Why Monolithic Prompts Fail in Financial Markets
Single Models Hallucinate.
Deliberative Committees Converge.
When a single language model attempts to summarize fundamentals, score technical momentum, and size a trade inside one prompt loop, confirmation bias and specification errors compound directly into portfolio drawdowns.
Unchallenged Single-Agent Inference
Single-agent prompts routinely anchor on surface-level headlines while overlooking balance-sheet dilution, wide bid-ask spreads, or adverse sector correlation. Without structured adversarial critique, noisy signals masquerade as high-conviction setups.
Structured Bull, Bear & Risk Debate
Starling Quantitative organizes specialized agents into an explicit multi-stage deliberation pipeline: factor analysts propose candidates, dedicated Bear auditors construct the strongest disconfirming case, and a deterministic Risk Officer enforces hard exposure ceilings.
3-Stage Systematic Consensus Pipeline
Separation of Concerns.
Factor Screening, Debate & Sizing.
Our architecture separates quantitative factor screening, multi-agent adversarial review, and portfolio risk allocation into three isolated stages. Proprietary execution parameterization is maintained strictly off-public-ledger.
Classical Cross-Sectional & Event Factors
Domain-specific screening modules evaluate established empirical anomalies across liquid U.S. equities and event venues, including trend breakouts, volatility contraction, post-earnings drift, RSI mean-reversion, and devigged prediction-market probabilities.
Multi-Agent Thesis Stress-Testing
Candidate setups enter a structured debate loop where independent Bear and Fundamental audit agents challenge earnings quality, macro regime alignment, and transaction-cost drag before scoring consensus conviction.
Constrained Sizing & Disciplined Rebalancing
Approved positions are sized using Quarter-Kelly (`c = 0.25`) principles subject to sector concentration caps, systematic stop-loss discipline, and out-of-sample Brier score calibration via Discounted Thompson Sampling.
Empirical Foundations & Academic Literature
Grounded in Empirical Finance.
Validated Out-of-Sample.
Rather than relying on black-box curve fitting, our research sleeves build upon decades of peer-reviewed market anomalies combined with structured multi-agent verification.
Multi-Agent Committee Console
Inspect Quorum Deliberation
In Dynamic Real Time.
Simulate how candidate factor signals pass through Bull, Bear, and Risk Officer nodes before approval.
Published System Framework
Disciplined Governance.
Audited Outcomes.
While proprietary execution timing and microstructure parameters remain confidential trade secrets, every Showcase outcome is audited publicly via third-party ledger.
Adversarial Multi-Agent Quorums & Systematic Factor Consensus
Role-separated Bull, Bear, and Risk Officer governance across Momentum, PEAD, and Mean-Reversion sleeves.
Direct Inquiries & Collaboration
Connect with the Architect.
Starling Quantitative combines multi-agent consensus, empirical factor research, and disciplined risk management across equities and event markets.
Greg Meade
Founder & Quantitative Systems Architect • Autonomous Trading Systems • Distributed Cloud Infrastructure
Interested in quantitative research discourse or platform integration?
We collaborate with quantitative research teams, copy-trading marketplaces, and data partners. Direct inquiries are welcome below.