A hybrid optimization engine combining quantum-inspired Ising-model selection with convex portfolio weighting to rebalance large asset universes efficiently on existing GPU infrastructure.
An Elevated Quantum Core Intelligent System
Requests are authorized with API keys and intended for controlled production use.
Each call can be traced through request identifiers and structured operational logs.
Market data and intermediate results can be served through cache-aware workflows.
The service can be evaluated in containerized environments that mirror production setups.
# authenticated optimization request
curl -X POST "https://api.eleqor.io/optimize_matrix" \
-H "X-API-Key: $ELEQOR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"tickers": ["AAPL", "MSFT", "NVDA"],
"mu": [0.0011, 0.0008, 0.0016],
"max_weight": 0.08,
"sector_caps": {"Technology": 0.35}
}'The service is designed for teams that need optimization outputs to move cleanly into portfolio management, execution, and operational monitoring workflows.
Submit a universe with portfolio rules, or use matrix mode with client-supplied expected returns and covariance inputs. Existing positions and policy constraints can be included in the same request.
Authenticated requests are processed by a GPU-accelerated optimization service that enforces hard constraints, records request metadata, and returns outputs intended for operational use rather than one-off analysis.
Results can include target weights, diagnostics, benchmark context, and trade scheduling inputs so downstream systems can evaluate, route, or execute the optimization output with clear auditability.
The current API surface covers single-fund optimization, matrix-mode optimization, multi-fund allocation, trade scheduling, and operational monitoring.
Single-fund optimization with institutional constraints, analytics, and practical outputs for portfolio review and downstream execution.
Matrix-mode optimization using client-supplied expected returns and covariance inputs when teams want tighter control over the optimization inputs.
Allocate across multiple funds under shared capital and policy constraints while keeping fund-level outputs separated for review and execution.
Translate target weights into execution-aware trade schedules with participation guidance and practical sequencing for the trading workflow.
Service health monitoring for readiness checks, uptime workflows, and basic operational validation without requiring authentication.
Operational observability and system metrics for authenticated monitoring, dashboards, and environment-level performance review.
Benchmarks are presented as workflow guidance rather than headline claims. The focus is latency, reproducibility, and practical integration into portfolio operations.
Benchmark interpretation depends on the objective, constraint set, hardware profile, and surrounding workflow. EleQor is intended to be evaluated in the context of those operational requirements.
Protected routes are intended to be accessed through API keys so teams can control usage across environments and clients.
Request identifiers and structured logging make it easier to debug runs, monitor behavior, and integrate with standard operational tooling.
Redis-backed caching supports repeatable workflows that benefit from reduced latency and more predictable data access patterns.
The service is suited to Docker-based deployment models so teams can evaluate and operate it in environments that resemble production.
Constraint support is framed around the controls portfolio teams and risk groups expect in live workflows.
Per-stock max weight, minimum position size, banned tickers, short-selling restrictions.
Gross exposure caps, net exposure floors, cash buffer requirements, leverage multipliers.
Per-GICS-sector max weight caps. Prevent concentration in tech, energy, or any sector slice.
Portfolio-level carbon budget (tCO₂e/revenue). ESG score floors. Exclude brown industries.
Max % of average daily volume per position. Prevents illiquid over-concentration and ownership creep.
Max % of a company's market cap. Critical for large AUM strategies to avoid market impact and 13F thresholds.
Factor exposure caps and floors (momentum, value, quality, beta). Build targeted smart-beta portfolios.
Cap one-way turnover per rebalance. EleQor accounts for current holdings and minimizes unnecessary trading automatically.
The platform is easier to assess when deployment expectations are explicit. This section summarizes how teams can think about operating the service.
| Capability | Managed API | Private Network | Self-hosted Container | Sandbox |
|---|---|---|---|---|
| API key authentication | ✓ | ✓ | ✓ | ✓ |
| Request IDs and logging | ✓ | ✓ | ✓ | ~ |
| Redis-backed caching | ✓ | ✓ | ✓ | ✗ |
| Docker-based deployment | ✓ | ✓ | ✓ | ~ |
| Protected metrics endpoint | ✓ | ✓ | ✓ | ✗ |
| Public health endpoint | ✓ | ✓ | ✓ | ✓ |
| Environment isolation | ~ | ✓ | ✓ | ✗ |
| Benchmark and evaluation workflow | ✓ | ✓ | ✓ | ✓ |
✓ = typical fit | ~ = limited or engagement-dependent | ✗ = generally not the intended model.
The API is designed to be straightforward to evaluate: authenticate with X-API-Key, submit a request, and inspect the response alongside request-level metadata.
# authenticated matrix-mode request
curl -X POST "https://api.eleqor.io/optimize_matrix" \
-H "X-API-Key: $ELEQOR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"tickers": ["AAPL", "MSFT", "NVDA"],
"mu": [0.0011, 0.0008, 0.0016],
"cov": [
[0.0120, 0.0021, 0.0014],
[0.0021, 0.0102, 0.0011],
[0.0014, 0.0011, 0.0186]
],
"max_weight": 0.08,
"sector_caps": {"Technology": 0.35}
}'{
"request_id": "req_01HQ8M4Q9S8M3J7B2A",
"solve_time_ms": 14.2,
"data_cached": true,
"weights": {
"AAPL": 0.32,
"MSFT": 0.28,
"NVDA": 0.40
},
"sharpe": 0.3479,
"constraints_applied": [
"max_weight",
"sector_caps"
],
"factor_names": null,
"analytics": {
"portfolio_variance": 0.0118,
"tracking_error_vs_equal": 0.0324
}
}
Production endpoints include /optimize, /optimize_matrix, /multi_fund, /schedule_trades, public /health, and protected /metrics.
Use the service to run constrained optimization and trade planning for long/short or long-only books that need practical outputs for review and execution.
Coordinate multi-fund allocation workflows when capital sharing, fund-specific rules, and operational consistency all matter.
Apply carbon, ESG, exclusion, and exposure rules as part of the optimization workflow when policy controls are not optional.
Evaluate allocations under liquidity, ownership, and mandate constraints in environments where auditability and deployment controls matter as much as raw optimization output.
Request access to review the API, benchmark methodology, and deployment options.
Access reviews can include API behavior, benchmark assumptions, and deployment approaches for managed, private-network, or self-hosted environments.