DeepSeek-R1-GGUF
unsloth · 工具包
671B 参数 DeepSeek-R1 推理模型的 GGUF 版本,带 1.58-bit 和 2-bit 动态量化,可在内存足够的本机上用 llama.cpp 运行。

Virat Singh 的开源概念验证项目,做的是一只由 AI 驱动的对冲基金。大语言模型投资者智能体和量化模型各自给出判断,基金既可以回放过去的交易日,也可以在真实的交易日里用虚拟资金做模拟交易。它不会下任何真实订单。
AI Hedge Fund 对冲基金概念验证 是一款 GitHub 上的开源项目,属于财务类,由 Mindket 上架。可免费获取。当前为第 2 版,最近更新于 。按它的 README 从 GitHub 安装。Mindket 的设置说明可在你的 Mindket 账户获取,以 zip 压缩包下载。
pipx install aihf aihf
AI Hedge Fund is a proof of concept by Virat Singh (virattt) that explores using AI to make trading decisions. You install it as the aihf command, and it opens an interactive terminal app. A fund there is a set of strategies. Each strategy blends the views of analysts, which are LLM investor agents such as Warren Buffett and Charlie Munger, or quant models such as post-earnings drift. The README says the project is for educational purposes only and that the system does not actually make any trades.
Developers, students and quant hobbyists who want to see how an LLM-plus-quant trading system is built, run it on a few tickers and read or extend its code. The roadmap invites new analysts, strategies and data sources. Skip it if you want a bot that trades for you. The README says it is not for real trading, and live brokers are only planned.
It runs in a terminal on Python 3.11 or newer. The maker’s README and docs give no steps for Claude Code, Codex, Cursor or other coding agents, and no skill, plugin or MCP server.
Get it saves a zip of Minda’s setup notes in your downloads folder, with the install and key steps from the maker’s README and docs. You install the aihf package with pipx, uv or pip, then run it.
| Need | Detail |
|---|---|
| Python | 3.11 or newer |
| An installer | pipx, uv or pip |
| Market data | A Financial Datasets API key, or, from the main branch only, a Base wallet that holds USDC for x402 pay-per-call (last 365 days of data, non-professional use) |
| A model | One API key from Anthropic, OpenAI, DeepSeek, Google, xAI, Kimi or TypeSafe (Jev) |
aihf to open the app.~/.hedge-fund/.env, and keys exported in your shell always win over that file.pipx install aihf aihf
The README also gives uv tool install aihf, and pip install aihf into an environment of your choice. For the x402 option the README on the main branch gives this line. The PyPI release, aihf 2.6.4, lists no x402 extra and its README does not mention x402, so until a release adds it, the x402 option is only on the repository’s main branch.
pip install 'aihf[x402]'
Start with aihf and pick a mode. In the paper trading list, highlight a fund to see what its next run will do, then press enter to run it through the next completed session. An approval step shows the exact decision before anything trades.
| Mode | What it does | Where results go |
|---|---|---|
| Paper trading | Runs your funds through real market days with fake money, one completed session at a time | A hash-chained ledger in ~/.hedge-fund/paper/<name>/ |
| Backtesting | Replays a fund over history and draws its equity curve against its benchmark | Result files in ~/.hedge-fund/research/ |
Fund definitions live in ~/.hedge-fund/mandates/ and carry no tickers. You pick the universe per backtest, or when you deploy a paper fund. You can build a new fund by choosing its strategies, capital, cadence and tickers.
You want to see how a value-and-drift fund behaves. The repository’s sample mandate has two strategies. Deep-value is a long-only pod led by the Graham agent, with Buffett and Munger beside it. Earnings-drift is a systematic pod that runs the post-earnings drift model, long and short. The fund starts with 100000 in capital, rebalances weekly, is measured against SPY and lets no single name pass 25% of equity.
You point it at tickers and a window. The engine README gives this backtest line, written for a checkout. With an x402 wallet this 2024 window fails: x402 serves only the last 365 days, and older data needs a Financial Datasets API key.
poetry run aihf backtest ~/.hedge-fund/mandates/example.yaml \
--universe AAPL,MSFT,NVDA --start 2024-01-02 --end 2024-06-28It prints the full result JSON, a record for every session, and saves a copy to ~/.hedge-fund/research/. The docs do not say whether an install puts example.yaml in ~/.hedge-fund/mandates/. The repository’s sample mandate is hedge_fund/fund/example.yaml. To paper trade the same fund, create it and tick it once per completed session.
poetry run aihf paper create alpha --mandate ~/.hedge-fund/mandates/example.yaml --universe AAPL,MSFT poetry run aihf paper tick alpha
A tick that finds a new completed session adds one record to the fund’s ledger.
新手:In this app a mandate is a file that names a fund’s strategies, risk limits, capital and how often it trades. The money is pretend. Paper trading runs a fund on real market days with fake money, and a backtest replays it over past days. Start with aihf and the two modes, and read the disclaimer first. Nothing here places a real order.
专家:Every session is one verb, advance, called once per completed session in both backtest and paper mode. A decision made at session T’s close is executed at T+1’s close. The engine README says stepping a paper fund through the same sessions produces byte-identical records.
Language models never touch the trade. Agents form views and write theses, deterministic code sizes and places orders, and risk limits are hard gates. The engine README makes point-in-time data a principle: on a simulated date only data filed by then is visible, filtered on filing date and not report period. The roadmap still marks point-in-time data correctness as in progress.
The ledger is append-only and hash-chained. Replay verifies the chain and refuses an altered record, and the paper broker’s book is reconciled against the ledger before anything trades. A mismatch raises BookMismatch rather than being repaired, and any failure inside advance halts the fund.
To add an analyst, implement AlphaModel.predict(ticker, date, data_client) and return a Signal with a value from -1 to +1. A new strategy needs no code: a YAML file in strategies/ bundles existing models with a blend policy.
Market data comes from Financial Datasets and model calls go to the provider you choose. The README gives no cost for a run.
这个工具包里只有安装说明,由 Mindket 编写。AI Hedge Fund 应用要从作者的仓库和 PyPI 安装:https://github.com/virattt/ai-hedge-fund
这些说明不是 Virat Singh(virattt)写的,也没有得到他的认可。README 的开头是“This is a proof of concept for an AI-powered hedge fund.”,意思是这是一个由 AI 驱动的对冲基金的概念验证。它还说这个项目只用于教育目的,不用于真实交易或投资,系统实际上不会进行任何交易。这些页面都在仓库的 main 分支上,读取于 2026 年 11 月 3 日。
许可证:MIT。LICENSE 文件(https://github.com/virattt/ai-hedge-fund/blob/main/LICENSE)写的是“MIT License”和“Copyright (c) 2024 Virat Singh”。
README 的 Disclaimer 一节(https://github.com/virattt/ai-hedge-fund/blob/main/README.md)说这个项目只用于教育和研究目的,并列出:
VISION.md(https://github.com/virattt/ai-hedge-fund/blob/main/VISION.md)补充说,这些投资者智能体是对知名投资者公开理念的风格化近似,并不是这些人本人,也不代表他们的背书。
aihf Python 包(https://pypi.org/project/aihf/),给你一个命令 aihf。它的元数据要求 Python 3.11 或更高版本,低于 4.0。来源见 https://pypi.org/pypi/aihf/json 和 https://github.com/virattt/ai-hedge-fund/blob/main/pyproject.toml~/.hedge-fund/ 下:你的密钥(.env)、基金定义(mandates/)、回测结果(research/)、模拟盘基金(paper/<name>/)和 API 响应缓存(cache/)。来源见 https://github.com/virattt/ai-hedge-fund/blob/main/README.md 和 https://github.com/virattt/ai-hedge-fund/blob/main/hedge_fund/README.mdpipx install aihf
然后在任何地方运行它:
aihf
来源:https://github.com/virattt/ai-hedge-fund/blob/main/README.md(“How to Install”)
README 和文档没有给出 Claude Code、Codex、Cursor 或其他编程智能体的步骤,也没有技能、插件或 MCP 服务器。它是一个终端应用,所以这些说明跟着作者记录的安装和运行方式走。
README 没有给出更新或卸载步骤。请用你的安装工具自己的命令。应用的文件都在 ~/.hedge-fund/ 下:这个文件夹里有你保存的密钥和每只模拟盘基金的账本,删除之前先留好要保留的东西。
README.md 4 KB setup/api-keys-and-models.md 3 KB setup/from-source.md 2 KB setup/install.md 1022 B setup/paper-trading-and-backtests.md 4 KB
Updated by Mindket to the current version of this kit.
Hacker News、Bluesky、X、Reddit、Facebook 和 YouTube 上提到这个项目的帖子和评论,来自每周一次的搜索和 Trendshift。它们不是 Mindket 写的,也未经核实,点开即可在原帖处阅读。
unsloth · 工具包
671B 参数 DeepSeek-R1 推理模型的 GGUF 版本,带 1.58-bit 和 2-bit 动态量化,可在内存足够的本机上用 llama.cpp 运行。
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