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Knowledge Network

The Knowledge Network is KEK's domain-specific intelligence layer.

It aggregates market data, processes signals, and provides structured context to AI agents and validation systems. This layer informs strategy generation and refinement — it does not execute trades, allocate capital, or control funds.

The Knowledge Network exists to answer one question:

"What is currently relevant — and why?"

What this page covers

  • What the Knowledge Network is
  • The types of data it processes
  • How signals are generated and distributed
  • How feedback improves future intelligence
  • What it does (and does not) do

What the Knowledge Network is

The Knowledge Network is KEK's shared context and research infrastructure. It ensures strategy intelligence and validation systems operate from a consistent understanding of market conditions.

Interactive Visualization + 150+ Reports

The Knowledge Network includes an interactive visualization featuring 150+ self-updating reports on currency, trading, and technical analysis. These reports act as a continuously evolving reference base for:

  • market structure and regime behavior
  • indicator research and pattern libraries
  • execution and risk concepts
  • strategy design frameworks
  • crypto ecosystem mechanics

This is KEK's "living research layer" — designed to stay current and expandable.

Role in the KEK system

The Knowledge Network supports the strategy lifecycle by supplying structured context to KEK's intelligence and validation layers:

Knowledge Network → AI Agents → Strategy Generation → Validation → Monitoring → Refinement

It improves the quality of strategy hypotheses by narrowing ambiguity and aligning the system to what matters right now.

Data ingestion

The Knowledge Network ingests data from multiple categories, including:

  • Market price and volume data
  • Volatility and liquidity metrics
  • Structural market indicators
  • Asset-level behavior across regimes
  • Cross-asset structure signals (where applicable)

Data sources are treated as inputs — not truth.

Signals are produced probabilistically and remain contextual.

Signal processing

Raw data is transformed into structured signals through:

  • Cleaning and normalization
  • Feature extraction
  • Context-aware filtering
  • Regime-conditional interpretation
  • Aggregation into machine-consumable outputs

Feature extraction is the process of transforming raw data into informative characteristics ("features") that models and systems can use more efficiently.

Signals are probabilistic and contextual — not deterministic predictions.

Market regime context

The Knowledge Network continuously evaluates market regime conditions to reduce false assumptions during transitions.

This enables:

  • Differentiation between trending, ranging, and volatile environments
  • Context-aware interpretation of signals
  • Reduced false positives during regime shifts
  • Strategy behavior alignment with current conditions

Regime awareness informs how strategies should behave — not when to trade.

Narrative and context tracking

The Knowledge Network tracks macro and sector-level narratives to provide contextual awareness beyond pure price-action.

This includes:

  • Identifying dominant themes
  • Measuring narrative persistence and decay
  • Assessing asset relevance within narratives
  • Weighting relevance under different regimes

Narratives are treated as context signals — not trade instructions.

Distribution to agents (MCP)

Processed intelligence is distributed to KEK's AI agents through the MCP (Model Context Protocol) Agent Server.

MCP is an open standard that enables secure connections between AI systems and external data/tools, allowing systems to share structured context and invoke workflows consistently.

This approach ensures:

  • Shared context across specialized agents
  • Consistent interpretation of signals
  • Structured message passing and tool coordination
  • Versioned, auditable inputs throughout the lifecycle

Agents do not need to query raw sources directly — they receive structured intelligence from the Knowledge Network.

Feedback from performance

The Knowledge Network improves over time by incorporating feedback from observed strategy outcomes.

Performance signals from validation and paper trading are used to:

  • Re-weight signals based on measured usefulness
  • Adjust relevance scoring by regime
  • Detect signal decay and changing effectiveness
  • Improve regime sensitivity and feature selection

Learning is grounded in measured outcomes, not assumptions.

What the Knowledge Network does not do

The Knowledge Network does not:

  • Execute trades
  • Predict prices
  • Allocate capital
  • Override validation requirements
  • Function as an "authority layer"

It supports intelligence — not action.

Why this matters

This design:

  • Reduces noise and overfitting risk by keeping intelligence contextual
  • Improves interpretability through structured signals
  • Enables disciplined strategy development via shared research and consistent context
  • Builds long-term system learning through feedback from real outcomes
  • Keeps execution downstream and permissioned

KEK treats knowledge as infrastructure, not prediction.

Where to go next