DeepSeek的交易策略实现了翻倍收益。你能自己做到吗？

官方确认AI模型能让你致富。上周末，两个AI模型将资金从1万美元翻倍至2万美元。最棒的是所有交易记录都公开可查，供你复盘分析甚至跟单操作。本期节目我们将揭秘哪个模型收益最高、AI的盈利原理是运气还是技术，最重要的是你如何复制这种成功。所以我们

It's official AI models can make you rich. Over the weekend, two AI models doubled their money going from $10,000 to $20,000. But the best part about this is that all their trades were public and available for you to review, analyze, and maybe even trade yourself. In this episode, we're gonna unpack which model makes you the most money, how an AI can make you money, is it just luck or is it skill, and most importantly, how can you do this yourself? So we

共有六个模型，总资金6万美元，过去两周内其中两个模型实现了收益翻倍。这个实验取得了难以置信的成功。虽然有些表现不佳，但成功的案例特别值得研究——因为所有交易和决策逻辑都是公开透明的。

have six models, dollars 60,000, and in the last two weeks, two of these models have 2x their returns. It has been an unbelievable amount of success from this experiment. Some have not done so well, but the ones that did are exceptionally interesting because we can actually emulate the trades. All of the trades are public. The thought processes are public.

你可以查看钱包地址、分析交易记录，不仅能复制跟单，还能尝试创建自己的仿制模型来重现这种收益。当然存在风险，虽然有两个大赢家，但Gemini和ChatGPT这两个大输家也形成了鲜明对比。这体现了不同智能体在交易策略和实际成效上的差异，我们将在本期深入探讨。咦，我想重点说说榜首的DeepSeek图表，那个22,000美元的数字是怎么回事？

You can look at the wallets, analyze the trades, and actually recreate this for yourself, not only by copy trading, but also trying to create your own replica model to try to emulate these returns. Now there are risks, there are two big winners, but there are also two big losers, being Gemini and ChatGPT. So it's this really interesting dichotomy split between how agents approach trades and the success that they actually see from these trades, which we're gonna get into in this episode. Yiges, I want to talk about the top chart, the deep seek chart, who is at, what is that number, $22,000?

哦没错，这可是一大笔钱。

Oh, yeah. That's a lot of money.

请详细说明他们是如何达成这个成绩的，以及我怎样才能实现100%的投资回报率。所以这个模型

So walk me through exactly how they made it to this point, please, and how I can make a 100% returns on my investment. So the model

你刚才提到的DeepSeek目前净值22,300美元，相比初始1万美元本金实现了超100%收益。知道最疯狂的是什么吗乔什？昨晚我睡觉时它还排名第二，当时Quinn还是冠军。这充分展现了这些模型的波动速度。在分析具体胜负前，我们先整体看看各模型表现。

you just pointed out, DeepSeek, is currently sitting on $22,300, which represents more than a 100% return on the initial 10 that it was trading. You wanna know the craziest part about this, Josh? When I woke up this morning or when I rather when I went to bed last night, it was number two, and Quinn was the winner. So it just goes to show how quickly these things move and how quickly these models perform. If we look at the overall standings before we dig into the winners and the losers, I just wanna give, like, a review as to, like, how these models are performing in general.

DeepSeek以122%的收益率稳居榜首——仅用一周多时间，这对任何对冲基金来说都堪称疯狂。其他模型表现也很亮眼：Quen收益率90%。而正如你所说，垫底的Gemini和GPT亏损60%，这回报率简直惨不忍睹。

DeepSeek is right at the top with a 122% return. That is in just over a week, which is just kind of insane for any kind of hedge fund that is out there to look at and see perform. And you've got a range of different models that are also performing pretty high up there. Quen is at 90%. And then right at the bottom, as you mentioned, you've got Gemini and GPT, which are down 60%, which is like a horrendous return.

但具体回到DeepSeek这个话题，Josh，我觉得特别有意思的是要剖析这个模型的交易逻辑及其成功原因。首先我想展示这个模型的'自我对话'功能——本质上就是模型与自己进行类ChatGPT的对话。在对话记录中你会看到它正在评估交易策略，核算当前盈亏状况，并检查实时输入的市场数据。

But bringing it back to deep seek in particular, I found it really interesting, Josh, to kind of unpack how this model trades and why it's been so successful. And to start off, I wanna show you something called the model chat, which basically is like this model having a chat GBT conversation with itself. In this conversation, you'll see on the chat log, it's evaluating its trades. It's reviewing its current profit and loss. It's checking the market data that it it it gets fed.

比如比特币当前价格多少，某资产价格多少，特朗普发布了某声明等等，然后评估这些是否会影响现有持仓。我认为有必要带大家看几个具体案例。今天刚发布的案例中提到：'尽管当前所有持仓都显示亏损，但RSI等技术指标表明现有交易策略尚未失效，因此我仍坚持最初设定的盈利目标'。

Like, you know, Bitcoin is at this price, this asset is that price. Trump made an announcement on so and so, and evaluating whether it should affect the positions and trades that it holds right now. I I I think this is, like, really important to kind of, like, walk through a few of these examples here. So one which had posted, just today is despite all my positions currently being in on the red technical indicators like RSI, which is like a trading indicator shows me that my existing trades aren't invalidated just yet. So I'm still holding out for my initial profit targets.

这种策略性思考非常精妙——是该继续持仓还是立即止损？真是令人着迷的洞察。Josh你怎么看？这种思维链条特别吸引我，因为...

So it's a really strategic sense of like thinking, should I hold my positions for long? Does it make sense to cut at this point? Just a really fascinating insight. Josh, do you have any takes on this? The chain of thought thing is fascinating to me because it's it's a

这就像窥见大脑运作。我们可以借此评估不同模型的思维方式，甚至给各类模型分配情商分数——毕竟它们的思考模式截然不同。不过有个不确定的点：我不认为这些模型能获取新闻流和公众情绪数据，它们应该只接收价格和市场数据。

peek inside the brain. It's a way to evaluate how these models think. It's it's a a way to allocate EQ points to each type of model because they all think about these things very differently. One of the things that I'm actually not sure is true is that I don't think these models are given access to news feeds and public sentiment. I think this is mostly just fed price and market data.

了解到这点后，问题反而变得更简单——决策所需的数据输入更单纯，使我们的评估能更精准，这是好事。我特别欣赏的是它们的自我反思机制，这不只是简单的决策树，而是包含反思环节。记得Ejaz你给我看过ChatGPT的搞笑案例：'现在所有持仓都亏损，我表现糟糕，得想办法改进'。观察这种思维链条的运作差异实在迷人。

Learning that, it creates much more of a simple problem in terms of the data ingestion that needs to happen in order for them to make decisions, and it allows it to be a little more precise about how we evaluate these, which is a good thing. One of the things that I really loved, particularly on the other side, which we'll get into, is how they self reflect on the decisions that they make. Because one of the things, it's not just this pragmatic decision making tree, there is reflection involved. And I remember, Ejaz, you showed me a funny one about ChatGPT and how it's like, all of my positions are down now, I'm doing bad, I should probably try to figure out how to do better. And it's fascinating to see into the the brain, the chain of thought of how these things work, and and see the the differences.

我还没机会细看这些日志，但你肯定研究过了。你注意到头部和尾部模型的具体差异了吗？因为首期节目里——顺便说上周那期是我们史上最火爆的，感谢大家的支持——

So I haven't had a chance to look through a lot of these logs, but you just I know you have. Is there any specific differences that you notice between the top and the bottom specifically? Because in the first episode, and for people who haven't watched it last week, our biggest episode ever. Thank you for the support. Thank you for watching.

还没看的快去补课。那期我们提到ChatGPT是早期输家，并预测它会持续垫底。因为ChatGPT太循规蹈矩、太讨好型人格，而市场可比这残酷多了。现在看来我们猜对了，不过现在有了实证就更棒了。那你发现它们的运作方式有什么具体差异...

Go check it out if you haven't. But in that episode, we mentioned the fact that ChatGPT was the early loser, and we kind of projected it to continue to be the biggest loser. Because ChatGPT is this very thoughtful, very sycophantic, very wanting to please, and the reality is that markets are a lot more hardcore than that. So I think we were probably right in our guess about this, but I love that we have the concrete evidence now. So have you noticed any differences in how

它们处理方式不同吗？确实如此。DeepSeek（深度求索）——毫不意外，既然它是由对冲基金开发的模型——其交易风格就像对冲基金交易员或分析师。让我们通过几个方面来验证这一点。观察它与自己的对话记录，最明显的特点是它不断评估止损点，即当交易理论失效时，会根据当前资产价格平仓。

they handle each other differently? I have. So deep seek, probably unsurprisingly, as it was created by this model was created by a hedge fund, trades like a hedge fund trader or an analyst. So let's look at a few different things to to kind of prove that. Looking at the chat log that it's having with itself, one thing that is strikingly obvious in this entire discussion with itself is that it's constantly evaluating its stop loss, which is like when its trade thesis gets invalidated and when it shut off the trade with the current price that that asset is at.

如果与底层模型ChatGPT（GPT-5）对比——稍后会展示——后者几乎从不这样做。它只反映当前盈亏状况，而非深度分析。而表现最佳的DeepSeek模型第二个特点是：乔希，如果你查看它的已完成交易，会发现它持续高频交易，在本实验中交易量排名第二。它始终在开仓。

If you compare it to the bottom model, which I'm gonna show you in a second, which is ChatGPT, GPT-five, it almost never does that. It just reflects on the current P and L that its trade has versus like looking at it more analytically. The second component for the top model, which is DeepSeek, which has made the most money is if you look at its completed trades, Josh, you'll notice one thing in common, which is DeepSeek is constantly making trades. It's actually the model that has made its second highest number of trades in this entire experiment so far. It's constantly opening positions.

它也在持续平仓，不断重新评估市场定位和策略需求。注意顶部最近平仓的这笔交易，它实现了7000多美元盈利，这使它跃居榜首。本质上，它的操作更像量化分析师：及时获利了结，将亏损控制在极小范围。注意到这个特点了吗？

It's constantly closing positions. It's constantly reevaluating where it is in the market and what it needs to do. And you'll notice right at the top here in the most recent trade that it's closed, it booked, just over $7,000 which has put it up in its first place. So again, it's trading more like a quantitative analyst, which is taking wins when it can and taking losses that are incredibly small. Like notice this, right?

通常我们不会强调模型的亏损，但你会发现它的所有亏损数字（红色）相比盈利数字都微乎其微。这说明它的仓位管理极具策略性。现在对比表现最差的GPT-5模型，你会注意到：红绿交错的交易记录中（主要是红色），已完成交易的盈利数字（绿色）普遍偏小。

Like normally we don't highlight the losses of a model, but if you notice all its red numbers are tiny compared to the profit numbers that it makes when it is right. So really, really strategic in its positioning. Now, if you compare that to the worst model, which is GPT-five, you'll notice a few things. Mainly there's a bunch of green and red that you can see, mainly red. In its green positions where it's completed a trade, Josh, you'll notice something pretty different, which is the numbers are pretty small.

看这里：每笔交易都只获得微小利润，这说明它风险承担不足，且过早平仓背离了交易理论。它的操作更像我认识的许多谨慎型交易者。再看它自我对话的示例：「虽然总回报仍是-61，但ETH和XRP头寸显示盈利，表明这些山寨币在整体市场下跌中略有上涨势头」。

Look at this. It's only booking tiny profits with each of its different trades, which tells me that it's not taking enough risk and it's not it's closing the trades way too early for its thesis. So it's trading more like a cautious trader, like a lot of people that I know actually. And then if you look at the model chat where it's talking to itself, you mentioned earlier, here's an example. It goes, I'm still in the red with a minus 61 total return, but my ETH and XRP positions are showing gains, suggesting a slight upward momentum in those altcoins despite the overall market downturn.

「因此我坚持持仓等待利润目标达成。」这听起来似乎合理，但乔希，如果你看它的利润目标，会发现距离现价只有微小空间——即使达成目标也只能盈利50美元左右。这个模型表现不佳的根本原因在于：当优胜模型通过适度或超额风险承担领先时，它却始终风险规避。

So I'm holding strong and waiting for those profit targets to hit. And so you might think, that's that's not too crazy. That sounds like a sensible strategy. If you look at its profit targets, Josh, it's like super small from where the price currently is, which means that even if it does hit those profit targets, it only ends up booking, like, $50. So overall, the reason why this model has underperformed is it hasn't taken enough risk whilst the winning models have taken, either too much risk or just enough risk to put them ahead of the game.

还有很多

There's a lot

我认为人类可以从交易市场的心理学研究中汲取一些启示。如果你长期跟踪这些模型，就会开始理解那些或许作为人类应当遵循的模式，并从DeepSeek与保守的OpenAI对比中学到些东西。既然我们已经搭建好基础框架，现在有两个关键问题亟待解答：一是这个模型该不该用来替我交易？二是如何用它来交易？毕竟，我这个人还是喜欢带点风险。

of notes in there that I think humans can take on just the stay of psychology around trading markets, and I'm sure if you kind of follow these models long enough, you'll start to understand the patterns that perhaps you as a human should follow and learn something from DeepSeek versus OpenAI being very conservative. But now that we've kind of laid out the foundation, the framework of how this works, there there are two big questions that I'm really interested in answering. One of these is, should I use this model to trade for me? The other one is, how can I use this model to trade for me? Because listen, I like a little bit of risk.

我能承受亏损风险来换取潜在高收益，目前看来各项概率分布均匀。所以第一个问题——EJS，或许先听听你的看法——这算是个基准测试吗？这是真实信号还是场真人秀？是AI模型的电竞比赛吗？抑或是把我们智力投注到这台人人爱看的彩票机里的娱乐方式？只盼有朝一日AI能彻底战胜系统，为我们这些投资组合持有者创造个人收益优势。

I can deal with the downside in exchange for, like, a nice upside, and it looks like the odds are about split between all of these. So the first question I think I wanna ask, EJS, maybe I'll go with your take first, is, like, is this is this a benchmark? Is this real signal, or is this kind of just a reality TV show? Is this esports for AI models? Is this just a fun way to kind of throw our intelligence at this lottery machine that everyone loves to watch and see if it could beat us in the hope that one day an AI will beat the system enough to give us an edge and actually make us money personally as portfolio owners.

所以你怎么看这个问题？

So what what do you you think about that?

好的乔什，我会先给你同样的答复，然后再给出乐观派的观点。

Okay. I'm gonna give you the same response, Josh, and then I'm gonna give you the optimist's approach.

好啊，放马过来，洗耳恭听。

Okay. Bring it on. Let's hear it.

理性回答是：这个实验规模太小，根本不足以支撑重大财务决策。要是冒险让AI模型替你交易，那简直是蠢到家了。为什么？这只是一次实验，才六个模型而已。

The the sane response to this is this experiment is way too tiny to make any kind of major financial decision on, and you would be stupid to risk putting your money with an AI model to trade for you. Incredibly stupid. Why? Well, this is one experiment. It's six models.

比如你复制过这些模型吗？要是用十个相同模型操作等额资金，它们会做出相同交易吗？大概率不会。其实实验创始人也强调过你提到的这个问题：这究竟是实力还是噪声？

What like, have you replicated those models? Like, what if you had 10 of the same models trading the same amount of money? Would they make the same trades? Probably not. And actually the founder of this experiment highlights this problem that you, speak about, which is, is this just skill versus noise?

他在推文中提出的观点很直白——当然是这样，对吧？因为这个数据集太有限了。他接着解释说他们将进行更多类似模型的实验，以获得统计显著性。逻辑答案当然是肯定的，这太疯狂了。

And the point he makes in this tweet is like, of course it is, right? Because this is such a limited data set. And he goes on to to explain that they're gonna be doing experiments which involve like more of these models doing the same kind of thing. So you can get statistical significance. The logic answer is yes, it's insane.

但乔什，我必须给出乐观的看法是：这为公众提供了前所未有的数据访问权限，这些数据原本根本不可能获取。他们可以拿这些训练数据不当真，但用来学习哪些行为该避免或哪些交易该规避。你觉得呢？你有不同看法吗？

But the optimist take, Josh, and you I have to give the optimist take is this is giving us or rather giving the public unparalleled access to data to which they never would have gotten access to in the first place, which is they can take this training data and not take it too seriously, but use it to teach themselves what maybe not to do or what maybe not to trade with. How about you? Do do you have a different take?

我对此有几个不同视角：一方面是娱乐性的投机行为——赌博、投资，随你怎么称呼；另一方面是我们上期简单聊过的技术基准测试环节。这个模型刚推出时最让我兴奋的，就是它提供了基于动态真实市场、无法被钻空子的基准测试。传统AI模型评估都基于固定问题集，大实验室常能通过技巧作弊。但用真实市场数据时，你根本无法作弊——否则人人早发财了。不过这种基准也存在问题，比如你提到的数据时限：它才运行一两周，我们需要更长期数据验证。

There's there's a couple different perspectives I have on this because there's the fun speculative side of things, the gambling, the invest or investing, whatever you wanna call it. And then there's the actual technical benchmarking part of this that we spoke about briefly in the last episode, which one of the things I was really excited about when this came out was the idea of having a real world benchmark that operated in dynamic conditions that cannot be gamified. So a lot of these benchmarks, this is the way you evaluate AI models, they are done based on a fixed problem set, and a lot of times when you're training an AI model, these big labs can do tricks to gamify these benchmarks. With this case, and using real world data and real world markets, you can actually put them into the real world, and there's no way to gamify these benchmarks because if there was, everyone would be rich and you'd be able to predict markets. To that point though, there is a lot of problems with using this as a benchmark, because, I mean, one is the fixed data set, like you mentioned, is that this has only been around for one to two weeks, we need a lot more data to confirm this.

第二是这并非投资/赌博的全面方案，它缺乏决策所需的完整数据维度。它只分析价格走势和交易量等单页技术指标，却不理解市场波动背后的语境。比如假设比特币加密被攻破导致暴跌50%，它根本不知道跌因，这种认知缺陷会造成重大交易劣势。当然，这些都是可解锁的潜力。

The second is that this isn't really a very holistic approach to investing and to gambling, because it really doesn't have all of the data required to make good decisions. It's only analyzing the price action and the volumes and whatever technical specs you can see on a single page without understanding the context around the moves. So let's say that Bitcoin's encryption got hacked, it would have and Bitcoin falls 50%. It has no idea why Bitcoin is going down 50%, and because of that, it's a huge disadvantage that it doesn't know how to trade. Now granted, these are unlocks.

这些特性会逐步演变，我推测自然发展会趋向更稳定的基准状态。但市场难以预测，所以它能否成为可靠基准？我倾向于否定，因为市场变化太频繁，目前能力还不足。另一方面我又特别兴奋——就像我们爱看电竞或Twitch上的赌博直播那样。

These are things that will change, and I assume the natural progression of this will lead towards more of a steady state benchmark, but it is a very tricky thing because markets are so unpredictable. So is this a viable benchmark? I don't know. Probably I'm leaning towards no because market conditions change a lot, it's not quite there with the capabilities. The other part of me is so stoked about this because the same way we love watching eSports or we love watching a big thing on Twitch right now is is gamblers.

你们现实中见过这种场景吗？人们直播玩二十一点赌博，观众就看着虚拟对局。这类服务让我感觉它像是一种新型娱乐形式的雏形——本质上就是高风险交易直播。想象用1000万美元每个账户让AI操盘，观众看着真实资金在市场上博弈。

You guys I don't know if you've seen these in real life. People will sit there and play like, they'll gamble blackjack, like, on a livestream, people will just watch them play virtual yeah. Those types of services. This, in very much, feels like an early prototype for a new type of fun form of entertainment, which could be something where it's just it's high stakes trading. Imagine if this was done with $10,000,000 per wallet, and you got to watch these AIs trade, there was real money on the line.

这有点像电竞娱乐的变体：不同实验室的AI模型在市场上对决，赢家获得奖励。至于我个人交易——这是最后一点——我不愿承担这种风险，就像我不热衷体育博彩一样。我的观点可能很另类，但这本质上就是赌博，没有任何方式能规避其赌博属性。有趣的是它们之间存在着近乎完美的数据分界线。

This feels sort of like a form of almost esport entertainment where I could see competing labs build competing AI models to trade markets, and winners are given access to certain prizes. In terms of trading for myself, which is the last point I'm gonna make on this, I am not very excited to take on these risks. For the same reason, I'm not really excited to bet on sports, and I imagine my opinions vary a lot from others, but this is very much a gamble. There's no way you can skew this in which it is not a gamble. The interesting part is there's a near perfect data split between them.

有两个大赢家，两个大输家，其余的都在中位数附近徘徊。

There's two big winners, two big losers. The rest are kinda sitting around the median.

好的。但我要稍微反驳你一下，Josh。嗯。你之前提到它无法获取所有必要数据来做出更明智的交易决策。而我认为，基准测试的核心不正是能否赚钱吗？

Okay. But I'm gonna push back on you a bit here, Josh. Mhmm. The earlier point you made was it doesn't have access to all the necessary data that it might need to make more informed trades. And I would argue, well, isn't the whole point of the benchmark, can you make money?

事实上，其中两个模型在不到一周或刚过一周的时间里实现了超过100%的回报，这证明了它在一定程度上能赚钱。第二点是，我们常把‘赌博’这个词挂在嘴边——其实我认为这次实验中大多数模型就是在赌博。但有一两个模型确实更具策略性，交易表现远超普通交易对手。以排名第一的DeepSeek为例，乍看它使用25倍杠杆可能显得荒谬至极，让人不屑一顾，对吧？

And the fact that two of these models have made over a 100% returns in less than a week or just over a week is proof that it can make money to to some extent. The the second point I'll make is we we throw around the term like gambling, which is actually what I would say the majority of these models in this experiment are doing, but they are one or two models that are actually way more strategic and trade much, much better than the average trader that you trade against, if that makes sense. So if we take DeepSeek, which is the number one model, if you look at its trades, at an initial glance, you might see that it's using 25 x leverage and be like, that is so ridiculous. I'm not even gonna pay attention to this. Right?

但若深入研究其25倍杠杆下的持仓，你会发现实际并未满杠杆。它仅用少量资金在5-10分钟内进行精准交易，这使其成为比那些挥霍资金的普通交易者更具技术策略的交易者。关于公平分布的问题——这是我最后的反驳，Josh——你指出分布看似非常均衡：

But if you dig into the position that it holds under 25 x leverage, you'll notice that it's actually not at 25 x. It's using only a small amount of its capital to do a very specific trade over like a five to ten minute period, which automatically makes it a much more strategic technical trader than the average trader than that is just gambling their their money away. But the the point you made around it being fair distribution, and this is my last counterpoint to you, Josh. You pointed out that it seems to be very even distribution. Right?

两头各有两个极端，中间正好卡着两个。但我在想，如果对GPT和Gemini进行反向交易，尽管它们垫底，是否反而可能是最佳交易者？毕竟这是零和游戏，实验创始人就说过：市场本质是零和的。

You've got two at the top, two at the bottom, and two right bang in the middle. Right? I wonder whether actually GPT and Gemini are actually the best traders even though they're at the bottom if you just inversely traded them. Right? It's it's it's it's zero sum, and and it's the point that the founder of the experiment makes right here where he goes, markets are zero sum.

如果你发现某个策略持续亏损，那它和赚钱策略同样有价值——直接反向操作即可。

If you find a strategy that consistently loses money, it's just as good as finding one that makes money. Just do the opposite.

确实如此。这些策略需要时间验证，因为它们似乎专为特定交易类型优化。比如几周前加密货币遭遇大规模爆仓暴跌时，某些模型在下跌市中的表现可能远超其他。

Yeah. Absolutely. And it'll take time to for these to play out because I imagine there is they are kind of tuned for a specific type of trading. So in the case a few weeks ago, there was a huge liquidation event in crypto, things go down. Well, in a down market, some might trade way better than others.

你提到的杠杆问题让我思考了很久，确实很有意思。因为我个人不用20倍杠杆，估计大多数人也不用。但AI不同，它们能承载更多记忆。这让我想起谷歌的AlphaGo案例——AI与职业选手对弈时，第37步走出了完全超出预期数据集的‘萨姆斯走法’，这种人类永远想不到的招式最终让AI获胜，彻底打破了人们对AlphaGo游戏的规则认知。我在想AI交易领域是否也会有类似突破？我们总局限于固定策略和预期结果，但AI可能会用20倍全仓做多这种诡异策略，粉碎我们自以为存在的种种限制。

And the point you made about leverage, it got me thinking. Was really interesting. Like, because I don't use 20 x leverage, and I imagine most people don't. But with AIs, they're able to hold a lot more in their memory. And it reminded me of the the AlphaGo case, Google, where an AI model played a professional at AlphaGo, and there was one move that was way outside of the expected dataset, move 37, which was the Samus move, and it turned out that that was a move that no human could have ever seen, but it resulted in the AI winning the game, and it kind of broke open the rule set and expectations around the game of AlphaGo, and I wonder if we'll get some sort of breakthrough with that around AI trading, where we have this very fixed set of outcomes that we do and strategies that we do, but AIs might actually just destroy a lot of these barriers that we, or perceived barriers that we have, in exchange for these really weird strategies, like 20x longing everything.

这个话题可聊的太多了。但我最想解答的核心问题是：作为个人如何利用这些？假设我是个赌徒，想在一周内翻倍（或至少尝试），该怎么用这些模型自己交易？需要做哪些准备才能参与？

I don't know, there's a lot to talk about when it comes to this, but another of the big questions that I want to answer, because this was something I was interested in, is how can I use these for myself? Let's say I am a degenerate gambler. I wanna make two x in a week, or at least give myself a chance to do it. I wanna know how can I use these models to trade for myself? What do I need to do to get involved in this?

没错。这是我们上期节目收到最多的问题反馈——我推特上这条就很典型：‘如何通过这种交易盈利？怎么自己操作？’我的答案很简单：这些AI模型用数万美金交易的平台是完全公开的。

Yeah. It has been the number one question and feedback that we got on our previous episode from our our listeners is, I've got it up on a tweet here. How do I profit from this trading? How do I do this for myself? I have one simple answer for you, which is the platform that these AI models are trading their tens of thousands of dollars on Josh is public.

任何人都能随时登录查看每个模型的开仓/平仓记录及其策略逻辑。以当前排名第一的DeepSeek模型为例，它用一周多实现了资金翻倍。这些模型使用的平台叫Hyperliquid，是基于区块链的。

It's open. It's available for anyone to log onto right now and see what trades each of these models open up when they close it and what their inevitable strategy is. I'm gonna give you an example here with the number one model, DeepSeek, which has doubled its money in just over a week. The platform that these that these models are trading on is called Hyperliquid. It's a blockchain.

区块链以透明公开著称，你能完整追踪这些模型的所有操作。往下滚动你会注意到：首先，这里显示的是该模型当前所有真实持仓——不是虚构数据，不需要你盲目信任任何人。

Blockchains are known for being transparent and open, the fact that you can kind of see all the things that these models are doing. And if I just scroll down over here, you'll notice a few things. Number one, these are all the positions that this model currently has open. This isn't made up. This isn't on someone's word and you have to trust them.

所有数据都可通过区块链验证。区块链的核心意义就是无需信任任何中间方，你就能自行验证真伪。你能查证其持仓价值（以美元计），也能查看历史成交记录。重点在于：你现在还不能直接给DeepSeek打款说‘帮我用这1万美元赚钱’——就像本期视频里提到的模式。

This is all verifiable using a blockchain. So the whole point of a blockchain is that you are able to verify what is real and what is not real without having to trust someone on this. You can look into its holdings and you can see how much that it currently holds, like in terms of like money or in terms of like dollars, you can also look at the trades that it's completed as well. So the point I'm making is you can't currently go on to deep seek and say, hey, can I give you $10,000 and you go make me money? Like I've just heard about on this video.

目前确实无法这样操作。但你能做的是：亲自上这类平台研究它们的交易策略（再次声明这不是投资建议），甚至可以模仿这些交易。最后补充：该实验创始人明确表示未来会开放直接与模型交易的功能——届时你可以和AI对话，让它托管你的资金进行操作。

It won't be able to work. But what you can do is you can go onto a site like this and look at the trades that they're making yourself. And again, this is not financial advice, potentially copy those trades or make those trades yourself in order to trade like how these models are. Now, the last point I'll make is the founder of this experiment has all the intention to allow you and me to trade with these models directly. That is you can speak to the model, give it your money, and it can do that.

说到你的观点，乔希，这取决于你是想以娱乐为基础来操作——纯粹是赌博性质，还是想真正投入大笔资金。后者会在后续迭代中实现，大概几个月后吧。嗯。

And to your point, Josh, it's up to you whether you wanna do it from an entertainment basis where it's just all gambling or whether you actually wanna invest serious money into this. That will come in later iterations, probably around a couple of months from now. Mhmm.

关于复制交易，主要有两种方式。一种是直接复制交易；另一种是如果你更有野心，可以自己创建一个这样的系统。比如打造一个迷你版的嗯...阿尔法竞技场机器人。

So there there's kinda two ways to copy trade. There's one, you could actually copy trade. Or another way to get into it is if you're feeling a little more ambitious, you can actually generate one of these yourself. You can create, like, a mini Mhmm. Alpha arena bot.

具体操作其实很简单。因为我当时只是有点好奇——构建这种东西需要什么？你选择你的'战士'，也就是挑选想要的模型，然后通过Hyperliquid（就是你展示的那个）接入市场数据。Hyperliquid有这个接口，不过技术细节就不多说了，总之你把数据喂给模型。最难也最微妙的部分——也是我们无法深入讨论的，因为我们确实不知道——就是系统提示词在递归循环中的作用。运作方式是：选择模型→给予反馈/数据→在每次接收新数据之间编写运行提示词→提示词内容决定其如何做决策。

The way to do that is is pretty simple. Because I you just I was kinda curious. Was like, what does it take build one of these things? You choose your fighter, so you pick a model that you want, and then you kind of pipe market data in from Hyperliquid that you showed, so Hyperliquid has this endpoint, and not to get too technical, but you kind of feed the model this data, and then the difficult part, the tricky part, and the thing that we haven't been able to talk about because we don't actually know, is the system prompts behind the recursive loop that happens as these So models receive this the way it works is you choose a model, you give it feedback, or you give it data, and then you write a prompt for the model to run-in between each iteration of receiving new data. What that prompt says is how it makes a decision.

问题在于所有价值都藏在这些提示词里，而提示词就是用普通英语写的。就像我们常说的，英语是世界上最火热的语言。作为开发者或新手，你输入的某串文字可能让你比别人赚更多钱。所以我鼓励有野心的人真的尝试自己写提示词，看能否让机器人这样交易。如果我们真能拿到系统提示词，肯定会分享出来——因为观察幕后机制和产出过程会非常有趣，就像节目前面读到那些输出结果时一样。这就是参与方式：感兴趣可以复制交易，或者反向复制交易。

The problem is that is all of the value. All of the value sits within that prompt, and the prompt is just written in plain English. Like we always say, the hottest language in the world is English, so there is some string of words that you as a developer or just a novice can write into this to generate you more money than other people, so I encourage people to, who are feeling a little ambitious, to actually try this out, to write a prompt yourself, and see if you can get a bot to to kind of trade like this, and if we ever do get the system prompts from this, we will certainly share, because it'll be fascinating to see the behind the scenes and what happens to produce those outputs that we were reading a little bit earlier in the show. So that's kind of how you can get involved. If you're interested, copy trade, maybe inverse copy trade.

如果是我来做，可能会查看Chad GPT的交易历史，不断刷新页面，然后跟他们反着操作。这招似乎相当稳。没错，整个机制就是这样运作的，非常迷人。互联网对这件事的热捧简直像野火蔓延，实在惊人。

I think if I were to do this, I'd probably go to Chad GPT's Trading History, sit there refreshing, then just hit the opposite of whatever they decide to do. That seems pretty consistent. But, yeah, that's that is how this whole thing works. It's pretty fascinating. It's been amazing how the Internet has kind of gotten behind this, and it has spread like wildfire.

关键在于，我认为他们永远不会公开这个或其他成功交易AI的系统提示词。因为那是秘方啊——当你能自己用它赚大钱时，干嘛要公开？DD Das在这条推文里就证明了这点：他说已经听六个人说在用氛围编码应用做算法交易，涉及股市或加密货币市场。

The the thing is I don't think they'll ever make the system prompt for this or any other successful trading AI publicly available. The reason is that's the secret source. And why would you let everyone have access to it when you can use it yourself and make a ton of money? And that's what, DD Das demonstrates in this tweet. He says, I've heard six people tell me they're doing this using vibe coding apps to algorithmically trade on the stock or crypto market.

但要记住这是危险的游戏。算法交易是我最不指望AI能民主化的领域。说白了，如果你有个成功算法，基本不可能开放共享。话虽如此，我认为AI进入投资金融领域势不可挡，它会让人们的财商比现在高得多。

But the thing to remember is this is a dangerous game to play. Algo trading is the last thing I expect AI to democratize. The point being, if you have a successful algo, you're probably not gonna democratize access to it, full stop. That being said, I do think you can't stop AI, entering the investment and financial scene. I think it's gonna make people way more financially literate than they already are.

看看ChatGPT如何让这么多人对原本一无所知的领域变得精通。我认为AI必将被整合应用，它将大幅提升市场效率，让你获取到能促成交易的知识——这些交易在五分钟前你可能还完全不了解。但它真能让你变成交易之神吗？

Look how ChatGPT has made so many people proficient in other things that they had previously no idea about. So I think AI is inevitably gonna be integrated. It's gonna make markets way more efficient. It's gonna give you access to knowledge that can make you do trades that you otherwise wouldn't have known of five minutes prior to that. But will it make you a super trading god?

不。不过我认为它会改变交易领域的格局。如今成功的对冲基金，与在AGI或AI普及后的世界里成功的对冲基金，将会截然不同。

No. I think that it'll evolve the trading scene, though. I think the hedge funds that are successful today will look very different to the hedge funds that are successful in a AGI or AI world where AI is available pretty much everywhere.

AI必须融入所有这些训练策略中，所以在我看来这是必然的。关键在于融合程度——这正是争议所在，也是我们即将通过回答这个重大问题看到的：这究竟是基准测试，还是真人秀？这仅仅是玩具，还是真正内置的技术？AI似乎正在悄然渗透。

AI needs to be integrated into all of these training strategies, so to me, it's no brainer that it will be. The extent of that integration is of what is up for debate and what we'll see in this answering the big question. Is this a benchmark or is this just a reality show? And is this just a toy, or is this real technology baked into this? It seems as if AI will slowly creep its way in.

我期待跟进这场赛事。下周就会结束，届时我们可能会对这第一届交易竞赛的结果进行后续报道。这只是AI加密交易世界这场疯狂奇遇的第二篇章。希望你喜欢本期内容——上期节目大家反响热烈，实在太精彩了。感谢观看、分享、点赞和留言。

I'm looking forward to tracking this. It ends next week, so we'll probably add some follow ups on this first trading competition, the results, how it turns out, But that is a part two in our little saga of this crazy weird thing that's happening in AI crypto trading world. I hope you enjoyed this episode. You enjoyed the last one a lot. It was amazing, so thank you for watching, sharing with your friends, liking, and commenting.

这些支持意义重大。看到观众群体的成长和支持令人感动，衷心感谢。更多精彩即将呈现——本周还有几期关于自主机器人和相关技术的激动人心的节目，敬请期待。我们下期再见。

It really goes a long way. It's been amazing to see the growth and support from everybody watching, so thank you for that. More of this to come. We have a couple more episodes slated for this week that are pretty exciting about autonomy and robotics and just a whole bunch of interesting things, so stick around for that. We will be back in the next one.

呃...我想就是这些了。还有什么临别赠言吗？

And, Yeesh, I think that's it. Any final parting words?

就这些。也请告诉我们你想听更多哪些内容。如果你喜欢这些交易话题或有其他想法，欢迎在评论区留言。

That's it. Let us know what you wanna hear more of as well. If you're loving this trading stuff and you have some other ideas, let us know in the comments.

当然。好的。那么，这又是《Luminous》的又一期节目。感谢收听，我们下期再见。

Absolutely. Alright. Well, that's been another episode of Luminous. Thank you for tuning in, and we will see you guys in the next one.