Multi-Agent Intelligence · v1.0

MultiMind Trading

Adversarial multi-agent debate system for robust trade signal generation. Six specialized agents argue, challenge, and converge toward high-conviction decisions.

6 Agents
3 Debate Rounds
Bayesian Consensus
78.4% Backtest Accuracy

System Architecture

How adversarial multi-agent debate improves trading decisions

Core Thesis: Single-model trading systems inherit the biases of their training data. By decomposing investment analysis into specialized agents with adversarial incentives, MultiMind forces rigorous examination of every trade thesis — producing signals that survive cross-examination rather than echo chamber reinforcement.

📊 Market Data
→
🔍 Agent Analysis
→
⚔️ Adversarial Debate
→
🎯 Bayesian Consensus
→
📈 Trade Signal
DATA INGESTION LAYER Price & Volume Fundamentals News & Filings Social Sentiment Options Flow Macro Indicators AGENT LAYER — INDEPENDENT ANALYSIS 🐂 Bull Analyst Growth & Momentum w = 0.18 Confidence: 0.82 🐻 Bear Analyst Risk & Downside w = 0.18 Confidence: 0.76 📐 Quant Statistical Edge w = 0.22 Confidence: 0.89 🌍 Macro Strategist Regime & Cycles w = 0.15 Confidence: 0.71 📡 Sentiment Crowd Psychology w = 0.12 Confidence: 0.65 🛡️ Risk Manager Position & Portfolio w = 0.15 Confidence: 0.84 ADVERSARIAL DEBATE LAYER — 3 ROUNDS Round 1: Initial Theses Round 2: Cross-Examination Round 3: Final Arguments CONSENSUS & OUTPUT LAYER Bayesian Aggregation Supra-Bayesian pooling Confidence Calibration Shannon entropy filter TRADE SIGNAL BUY / SELL / HOLD + conf%

Why Multi-Agent?

Traditional AI trading systems use a single model that conflates analysis, risk assessment, and decision-making. This creates confirmation bias — the model seeks evidence supporting its initial hypothesis.

MultiMind decomposes the problem into 6 specialized agents with structurally adversarial roles. The Bull and Bear agents are incentivized to disagree. The Quant demands statistical evidence. The Risk Manager has veto power over position sizing. Only arguments that survive 3 rounds of cross-examination become signals.

Inspired By

  • Debate-based alignment — Irving et al. (2018), AI safety through adversarial debate
  • Mixture of Experts — Jacobs et al. (1991), gated specialist networks
  • Prediction markets — Hanson (2003), information aggregation via markets
  • Ensemble methods — Dietterich (2000), combining multiple classifiers
  • Supra-Bayesian pooling — Morris (1977), expert opinion aggregation
  • Grok multi-agent reasoning — xAI (2025), adversarial chain-of-thought

Key Design Principles

⚔️
Structural Adversarialism
Agents are designed to disagree. Bull vs Bear is not optional — it's the core mechanism for surfacing overlooked risks and opportunities.
🔄
Iterative Refinement
Three debate rounds force agents to respond to counterarguments, update priors, and strengthen or abandon positions. Weak theses collapse under scrutiny.
📊
Calibrated Confidence
Final signals carry entropy-adjusted confidence scores. High-entropy debates (genuine disagreement) produce lower confidence — correctly reflecting real uncertainty.

Agent Roster

Six specialized agents with distinct analytical perspectives and structural incentives

Agent Interaction Matrix

Natural tension between agents drives more thorough analysis. Bold = primary adversarial pairing.

🐂 Bull 🐻 Bear 📐 Quant 🌍 Macro 📡 Sentiment 🛡️ Risk Mgr

Weight Calibration History

Agent Accuracy by Market Regime

Debate Arena

Watch agents deliberate in real-time on a specific trade thesis

Ticker
Time Horizon
Select a ticker and click Run Debate to start the multi-agent deliberation.

Live Consensus

Buy 33% Hold 34% Sell 33%

Agent Positions

Waiting for debate…

Debate Metrics

Round —
Entropy —
Conviction —
Agreement —

Signal Board

Current consensus signals across tracked assets

Filter
Sector
Min Confidence
Ticker Company Signal Confidence Bull/Bear Split Sector Horizon Entropy

Signal Distribution

Confidence vs Entropy

Consensus Engine

Bayesian opinion aggregation with calibrated confidence scoring

Supra-Bayesian Pooling: Each agent's opinion is treated as a probability distribution over outcomes {Buy, Hold, Sell}. The consensus engine applies logarithmic opinion pooling with agent-specific weights, then calibrates the final distribution using Shannon entropy to produce a confidence-adjusted signal.

Agent Weights

Adjust weights to see how consensus shifts. Weights are normalized to sum to 1.

Weighted Consensus

Mathematical Formulation

Logarithmic Opinion Pool

P(θ) ∝ ∏ᵢ pᵢ(θ)^wᵢ

where wᵢ = base_weight × accuracy_ᵢ × regime_adjust_ᵢ
and Σwᵢ = 1

Each agent i provides a distribution pᵢ over outcomes θ ∈ {Buy, Hold, Sell}. The logarithmic pool multiplicatively combines these distributions, giving more weight to agents with higher historical accuracy in the current market regime.

Confidence Calibration

H(P) = −Σ P(θ) log P(θ)

Confidence = 1 − H(P) / log(3)
Signal iff Confidence > τ (default: 0.55)

Shannon entropy H measures disagreement in the pooled distribution. Maximum entropy (uniform) yields confidence = 0. Perfect agreement yields confidence = 1. Only signals above threshold τ are emitted — uncertain debates produce "HOLD" by default.

Debate Round Dynamics

How agent positions evolve across 3 debate rounds (simulated NVDA analysis)

Performance

Backtest results and historical signal accuracy

Overall Accuracy
78.4%
326 / 416 signals correct
Sharpe Ratio
1.87
vs S&P 500 Sharpe 1.12
Max Drawdown
−12.3%
vs S&P 500 −18.7%

Cumulative Returns — MultiMind vs Benchmarks

Accuracy by Confidence Bucket

Monthly Returns

Performance Breakdown by Signal Type

Signal Count Correct Accuracy Avg Return Avg Confidence Win/Loss Ratio
BUY 178 144 80.9% +4.2% 76% 2.34
SELL 112 85 75.9% −3.8% 72% 1.98
HOLD 126 97 77.0% +0.3% 63% —

Single Agent vs Ensemble — Why Debate Wins

References

Academic foundations for multi-agent debate systems in finance