LatticeQuant applies MPS/PEPS tensor network architectures to high-dimensional volatility surfaces. Model 500+ correlated assets where classical optimizers fail at 50.
Mean-variance optimization was designed for 20-asset portfolios in 1952. Today's funds hold hundreds of correlated instruments across asset classes, geographies, and time horizons. The covariance matrix explodes exponentially. Classical solvers approximate. They miss non-linear dependencies. They break under regime shifts.
Tensor networks exploit the entanglement structure of financial correlation matrices. Matrix Product States (MPS) decompose high-dimensional surfaces into chains of low-rank tensors. The full state is never materialized. Computation scales polynomially. Non-linear correlations are preserved, not discarded.
MPS/PEPS architectures map multi-asset volatility surfaces into compressed tensor representations. Exact portfolio variance computation without Monte Carlo approximation for up to 500 correlated assets.
Upload historical time-series data. Select allocation models. Run accelerated Monte Carlo simulations. Output exact Sharpe ratios, maximum drawdown, and Value at Risk across configurable time horizons.
Background market surveillance evaluates variance metric shifts across asset lists. Autonomous rebalancing calculations trigger when strategy parameters drift beyond user-defined thresholds.
Two execution tracks: pre-built algorithmic templates (QTN Mean-Variance, Adaptive Regime Switching) or upload Python-syntax rule scripts for the execution engine to run against your data.
PostgreSQL Row-Level Security on every schema. No client fund can query, view, or access another partition. Period.
Client portfolio structures and proprietary weights are processed in isolated server-side computation. Raw financial data never touches external APIs, LLM logging, or third-party training loops.
Every byte encrypted. Authentication via salted bcrypt hashes and secure JWT access tokens. Enterprise audit logging on all data access.
Strict input validation on all financial file parsers. CSV and JSON tick feeds are sanitized, schema-validated, and processed through isolated pipelines before touching any computation engine.
LatticeQuant brings quantum tensor network mathematics to institutional portfolio management. Not as a promise. As production infrastructure.