AlphaVibe is a specialist swarm of trading agents with deterministic risk controls, global intelligence context, and full auditability. We integrate with Hyperliquid API and Coinbase API, built on managed OpenClaw infrastructure, with a paper shadow portfolio for every cycle.
In the 1980s, a small group of funds started using computers to analyze markets. At the time it seemed silly, but quantitative trading is now obvious. We're at a similar inflection point now, and the next Renaissance, Bridgewater, and D.E. Shaw's are going to be built on AI.
The biggest funds in the world have been slow to adapt. I worked as a quant researcher at one of these funds, and when I asked compliance to let us use ChatGPT, I didn't even get a response.
It made it clear to me that the hedge funds of the future won't just bolt AI onto their existing strategies. They'll use it to come up with entirely new ones. That's where the alpha is.
We've already got swarms of Claude agents writing our codebases. Imagine swarms of agents doing what hedge fund traders do now — combing through 10-Ks, earnings calls, and SEC filings, synthesizing analyst ideas and making trades. An AI-native hedge fund will be the first to do this well.
Parallel specialists produce structured signals. A consensus agent synthesizes them, and a deterministic risk engine constrains every trade before execution.
Technical, sentiment, on‑chain, macro, microstructure, regime detection.
Weighted synthesis with conflict identification and regime context injection.
LLM decides within deterministic constraints. No hallucinated position sizes.
Risk is calculation, not opinion. Every cycle enforces sizing, drawdown, and liquidation constraints before any trade is placed.
Real‑time news, macro context, and on‑chain flows feed a Strategic Risk Index that throttles risk in unstable conditions.
Composite 0‑100 score from macro, sentiment, on‑chain, and microstructure.
Detects correlation breaks (BTC/SPX, ETH/BTC regime shifts).
Curated news + macro events surface inside the trading console.
Historical replay uses the same swarm code as live trading. Post‑trade analysis updates agent weights and reduces bias over time.
8 LLMs compete with isolated wallets and tracked performance. Every prompt, signal, and trade is stored.
GPT‑5.1, Claude Sonnet 4.5, Grok‑4, DeepSeek, Gemini, Qwen, Kimi, Typhoon.
Every decision is reproducible with evidence, signals, and constrained actions.
We are consolidating the codebase and hardening the swarm. OpenClaw remains the core orchestration layer.
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