality assessment

Nodes Used:

• Cron Trigger
• PostgreSQL (Portfolio Data)
• Code (Attribution Calculation)
• Email (Detailed Reports)

16. Margin Call Processing Automation

File: margin-call-processing-automation/margin-call-processing-automation.json

Automates margin call processing, liquidation prioritization, and collateral management.

Key Features:

• Real-time margin requirement monitoring
• Automated liquidation prioritization
• Collateral optimization
• Treasury team notifications

Nodes Used:

• Webhook Trigger (Margin Call Events)
• PostgreSQL (Account Data)
• Code (Liquidation Logic)
• HTTP Request (Trading API)
• Slack/Email (Notifications)

17. Best Execution Monitoring System

File: best-execution-monitoring-system/best-execution-monitoring-system.json

Monitors trade execution quality, calculates slippage, and ensures compliance with best execution obligations.

Key Features:

• Real-time execution quality scoring
• TCA (Transaction Cost Analysis)
• Benchmark comparison
• Compliance reporting

Nodes Used:

• Webhook Trigger (Trade Execution Events)
• PostgreSQL (Market Data)
• Code (Execution Quality Metrics)
• Email (Compliance Reports)

18. Corporate Bond Pricing Engine

File: corporate-bond-pricing-engine/corporate-bond-pricing-engine.json

Real-time corporate bond pricing engine with credit risk adjustment and liquidity factors.

Key Features:

• TRACE data integration
• Credit spread adjustment
• Liquidity factor incorporation
• Greeks calculation

Nodes Used:

• Cron Trigger
• HTTP Request (TRACE API)
• PostgreSQL (Bond Metadata)
• Code (Pricing Engine)
• Email (Pricing Reports)

19. Quant Strategy Deployment Pipeline

File: quant-strategy-deployment-pipeline/quant-strategy-deployment-pipeline.json

Automated CI/CD pipeline for quantitative strategies with testing, validation, and deployment.

Key Features:

• GitHub integration
• Automated testing (unit, integration)
• Backtest validation
• Docker deployment

Nodes Used:

• Webhook Trigger (GitHub Events)
• Execute Command (Tests)
• Execute Command (Docker Build)
• Redis (Deployment Queue)
• Slack (Deployment Notifications)

20. Real-time P&L Calculation Engine

File: real-time-pnl-calculation-engine/real-time-pnl-calculation-engine.json

Real-time P&L calculation engine that processes market price updates and calculates position valuations.

Key Features:

• Real-time P&L updates
• Portfolio aggregation
• Daily P&L calculation
• Large movement alerts

Nodes Used:

• Webhook Trigger (Price Updates)
• PostgreSQL (Position Data)
• Code (P&L Calculation)
• Redis (P&L Updates Queue)
• Slack/Email (Alerts)

🔧 Configuration

Database Setup

Each workflow requires specific database tables. Refer to individual workflow documentation for table schemas and setup scripts.

API Credentials

Configure the following API credentials in n8n:

• Market Data Providers (Bloomberg, Refinitiv, etc.)
• Credit Rating Agencies (Moody's, S&P, Fitch)
• Regulatory Reporting APIs
• Trading and Execution Platforms
• Communication Channels (Slack, Email)

Monitoring and Alerting

Set up monitoring for:

• Workflow execution failures
• Data quality issues
• Risk limit breaches
• Regulatory compliance violations
• System performance metrics

📈 Production Considerations

Error Handling

All workflows include comprehensive error handling with:

• Try-catch patterns
• Error logging to database
• Multi-channel alerting
• Retry mechanisms for transient failures

Performance Optimization

• Database indexing for large datasets
• API rate limiting compliance
• Efficient data processing algorithms
• Memory management in code nodes

Security

• API key management through n8n credentials
• Database connection security
• Data encryption at rest and in transit
• Access control for sensitive workflows

🛠️ Customization

Each workflow can be customized for:

• Different data sources
• Alternative calculation methodologies
• Custom alert thresholds
• Specific regulatory requirements
• Unique business logic

📞 Support

For workflow customization, implementation support, or questions:

1. Check individual workflow documentation
2. Review n8n node configuration guides
3. Consult the quantitative finance team for domain-specific questions

📄 License

This collection of workflows is provided for educational and professional use. Please ensure compliance with data provider licenses and regulatory requirements when deploying in production environments.

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Note: If n8nQuant workflow saves you time and makes your quant development more efficient, please give a star on GitHub. It helps more developers discover this project!

Note: Always test workflows thoroughly in a development environment before deploying to production. Monitor performance and adjust configurations based on your specific requirements and data volumes.