Beta warning

⚠️ Beta / Active Development - Not for Production Use

This product is in Staging and is NOT intended for production deployments.

  • Subject to breaking changes without notice
  • Not covered by service level agreements
  • May contain bugs, errors, or security vulnerabilities
  • Features may be modified or removed
  • Provided "AS IS" without warranties

For production use, select Generally Available (GA) products only.

All Solutions

SIGQ

Staging

Quantum-Enhanced Stock Market Prediction System

SIGQ is a hybrid quantum-classical stock prediction platform combining Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN) with classical ML models (LSTM, Transformer, Bayesian Neural Networks) and 97 technical indicators across price, volume, momentum, volatility, pattern, and market microstructure features. Every prediction includes comprehensive explainable AI reasoning — not just a signal, but why. The system validates prediction quality and rejects outputs below 50% confidence threshold. Features PQC-secured authentication using CRYSTALS-Dilithium (ML-DSA, NIST FIPS 204) signed JWT tokens, CRYSTALS-Kyber (ML-KEM) key exchange, and AES-256-GCM symmetric encryption. Real-time WebSocket streaming delivers BUY/SELL/HOLD signals with entry/exit points, stop-loss, take-profit, and risk metrics (VaR, CVaR, Sharpe, Sortino, max drawdown). Supports IBM Quantum, Google Sycamore, Azure Quantum, and local simulator backends. Multi-source market data from Yahoo Finance, Alpha Vantage, Finnhub, Polygon, Twelve Data, EOD Historical, Marketstack, and FMP.

97

Technical Indicators

4

Quantum Backends

Dilithium3

PQC Auth

Real-time

Signal Latency

4

Model Profiles

Staging

Stage

Stack Capabilities

What SIGQ delivers

Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN) with 4-7 qubit configurations

97 technical indicators: price, volume, momentum, volatility, pattern, and market microstructure features

Explainable AI: every prediction includes comprehensive reasoning (WHY, not just WHAT)

Quality validation: rejects predictions below 50% confidence — no false confidence

PQC-secured authentication: CRYSTALS-Dilithium (ML-DSA FIPS 204) signed JWT tokens on every API call

Real-time WebSocket streaming with BUY/SELL/HOLD signals, entry/exit points, stop-loss, and take-profit

Enterprise risk metrics: VaR (95%), CVaR, Sharpe ratio, Sortino ratio, max drawdown, rolling volatility

Multi-source market data: Yahoo Finance, Alpha Vantage, Finnhub, Polygon, Twelve Data, EOD, FMP

Quantum backends: IBM Quantum (16 qubits), Google Sycamore (53 qubits), Azure Quantum, local simulator

4 model profiles: Classical (15ms), Quantum-Fast (4q/2L), Quantum-AI (5q/3L), Quantum-Max (7q/4L/97 features)

Market regime detection: Bull/Bear/Sideways/Volatile with adaptive strategy switching

Prediction tracking with accuracy verification, adaptive learning loops, and concept drift detection

Interfaces & Modules

Integration surfaces

Prediction API
WebSocket stream
Signal API
Risk API
Auth API

Deployment

Where it runs

SaaS, VPC, or on-premises with quantum backend integration (IBM, Google, Azure, simulator)

Managed SaaS
Private / VPC
Air-Gapped

Total Addressable Market (Yr.2030)

$200B

Third-Party Services & Dependencies

CUI Labs products integrate with and depend on third-party services including blockchain networks, cloud infrastructure providers, cryptographic libraries, identity providers, and certificate authorities.

CUI Labs is not responsible for:

  • Availability, performance, or security of third-party services
  • Changes to third-party APIs, protocols, or standards
  • Third-party service outages, breaches, or failures
  • Costs associated with third-party services
  • Compliance of third-party services with applicable laws

Performance metrics and capabilities may be affected by third-party service limitations. Customers are responsible for evaluating and accepting risks associated with third-party dependencies.

Interested in SIGQ?

Discuss deployment options, technical evaluations, and structured pilots with the CUI Labs engineering team.