Quantum-Enhanced Stock Market Prediction System
SIGQ
Quantum-Enhanced Stock Market Prediction System
- Availability
- In development
Overview
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.
What exists in the repository
TypeScript platform
- PQC verification report: ML-KEM-1024 key exchange implemented
- Audit, integration-test and PQC verification reports in-repo
Repository state as of its latest mounted snapshot. Not an availability claim.
- 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 and 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)
Third-Party Services & Dependencies
HEOSSI solutions integrate with and depend on third-party services including blockchain networks, cloud infrastructure providers, cryptographic libraries, identity providers, and certificate authorities.
HEOSSI 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.
FAQ
Common questions about SIGQ
How does SIGQ use quantum computing for predictions?
SIGQ uses Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN) combined with classical ML models (LSTM, Transformer, Bayesian) for hybrid quantum-classical predictions. It processes 97 technical indicators with PQC-secured authentication (CRYSTALS-Dilithium ML-DSA) and provides explainable AI outputs.
Which quantum hardware backends does SIGQ run on?
SIGQ supports IBM Quantum (16 qubits), Google Sycamore (53 qubits), Azure Quantum, and a local simulator backend. Four model profiles let you trade latency for fidelity: Classical (15ms), Quantum-Fast (4 qubits / 2 layers), Quantum-AI (5 qubits / 3 layers), and Quantum-Max (7 qubits / 4 layers / all 97 features).
How does SIGQ avoid false-confidence predictions?
Every prediction is gated by a 50% confidence threshold - outputs below it are rejected rather than shipped as a weak signal. Each accepted signal carries explainable AI reasoning plus enterprise risk metrics: VaR (95%), CVaR, Sharpe, Sortino, max drawdown, and rolling volatility. Market regime detection (Bull/Bear/Sideways/Volatile) and concept-drift tracking adapt the model when the regime shifts.
Next step
Interested in SIGQ?
Discuss deployment options, technical evaluations, and structured pilots with the HEOSSI engineering team.