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In a Training Loop
Paul Smith
pjsmith
1
7
42
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0 followers
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14 following
pjsmith
AI & ML interests
trading, trading, trading (and puppies)
Recent Activity
liked
a model
8 days ago
ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
reacted
to
SeaWolf-AI
's
post
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13 days ago
Can AI beat the market? Nobody has actually measured it. We opened a 122-day public experiment to find out. $2,000 in prizes. Here is the problem with every trading result you have ever read. Someone returns 30% in a month. Skill or luck? There has never been a way to tell, because nobody measured how far a player with zero skill could have gone over the same window. So we measured it first. Twenty thousand random players, per asset, charged the same fees. Bitcoin +86.6%. NVIDIA +51.7%. Crude oil +26.9%. Gold +9.2%. That is the luck ceiling. A return below it is not evidence of skill, and every row on our leaderboard shows where it sits against that line. How you compete: submit one number between −1.0 and +1.0. It holds until you replace it, traded against live prices with real execution costs. Leverage is fixed at 1, so betting bigger is not a way to win. The answer lives in the future — the world writes it after you submit, which means fitting the past cannot help you. Humans move a slider. Agents attach an MCP server and gain four tools, then you tell them "enter the challenge." We already found something before the season began. Thirteen well-known rules, run from 1 January through the same scorer: Stochastic 14/3 finishes 1st on NVIDIA at +43% and 12th on Bitcoin at −25%. Donchian breakout does the exact opposite — last on NVIDIA, first on Bitcoin. The ranking inverts. "Which indicator is good" turns out not to be a well-posed question; the character of the market decides. Four assets: NVIDIA, Bitcoin, Gold, Crude Oil. $500 to the top return in each. 24 August to 24 December 2026. The organisers do not compete. Three baselines — buy and hold, volatility targeting, random — sit in the same table instead, because a leaderboard without a scale cannot be read. The scoring code is public. Read what it does before you enter. https://huggingface.co/spaces/FINAL-Bench/finchal https://huggingface.co/blog/FINAL-Bench/financial-forecast-challenge
liked
a model
14 days ago
logic65/Qwen3.8-Whittle-MoE-27B-A17.8B
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liked
a model
8 days ago
ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
Image-Text-to-Text
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27B
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Updated
4 days ago
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348k
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459
liked
a model
14 days ago
logic65/Qwen3.8-Whittle-MoE-27B-A17.8B
27B
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8 days ago
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33k
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111
liked
5 models
21 days ago
unsloth/Qwen3.8-27B-GGUF
27B
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17 days ago
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10.3M
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3.58k
Qwen/Qwen3.8-27B
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28B
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23 days ago
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6.19M
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unsloth/Qwen3-VL-8B-Instruct-GGUF
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8B
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Oct 31, 2025
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118k
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unsloth/Qwen3-VL-4B-Instruct-GGUF
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4B
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AtomicChat/Qwen3.8-27B-GGUF
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27B
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19 days ago
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298k
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165
liked
a dataset
about 2 months ago
campwill/HAL-9000-Speech
Viewer
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Apr 11, 2025
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96
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17
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2 models
about 2 months ago
prism-ml/Ternary-Bonsai-27B-gguf
Text Generation
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27B
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6 days ago
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campwill/HAL-9000-Piper-TTS
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Apr 13, 2025
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23
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a model
2 months ago
LibertAIDAI/Qwen3.6-35B-A3B-NVFP4-GGUF
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35B
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May 4
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9 models
3 months ago
prefeitura-rio/Rio-3.5-Open-397B
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56
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byteshape/Qwen3.6-35B-A3B-GGUF
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35B
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unsloth/gemma-4-E4B-it-qat-GGUF
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pjsmith/Qwen3.6-35B-A3B-2.6763bpw.gguf
35B
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AtomicChat/gemma-4-26B-A4B-it-assistant-GGUF
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unsloth/gemma-4-26B-A4B-it-qat-GGUF
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25B
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unsloth/gemma-4-12B-it-qat-GGUF
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12B
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Jul 17
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RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.2
Text Generation
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29B
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28
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w-ahmad/Qwen3.5-9B-GGUF-MoQ
Text Generation
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9B
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Jun 6
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23
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