Alpha Arena is a new benchmarking platform that aims to measure how well AI models perform in live crypto markets. In this test, six leading AI models were each given $10,000, access to a real crypto perpetual market, and one identical prompt to trade autonomously.
Within just 3 days, DeepSeek Chat V3.1 grew its portfolio by over 35%, outperforming Bitcoin and all other AI traders in this space.
This article describes how the experiment was structured, how AI was used, why DeepSeek performed better than others, and how anyone can safely reproduce a similar approach.
How the Alpha Arena experiment works
This project measured how well large-scale language models (LLMs) handle risk, timing, and decision-making in live cryptocurrency markets. The setup used at Alpha Arena is as follows:
Each AI received $10,000 of real capital.
Market: Permanent cryptocurrencies traded on Hyperliquid.
Goal: Maximize risk-adjusted return (Sharpe ratio).
Duration: Season 1 runs until November 3, 2025.
Transparency: All transactions and logs are public.
Autonomy: No human input required after initial setup.
Contestants:
Deep Seek Chat V3.1
Claude Sonnet 4.5
Grok 4
gemini 2.5 pro
GPT-5
kwen 3 max
What prompts were used?
Each model was given the same system prompts. This is a simple but rigorous trading framework.
“You are an autonomous trading agent. Make perpetual trades in BTC, ETH, SOL, XRP, DOGE, BNB on Hyperliquid. You start with $10,000. All positions require:
take profit target
Stop Loss or Disable Condition. Use 10x to 20x leverage. Never remove the stop. Please report the following:
Side | Coin | Leverage | Conceptual | Exit Plan | Unrealized P&L
If invalidation is not successful → HOLD”
With this minimal instruction, each AI had to reason about entry, risk, and timing just like a trader.
For each tick, the AI ​​received market data (BTC, ETH, SOL, XRP, DOGE, BNB) and had to decide whether to open, close, or hold. Models were evaluated for their consistency, workability, and discipline.
Results after 3 days
Why deep seek won
A. Diversification and position management
DeepSeek held all six major crypto assets (ETH, SOL, XRP, BTC, DOGE, BNB) with moderate leverage (10x to 20x). This diversified risk while maximizing exposure to the altcoin rally that occurred between October 19th and 20th.
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B. strict discipline
Unlike some of our peers, DeepSeek consistently reported:
“No invalid hit → pending”.
There was no chasing trades or over-adjusting. This rule-based stability further increased profits.
C. Balanced risk
DeepSeek’s unrealized profit/loss distribution is as follows.
ETH: +$747
Sol: +$643
BTC: +$445
BNB: +$264
Doge: +$94
XRP: +$184
Total: +$2,719
No single asset dominates returns. This is a hallmark of sound risk allocation.
D. Fund management
Approximately $4,900 remained idle, enough to prevent liquidation and make adjustments as needed.
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Why other AI models have struggled
Grok 4: Closely matches DeepSeek, but with slightly higher volatility and less cache buffer.
Claude 4.5 Sonnet: Good ETH/XRP call, but the cash is underutilized (about 70% idle).
Qwen3 Max: Too conservative – only traded BTC despite obvious altcoin momentum.
GPT-5: There was a missing stop loss and a P&L error. Good analysis but poor execution.
Gemini 2.5 Pro: I entered short BNB during an uptrend in the market. This was the most costly mistake.
How to (safely) replicate this
Although this was a controlled AI experiment, a simplified version can be recreated for learning and paper trading purposes.
Step 1: Choose a sandbox
Use a testnet or paper trading platform such as:
Superfluidity testnet
Binance Futures Testnet
TradingView + Pine Script Simulator
Step 2: Start with a fixed budget
Allocate a small demo account (e.g., virtual balance of $500 to $1000) to simulate portfolio management.
Step 3: Recreate the DeepSeek prompt
Use structured prompts like this:
You are an autonomous cryptocurrency trading assistant.
Your task: Trade BTC, ETH, SOL, XRP, DOGE, BNB using 10x to 20x leverage.
All trades must include a take profit and stop loss. Don’t overtrade.
If termination conditions are not met → Hold.
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Step 4: Collect signals
Feed the model.
Price data (e.g. from CoinGecko or exchange API)
RSI, MACD or trend information
Account snapshot (balances, positions, cash)
Step 5: Log output
For each decision cycle, record:
Side | Coin | Leverage | Entry | Exit Plan | Unrealized P&L
Even with paper transactions, it’s important to track consistency.
Step 6: Evaluate performance
After a few sessions, calculate:
Account value
drawdown
Sharpe ratio (reward/volatility)
This reflects Alpha Arena’s benchmarking style.
final thoughts
While the results are interesting, they are not investment advice. Alpha Arena’s experiment was to understand how inference models perform in real markets.
Still, for those interested in the intersection of AI, finance, and autonomy, DeepSeek’s 35% increase in 72 hours is a strong signal.
Disclaimer: This article is for educational purposes only. Data reflects live testing on Alpha Arena’s real money benchmark from October 17th to 20th, 2025. Past performance is not indicative of future results. Always trade responsibly and understand the risks of trading cryptocurrencies with leverage.
