NaviMod Agentic Fund Framework

Agentic Multi-Layer Trading Architecture

Autonomous AI-Driven Trading System Design
Version 2.0 · November 2025
1 · Introduction

What is Agentic Trading Architecture?

The NaviMod Agentic Fund Framework goes beyond traditional algorithmic trading approaches by offering an AI-Driven, multi-layered, autonomous architecture composed of specialized agents. Instead of a single "large model", the architecture employs specialized agents including LLM-powered reasoning agents—each responsible for a specific task. Agents execute stages such as data collection, signal generation, risk management, portfolio optimization, and order management in an independent yet coordinated manner.

This approach facilitates adaptation to rapid market changes, error isolation, and continuous improvement. Acting as the "brain" of the system, the Management & Orchestration Layer synchronizes all agents and continuously monitors performance.

Data → Alpha → AI Prediction → Risk → Portfolio → Execution
2 · Architecture Blueprint

Six-Layer System Overview

The diagram below shows how the system is organized from data acquisition to order execution. Each layer takes the output from the previous layer, processes it, and passes it to the next layer.

Layer 0

Management & Orchestration

Central coordinator of the system; schedules all agents, manages memory, and monitors models.

Layer 1

Data & Alpha Factory

Raw data collection, cleaning, enrichment, and dynamic feature generation.

Layer 2

AI-Based Strategic Prediction

Market direction (regime) prediction and stock-based return/direction forecasts with AI & ML models.

Layer 3

Risk & Selection

Filters risks of predicted stocks and selects suitable candidates.

Layer 4

Portfolio & Strategy

Long/short balance, inter-strategy weighting, and net exposure adjustment.

Layer 5

Execution & Market Interaction

Order type, microstructure management, broker communication, and slippage minimization.

3 · Layer Details

Each Layer's Roles and Responsibilities

Layer 0

Management & Orchestration Layer

The brain of the system; coordinates all agents, manages scheduling and memory.

  • Plans and triggers which agents run when.
  • Switches modes (bull/bear/neutral) based on market regimes.
  • Monitors model performance and detects concept drift.
  • Stores regime information, performance metrics, and historical feature behaviors in system memory.
Layer 1

Data & Alpha Factory Layer

Collects, cleans, enriches data, and generates dynamic alpha factors.

  • Combines price, volume, fundamental analysis, macro, and alternative data sources.
  • Feature Generation Agent: Generates thousands of features (technical, fundamental, sentiment, macro) using LLM-based reasoning.
  • Feature Selection Agent: Selects which feature set to use based on regime.
  • Detects and flags low-quality data via Data Quality agent.
Layer 2

AI-Based Strategic Prediction Layer

Predicts overall market direction and individual stock movements with AI & ML models.

  • Market Compass Agent: Detects market regime (Bull/Bear/Neutral), adapting the entire system's behavior accordingly.
  • Single Stock Agent: Forecasts future returns and direction (long/short) for each stock.
  • Output: Expected return forecast and direction signal for each stock.
Layer 3

Risk & Selection Layer

Filters risky stocks from predictions and creates candidate pool for portfolio.

  • Evaluates criteria like volatility, liquidity, correlation, model uncertainty (confidence) and sentiment scores through LLM-based reasoning.
  • Risk Management Agent: Filters stocks based on risk level.
  • Output: "Clean" candidate list entering portfolio optimization.
Layer 4

Portfolio & Strategy Layer

Determines fund's long/short balance and weight distribution across strategies.

  • Portfolio Agent (Optimizer): Calculates weights using mean-variance, Black-Litterman, or RL-based models.
  • Adjusts net exposure level (long/short ratio) based on regime signal.
  • Combines different strategies into a single basket.
  • Output: Target position size and direction for each stock.
Layer 5

Execution & Market Interaction Layer

Converts portfolio decisions into real market orders; manages microstructure.

  • Execution Agent: Selects order type and submission speed (limit / market / TWAP / VWAP).
  • Slices orders based on liquidity, chooses routes and exchanges.
  • Estimates and monitors transaction costs and slippage.
  • Communicates securely and trackably with broker APIs.
4 · Agent Roles

Specialized Agents' Layer Relationships

These layers are managed by specific "Agents" that make autonomous decisions. Each agent has a clear place in this architecture:

Specific Agent Role Related Architecture Layer Main Task
Feature Generation Agent Data & Alpha Factory Layer Derive thousands of new and creative 'alpha' factors from raw data.
Feature Selection Agent Data & Alpha Factory Layer Select informative, noise-free features.
Market Compass Agent AI-Based Strategic Prediction Layer Determine overall market direction (Bull/Bear/Neutral).
Single Stock Agent Strategic Prediction Layer Predict individual stocks' direction and return potential.
Risk Management Agent Risk & Selection Layer Filter stock and market risks to create candidate pool.
Portfolio Agent (Optimizer) Portfolio & Strategy Layer Optimize weight allocation to selected stocks.
Execution Agent Execution & Interaction Layer Execute orders in the market with minimal impact (slippage).
6 · Comparison with Traditional Approach

Traditional Algo-Trading Systems vs Agentic Architecture

The table below compares the classic single-model approach with the agentic, multi-layered structure.

Criterion Traditional Algo-Trading Agentic Multi-Layer Architecture
Architecture Single model, monolithic system. Multi-layered, flexible structure divided into specialized agents.
Data & Signals Fixed indicator set, limited feature generation. Dynamic feature generation, regime-based signal set (Alpha Factory).
Risk Management Fixed stop/limits, risk added at end of process. Embedded in decision process, proactive risk filter (Risk Layer).
Regime Adaptation Usually absent or dependent on manual intervention. Automatic bull/bear/neutral mode adjustment via Market Compass Agent (Prediction Layer).
Model Management Manual retraining and changes, limited monitoring. Continuous performance monitoring and autonomous coordination (Management Layer).
Portfolio & Strategy Fixed weights or simple optimization. Regime-adaptive long/short balance optimization (Portfolio Layer).
Fault Tolerance Single model failure directly impacts fund performance. Agent-based isolation; failure can remain in single layer.
7 · Key Strengths

Advantages

Modularity & Flexibility

Each agent is responsible for a single task. Development, testing, and deployment processes can proceed independently.

Fault isolation Easy versioning Step-by-step expansion

Regime-Sensitive Decision Making

Thanks to the Compass agent, the system doesn't apply "same strategy in all weather"; it selects appropriate net exposure and strategy mode based on regime.

Bull / Bear / Neutral Net exposure control Dynamic adaptation

Risk-First Approach

Risk isn't just managed with stop-loss at the end; it's embedded in the selection process. Risky stocks are eliminated before entering portfolio calculations.

Proactive risk Quality candidate pool Continuous monitoring

Continuous Learning & Memory

Model governance and memory layer record regimes, performance metrics, and feature behaviors, enabling the system to mature over time.

Concept drift tracking Data-driven improvement Autonomous evolution