# The Most Powerful AI Trading Bot Possible (Opus 5.5 + Jev)
**作者**: Miles Deutscher
**日期**: 2026-09-15T18:17:52.000Z
**来源**: [https://x.com/milesdeutscher/status/2104937986869567565](https://x.com/milesdeutscher/status/2104937986869567565)
---
Opus 5.5 + Jev is actually the most insane AI trading bot you could build right now.
I've spent the last week trying to crack the code on how I can combine these two models to build the ultimate trading bot, and I finally did it.
In this article, I'll show you how it works and how to set up your own version of the high-frequency trading bot I've built.
I'm quite excited about this one. It's unlike any of my previous AI trading bot builds. Jev has introduced an entirely new element to probabilistic trading.
You can use the latest Opus 5.5 model as the brain, and take advantage of Jev's cheap inference decision-making to make fast, cheap, high-quality trading decisions.
For reference, here's the bot trading $HYPE just last week:
<video controls preload="metadata" poster="/doc/8882/image/img_54335d8ce9dc.jpg" src="/videos/1796.mp4"></video>
My Trading Bot *live*
What I'm covering today:
- Brief Intro to Jev
- Trading Bot Architecture
- Setting up the Technicals
- Finding Profitable Strategies
- News Trading with Jev (use case)
- Final Tips
Let's get right into things:
## Brief Intro to Jev
If you haven't heard of Jev, let me be the first to introduce it to you.
Jev is essentially a new type of AI model from TypeSafe AI that works quite differently from the LLMs you're probably used to.
> **Diogo Almeida@CompleteSkeptic**: [原文链接](https://x.com/CompleteSkeptic/status/2099925682726002904)
>
> After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
> I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
> • 20-200x faster
> • 40-400x
>
> 
Jev doesn't write any text at all; TypeSafe calls it a "System One" model.
In simple terms, I like to think of Jev as an AI decision framework.
You give it possible decisions upfront, then Jev evaluates the outcomes and returns a probability score.
Jev Explained
In the example of trading, that could look like:
→ Is this headline bullish, bearish, or neutral? 72% bearish
→ Is buying pressure building? 81% yes
→ Which way is price moving? Up, 64%
Jev versus a Typical LLM
The easiest way to understand Jev is to compare Jev versus a typical LLM.
At its core, Jev does not try to “predict” tokens the same way a model like Claude would.
Instead, it grades the fixed set of outcomes you provide.
Jev ELI5
Why use Jev for Trading?
Put simply: Speed and cost.
Claude and ChatGPT can take several seconds to reason through a decision and are quite expensive to do so.
Jev is 200-400x cheaper AND faster than models from GPT/Claude.
Jev answers in roughly 70-500 milliseconds and costs around $0.042 per million input tokens, with output free. In my demo above, Jev is running at ~400 milliseconds.
For high-frequency and news trading, Jev is perfect. It's fast and cheap, and it was designed to basically assign probabilities to fixed outcomes (trading is essentially just up/down, so this is great).
## Trading Bot Architecture
The actual trading bot setup is made up of four key components:
1. Claude (Opus 5.5) is the brain. It runs the backtests and designs the final strategy that Jev runs on.
2. Jev is the decision layer. It ingests live market data and returns a probability for a trade setup.
3. Your strategy (stop loss, take profit, etc.)
4. The exchange serves as the execution layer.
Trading Bot Architecture
How a trade actually fires
Claude designs the strategy → Claude turns the strategy into questions to ask Jev → Jev reads live market data and answers with probabilities → Trades fire
Why I've designed the architecture this way
Opus 5.5 is the LLM that handles the thinking that happens once (high-level strategy), and Jev handles the decisions that happen constantly.
You get the best of both worlds here.
The best decision model to design the strategy, and the cheapest/fastest model to actually execute it.
Possibilities with Jev + Opus 5.5
With this trading architecture, the possibilities are endless.
Some ideas:
- Jev executing trades based on news headlines (I built this - shown below)
- High-frequency trading
- Prediction market probability trading
- Momentum/volatility-based trading
- General investing/portfolio management
Jev Newsroom
Obviously, in this article I'm covering how to use Jev as a high-frequency trading bot, but don't be afraid to get creative and use it for these other use cases too.
## Setting up the Technicals
Now that you understand the high-level architecture, here's how to set it up in practice.
The setup essentially runs off a single mega prompt that you can drop into Claude Code (or Codex if you want to use Astra).
The full setup prompt is too long to attach here on 𝕏, so I put it for free in my Skool community.
Just head over to Classroom → Free Asset Library → Scroll down to Opus 5.5 + Jev.
<video controls preload="metadata" poster="/doc/8882/image/img_636666ea746d.jpg" src="/videos/1797.mp4"></video>
https://www.skool.com/milesdeutscherfinance
The prompt does three things:
1. Installs Jev and walks you through deploying your bot on Vercel, so you don't have to work it out yourself
2. Builds a strategy-tester harness dashboard
3. Connects everything to your exchange for execution
Two routes for actual trade execution
Whichever route you pick, I recommend starting with paper trading.
Path 1: Alpaca (the quick start) Alpaca has both paper and live trading, plus crypto and stocks.
Alpaca
Just take your API key and put it in the .env file that Claude Code prompts you to create. Claude feeds the data to Jev, and Jev's signals go to your paper account.
Path 2: An always-on server (more complex)
Your second option is to deploy a VPS with a provider like Hostinger, so your bot can run 24/7 without you.
You can run this on older hardware, such as a Mac Mini.
You have the option here to use a tool like TriggerTrade, which essentially triggers trades in the cloud based on a predetermined Pine Script.
If you go this route, Opus 5.5 can guide you through the exact steps here.
Path 1 versus Path 2
## Finding Profitable Strategies
Probably the most important part of this entire article. How to actually make money.
Your strategy is everything. Without a good strategy, you're just automating losses.
The way you should think about your strategy is that it's the "rulebook" Jev will use to generate buy/sell signals.
There are a few good places to find/backtest profitable trading strategies that you can feed AI, but the crux of it is this:
- Backtest hundreds or thousands of ideas - this is the volume game
- Filter the survivors
- Forward test them
- Paper trade test them
- Go live with small capital to test execution
In the article below, I go more in-depth as to how to find winning strategies:
> **Miles Deutscher@milesdeutscher**: [原文链接](https://x.com/milesdeutscher/status/2082095561176703135)
>
Some places worth searching for strategies:
- TradingView public communities
- Reddit
- AI (use Claude to backtest real data)
- GitHub repos
How I personally find strategies (backtesting engine)
I use a backtesting engine I built myself. I can ask it to design a backtest across various assets, tell it the kind of strategy I'm after, and let it run multi-hour searches that return a shortlist.
I may release it at some point, but for now it's just for my own use.
The good news is, you don't need mine. You can open Claude Code, ask it to build a backtesting engine harness, and use the strongest model you have to backtest market data.
Once you have a strategy you want to test, inject it into your Claude chat where you're building your trading bot so it can ingest and adopt it.
## News Trading with Jev (use case)
How a news trading engine works
Live news feed → Jev analyses → Scan your strategy → Trade based on set parameters
News Trading Engine
This is one of the coolest use cases I found for Jev in financial markets.
News trading is a probability game. A headline drops, and you have a very short window to work out what it means before the market prices it in.
I basically built a dashboard where I can watch headlines come in, see Jev label each one, and gain a real-time market edge with Jev's speed + execution.
<video controls preload="metadata" poster="/doc/8882/image/img_6c15cd8244ef.jpg" src="/videos/1798.mp4"></video>
News Trading Engine *live*
## Final Tips
• Before any strategy goes live, set the rules that limit the damage when it's wrong. Build your risk management framework into your strategy. md file
• Things to include in your strategy. md: max position size, daily loss limit, kill switch, manual approval for certain trades
• Use API keys with trading permission only and withdrawals switched off.
• Set up alerts (via BotFather on TG)
• Keep a human in the loop on bigger trades
• I personally use Bybit for execution, but for this to work properly, you need any exchange that allows AI connectivity via an MCP + API.
I hope you've found this article helpful.
If you did, be sure to follow me here @milesdeutscher. . Every single day I post about how you can actually use AI to gain an edge in financial markets.
For deeper AI insights, follow me over on@aiedge_.
Reminder: You can download the full mega prompt to send to Claude now to set up your Opus 5.5 + Jev trading bot.
Posted for free in my Skool community.
👉https://www.skool.com/milesdeutscherfinance
## 相关链接
- [Miles Deutscher](https://x.com/milesdeutscher)
- [@milesdeutscher](https://x.com/milesdeutscher)
- [716K](https://x.com/milesdeutscher/status/2104937986869567565/analytics)
- [$HYPE](https://x.com/search?q=%24HYPE&src=cashtag_click)
- [Sep 16](https://x.com/CompleteSkeptic/status/2099925682726002904)
- [40M](https://x.com/CompleteSkeptic/status/2099925682726002904/analytics)
- [https://www.skool.com/milesdeutscherfinance](https://www.skool.com/milesdeutscherfinance)
- [Jul 28](https://x.com/milesdeutscher/status/2082095561176703135)
- [220K](https://x.com/milesdeutscher/status/2082095561176703135/analytics)
- [@milesdeutscher](https://x.com/@milesdeutscher)
- [@aiedge_](https://x.com/@aiedge_)
- [https://www.skool.com/milesdeutscherfinance](https://www.skool.com/milesdeutscherfinance)
- [Upgrade to Plus](https://x.com/i/premium_sign_up)
- [10:14 PM · Sep 29, 2026](https://x.com/milesdeutscher/status/2104937986869567565)
- [716.7K Views](https://x.com/milesdeutscher/status/2104937986869567565/analytics)
- [View quotes](https://x.com/milesdeutscher/status/2104937986869567565/quotes)
---
*导出时间: 2026/10/26 10:08:40*
---
## 中文翻译
# 最强AI交易机器人（Opus 5.5 + Jev）
**作者**： Miles Deutscher
**日期**： 2026-09-15T18:17:52.000Z
**来源**： [https://x.com/milesdeutscher/status/2104937986869567565](https://x.com/milesdeutscher/status/2104937986869567565)
**阅读**： 716738
**点赞**： 337
**转发**： 52
**回复**： 22
---
Opus 5.5 + Jev 是目前你能搭建出的最疯狂、最强大的AI交易机器人。
过去一周，我一直在研究如何将这两个模型结合起来，打造终极交易机器人，现在我终于做到了。
在这篇文章中，我将向你展示它是如何工作的，以及如何搭建你自己版本的、我所构建的高频交易机器人。
我对这个项目感到相当兴奋。它与我以往构建的任何AI交易机器人都不一样。Jev 为概率交易引入了一个全新的元素。
你可以使用最新的 Opus 5.5 模型作为大脑，并利用 Jev 廉价的推理决策能力，做出快速、低成本、高质量的交易决策。
作为参考，以下是该机器人上周交易 $HYPE 的实况：
<video controls preload="metadata" poster="/doc/8882/image/img_54335d8ce9dc.jpg" src="/videos/1796.mp4"></video>
我的交易机器人 *实盘运行中*
今天我要讲的内容：
- Jev 简介
- 交易机器人架构
- 技术设置
- 寻找盈利策略
- 用 Jev 进行新闻交易（应用案例）
- 最后的建议
让我们直接进入正题：
## Jev 简介
如果你还没听说过 Jev，那就让我第一个来介绍它。
Jev 本质上是 TypeSafe AI 推出的一种新型AI模型，它的工作方式与你熟悉的 LLM 有很大不同。
> **Diogo Almeida@CompleteSkeptic**: [原文链接](https://x.com/CompleteSkeptic/status/2099925682726002904)
>
> 在共同发明 ChatGPT 之后，我一直在问自己：为什么超越人类的聊天模型没有带来 AGI？
> 过去两年，我一直在隐秘开发一种新的模型训练方法（RLCD），以及一种我们今天发布的新型前沿AI模型：Jev
> • 快 20-200 倍
> • 40-400 倍
>
> 
Jev 完全不生成任何文本；TypeSafe 称之为“系统一（System One）”模型。
简单来说，我喜欢把 Jev 看作一个AI决策框架。
你预先给它提供可能的决策选项，然后 Jev 评估各种结果并返回一个概率分数。
Jev 解析
以交易为例，它看起来像这样：
→ 这条新闻标题是利多、利空还是中性？利空概率 72%
→ 买盘压力正在积聚吗？是的，概率 81%
→ 价格往哪个方向走？上涨，概率 64%
Jev 与典型 LLM 的对比
理解 Jev 最简单的方法，就是将它与典型的 LLM 进行比较。
本质上，Jev 不像 Claude 这样的模型那样去“预测”token。
相反，它会对您提供的一组固定结果进行评分。
Jev ELI5
为什么用 Jev 进行交易？
简单来说：速度和成本。
Claude 和 ChatGPT 推理一个决策可能需要几秒钟，而且成本相当高。
Jev 比 GPT/Claude 的模型便宜 200-400 倍，而且更快。
Jev 大约在 70-500 毫秒内给出答案，每百万输入 token 的成本约为 0.042 美元，输出免费。在我上面的演示中，Jev 的运行速度约为 400 毫秒。
对于高频交易和新闻交易来说，Jev 非常完美。它快速且廉价，而且它的设计初衷就是为固定结果分配概率（交易本质上就是涨/跌，所以这非常契合）。
## 交易机器人架构
实际的交易机器人由四个关键组件构成：
1. Claude（Opus 5.5）是大脑。它负责运行回测，并设计 Jev 所运行的最终策略。
2. Jev 是决策层。它摄取实时市场数据，并返回某个交易形态的概率。
3. 你的策略（止损、止盈等）
4. 交易所作为执行层。
交易机器人架构
一笔交易是如何实际触发的
Claude 设计策略 → Claude 将策略转化为要问 Jev 的问题 → Jev 读取实时市场数据并以概率形式回答 → 交易触发
为什么我这样设计架构
Opus 5.5 是处理一次性思考（高层策略）的 LLM，而 Jev 处理的是持续不断发生的决策。
这样你就能两全其美。
用最好的决策模型来设计策略，用最便宜/最快的模型来实际执行它。
Jev + Opus 5.5 的可能性
有了这个交易架构，可能性是无穷的。
一些想法：
- Jev 根据新闻标题执行交易（我构建了这个——见下文）
- 高频交易
- 预测市场概率交易
- 基于动量/波动率的交易
- 一般投资/投资组合管理
Jev 新闻编辑室
显然，在本文中我讲的是如何将 Jev 用作高频交易机器人，但不要害怕发挥创意，将它用于这些其他用例。
## 技术设置
既然你已经了解了整体架构，下面是如何在实践中进行设置。
这个设置本质上依赖于一个大型提示词（mega prompt），你可以将其放入 Claude Code（如果你想使用 Astra，也可以用 Codex）。
完整的设置提示词太长，无法在这里贴出，所以我把它免费放在了我的 Skool 社区里。
只需前往 Classroom → Free Asset Library → 向下滚动到 Opus 5.5 + Jev。
<video controls preload="metadata" poster="/doc/8882/image/img_636666ea746d.jpg" src="/videos/1797.mp4"></video>
https://www.skool.com/milesdeutscherfinance
这个提示词做三件事：
1. 安装 Jev，并引导你在 Vercel 上部署你的机器人，你无需自己摸索
2. 构建一个策略测试器工具面板
3. 将所有东西连接到你的交易所以执行交易
实际交易执行的两条路径
无论你选择哪条路径，我都建议从模拟交易开始。
路径一：Alpaca（快速入门）Alpaca 同时提供模拟交易和实盘交易，还支持加密货币和股票。
Alpaca
只需获取你的 API 密钥，并将其放入 Claude Code 提示你创建的 .env 文件中。Claude 将数据提供给 Jev，Jev 的信号则发送到你的模拟账户。
路径二：全天候运行的服务器（更复杂）
你的第二个选择是部署一个 VPS（例如通过 Hostinger 这样的提供商），这样你的机器人就可以在没有你参与的情况下 24/7 全天候运行。
你可以在较旧的硬件上运行它，比如 Mac Mini。
在这里你可以选择使用 TriggerTrade 这样的工具，它本质上基于预先设定的 Pine Script 在云端触发交易。
如果你选择这条路径，Opus 5.5 可以引导你完成具体步骤。
路径一与路径二
## 寻找盈利策略
这可能是整篇文章中最重要的部分：如何真正赚钱。
你的策略就是一切。没有好的策略，你只是在自动化地亏损。
你应该这样看待你的策略：它是 Jev 用来生成买入/卖出信号的“规则手册”。
有一些不错的地方可以用来寻找/回测可盈利、可供AI使用的交易策略，但其核心在于：
- 回测成百上千个想法——这是数量游戏
- 过滤出幸存者
- 对它们进行前瞻性测试
- 对它们进行模拟交易测试
- 用小额资金实盘，测试执行情况
在下面的文章中，我更深入地讲解了如何找到制胜策略：
> **Miles Deutscher@milesdeutscher**: [原文链接](https://x.com/milesdeutscher/status/2082095561176703135)
>
一些值得寻找策略的地方：
- TradingView 公共社区
- Reddit
- AI（用 Claude 对真实数据进行回测）
- GitHub 仓库
我个人如何寻找策略（回测引擎）
我使用的是一个我自己构建的回测引擎。我可以让它针对各种资产设计回测，告诉它我想要什么样的策略，然后让它运行数小时的搜索并返回一份入围名单。
我可能会在某个时候发布它，但目前仅供我个人使用。
好消息是，你不需要我的。你可以打开 Claude Code，让它构建一个回测引擎工具，并使用你手头最强的模型来回测市场数据。
一旦你有了想测试的策略，将其注入到你正在构建交易机器人的 Claude 对话中，以便它可以摄取并采用它。
## 用 Jev 进行新闻交易（应用案例）
新闻交易引擎是如何工作的
实时新闻流 → Jev 分析 → 扫描你的策略 → 根据设定参数进行交易
新闻交易引擎
这是我发现 Jev 在金融市场中最酷的用例之一。
新闻交易是一场概率游戏。一条新闻标题发布后，你只有很短的时间窗口去判断它的含义，否则市场就会将其消化定价。
我基本上构建了一个仪表板，可以看着新闻标题不断涌入，看到 Jev 为每一条贴上标签，并借助 Jev 的速度 + 执行力获得实时的市场优势。
<video controls preload="metadata" poster="/doc/8882/image/img_6c15cd8244ef.jpg" src="/videos/1798.mp4"></video>
新闻交易引擎 *实盘运行中*
## 最后的建议
• 在任何策略实盘之前，先设定好限制错误时损失程度的规则。将你的风险管理框架构建到你的策略中。md 文件
• 策略 md 中应包含的内容：最大仓位大小、每日亏损限额、紧急终止开关、某些交易需人工批准
• 使用仅有交易权限、已关闭提现功能的 API 密钥。
• 设置警报（通过 TG 上的 BotFather）
• 对较大的交易保持人工在环
• 我个人使用 Bybit 执行交易，但要使其正常工作，你需要任何支持通过 MCP + API 进行AI连接的交易所。
希望这篇文章对你有所帮助。
如果有帮助，请务必在这里关注我 @milesdeutscher。我每天都在发布关于如何真正利用AI在金融市场中获得优势的内容。
想获取更深入的AI洞察，请在 @aiedge_ 上关注我。
提醒：你现在就可以下载完整的大型提示词发送给 Claude，以搭建你的 Opus 5.5 + Jev 交易机器人。
免费发布在我的 Skool 社区中。
👉https://www.skool.com/milesdeutscherfinance
## 相关链接
- [Miles Deutscher](https://x.com/milesdeutscher)
- [@milesdeutscher](https://x.com/milesdeutscher)
- [716K](https://x.com/milesdeutscher/status/2104937986869567565/analytics)
- [$HYPE](https://x.com/search?q=%24HYPE&src=cashtag_click)
- [Sep 16](https://x.com/CompleteSkeptic/status/2099925682726002904)
- [40M](https://x.com/CompleteSkeptic/status/2099925682726002904/analytics)
- [https://www.skool.com/milesdeutscherfinance](https://www.skool.com/milesdeutscherfinance)
- [Jul 28](https://x.com/milesdeutscher/status/2082095561176703135)
- [220K](https://x.com/milesdeutscher/status/2082095561176703135/analytics)
- [@milesdeutscher](https://x.com/@milesdeutscher)
- [@aiedge_](https://x.com/@aiedge_)
- [https://www.skool.com/milesdeutscherfinance](https://www.skool.com/milesdeutscherfinance)
- [Upgrade to Plus](https://x.com/i/premium_sign_up)
- [10:14 PM · Sep 29, 2026](https://x.com/milesdeutscher/status/2104937986869567565)
- [716.7K Views](https://x.com/milesdeutscher/status/2104937986869567565/analytics)
- [View quotes](https://x.com/milesdeutscher/status/2104937986869567565/quotes)
---
*导出时间： 2026/10/26 10:08:40* 
