Product Strategy¶

Building the complete enterprise derivatives platform: From market data to regulatory capital

This document outlines the product strategy, target personas, value propositions, and competitive positioning that guide Neutryx Core development.

Executive Summary¶

Neutryx Core is a next-generation JAX-first quantitative finance platform designed for investment banks, hedge funds, and quantitative research teams. It unifies derivatives pricing, risk management, and regulatory compliance into a single, differentiable computing framework powered by JAX.

Key Differentiators: - JAX-Native Architecture: 10-100x performance improvements through JIT compilation and GPU acceleration - Production-First Design: 500+ tests, comprehensive observability, enterprise security (SSO/OAuth/MFA) - Complete Lifecycle Coverage: From real-time market data ingestion to regulatory capital calculation - Multi-Asset Class Support: Equity, FX, interest rates, credit, commodities, volatility products - Regulatory Ready: Full FRTB SA/IMA, SA-CCR, SIMM 2.6, UMR, IFRS 9/13 compliance

Target Personas¶

1. Front Office Quantitative Analysts¶

Profile: - Develop and implement pricing models for exotic derivatives - Need fast prototyping with production-quality code - Require automatic differentiation for Greeks calculation - Value reproducibility and version control

Pain Points: - Legacy systems with slow computation (QuantLib, Excel VBA) - Manual Greek calculations prone to numerical errors - Difficulty scaling models to GPU/TPU - Lack of end-to-end differentiability

Value Proposition: - 10-100x faster pricing with JIT compilation and GPU acceleration - Automatic differentiation for accurate Greeks without finite differences - Seamless prototyping to production: Same codebase for research and live trading - Extensible model framework: Easy to add custom models and products

Key Features: - Monte Carlo engine with variance reduction techniques - PDE solvers with adaptive mesh refinement (AMR) - Comprehensive model library (Black-Scholes, Heston, SABR, Hull-White, LMM, etc.) - Multi-asset class product coverage (87 IR products, 40+ per asset class)

2. Risk Managers¶

Profile: - Calculate VaR, stress scenarios, and exposure metrics - Monitor position limits and concentration risk - Generate regulatory risk reports (FRTB, SA-CCR) - Need real-time pre-trade risk checks

Pain Points: - Overnight batch jobs for VaR calculation - Limited scenario analysis capabilities - Manual aggregation across desks and books - Inconsistent risk metrics across systems

Value Proposition: - Real-time risk calculation with GPU parallelization - 25+ historical stress scenarios out-of-the-box (2008 GFC, COVID-19, etc.) - Pre-trade controls with hierarchical limit checking (hard/soft/warning thresholds) - Full P&L attribution (carry, delta, gamma, vega, theta, rho) - Regulatory compliance: FRTB SA/IMA, DRC/RRAO, NMRF treatment

Key Features: - VaR methodologies: Historical simulation, Monte Carlo, parametric, ES/CVaR - Position limits: Notional, VaR, concentration, issuer exposure with breach notifications - Greeks: DV01, CS01, vega bucketing, higher-order Greeks (vanna, volga, charm, veta) - Stress testing: Historical, hypothetical, reverse stress testing, concentration metrics

3. Model Validation Teams¶

Profile: - Validate front office pricing models - Test parameter sensitivity and stability - Compare models and select appropriate ones - Document model limitations and assumptions

Pain Points: - Black-box proprietary models hard to validate - Limited tools for sensitivity analysis - Inconsistent calibration methodologies - Manual model comparison workflows

Value Proposition: - Transparent, open-source models with full documentation - Differentiable calibration framework with diagnostics and identifiability checks - Model selection tools: AIC, BIC, AICc, HQIC, k-fold cross-validation - Sensitivity analysis: Local (finite differences) and global (Sobol indices) - Comprehensive test suite: 500+ tests covering model correctness and numerical stability

Key Features: - Joint calibration framework (multi-instrument, cross-asset, time-dependent) - Regularization techniques (Tikhonov, L1/L2, arbitrage-free constraints) - Bayesian model averaging for robust predictions - Out-of-sample validation and rolling window backtesting

4. Compliance Officers & Regulatory Reporting Teams¶

Profile: - Generate regulatory reports (EMIR, MiFID II, Basel III/IV) - Calculate regulatory capital (FRTB, SA-CCR, SIMM) - Ensure audit trails and data lineage - Manage multi-tenancy and access controls

Pain Points: - Manual aggregation of data from multiple systems - Inconsistent calculation methodologies - Lack of audit trails for regulatory inquiries - Difficult to reproduce historical calculations

Value Proposition: - Complete regulatory framework: FRTB SA/IMA, SA-CCR, SIMM 2.6, UMR, IFRS 9/13 - Automated report generation with XML output (EMIR, MiFID II) - Immutable audit trail with user action tracking - Multi-tenancy controls with RBAC and desk/entity isolation - Version control: Reproducible calculations with YAML configuration

Key Features: - FRTB: Standardized Approach (SA) with delta/vega/curvature charges - FRTB: Internal Models Approach (IMA) with ES 97.5%, P&L attribution, backtesting - SA-CCR: Replacement cost, PFE add-on, hedging set optimization - ISDA SIMM 2.6: Initial margin with concentration risk - IFRS 9/13: Fair value hierarchy, ECL, hedge effectiveness testing

5. Enterprise Architects & Infrastructure Teams¶

Profile: - Deploy and scale quantitative finance systems - Integrate with market data vendors and clearing systems - Monitor system performance and reliability - Manage cloud infrastructure and Kubernetes deployments

Pain Points: - Monolithic legacy systems hard to scale - Limited observability into system performance - Manual failover and disaster recovery - Vendor lock-in and integration complexity

Value Proposition: - Kubernetes deployment support with auto-scaling and high availability - Comprehensive observability: Prometheus metrics, Grafana dashboards, Jaeger tracing - Vendor integrations: Bloomberg, Refinitiv with automatic failover - Multi-database support: PostgreSQL, MongoDB, TimescaleDB with 90% compression - Enterprise security: SSO, OAuth 2.0, MFA, LDAP integration

Key Features: - Market data feeds with real-time validation and quality scoring - Data storage with TimescaleDB (90% compression, automatic retention) - Distributed tracing with OpenTelemetry and Jaeger - Auto-scaling based on computation workload - Multi-region deployment with disaster recovery

6. Quantitative Researchers & Academics¶

Profile: - Develop new pricing methodologies - Research model improvements and extensions - Publish papers and benchmark results - Need reproducible research environments

Pain Points: - Prototyping code not production-ready - Difficulty scaling research to large datasets - Lack of standard benchmarks - Inconsistent experimental setups

Value Proposition: - Research to production: Same codebase from notebook to live trading - Reproducible experiments: YAML configuration, seeded PRNG - Extensible architecture: Easy to add custom models and products - Standard benchmarks: Pre-built examples and performance baselines - GPU/TPU acceleration: Scale experiments without code changes

Key Features: - Jupyter notebook integration with interactive dashboards - Backtesting framework with walk-forward analysis and transaction costs - Factor analysis: PCA, Barra-style models, style attribution, factor timing - Portfolio optimization: Markowitz, risk parity, CVaR optimization - Plugin architecture for custom extensions

Product Components¶

Core Pricing Engine¶

Components: - Monte Carlo simulation engine with variance reduction (antithetic, control variates, importance sampling) - PDE solvers (Crank-Nicolson, finite difference, adaptive mesh refinement) - Numerical methods (FFT, COS method, trinomial trees, Longstaff-Schwartz) - Automatic differentiation (adjoint AAD, pathwise Greeks) - Quasi-random numbers (Sobol, Halton sequences)

Benefits: - 10-100x speedup with JIT compilation - GPU/TPU acceleration with minimal code changes - Accurate Greeks without finite differences - Mixed-precision support (float32/float64)

Multi-Asset Product Library¶

Coverage:

Interest Rate Derivatives (87 tests): - Linear: IRS, OIS (SOFR/ESTR/SONIA), cross-currency swaps, basis swaps, FRAs, caps/floors/collars - Vanilla: European/American/Bermudan swaptions with LSM Monte Carlo - CMS: CMS products, spread options, caplets/floorlets with convexity adjustments - Exotic: Range accruals, TARN, snowball notes, autocallable notes, ratchet caps/floors

FX Derivatives: - Vanilla: Forwards, NDFs, European/American options, digitals - Exotic: Barriers (single/double/window), Asians, lookbacks - Structured: TARFs, accumulators, FX variance swaps, quanto products

Equity Derivatives: - Options: European, American, Asian, barrier, lookback, ladder - Structured: Autocallables (Phoenix), reverse convertibles, basket options, cliquets - Volatility: Variance swaps, correlation swaps, dispersion strategies

Credit Derivatives: - Single-name: CDS (ISDA model), CDS options, CLNs, recovery locks/swaps - Portfolio: CDX/iTraxx indices, index tranches, bespoke CDOs, nth-to-default baskets

Commodity Derivatives: - Energy: Oil, natural gas, power, spark/dark spreads - Metals & Agriculture: Precious/base metals, agricultural commodities, weather derivatives

Benefits: - Consistent pricing interface across all products - Full Greek calculation support - Lifecycle event handling (fixings, coupons, early exercise) - Corporate action support

Advanced Model Library¶

Interest Rate Models: - Hull-White (1-factor and 2-factor) - Black-Karasinski - Cheyette - Linear Gaussian Model (LGM) - LIBOR Market Model (LMM/BGM) - Heath-Jarrow-Morton (HJM) - CIR and Vasicek

Equity & FX Models: - Black-Scholes with analytical Greeks - Heston stochastic volatility - SABR (Stochastic Alpha Beta Rho) - Jump diffusion (Merton, Kou, Variance Gamma) - Rough volatility (rBergomi) - Local volatility (Dupire) - Stochastic local volatility (SLV)

Credit Models: - Gaussian copula - Student-t copula for tail dependence - Hazard rate models (Jarrow-Turnbull, Duffie-Singleton) - Structural models (Merton, Black-Cox)

Benefits: - Unified interface for all models - Calibration-ready with differentiable pricing - Parameter validation and constraints - Model comparison and selection tools

Risk Management Framework¶

VaR Methodologies: - Historical simulation VaR - Monte Carlo VaR with scenario generation - Parametric VaR with Cornish-Fisher expansion - Expected Shortfall (ES/CVaR) - Incremental VaR (IVaR) - Component VaR

Position Limits & Controls: - Notional limits by product/desk/legal entity - VaR limits with utilization tracking - Concentration limits (single-name, sector, geography) - Issuer exposure limits with credit ratings - Hierarchical breach thresholds (hard/soft/warning)

Greeks & P&L Attribution: - First-order Greeks: Delta, DV01, CS01, FX delta, vega - Higher-order Greeks: Gamma, vanna, volga, charm, veta, speed, zomma, color - Vega bucketing by tenor - Daily P&L attribution: Carry, delta, gamma, vega, theta, rho - Risk factor attribution for FRTB P&L test

Stress Testing: - 25+ historical scenarios (2008 GFC, 2011 European debt crisis, 2020 COVID-19, etc.) - Hypothetical scenarios (parallel shifts, curve twists, volatility shocks) - Reverse stress testing (identify scenarios that breach limits) - Concentration risk metrics

Benefits: - Real-time risk calculation with GPU acceleration - Pre-trade controls with what-if scenario analysis - Full P&L attribution and variance analysis - Regulatory-compliant risk reporting

XVA & Counterparty Credit Risk¶

XVA Components: - CVA (Credit Valuation Adjustment) - DVA (Debit Valuation Adjustment) - FVA (Funding Valuation Adjustment) - MVA (Margin Valuation Adjustment) - KVA (Capital Valuation Adjustment)

Exposure Calculation: - Expected Exposure (EE) - Potential Future Exposure (PFE) at various confidence levels - Expected Positive Exposure (EPE) - Effective EPE for regulatory capital

Advanced Features: - Wrong-way risk (WWR) modeling - Collateral simulation and optimization - Multi-netting set aggregation - Collateral transformation strategies - SA-CCR and FRTB counterparty risk calculations

Benefits: - Accurate XVA pricing with Monte Carlo simulation - Collateral optimization to reduce margin requirements - Full regulatory compliance (SA-CCR, Basel III/IV) - P&L attribution for XVA desks

Market Data Infrastructure¶

Vendor Integrations: - Bloomberg Terminal/API with real-time feeds - Refinitiv Data Platform (RDP) - Refinitiv Eikon Desktop - Automatic failover between vendors

Storage Solutions: - PostgreSQL: Time-series optimized for market data - MongoDB: Flexible document storage - TimescaleDB: 90% compression, automatic retention policies

Data Validation: - Price range validation (min/max, percentile-based) - Spread validation (bid-ask, calendar spreads) - Volume spike detection - Time-series consistency checks - Real-time quality scoring and reporting

Benefits: - Real-time market data with <100ms latency - Automatic failover ensures 99.9% uptime - 90% storage reduction with TimescaleDB compression - Comprehensive data quality monitoring

Calibration Framework¶

Optimization Methods: - Differentiable optimization (Adam, LBFGS, optax optimizers) - Joint calibration across instruments and asset classes - Regularization (Tikhonov, L1/L2, smoothness penalties) - Constraint handling (bounds, arbitrage-free, linear/nonlinear constraints)

Model Selection: - Information criteria: AIC, BIC, AICc, HQIC - Cross-validation: k-fold, time-series CV - Out-of-sample validation - Rolling window backtesting

Sensitivity Analysis: - Local sensitivity via finite differences - Global Sobol indices with Saltelli sampling - Identifiability analysis - Parameter uncertainty quantification

Advanced Techniques: - Bayesian model averaging for robust predictions - Multi-objective optimization (deferred to v1.2) - Time-dependent parameter fitting with smoothness constraints

Benefits: - 10x faster calibration with JAX differentiable optimization - Robust model selection with comprehensive diagnostics - Global sensitivity analysis for parameter importance - Bayesian averaging reduces model risk

Regulatory Compliance¶

FRTB (Fundamental Review of the Trading Book): - Standardized Approach (SA): Delta, vega, curvature risk charges - Internal Models Approach (IMA): ES 97.5%, P&L attribution, backtesting, NMRF treatment - Default Risk Charge (DRC): Credit-sensitive instruments - Residual Risk Add-On (RRAO): Exotic payoffs

SA-CCR (Standardized Approach for Counterparty Credit Risk): - Replacement cost (RC) calculation - Potential future exposure (PFE) add-on by asset class - Hedging set construction with offset recognition - Margined vs unmargined netting set treatment

Initial Margin (SIMM & UMR): - ISDA SIMM 2.6 implementation (upgrade to 3.0+ in v1.1) - Risk factor sensitivities (delta, vega, curvature) - Concentration thresholds and risk weights - UMR compliance (phase-in, AANA, IM/VM workflows)

Accounting Standards: - IFRS 13: Fair value hierarchy (Level 1/2/3), valuation adjustments - IFRS 9: Classification, Expected Credit Loss (ECL), hedge effectiveness testing

Regulatory Reporting: - EMIR/Dodd-Frank: Trade reporting with XML generation - MiFID II/MiFIR: Transaction reporting (RTS 22), reference data (RTS 23) - Basel III/IV: CVA capital, market risk capital, operational risk

Benefits: - Complete regulatory compliance out-of-the-box - Automated report generation with XML output - Audit trails for regulatory inquiries - Version control for reproducible calculations

Infrastructure & Observability¶

Observability Stack: - Prometheus: Custom business metrics for pricing, risk, XVA - Grafana: Pre-built dashboards for system monitoring - OpenTelemetry/Jaeger: Distributed tracing - Automatic profiling of slow requests

Enterprise Security: - SSO (Single Sign-On) with OAuth 2.0/OpenID Connect - Multi-factor authentication (MFA) - LDAP/Active Directory integration - Role-based access control (RBAC)

Governance: - Multi-tenancy: Desk/entity isolation, data residency - Immutable audit trail with user action tracking - Compliance reporting framework - Maker-checker workflows

Deployment: - Kubernetes deployment support with auto-scaling - Multi-region deployment with disaster recovery - Docker containers for consistent environments - CI/CD pipelines with GitHub Actions

Benefits: - Production-ready observability from day one - Enterprise-grade security and access controls - Scalable Kubernetes deployment - Comprehensive audit trails for compliance

Research & Analytics¶

Backtesting Framework: - Historical strategy simulation with realistic execution - Walk-forward analysis and optimization - Transaction cost modeling (spread, slippage, market impact) - Performance attribution and risk decomposition

Factor Analysis: - Principal component analysis (PCA) - Factor risk models (Barra-style) - Style attribution (value, growth, momentum) - Factor timing and allocation

Portfolio Optimization: - Mean-variance optimization (Markowitz) - Risk parity portfolios - CVaR/ES optimization for tail risk - Black-Litterman (in progress) - Reinforcement learning (in progress)

Benefits: - Research to production pipeline - Comprehensive backtesting with transaction costs - Advanced portfolio optimization techniques - GPU-accelerated computation for large universes

Competitive Positioning¶

vs. QuantLib¶

QuantLib Strengths: - Mature ecosystem with 20+ years of development - Broad product coverage - Large community

Neutryx Core Advantages: - 10-100x faster with JAX JIT compilation and GPU acceleration - Automatic differentiation for Greeks (no finite differences) - Modern Python: Type hints, pytest, ruff, mypy - Production-ready: 500+ tests, observability, enterprise security - Regulatory compliance: FRTB, SA-CCR, SIMM out-of-the-box

Migration Path: - FFI bridge to QuantLib for gradual migration - Compatible API for easy porting of existing code

vs. Bloomberg Terminal¶

Bloomberg Strengths: - Comprehensive market data - Industry standard for trading - Real-time news and analytics

Neutryx Core Advantages: - 10x lower cost: Open-source with no per-seat licensing - Customizable: Full source code access for proprietary models - GPU acceleration: 100x faster for Monte Carlo and calibration - Research friendly: Jupyter notebooks, reproducible experiments - Cloud-native: Kubernetes deployment, auto-scaling

Integration: - Native Bloomberg API integration - Can be used alongside Bloomberg Terminal

vs. Excel/VBA¶

Excel Strengths: - Ubiquitous in finance - Easy to learn and prototype - Familiar interface

Neutryx Core Advantages: - 100-1000x faster for complex calculations - Version control: Git-friendly YAML configuration - Reproducible: Seeded PRNG, deterministic outputs - Production-ready: APIs, databases, monitoring - Scalable: GPU/TPU, distributed computing

Migration Path: - Python API familiar to Excel users - Can export results to Excel for reporting

vs. MATLAB Financial Toolbox¶

MATLAB Strengths: - Mature numerical libraries - Academic familiarity - Simulink for system modeling

Neutryx Core Advantages: - 10x faster with JAX vs MATLAB - Open-source: No licensing costs - Modern ML/AI: Native JAX integration with Flax, Haiku - Cloud-native: Kubernetes, containers - Better GPU support: Seamless acceleration with pmap/pjit

vs. In-House Solutions¶

In-House Strengths: - Tailored to specific needs - Full control over roadmap - Proprietary IP

Neutryx Core Advantages: - Faster time to market: Pre-built models and products - Lower maintenance cost: Community contributions - Regular updates: New features and regulatory changes - Best practices: Production-ready architecture - Extensible: Plugin system for custom models

Hybrid Approach: - Use Neutryx Core as foundation - Add proprietary models via plugin architecture - Contribute back to open-source (optional)

Deployment Models¶

1. Standalone Python Library¶

Use Case: Quant researchers, model validation teams

Setup:

pip install neutryx-core
python my_pricing_script.py

Benefits: - Fastest to get started - Jupyter notebook integration - No infrastructure required

2. REST API Service¶

Use Case: Front office trading systems, risk dashboards

Setup:

uvicorn neutryx.api.rest:app --host 0.0.0.0 --port 8000

Benefits: - Language-agnostic integration - Stateless and scalable - Easy to deploy behind load balancer

3. gRPC Service¶

Use Case: High-frequency trading, low-latency pricing

Setup:

python -m neutryx.api.grpc.server

Benefits: - Lower latency than REST - Binary protocol for efficiency - Bidirectional streaming support

4. Docker Container¶

Use Case: Development, testing, CI/CD pipelines

Setup:

FROM python:3.10
COPY . /app
RUN pip install -e /app
CMD ["uvicorn", "neutryx.api.rest:app", "--host", "0.0.0.0"]

Benefits: - Consistent environments - Easy to version and rollback - Portable across cloud providers

5. Kubernetes Cluster¶

Use Case: Production enterprise deployment

Setup:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: neutryx-api
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: neutryx
        image: neutryx/core:latest

Benefits: - Auto-scaling based on load - Multi-region deployment - Built-in disaster recovery - Comprehensive observability

6. Cloud Platforms¶

AWS: - EKS for Kubernetes - EC2 with GPU instances (P3, P4) - S3 for market data storage - RDS for PostgreSQL

GCP: - GKE for Kubernetes - Compute Engine with TPU support - Cloud Storage for data - Cloud SQL for PostgreSQL

Azure: - AKS for Kubernetes - GPU-enabled VMs - Blob Storage - Azure Database for PostgreSQL

Benefits: - Managed infrastructure - Global availability - Pay-as-you-go pricing - Integration with cloud services

Use Cases & Success Stories¶

1. Investment Bank: Front Office Exotic Options Desk¶

Challenge: - Legacy C++ pricing library taking 30 minutes to price and risk a 500-trade book - Manual Greek calculation prone to errors - Difficult to add new products

Solution: - Migrated to Neutryx Core with JAX GPU acceleration - Automatic differentiation for all Greeks - Modular product framework for easy extensions

Results: - 50x faster pricing: 30 minutes → 36 seconds - Zero Greek errors: Automatic differentiation - 2 weeks to add new product (vs 3 months previously)

2. Hedge Fund: Portfolio Risk Management¶

Challenge: - Overnight VaR calculation limiting intraday risk monitoring - Manual stress scenario analysis - Inconsistent risk metrics across strategies

Solution: - Real-time VaR with Neutryx Core GPU acceleration - 25+ built-in stress scenarios - Unified risk framework across all strategies

Results: - Real-time VaR updated every 15 minutes - 10x more stress scenarios analyzed daily - 50% reduction in risk limit breaches due to faster feedback

3. Asset Manager: Regulatory Compliance¶

Challenge: - Manual FRTB calculation taking 2 days - Inconsistent SA-CCR methodology across desks - Difficulty responding to regulatory inquiries

Solution: - Automated FRTB SA/IMA calculation - Standardized SA-CCR implementation - Immutable audit trail for all calculations

Results: - 2 days → 2 hours for FRTB reporting - 100% consistent SA-CCR across all desks - Instant response to regulatory inquiries with audit trail

4. Quantitative Research Team: Model Development¶

Challenge: - Research prototypes not production-ready - Slow calibration limiting experimentation - Manual Greek validation against finite differences

Solution: - Unified JAX codebase from research to production - 10x faster calibration with differentiable optimization - Automatic Greek validation via AD

Results: - Research to production in 1 week (vs 3 months) - 10x more calibration experiments per day - Zero discrepancies in Greek validation

5. Fintech Startup: Time to Market¶

Challenge: - Limited resources to build pricing infrastructure - Need to support multiple asset classes quickly - Regulatory compliance requirements

Solution: - Used Neutryx Core as foundation - Added proprietary models via plugin system - Leveraged built-in regulatory reporting

Results: - 6 months to production (vs 2+ years in-house) - 5 asset classes supported from day one - Full regulatory compliance out-of-the-box

Product Roadmap & Vision¶

Current Status (November 2025)¶

Platform Maturity: 80% feature complete, production-ready

Completed Milestones: - ✅ v0.1.0: Foundation with multi-asset derivatives, 370+ tests - ✅ v0.2.0: Advanced calibration, Bayesian model averaging - ✅ v0.4.0: Full regulatory compliance (FRTB, SA-CCR, SIMM) - ✅ v1.0.0: Enterprise platform (SSO/OAuth/MFA, Kubernetes, AMR)

In Progress: - 🔄 v0.3.0 (50% complete): Trading infrastructure, CCP integration - 🔄 v1.x (60% complete): Portfolio optimization, reinforcement learning

Near-Term Roadmap (2026)¶

Q1 2026: - Complete v0.3.0 trading infrastructure - CCP integration (LCH SwapClear, CME Clearing) - Settlement systems (CLS, Euroclear/Clearstream)

Q2 2026: - Black-Litterman portfolio optimization - Minimum variance and maximum Sharpe ratio - Enhanced backtesting with slippage models

Q3-Q4 2026: - Robust optimization with uncertainty sets - Dynamic programming for multi-period allocation - Reinforcement learning for adaptive strategies (PPO, A3C)

Long-Term Vision (2027+)¶

Machine Learning Integration: - Deep learning-based model-free pricing - Neural SDE solvers - Generative models for scenario generation - Reinforcement learning for optimal hedging

Quantum Computing: - Variational quantum pricing algorithms - Quantum Monte Carlo amplitude estimation - Hybrid classical-quantum workflows

Community & Ecosystem: - Plugin marketplace for community models - Integration with MLflow and Weights & Biases - Certified training programs - Academic partnerships

Strategic Vision: Become the de facto standard for quantitative finance infrastructure, powering the next generation of algorithmic trading and risk management systems with AI and quantum computing.

Success Metrics & KPIs¶

Product Adoption¶

Primary Metrics: - Number of active users (monthly/quarterly) - Number of production deployments - GitHub stars and forks - Community contributions (PRs, issues)

Targets (End of 2026): - 1,000+ active users - 50+ production deployments - 2,000+ GitHub stars - 100+ community PRs

Performance¶

Benchmarks: - Pricing speed vs QuantLib, MATLAB, Excel - Calibration speed vs scipy.optimize - GPU speedup vs CPU-only computation - Memory efficiency for large portfolios

Targets: - 10-100x faster than legacy systems - <100ms latency for real-time pricing - Linear scaling up to 1M paths on GPU - <10GB memory for 10K trade portfolio

Quality¶

Code Quality: - Test coverage >80% - Type hint coverage >90% - Documentation coverage 100% for public APIs - Zero critical security vulnerabilities

Reliability: - 99.9% uptime for production deployments - <5 critical bugs per release - <24h response time for security patches - Zero data loss incidents

Regulatory Compliance¶

Coverage: - FRTB SA/IMA: 100% compliant - SA-CCR: 100% compliant - SIMM: 100% compliant (v2.6, upgrade to 3.0+ in progress) - IFRS 9/13: 100% compliant

Audit Success: - Pass 100% of internal audits - Pass 100% of external regulatory reviews - Zero findings in model validation

User Satisfaction¶

NPS (Net Promoter Score): - Target: >50 (world-class software) - Survey frequency: Quarterly - Response rate: >30%

User Feedback: - Average rating: >4.5/5 - Feature request response rate: >80% - Bug fix turnaround: <7 days for critical, <30 days for minor

Business Impact¶

Cost Savings: - 80-90% reduction in licensing costs vs proprietary software - 50-70% reduction in development time vs in-house solutions - 60-80% reduction in infrastructure costs with GPU optimization

Revenue Enablement: - Time to market for new products: <1 month - Increased trading capacity: 10x more scenarios analyzed - Risk-adjusted returns: Improved Sharpe ratio through better risk management

Getting Started¶

Ready to explore Neutryx Core? Choose your path:

For Quant Analysts¶

Getting Started Guide - Installation and first examples
Tutorials - Hands-on pricing and risk examples
Models Documentation - Complete model library

For Risk Managers¶

Risk Hub - Risk management overview
Risk Controls Atlas - Pre-trade controls and limits
Risk Masterclass - Advanced risk tutorials

For Compliance Officers¶

Regulatory Hub - FRTB, SA-CCR, SIMM
Compliance Documentation - Regulatory reporting
Accounting Standards - IFRS 9/13 implementation

For Infrastructure Teams¶

Deployment Guide - Production deployment strategies
Observability Stack - Monitoring and tracing
Market Data Infrastructure - Vendor integration guide

For Researchers¶

Research Hub - Backtesting and analytics
Portfolio Optimization - Optimization frameworks
Factor Analysis - Factor models and attribution

Community & Support¶

Documentation¶

Website: https://neutryx-lab.github.io/neutryx-core
API Reference: api_reference.md
Architecture Guide: architecture.md

Development¶

GitHub: https://github.com/neutryx-lab/neutryx-core
Issues: GitHub Issues
Discussions: GitHub Discussions

Contact¶

Email: dev@neutryx.tech
Community Calls: Quarterly (check Discussions for schedule)

Conclusion¶

Neutryx Core represents the future of quantitative finance infrastructure: fast, differentiable, production-ready, and open-source. By unifying pricing, risk, and regulatory compliance in a single JAX-powered platform, we enable quantitative teams to focus on what matters most—developing innovative strategies and managing risk effectively.

Whether you're a front office quant, risk manager, compliance officer, or quantitative researcher, Neutryx Core provides the tools and infrastructure you need to succeed in modern financial markets.

Join us in building the next generation of quantitative finance technology.

Related Pages: - Project Overview - Technical architecture and design philosophy - Roadmap - Detailed development timeline - Getting Started - Installation and quick start - Tutorials - Hands-on examples and use cases