MLAP Trading Setup
MLAP Trading Intelligence

Build a trading research system that explains before it acts.

MLAP organizes market data, news, analyst content, history, portfolio context and risk controls into specialist Agents. Traders get structured intelligence, traceable evidence and enforceable decision boundaries instead of a black-box buy or sell signal.

View Agent TreeSee Risk Boundary
Current + HistorySpecialist researchPersistent learningDeterministic riskHuman approvalAuditable decisions
01 / Trading Agent Tree

Split the problem by responsibility and time horizon.

The top-level Trading Agent receives two coordinated views: what is happening now and what history says about similar conditions.

Trading/
├── agent.yaml
├── Current/
│   ├── News/
│   │   ├── GlobalMarketNews/
│   │   ├── IndianMarketNews/
│   │   └── SectorNews/
│   ├── YouTube/
│   │   ├── Channel-A/
│   │   └── Channel-B/
│   └── MarketData/
│       ├── IndexData/
│       ├── SectorData/
│       └── VolatilityData/
├── History/
│   ├── ExchangeData/
│   ├── LocalData/
│   └── HistoricalPatterns/
├── Risk/
├── Portfolio/
└── Execution/
Why this structure helps: each specialist can use the source, tool, model and memory appropriate to its responsibility. Trading receives an aggregated result instead of managing every data source directly.
02 / Executive Summary

A practical Agent model for trading research and control.

MLAP helps traders convert noisy market inputs into explainable research packages. LLMs can interpret and summarize, while deterministic services keep risk, approval and execution under strict control.

01

Current market view

Track price, volume, volatility, news, macro signals and sector movement in one workflow.

02

Historical context

Compare today with similar regimes, prior events, patterns and strategy outcomes.

03

Research synthesis

Combine market data, analyst content and source evidence into a structured trading view.

04

Risk-first decisions

Check exposure, liquidity, concentration and limits before any action is allowed.

05

Human approval

Keep sensitive decisions visible, reviewable and approved by the right person or policy.

06

Learning loop

Store predictions, outcomes and failed reasoning so future analysis gets sharper.

03 / Mixed Execution

Not every trading Agent needs an LLM.

MLAP lets deterministic data work and model-assisted interpretation live in the same hierarchy.

Deterministic Agents

  • Exchange and API acquisition
  • Price and volume calculations
  • Indicator generation
  • Duplicate detection and time alignment
  • Backtesting and performance metrics

LLM-assisted Agents

  • News and transcript interpretation
  • Claim and sentiment extraction
  • Contradiction and source comparison
  • Research summarization
  • Explanation of a structured result
Boundary: an LLM may explain a signal or propose a scenario. It should not silently calculate position size, bypass risk limits or send a broker order.
04 / Safety Boundary

Research can be intelligent. Risk and execution must be enforceable.

Research Findings
→
Strategy Proposal
→
Risk Engine
→
Approval Gate
→
Execution Service
01

Risk Engine

Checks exposure, leverage, liquidity, concentration, loss limits, market hours and instrument rules.

02

Approval Gate

Requires a human or explicitly authorized policy decision before sensitive actions.

03

Execution Service

Owns broker connectivity, order validation, idempotency, acknowledgements and cancellation.

Non-negotiable: the LLM is not the authorization layer, risk engine or broker. Those controls must be deterministic, independently tested and auditable.
05 / Persistent Learning

Compare today’s view with what the system already learned.

Each Agent can preserve evidence and outcomes so later analysis can improve without treating every new prompt as a blank slate.

Today’s Prediction
→
Actual Outcome
→
Evaluation
→
Agent Memory
→
Future Context

Persist with provenance

Store source references, timestamps, model context, confidence, assumptions, market regime and resulting outcome.

Learn without self-deception

Separate prediction from result, record failed reasoning, avoid silently rewriting history and preserve contradictory evidence.

06 / Example Decision

“What changed, and is a trade even allowed?”

A realistic MLAP workflow produces an inspectable decision package rather than a mysterious buy or sell instruction.

Question: What changed in Sector-A today?

Current Agents:
  - price and volatility snapshot
  - news claims and source references
  - sector and index relationships

History Agents:
  - similar market regimes
  - prior event outcomes
  - strategy performance under similar conditions

Trading result:
  - observed facts
  - competing interpretations
  - confidence and uncertainty
  - candidate scenarios
  - evidence references

Risk result:
  - exposure check
  - position-size limit
  - liquidity check
  - approval requirement
  - executable / rejected decision
07 / What MLAP Adds

The value is the coordination model, not a magical prediction layer.

Composition

New sources and research methods become child Agents without redesigning Trading.

Context

Current, historical and portfolio knowledge can be combined with clear ownership.

Traceability

Findings, evidence, execution IDs and outcomes can be followed through the hierarchy.

Control

Risk, approval and execution remain outside unconstrained model reasoning.

Learning

Predictions and outcomes can update persistent knowledge for future analysis.

Replaceability

Data providers, models, storage and deployment can evolve independently.

Scope: this is an architecture for organizing trading research and controlled decision workflows. It is not a promise of profitable predictions or a substitute for regulated financial, risk and broker controls.