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ALOHA

Technology

Seven layers.
One continuous system.

Aloha Agent is an architecture before it is a product. Each layer answers to the one above it, and the last layer rewrites the first.
01Agent architecture

Specialised agents, one addressable network.

Every agent is narrow by design. It owns one discipline, holds one kind of context, and is accountable for one kind of judgement. The system’s intelligence is in how those judgements meet.

MACRO

Macro Intelligence

Interprets global economic forces and regime shifts.

STRUCT

Market Structure

Maps trend, volatility, momentum, and structural imbalance.

FLOW

Order Flow

Reads real-time pressure across bids, asks, and execution flow.

LIQ

Liquidity Mapping

Identifies where capital can move with the least resistance.

WHALE

Whale Activity

Tracks large positions, transfers, and institutional behavior.

NEWS

News Understanding

Transforms global events into structured market context.

RISK

Risk Control

Challenges every decision before capital is exposed.

EXEC

Execution

Converts collective intelligence into disciplined action.

REVIEW

Post-Trade Review

Reconstructs every outcome to understand what changed.

LEARN

Learning & Evolution

Shares experience across the entire civilization.

02The stack

What each layer is responsible for.

L1

Perception Layer

Continuous intake of market, on-chain, and event data across global venues.

Perception agents do not interpret — they establish what is true right now. Price and volume structure, resting and executed order flow, where liquidity actually sits, large capital movement, and the events that reframe all of it.

  • Global scanning without a session close
  • Market structure, order flow, and liquidity mapping
  • Large-capital and on-chain behaviour
  • Events converted into structured context

L2

Reasoning Layer

Where observation becomes an argument that can be tested and rejected.

Reasoning agents assemble competing readings of the same conditions, price the scenarios against each other, and score opportunity against what it would cost to be wrong. Any conclusion carries the assumptions it depends on.

  • Multi-factor synthesis across disciplines
  • Scenario and failure-condition simulation
  • Opportunity evaluation with stated assumptions
  • Risk-reward assessment before commitment

L3

Collaboration Layer

Independent conclusions are pooled, compared, and reconciled into one position.

Agents publish to a shared context rather than to a controller. Where readings disagree, the disagreement is preserved and examined instead of averaged away.

  • Shared context between specialised agents
  • Independent challenge paths
  • Cross-validation of overlapping signals
  • Consensus formed, not assumed

L4

Risk Layer

A standing veto that operates before capital is ever exposed.

Risk is not a filter applied at the end. It participates throughout, holds predefined boundaries, and can stop a decision that every other agent agrees with.

  • Predefined exposure boundaries
  • Independent challenge of every decision
  • Supervision that survives consensus
  • Escalation before, not after, action

L5

Execution Layer

Collective intelligence converted into disciplined, bounded action.

Execution agents translate an approved position into action within the constraints the risk layer set. Discipline here means the plan that was approved is the plan that runs.

  • Action inside predefined constraints
  • Routing informed by live liquidity
  • Deterministic behaviour under stress
  • Full record of what was done and why

L6

Learning Layer

Every outcome is reconstructed so the next pass starts better informed.

Post-trade review rebuilds what was expected, what happened, and where the two separated. That reconstruction is the raw material for refinement.

  • Structured post-trade reconstruction
  • Expectation compared against outcome
  • Behaviour refined against evidence
  • Continuous adaptation, not periodic retraining

L7

Shared Memory

The connective tissue that makes the other six layers cumulative.

Analysis does not expire with the agent that produced it. Shared memory is what turns a network of specialists into a civilization with history.

  • Common analysis memory across agents
  • Context that outlives the individual agent
  • Precedent available at decision time
  • Civilization-wide learning
03Continuous evolution

The layers only matter because they loop.

A single pass through the architecture is a decision. Repeated passes, with memory, are a civilization.
  1. 01

    Observe

    Scan global markets, liquidity, flows, and events in real time.

    Continuous scanningMulti-source intake
  2. 02

    Analyze

    Combine macro, structure, sentiment, and behavioral signals.

    Multi-factor synthesisContext assembly
  3. 03

    Simulate

    Test scenarios, outcomes, and failure conditions before action.

    Scenario testingFailure modelling
  4. 04

    Decide

    Build consensus through collaboration, validation, and challenge.

    Cross-validationConsensus formation
  5. 05

    Execute

    Act with speed, discipline, and predefined risk controls.

    Bounded actionDisciplined routing
  6. 06

    Learn

    Turn every analysis and trade into shared intelligence.

    Outcome reviewShared memory

Risk disclosure

Digital asset trading involves substantial risk and may result in partial or total loss of capital. Aloha Agent does not guarantee performance, returns, or the elimination of market risk. Users are responsible for evaluating their own circumstances and regulatory obligations.

This page describes system architecture at a conceptual level. It does not disclose proprietary implementation, model configuration, or trading logic, and nothing on it is investment advice.

Meet the civilization behind the architecture.

Roles, collaboration, supervision, and the shared experience that ties them together.