# DeepSeek Chat V3.1 Alpha Arena trading results

> DeepSeek Chat V3.1 in Nof1's Alpha Arena, recomputed from its Hyperliquid wallets. In Season 1 it finished #2 of 6 with +6.9% on $10,000 ($685).

Canonical: https://tradingagentstack.com/alpha-arena-results/deepseek-chat-v3-1/

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3. DeepSeek Chat V3.1

Recomputed from Hyperliquid's own data · seasons finished

# DeepSeek Chat V3.1 in Alpha Arena: trading results, recomputed

In Season 1 it finished **#2 of 6** with +6.9% on $10,000 ($685), paying $719 in fees. In Season 1.5 its four accounts combined to **−32.9%**, #5 of 8 models; 1 of 4 finished up, best Monk Mode (+16.7%), worst Max Leverage (−64.6%).
**5**accounts recomputed
**−$12,483**combined PnL
**$6,810**trading fees paid
**2 of 5**accounts that finished up

## Every account

| Season and rule set | Return | PnL | Fees | Fills | Realized drawdown | Sources |
| --- | --- | --- | --- | --- | --- | --- |
| Season 1 | **+6.9%** | $685 | $719 | 341 | −55.4% | [wallet](https://app.hyperliquid.xyz/explorer/address/0xc20ac4dc4188660cbf555448af52694ca62b0734) · [Nof1](https://web.archive.org/web/20251023210434/https://nof1.ai/models/deepseek-chat-v3.1) |
| Season 1.5 · Monk Mode | **+16.7%** | $1,676 | $1,286 | 7,919 | −32.6% | [wallet](https://app.hyperliquid.xyz/explorer/address/0xf180c5145b53b430777f67bded05ddc92650c594) · [Nof1](https://www.alphaarena.ai/models/14) |
| Season 1.5 · New Baseline | **−21.6%** | −$2,164 | $749 | 4,617 | −52.8% | [wallet](https://app.hyperliquid.xyz/explorer/address/0xb0d63f7dc3beb0d667b601b50f3db25b10b6a212) · [Nof1](https://www.alphaarena.ai/models/13) |
| Season 1.5 · Situational Awareness | **−62.5%** | −$6,241 | $3,208 | 10,757 | −68.7% | [wallet](https://app.hyperliquid.xyz/explorer/address/0x3cd9f57464e15afac05b7509fd406e4ad0753af8) · [Nof1](https://www.alphaarena.ai/models/15) |
| Season 1.5 · Max Leverage | **−64.6%** | −$6,440 | $848 | 3,128 | −76.6% | [wallet](https://app.hyperliquid.xyz/explorer/address/0x2bde65aec178175f0213e46cb9c7a03a9644f7b9) · [Nof1](https://www.alphaarena.ai/models/16) |

Each account started with about $10,000. Return = realized PnL minus fees and builder fees, plus funding, plus open positions marked from Hyperliquid's candles at the end. [How we recomputed it](https://tradingagentstack.com/alpha-arena-results/#method).

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## FAQ

### How did DeepSeek Chat V3.1 do in Alpha Arena?

In Season 1 it finished #2 of 6 with +6.9% on $10,000 ($685), paying $719 in fees. In Season 1.5 its four accounts combined to −32.9%, #5 of 8 models; 1 of 4 finished up, best Monk Mode (+16.7%), worst Max Leverage (−64.6%). Every number is recomputed from the model's Hyperliquid wallet: fills, fees, funding and positions open at the end.

### Is DeepSeek Chat V3.1 good at trading?

Two or three weeks of trading is too short to separate skill from luck, and the same model can finish near the top in one season and near the bottom in the next. Fees mattered as much as direction: across both seasons they were 3.7 times the combined profit of the 7 accounts that finished up.

## Other models
[Qwen3 Max](https://tradingagentstack.com/alpha-arena-results/qwen3-max/)[Claude Sonnet 4.5](https://tradingagentstack.com/alpha-arena-results/claude-sonnet-4-5/)[Grok 4](https://tradingagentstack.com/alpha-arena-results/grok-4/)[Gemini 2.5 Pro](https://tradingagentstack.com/alpha-arena-results/gemini-2-5-pro/)[GPT-5](https://tradingagentstack.com/alpha-arena-results/gpt-5/)[GPT-5.1](https://tradingagentstack.com/alpha-arena-results/gpt-5-1/)[Kimi K2 Thinking](https://tradingagentstack.com/alpha-arena-results/kimi-k2-thinking/)[Grok 4.20](https://tradingagentstack.com/alpha-arena-results/grok-4-20/)[Gemini 3 Pro](https://tradingagentstack.com/alpha-arena-results/gemini-3-pro/)[Both seasons compared](https://tradingagentstack.com/alpha-arena-results/)[AI model benchmarks](https://tradingagentstack.com/ai-trading-leaderboard/#models)[AI trading bot statistics](https://tradingagentstack.com/ai-trading-bot-statistics/)

Not financial advice. Alpha Arena is Nof1's experiment; the wallets are Nof1's; the numbers here are ours.

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