LUMA QuantMarket Intelligence
LUMA maps relationships across multiple evidence layers, detects regime shifts and evaluates risk as conditions change.It is designed to recognise when previously stable relationships begin to weaken, disappear or reorganise.
LUMA does not depend on one signal or one fixed assumption. It observes, maps, learns and controls how evidence becomes decision-relevant.
For financial markets, the same analytical core can connect to macroeconomic, microstructural, capital-market, semantic, news, on-chain and off-chain data. LUMA transfers analytical mechanisms—not domain-specific predictions.
LUMA Trust Layer v1.0 connects selected architecture and product claims to public repositories, exact source states, CI checks and explicit disclosure boundaries. Technical transparency is not legal approval or an independent audit.
Domain-specific data enters a reusable analytical core that maps relationships, remembers prior states, tests scenarios and controls how evidence becomes decision-relevant.
This public mapping shows how existing analytical mechanisms can translate into financial intelligence without exposing proprietary weights, thresholds, ranking formulas or execution logic.
LUMA treats a market as a changing information state—not merely as a price series.
Combining macroeconomic conditions with market microstructure, asset-level behaviour and local liquidity signals.
Showing where independent evidence layers confirm each other and where they conflict.
The framework maps what is connected, what is changing and what should be present but is missing.
Mapping relationships between assets, sectors, factors and market variables beyond simple pairwise correlation.
Detecting moments where several independent market factors align and form a stronger combined signal.
Separating market behaviour into dimensions such as momentum, stability, liquidity, volatility, sentiment and structural risk.
Detecting missing confirmation, silent divergence, liquidity gaps, absent participation and structural weaknesses.
Reducing a large asset or scenario universe into a smaller, explainable research set.
Signals are evaluated through memory, subsequent outcomes and changing regimes—not as isolated events.
Maintaining structured memory of prior market states, transitions, failures, interactions and post-outcome learning.
Measuring how analytical rules would have behaved across known historical conditions.
Testing models sequentially on unseen future periods rather than relying only on retrospective optimisation.
Detecting new relationships before they become established long-term factors.
Comparing the expected state with the observed result and updating analytical memory.
A signal becomes decision-relevant only after regime, risk and cross-layer confirmation have been evaluated.
Evaluating whether a signal remains credible when examined across independent risk perspectives.
Identifying transitions between accumulation, expansion, instability, contraction, stress and recovery environments.
Testing outcome distributions, stress scenarios, tail risks and portfolio sensitivity under changing assumptions.
Exploring alternative analytical policies, parameter configurations and decision paths.
Evaluating branching scenarios and possible decision sequences under uncertainty.
Preventing a raw signal from becoming actionable until risk, regime and confirmation conditions have been checked.
Running large scenario, parameter and stress-test workloads efficiently across many potential states.
A market translation must be judged through historical evidence, sequential testing, regime-specific evaluation and learning from later outcomes.
Evaluate analytical behaviour across known market conditions and failure periods.
Test sequentially on unseen future intervals instead of relying only on retrospective optimisation.
Separate evidence across expansion, contraction, instability, stress and recovery states.
Compare expected states with realised outcomes and update analytical memory.
The goal is to build and validate a research and risk-intelligence layer before any stronger product or execution claim is considered.
Define macro, microstructure, semantic, news, on-chain and off-chain data contracts.
Translate entities, interactions, risk dimensions, regimes and ENMF memory states.
Run historical and sequential walk-forward experiments across changing conditions.
Build Monte Carlo stress analysis, cross-axis risk scoring and conflict detection.
Expose regime states, explainable universes, risk maps and no-trade or wait states.
The roadmap separates the implemented analytical foundation, the active Web3 workstream and the planned 2027 market-intelligence research path.