🧩 Solution Proposal: Autonomous Alpha Generation Agent using AZR and R\&D-Agent(Q) on the WorldQuant BRAIN Platform

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1. Overview

This proposal outlines the development of a modular, multi-agent system for autonomously generating novel, decorrelated alpha expressions using a compact LLM trained entirely through self-supervised interaction with the WorldQuant BRAIN platform. The agent will combine:

• The R\&D-Agent(Q) architecture for structured multi-agent task flow, and
• The Absolute Zero Reasoner (AZR) training method for curriculum-free, reward-driven optimization.

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2. Motivation

The alpha discovery process in quantitative finance suffers from:

• A diminishing pool of novel, decorrelated signals;
• Overreliance on complex, pretrained models that are difficult to audit or adapt;
• Empirical fragility in signal performance due to modeling artifacts (Buncic, 2024).

Recent breakthroughs in multi-agent LLM architectures (Zhang et al., 2024) and reward-driven reasoning without supervision (Chen et al., 2024) offer a promising alternative: compact models that learn from environment feedback, not labels.

This system will serve as a testbed for applying this integrated architecture to a real-world, high-stakes domain: alpha factor mining on the WorldQuant BRAIN platform.

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3. Proposed Solution

We propose a multi-agent LLM system that learns to generate Fast Expressions — the DSL used in BRAIN to construct alpha signals — entirely through interaction with the BRAIN backtesting environment.

🔧 Architecture: R\&D-Agent(Q)-Inspired System

Agent Role	Description
Proposer	LLM generates candidate Fast Expressions in valid DSL syntax.
Implementer	Wraps the expression into a BRAIN-compatible format and runs simulation via the Python API.
Validator	Extracts key metrics (Fitness, Sharpe, turnover, decorrelation) from backtest results.
Critic	Assesses novelty, stability, and adherence to constraints; filters poor outputs.
Scheduler (optional)	Bandit-based role switching (e.g., prioritize exploration, refinement, or high-confidence picks).

🧠 Training Method: Absolute Zero Reasoner (AZR)

The system uses AZR-style curriculum-free self-play:

• No pretraining, labeled examples, or human priors.
• Rewards are derived from simulator feedback (Sharpe, Fitness, decorrelation).
• LLM weights are updated via REINFORCE++, PPO, or similar techniques using trl or Unsloth.

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4. Technical Implementation

Phase 1 – MVP Bootstrapping

• Seed the proposer with trivial valid expressions (e.g., rank(close)).
• Set up BRAIN API wrapper (ace_lib) to run expressions and extract metrics.
• Build reward function using simulator outputs.

Phase 2 – Closed-Loop Training

• Implement AZR loop to propose, simulate, score, and learn.
• Maintain a replay buffer of all expression–reward pairs.
• Track correlation to prior alphas and penalize duplicates.

Phase 3 – Multi-Agent Integration

• Expand beyond single-loop AZR to R\&D-Agent(Q) roles:

  • Critic to filter redundant or trivial expressions.
  • Scheduler to toggle strategies (e.g., exploration vs. exploitation).

Phase 4 – Reporting and Export

• Generate a report/dashboard of discovered alphas.
• Log performance trends and agent reasoning improvements over time.

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5. Innovations and Contributions

Area	Contribution
Methodological	Demonstrates the effectiveness of AZR in a financial setting with zero pretraining.
Architectural	Combines AZR with the modular agent structure of R\&D-Agent(Q) for enhanced interpretability.
Practical	Produces high-Fitness, decorrelated Fast Expressions on real data using only API access.
Computational	Can run on Colab-tier compute using LoRA, TRL, and lightweight 1B models.

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6. Alignment with Research & Investment Goals

• Advances zero-data, verifiable-agent design in financial research.
• Reduces development time and human bias in alpha discovery pipelines.
• Lays foundation for general-purpose simulator-driven LLMs beyond finance (science, policy, etc.).