AlphaBot: a multi-LLM agent system for systematic alpha discovery

Status: draft, sanitized public version pending Cubist review.

What AlphaBot is

AlphaBot is a multi-LLM agent system that does alpha research the way a quantitative team does: it composes validated patterns into candidate trading signals, backtests them under hedge-fund-grade discipline, and ships only the survivors. Three frontier LLM families propose; the harness disposes.

It runs in two markets: US equities at Cubist (Point72), and an independent self-funded crypto deployment outside Cubist with an 18-month public live track.

Architecture, in layers

AlphaBot’s architecture is layered, not stepped — each layer is a typed surface the next one composes on:

• Data. Market data — prices, volumes, fundamentals, microstructure features, signals from filings — ingested into a normalized research store with deterministic replay.
• Centers of Expertise (CoE). A library of validated patterns and operators drawn from where signal-extraction has been studied seriously: statistics, physics, signal processing, electrical engineering, information theory, and adjacent domains. Each CoE is a compact declarative description of a regularity — a thematic primitive the language model can compose against. AlphaBot currently runs across 150+ CoEs.
• Skills. Composable research operations — backtesting under train/validate splits with overfitting controls, feature selection with statistical filters, ensemble prediction models across regression families, loss functions, and targets. Skills consume data and CoE-derived candidates, and produce structured research output.

A research solution sits on top of the three layers: trading-signal generation. Three LLMs propose candidate factor expressions conditioned on (data, CoE, skill); the candidates flow through the harness; survivors get traded.

Why a closed loop matters

The discourse on “LLM-driven research” tends to optimize for the demo. In a real research environment, what matters is whether the system produces signals that survive an out-of-sample regime the model has never seen. That requires a harness, not a chatbot:

• train / validate splits on different time periods,
• odd / even sample splits to catch overfit-to-regime,
• joint thresholds on train and validate Sharpe,
• a final out-of-sample holdout the brainstorm LLMs never saw.

The language models propose. The harness disposes.

Multi-LLM proposal: an empirical ranking

Three frontier model families propose in parallel: Claude Opus, GPT-5, Gemini 2.5 Pro. Across many alpha rounds against the same harness, three signals are stable:

• Research-output quality. On this task, Opus > GPT > Gemini.
• Orthogonality. The two strongest models propose substantially complementary candidates rather than redundant ones — the union of their proposals is materially wider than either alone.
• Cost-effectiveness. On a yield-per-dollar basis, Opus is the most cost-effective of the three for research-grade generation.

This is why AlphaBot runs three model families side-by-side: the harness, not the LLM, is the bar that decides what survives, so wider candidate surface is better at the proposal step.

Scale to date

AlphaBot has brainstormed over 100,000 candidate signals with reasoning across 150+ CoEs and three frontier LLM families, and has produced 400+ production-level signals in the equities deployment, spanning momentum, mean reversion, liquidity, information flow, and other risk-factor categories. Ensemble prediction models combining surviving signals attain promising Sharpe on both large- and small-cap US equities. (Specifics IP-protected; qualitative summary only.)

The cross-market check

The same architecture runs on a self-funded crypto research project outside Cubist (joint with Beier Liu). Live performance is public over an 18-month track on a self-funded testing account.

Two markets, same scaffolding — that’s the point: when the same agent architecture produces surviving signals in markets with very different microstructure (US equities vs mid-frequency crypto), the architecture is doing real work, not overfitting to a single regime.

Sanitized public version forthcoming.