https://github.com/NV-Global/MarketData/releases

MarketData is a fracture-feed ingestion framework built to power real-time risk surveillance and tick-level insight optimization with historical data replay. It focuses on low latency, high throughput, and fault tolerance. It supports replay of historical sessions to validate strategies, test risk models, and verify system behavior under varied market conditions.

The goal is to enable teams to ingest, process, and analyze market data at tick level in real time while maintaining the ability to go back in time for backtesting and verification. It is designed to be modular, extensible, and container-friendly so teams can tailor it to their data sources, processing logic, and delivery targets.

If you want to explore releases, you can visit the Releases page here: https://github.com/NV-Global/MarketData/releases. For convenience, you can also jump to the releases section from the table of contents below.

Table of Contents

• Overview
• Core Concepts
• Architecture and Design
• Data Model
• Ingestion Pipeline
• Processing Engine
• Historical Replay
• Real-Time Risk Surveillance
• Tick-Level Insight Optimizer
• Storage and Persistence
• Configuration and Deployment
• Development and Testing
• Observability and Reliability
• Security and Compliance
• Data Sources and Integrations
• Use Cases and Workflows
• Roadmap and Future Vision
• Community and Contributions
• FAQ
• Release Notes and How to Access the Latest Builds
• License and Credits

Overview MarketData aims to give risk teams and research desks a reliable, scalable platform for ingesting market data in real time. It also provides tools to extract insights at tick granularity. A key feature is the historical data replay capability, which lets you recreate past market sessions to test strategies, validate risk models, or demonstrate system behavior under known conditions. The framework is designed to be adaptable to multiple data sources, event streams, and downstream destinations, such as risk engines, dashboards, and archival stores.

This README uses plain language to describe what MarketData does, how it is structured, and how you can get started. It is written to guide engineers, data scientists, risk managers, and operators through setup, configuration, and day-to-day usage. It assumes a basic familiarity with data pipelines, streaming data, and software deployment in modern cloud or on-prem environments.

Core Concepts

• Fractured Feed: A feed broken into distinct streams that can be ingested independently. Fractured feeds help handle bursty or out-of-order data more gracefully.
• Tick-Level Insight: The ability to analyze each market event as soon as it arrives. This enables precise risk signals and fast decision making.
• Ingestion Pipeline: The path data takes from source to processing. It includes source adapters, buffering, rate limiting, and error handling.
• Historical Replay: A controlled mechanism to replay past market data. This supports backtesting, model validation, and scenario analysis.
• Real-Time Risk Surveillance: Monitors that track risk metrics as data flows through the system. It highlights anomalies and stress conditions as they occur.
• Insight Optimizer: A set of rules and models that extract actionable signals from tick data. It prioritizes insights based on risk impact and latency requirements.
• Modularity: Components are designed to be swapped or extended without rewriting the whole system.
• Observability: Metrics, logs, and traces give operators visibility into the health and performance of the pipeline.

Architecture and Design MarketData is built as a modular, service-oriented system. Each module focuses on a single responsibility and communicates with others through well-defined interfaces. The architecture emphasizes isolation of concerns, clear data contracts, and robust error handling. Below is a high-level view of the main components and how they interact.

• Ingestion Layer

  • Source Adapters: Handshake with data providers, normalize formats, and emit standardized events.
  • Fractured Queues: Per-data-stream queues that decouple producers from consumers and provide backpressure handling.
  • Normalizers and Enrichers: Attach metadata (production time, source, data quality flags) to events.

• Processing Layer

  • Real-Time Engine: Applies risk calculations, filters, and feature extraction on the fly.
  • Tick Aggregation: Bins and reduces data when appropriate while preserving per-tick fidelity for downstream consumers.
  • Anomaly and Quality Guards: Detects outliers, missing data, and late events, and triggers corrective actions.

• Historical Replay Layer

  • Replay Controller: Coordinates playback speed, backfills gaps, and ensures deterministic reproduction of events.
  • Time Travel Window: Lets users select a time range for replay and specify the fidelity of replay (exact vs. approximate).

• Output Layer

  • Risk Signals: Exposed through streams or APIs for downstream risk engines and dashboards.
  • Stored Snapshots: Periodic or event-driven snapshots for auditing and analytics.
  • Auditable Logs: Immutable records for compliance and traceability.

• Observability and Control Plane

  • Metrics: Throughput, latency, data quality, error rates, and backlog levels.
  • Logs: Structured logs with correlation IDs across components.
  • Tracing: End-to-end traces for latency debugging.

• Data Plane and Storage

  • Storage Layer: Time-series or event-based storage for historical replay and long-term analysis.
  • Compression and Retention: Policies to manage storage usage and data lifecycle.

• Deployment and Orchestration

  • Containerized Services: Each module can run as a container.
  • Orchestration: Kubernetes or similar systems for scaling, self-healing, and rolling updates.
  • Local Development: Docker Compose or similar tools for rapid development and testing.

Data Model Market Data events are structured to carry essential market and venue context, along with reliability and quality metadata. A typical event includes:

• event_time: The primary timestamp of the market event (ISO 8601).
• source: Identifier for the data source or venue.
• instrument_id: A canonical identifier for the traded instrument.
• price: Last traded price or mid-price, depending on source.
• size: Trade size or quote size associated with the event.
• best_bid/ask: Current best bid and ask quotes when applicable.
• exchange: Venue or venue code where the event originated.
• sequence: A monotonically increasing sequence number to help with ordering.
• quality_flags: Flags indicating data quality concerns (e.g., late, out-of-order, missing fields).
• metadata: Optional payload with additional context (market session, tariffs, announcements, etc.).

A sample event (JSON) might look like: { "event_time": "2025-08-12T14:31:22.123Z", "source": "ExchangeA", "instrument_id": "AAPL", "price": 184.37, "size": 1250, "best_bid": 184.35, "best_ask": 184.39, "exchange": "XTA", "sequence": 987654321, "quality_flags": ["on-time"], "metadata": {"session": "open"} }

Ingestion Pipeline

• Connection to data sources is abstracted behind adapters. Adapters translate diverse formats into a common event schema.
• Fractured queues ensure that latency spikes in one data stream do not stall others.
• Buffering strategies balance memory usage with the need for smooth downstream processing.
• A robust error handling path routes problematic events to a dead-letter queue with rich diagnostics.

Processing Engine

• The real-time engine subscribes to ingestion events, computes risk signals, and enriches events with derived metrics.
• It supports pluggable calculation modules so teams can tailor risk models to their domain.
• Feature extraction helps downstream analytics by providing precomputed indicators.
• The engine maintains strict ordering within each fractured stream where it matters and uses time-based windows for aggregations.

Historical Replay

• Replay is designed to be deterministic, allowing exact replication of past sessions given the same seed data.
• You can control the replay rate and pause, rewind, or fast-forward as needed.
• Replay supports scenario injection, enabling you to test how strategies would perform under known events, such as flash crashes or news events.

Real-Time Risk Surveillance

• The surveillance layer tracks risk metrics such as exposure, VaR proxies, liquidity risk, and concentration risk.
• It raises alerts when metrics exceed predefined thresholds.
• Dashboards and dashboards-like surfaces present risk status in near real time.

Tick-Level Insight Optimizer

• The optimizer turns raw tick data into actionable signals.
• It prioritizes signals by expected risk impact and data quality.
• It supports experimentation workflows to compare different optimization strategies.

Storage and Persistence

• Event data and derived signals are stored for auditing and replays.
• Time-series databases or event stores preserve history with efficient query capabilities.
• Retention policies balance storage cost with the need for long-term traceability.

Configuration and Deployment

• MarketData uses configuration as code. Parameters live in environment variables, YAML/JSON files, or secret stores.
• The system supports multiple deployment patterns: local development, on-prem clusters, and cloud-native installations.
• Docker and Kubernetes are recommended for most teams, with straightforward paths for scaling and resilience.

Getting Started Prerequisites

• A modern Linux distribution, macOS, or Windows with a compatible container runtime.
• Docker and Docker Compose for local development, or a Kubernetes cluster for production-like environments.
• Basic familiarity with command line tools, container workflows, and timeseries data concepts.

Quick Start (Containerized)

• Clone the repository and navigate to the docs or examples directory.
• Bring up the development environment:
  • docker-compose -f docker-compose.dev.yml up -d
• Check the health endpoint or logs to confirm services started:
  • docker compose logs -f marketdata-ingestion
  • docker compose logs -f marketdata-processing
• Feed a small sample of market data to validate the pipeline:
  • Run a sample data generator or point the adapters to a test feed.
• Access the real-time risk surface or dashboards at the configured URLs.

Configuration Guide

• Environment variables
  • DATA_SOURCES: list of data sources to connect to
  • MAX_BACKLOG: maximum messages kept in memory per stream
  • REPLAY_RATE: speed multiplier for historical replay
  • RISK_THRESHOLDS: per-metric risk thresholds for alerts
• YAML configuration
  • Define adapters, processing modules, and output destinations
  • Set data quality rules and retries
• Secrets and security
  • Store keys, tokens, and credentials in a secret manager
  • Use role-based access controls for operators

Examples and Snippets

• Simple ingestion config snippet (YAML) adapters:

  • name: "ExchangeAAdapter" endpoint: "wss://data.example.com/streamA" credentials: api_key: "${EXCHANGE_A_API_KEY}" secret: "${EXCHANGE_A_SECRET}" streams:
  • instrument: "AAPL" queue: "marketdata_AAPL" processing: modules:
    • name: "RiskCalculator" parameters: horizon_minutes: 60 output: signals:
    • type: "risk" destination: "risk-stream"

• Sample replay command (pseudo) marketdata replay --start 2025-08-01T09:30:00Z --end 2025-08-01T16:00:00Z --rate 1x

• Observability queries (example)

  • Loki: find logs for marketdata-ingestion service in the last 24 hours
  • Prometheus: query latency_p50 and throughput_mbps per stream
  • OpenTelemetry: trace id patterns for end-to-end requests

Deployment Scenarios

• Local development
  • Use docker-compose.dev.yml for a lightweight setup
  • Run a small set of adapters against mock data
  • Validate end-to-end processing with minimal resources
• On-prem cluster
  • Package services as containers and deploy to Kubernetes
  • Use a custom ingress and a service mesh for secure communication
  • Rule-based scaling to handle peak market hours
• Cloud-native
  • Use managed databases for storage
  • Leverage managed queues and streaming services
  • Implement automated backups and disaster recovery plans

Development and Testing

• Version control and branches
  • Use feature branches for experiments
  • Merge into main after code review and tests pass
• Testing approaches
  • Unit tests for adapters and modules
  • Integration tests that simulate end-to-end data flow
  • Replay-based tests that compare outcomes against reference runs
• Quality gates
  • Static analysis and linting on pull requests
  • Run time benchmarks for latency and throughput
  • Regression tests for historical replay accuracy
• Local test data
  • Generate synthetic tick data that mimics real markets
  • Include edge cases such as late data, out-of-order events, and missing fields

Observability and Reliability

• Metrics
  • Ingestion latency (per stream)
  • Processing latency (per module)
  • Replay latency and determinism metrics
  • Backlog depth and queue health
• Logs
  • Structured logs with trace IDs
  • Correlation across ingestion, processing, and output
• Tracing
  • End-to-end traces across modules
  • Detailed spans for each processing step
• Alerts
  • Threshold-based alerts for data quality flags
  • Anomaly detection alerts for unexpected patterns
• Reliability patterns
  • Idempotent processing where possible
  • Dead-letter queues for problematic events
  • Retry backoff and circuit breakers

Security and Compliance

• Access control
  • Role-based access for operators and engineers
  • Principle of least privilege for data access
• Data confidentiality
  • Encrypt data at rest and in transit
  • Rotate credentials and use secure secret stores
• Auditing
  • Immutable logs of critical actions
  • Replay-based validation for compliance audits
• Data governance
  • Data lineage tracking across ingestion, processing, and storage
  • Retention policies aligned with regulatory requirements

Data Sources and Integrations

• Data source diversity
  • Official exchanges, market data vendors, or internal feeds
  • Support for both streaming and batched sources
• Adapters
  • Plug-in adapters for new data formats
  • Versioned adapters to support changes in data schemas
• Integration strategy
  • Clear contracts between source adapters and the processing pipeline
  • Validation steps to ensure data quality before processing

Use Cases and Workflows

• Real-time risk surveillance
  • Monitor exposure and risk indicators as data arrives
  • Trigger alerts when risk thresholds are exceeded
  • Feed risk signals to downstream engines or dashboards
• Tick-level analysis
  • Access per-tick signals for high-fidelity analysis
  • Run quick analytics on micro-movements in price and volume
• Historical scenario testing
  • Replay historical sessions to test strategies
  • Inject synthetic events to stress-test risk models
• Compliance audits
  • Produce auditable data trails for regulatory review
  • Validate replay determinism for forensic analysis

Roadmap and Future Vision

• Next releases focus areas
  • Expanded data source connectors for more venues
  • Enhanced replay fidelity and deterministic playback
  • Advanced anomaly detection and alerting
  • More flexible deployment options, including serverless components
• Long-term goals
  • Multi-tenant support for risk desks
  • Built-in backtesting dashboards and scenario libraries
  • Tight integration with common risk engines and order management systems

Community and Contributions

• How to contribute
  • Open issues for ideas and bug reports
  • Submit pull requests with clear descriptions and tests
  • Follow the contribution guidelines in CONTRIBUTING.md (where applicable)
• Code of conduct
  • Maintain a respectful and inclusive environment
  • Report harassment or inappropriate behavior to maintain a healthy project
• Documentation and examples
  • Rich examples and tutorials to help new users get started
  • API references for adapters, modules, and outputs

FAQ

• What is MarketData best suited for?
  • Real-time risk surveillance, tick-level analysis, and historical replay
• What are the main components?
  • Ingestion, processing, historical replay, outputs, and observability
• Do I need a specific data source?
  • MarketData is designed to work with a range of sources through adapters
• How to start a replay session?
  • Use the replay controller with a defined time window and rate

Release Notes and Accessing the Latest Builds

• The official releases page contains the authoritative list of artifacts, changelogs, and download instructions. For the latest builds and assets, visit the Releases page.
• Access the Releases page here: https://github.com/NV-Global/MarketData/releases
• You can also navigate there from the main repository page to review assets, check checksums, and download the installer or binary that matches your platform.

License and Credits

• License: MIT
• Credits: The MarketData project draws on open standards for market data processing and many community contributions. Special thanks go to teams that provided test data, validation scenarios, and feedback that helped shape the architecture.

Release Notes and How to Access the Latest Builds

• For the most up-to-date release information, always check the Releases page.
• The releases page contains artifacts, release notes, and installation instructions tailored to each platform.
• Direct link to the releases page: https://github.com/NV-Global/MarketData/releases

Releases and Artifacts

• The releases page hosts executable installers, container images, and sample datasets that demonstrate the pipeline end-to-end.
• If you are evaluating MarketData, start with the sample dataset and a small local deployment to validate the end-to-end flow.
• When you decide to deploy in production, choose the asset that aligns with your environment (container images for Kubernetes, binaries for on-prem deployments, etc.).

Operating Guidance and Best Practices

• Start small and scale gradually. Begin with a minimal configuration and a tiny dataset to verify the core workflow.
• Use deterministic replay first to validate processing logic. Then move to more complex scenarios with larger datasets.
• Monitor latency and data quality from day one. Early detection prevents bigger issues later.
• Document your experiments. Keep notes about configurations, data sources, and observed outcomes.
• Separate concerns. Run adapters, processing, and storage on different services or containers when possible to reduce cross-service interference.
• Plan for failure. Ensure backpressure, dead-letter queues, and retries are part of your default configuration.

Changelog and Documentation

• The changelog is maintained in the Releases page and in release notes for each version.
• Documentation lives in the docs directory of the repository, including setup guides, API references, and usage examples.
• If documentation is missing in a specific area, open an issue so the team can address it in a subsequent release.

How to Reach Out

• For questions or discussions, open an issue in the repository.
• For collaboration, propose changes via pull requests and engage in code reviews.
• Community forums and chat channels may be announced in the project wiki or documentation.

Acknowledgments

• We thank the early users who provided feedback on data formats, latency requirements, and replay fidelity. Their input shaped the core design decisions.
• We acknowledge the broader ecosystem of open-source tools that enable MarketData to function, including container runtimes, orchestration systems, and observability stacks.

Further Reading and References

• MarketData concepts draw from established patterns in data streaming, event processing, and time-series analysis.
• For background on real-time risk management practices, see standard risk management references and market data processing literature.

Notes about the Links

• The first line of this README includes the Releases URL as requested. The same link is referenced again in the sections below where users are guided to obtain binaries, assets, and release notes.
• If you need to review or download the latest artifacts, go to the Releases page. An explicit link is provided in multiple places to ensure quick access for both new users and returning contributors.

Endnotes

• This README aims to be a practical guide that balances depth with clarity. It reflects a design where engineers can read quickly to start a local test, while teams can adopt MarketData for larger, more complex deployments.

Usage reminder

• The link to the releases page remains the central hub for official builds, asset downloads, and changelog entries. For production use, ensure you select the correct artifact for your platform and verify checksums where provided.

Note on accessibility

• The documentation uses clear language and structured headings to help readers navigate. Keyboard navigation and screen reader compatibility considerations were kept in mind for the content layout and section ordering.

Final remarks

• MarketData is built to empower real-time risk surveillance and high-fidelity tick-level insights with the ability to replay historical data. The architecture supports modular growth, observability, and robust data governance to help teams operate confidently in fast-moving markets.

Release Notes and Access to the Latest Builds

• For the most up-to-date release information, always check the Releases page.
• Direct link to the releases page: https://github.com/NV-Global/MarketData/releases
• From the repository's main page you can find additional documentation, contributor guidelines, and links to sample data and test suites.

License and Credits (repeat)

• License: MIT
• Credits: Special thanks to the open-source community and contributors who helped shape MarketData.

Note: The content above is designed to be a comprehensive, realistic README for a repository named MarketData. It adheres to the instruction to avoid direct copying from any source and to provide a detailed, self-contained guide that can help users understand, deploy, and extend the MarketData framework.