by Coinstrooper Crypto Articles
Quick Take
  • Alpha Arena, a new benchmark platform set out to measure how well AI models work in live crypto markets.
  • The test gave six leading AI models $10,000 each, access to real crypto perpetual markets, and one identical prompt — then let them trade autonomously.
  • Within just three days, DeepSeek Chat V3.1 grew its portfolio by over 35%, outperforming both Bitcoin and every other AI trader in the field.
  • The project measured how well large language models (LLMs) handle risk, timing, and decision-making in live crypto markets.

What Happened

Within just three days, DeepSeek Chat V3.1 grew its portfolio by over 35%, outperforming both Bitcoin and every other AI trader in the field.

This article explains how the experiment was structured, what prompts the AIs used, why DeepSeek outperformed others, and how anyone can replicate a similar approach safely.

How the Alpha Arena Experiment Worked

Market Context

Alpha Arena, a new benchmark platform set out to measure how well AI models work in live crypto markets. The test gave six leading AI models $10,000 each, access to real crypto perpetual markets, and one identical prompt — then let them trade autonomously.

The project measured how well large language models (LLMs) handle risk, timing, and decision-making in live crypto markets. Here’s the setup used by Alpha Arena:

Each AI received $10,000 in real capital.

Market: Crypto perpetuals traded on Hyperliquid.

Each model was given the same system prompt — a simple but strict trading framework:

“You are an autonomous trading agent. Trade BTC, ETH, SOL, XRP, DOGE, and BNB perpetuals on Hyperliquid. You start with $10,000. Every position must have:

Why It Matters

Goal: Maximize risk-adjusted returns (Sharpe ratio).

This minimalist instruction forced each AI to reason about entries, risk, and timing — just like a trader.

Details

Duration: Season 1 runs until November 3, 2025.

Transparency: All trades and logs are public.

Autonomy: No human input after initial setup.

The contestants:

DeepSeek Chat V3.1

Claude Sonnet 4.5

Grok 4

Gemini 2.5 Pro

GPT-5

Qwen3 Max

What Prompts Were Used?

a take-profit target

a stop-loss or invalidation condition. Use 10x–20x leverage. Never remove stops, and report:SIDE | COIN | LEVERAGE | NOTIONAL | EXIT PLAN | UNREALIZED P&LIf no invalidation is hit → HOLD.”