Wrestling the World Into Rows with Eric Mannes

[music] It's my pleasure to introduce Eric Manis. Eric has been at Gain Street for about a decade and he's had a really interesting range of roles here spanning trading and technology and these days thinking a lot about alt data which is a lot about what we're going to talk about today. So thanks for joining me.

[音乐]我很荣幸向大家介绍 Eric Manis。Eric 在 Gain Street 工作了大约十年，期间担任过一系列非常有意思的职位，横跨交易与技术领域。最近，他一直在深入思考另类数据，这也是我们今天讨论的主要内容。感谢你参加这次访谈。

Thanks for having me.

谢谢你的邀请。

So maybe to start with I just like to hear a little bit more about how you got to Jane Street in the first place.

首先，我想详细了解一下你最初是如何加入 Jane Street 的。

I studied math in college and I looked at what many of my peers did and a lot of them were going into academic research. um perhaps in Boston. Uh a lot of them were going into tech often in San Francisco and uh some of them were going into uh finance uh usually in New York City. And so I thought like well I have three summers in undergrad and I'll just spend one summer trying each of these and at the end I was like Jane

我大学主修数学。当时，我观察了身边许多同龄人的选择：很多人投身于学术研究，可能是在 Boston；很多人进入了科技行业，通常是在 San Francisco；还有一些人进入了金融行业，通常是在 New York City。由于本科期间有三个暑假，我决定每个暑假尝试其中一个领域。最终，我认为 Jane

Street is by far the most interesting of these and the best fit for me so I'll do that. So what about the trading experience struck you as so interesting?

Street显然是其中最有意思、也最适合我的，因此选择了这里。交易经历中的哪些方面让你觉得如此有意思？

So uh I was a trading intern and I was really struck by like how much I was learning.

当时我是交易实习生，令我印象非常深刻的是，我学到了非常多的东西。

But I mean I guess one of the interesting things about trading is like by and large it's not a thing that anybody knows, right? Like we hire software engineers and they've often done software engineering in other places but like the vast majority of people who come here have no experience in the trading world, right? And so um there was nothing really um outside that had you know given me like practice trading or any

交易的一个有趣之处在于，总体而言，人们对它并不了解。例如，我们招聘的软件工程师通常曾在其他地方从事软件工程工作，但来到这里的绝大多数人都没有交易领域的经验。因此，外界其实没有多少机会让我练习交易，或是

depth in thinking about markets. And um when I came here, I was struck by like how much time people at Gan Street like spent teaching the interns like about like the fundamentals of how markets work uh about um concepts like adverse selection, how to do research well and people use the word anti-inductive environment of like when you discover a law in when when you discover some effect in physics, it's not like that

深入思考市场。来到这里后，令我印象深刻的是，Jane Street的人会投入大量时间，向实习生讲授市场运作的基本原理、逆向选择之类的概念、如何做好研究，以及人们所说的“反归纳环境”。当你发现一条规律时，例如在物理学中发现某种效应，它并不会因为被发现就

effect goes away in finance like the um things that you find um the patterns that you find often get discovered and competed away over time and the things that you like knew stop being true or as important. Um and so you like have to keep discovering new things, right? And then what remains looks ever more and more like a random walk. This is why actually our signal to noise ratios are kind of miserable because

消失；但在金融领域，你发现的现象、模式和规律往往也会被其他人发现，随后在竞争中逐渐失效。随着时间推移，那些你原以为成立的规律也会不再成立，或者不再那么重要。因此，你必须不断发现新的规律。这样一来，剩余的部分看起来就会越来越像随机游走。这正是我们的信噪比如此之低的原因，因为

there's this whole system whose job is to like extract signal from the system and have it look more like a kind of pure representation of our remaining uncertainty which is sort of by its nature looks more random right and when you act in this system other people like respond uh and change their behavior in ways that like weren't visible in your training data.

有一整套系统会从中提取信号，使其更像是对剩余不确定性的纯粹呈现，而这种不确定性在本质上更具随机性。此外，当你在这个系统中采取行动时，其他人会作出反应并改变自己的行为，而这些变化并没有体现在你的训练数据中。

Yeah. Like kind of an amazing thing about like the world of trading is it's this interesting like compromise and incentive system that rewards people for learning things about the world but does it in a way that essentially forces them to give some of that information up. So you have this kind of information discovery system where all of these people trading and competing with each other feeds information into the markets

交易世界的奇妙之处在于，它构成了一套很有意思的平衡与激励机制，会奖励那些了解现实世界的人，但其运作方式实际上又迫使他们交出一部分信息。由此便形成了一种信息发现系统：所有相互交易、相互竞争的人都在不断将信息注入市场，

and then the markets now are a kind of more reliable gauge for what things are worth which tells you all sorts of things about what's happening in the world. I think that felt uh very real on um commodities and on in my current work on alt data where like we are looking at data from the real world to understand it better and incorporate that information into market prices. So you said that like you were struck by the

于是，市场便成为一种更可靠的价值衡量工具，能够反映现实世界正在发生的各种情况。我在大宗商品业务以及目前从事的另类数据工作中，对此有非常切实的感受。我们会研究来自现实世界的数据，以便更好地理解现实，并将这些信息纳入市场价格。刚才你提到，大家在

amount of effort that people put into teaching and I think teaching really is an important thing here and actually I think one of the interesting like facts about G& Street which is maybe not obvious from the outside is like the insane amount of effort that goes into the internship. Uh because we think like getting great people is incredibly important and we've kind of put almost a pathological amount of work into making

教学方面投入的精力给你留下了深刻印象。我认为，教学在这里确实非常重要。事实上，Jane Street有一个外界可能并不清楚的特点：我们为实习项目投入了惊人的精力。因为我们认为招到优秀人才至关重要，所以几乎以一种近乎偏执的程度投入工作，

the internship a really great experience. Um but I'm kind of curious like how did you feel like that played out? like what were examples of the kinds of things that happened in the internship that you felt like taught you more about how to think about markets and and and kind of what the job was like?

努力让实习项目成为一段非常出色的经历。不过，我很好奇你的实际体验如何。例如，实习期间发生了哪些事情，让你进一步学会了应当如何思考市场，并了解这份工作大致是什么样的？

One uh important part of the internship was like mock trading or trading at low stakes. You can talk about good decision-m in the abstract but no replacement for actually trying to do it live.

实习中很重要的一部分是模拟交易或低风险交易。你可以从抽象层面讨论如何作出好的决策，但这无法替代真正尝试实盘操作。

Yeah.

是的。

Yeah. [laughter] There's no replacement for actually trying to do it live. doing it in a context where you kind of commit to something and there are consequences to what you choose. I think provides like useful feedback, right? And I think this goes back to the fact that very early on at James Street, we felt like people the early people here learned an enormous amount from like being on the trading floors and in

确实如此。[笑声]置身于一个需要实际作出决定、而且自己的选择会带来后果的情境中，可以获得很有价值的反馈。这也让我想到Jane Street创立初期的情况。我们发现，这里早期的员工从交易大厅以及这种实盘交易环境中

this kind of live trading environment like kind of the nature of what trading is and how it works and the basics the kind of basics of adverse selection and information flow and all of that. But like you can't actually send everyone back to the trading floors. Many of them don't exist anymore. So like what does it actually look like being in a mock trading session?

学到了很多，包括交易的本质、交易的运作方式，以及逆向选择、信息流等方面的基础知识。但我们不可能真的把所有人都送回交易大厅，而且很多交易大厅已经不存在了。那么，参加一场模拟交易活动究竟是怎样的体验？

Yeah, this has improved a lot over time.

这些年来，模拟交易活动已经改进了很多。

But like uh one thing that we do is uh like we did do have like a simulated stock exchange where interns can trade and we'll have these like one or two hour like scenarios um that kind of distill like some important trading concept or thing that happens in real life into a like time boxed toy model of it. So, what's an example? Um, we have a uh trading card game, not trading card game. Well, it is trading and it is a

我们的一项做法是建立一个模拟证券交易所，让实习生在其中交易。我们还会设计一些持续一两个小时的情境练习，将现实中某个重要的交易概念或现象提炼成一个限时的简化模型。能举个例子吗？我们有一款交易卡牌游戏——并不是通常所说的那种集换式卡牌游戏。不过，它确实涉及交易，也确实是一款

card game. Um, [laughter] close enough.

卡牌游戏。[笑声]两者也算比较接近。

Uh, called Figgy. Um, where, you know, you and three other players um have each have some private information about what the different cards in the game are worth, and you do trades um based on that information.

它叫Figgie。在游戏中，你和另外三名玩家各自掌握一些私人信息，知道游戏中不同卡牌的价值，并根据这些信息进行交易。

And you pay attention to what other people are doing, what information uh that they have that is probably causing them to behave in that way. And think about whether like how you should update what you were doing or the trades that should happen when like you and someone else have different information or like reasons to value the same thing differently. It also, you know, gets people like used to market

同时，你还要留意其他人在做什么、掌握了哪些信息，以及这些信息为何可能促使他们采取相应行动。你还需要思考自己是否应当调整做法、应当如何调整，或者当你与其他人掌握不同的信息，或因不同原因对同一事物有不同估值时，应该进行哪些交易。此外，这也能让大家逐渐熟悉做市

making language and like putting themselves out there and being willing to buy or sell spades for seven ships.

术语，并敢于公开报价，愿意以七个筹码买入或卖出黑桃。

And I think like getting people comfortable with making decisions under uncertainty in these like kind of control controlled low stakes environments I think is good practice for doing it in the real world um without like the psychological pressure that would normally get in the way. Have you read the book Enders Game?

让大家习惯在不确定性下作出决策，并在这种可控的低风险环境中练习，是很有帮助的。这能够为在现实世界中作出决策打好基础，同时又不必承受通常会造成干扰的心理压力。你读过《安德的游戏》吗？

I have. Right. Um classic.

读过。这是一部经典之作。

Yeah. Like we can't actually do that in uh like have people do like mock trading and then slowly at some point discover the mock trading is actually real trading.

我们实际上无法这样做。例如，先让大家进行模拟交易，再让他们逐渐发现，所谓的模拟交易其实是真实交易。

Right. I I I think for licensing legal [laughter] supervisory reasons um it's problematic.

出于牌照、法律和监管方面的原因，这样做会有问题。

But I it is easier to teach uh intuitions about trading in this game like environment where you can like try things and see the outcome that isn't quite like the heat of battle, right? And and and I guess like like a game like there is an adversarial component to it, right? It's not like the markets aren't a zero- sum game.

不过，在这种环境下确实更容易传授交易直觉，因为它就像一场游戏。你可以尝试各种做法，观察会产生什么结果，而不必真正置身于激烈的实战之中。与游戏类似，其中也包含对抗性成分，但这并不意味着市场是一场零和博弈。

They're like a positive sum game. In the end, people bet all you know people all in benefit from participating in the markets but like small positive sum right and so if like player A is like winning a lot then player B who they're trading with is losing a lot and like that gets reflected in the kind of game structure as well I imagine yeah identifying when you are doing a positive some trade with someone versus

市场更像是一场正和博弈。归根结底，所有人都能从参与市场中受益，只不过正和的幅度很小。因此，如果玩家 A 赢了很多，与之交易的玩家 B 就会输掉很多。我想，这一点也会体现在游戏的结构中。关键在于判断，你是在与某人进行正和交易，还是身处另一种情形：

situations where you and someone who is just as smart as you disagree about some fact of the Right. One of you is wrong.

你和另一个与你同样聪明的人对某个事实持有不同意见，而你们中总有一个人是错的。

One [laughter] of you is wrong. And you should think hard about like why you think what you do.

你们中有一个人是错的，因此你应该认真思考自己为什么会这样判断。

Right. And why you're not the one who's making the mistake.

还要思考为什么犯错的不是自己。

That's right.

没错。

Okay. So, you learned a lot through this internship and you decided actually this is the thing that you wanted to do. What was it like going from having been an intern to like actually being here as a trader? It was like even more interesting than I expected because like there were all of these like real life trading decisions and strategies that like we couldn't get into the details of for IP reasons. But um [laughter]

那么，你在这次实习中学到了很多，也发现这确实是自己想做的事。从实习生转变为正式入职的交易员，是怎样的体验？实际上，这份工作甚至比我预想的还要有趣，因为其中涉及现实中的各种交易决策和策略。出于知识产权方面的原因，我们在实习期间无法深入了解这些细节。

I felt like I was learning a lot during the internship. And then once I like got to Jane Street, we could talk about like the trades that we were actually doing as a firm and the like decisions that we were making as a firm. And so I started off on the commodities desk um learning to trade uh oil and natural gas um ETFs and index ETFs which are about 50% like oil and uh energy related products and

实习期间，我觉得自己已经学到了很多。真正进入 Jane Street 后，我们便可以讨论公司实际进行的交易和作出的决策。我最初加入了大宗商品交易台，学习交易石油、天然气 ETF 和指数 ETF，其中约 50% 与石油和能源相关产品有关，同时也会开展相关研究。

looking at the trades that we were doing and thinking about like did they make sense? Were they good trades?

我们会研究实际进行的交易，并思考这些交易是否合理、是不是好交易。

um why were we doing them? Um if they were good trades, why didn't we do more of them? And if they were bad trades, why did we do so many? All of this happened like sitting next to you know a mentor who was an experienced trader who had been here a long time and you know a bunch of trades would happen and then he would say like okay well what do you think we should do here? how should we change our uh behavior? I would think

我们为什么要进行这些交易？如果是好交易，为什么不多做一些？如果是糟糕的交易，为什么又做了这么多？这一切都发生在这样的情境中：你坐在一位经验丰富的交易员导师旁边，他已经在这里工作了很长时间。一连串交易发生后，他会问：“你觉得现在应该怎么做？我们应该如何调整自己的做法？”我会认真思考。

for a bit and say something and he would say, "Well, okay, we're doing something else and then work through with me like, okay, well, you should also take into account like this, that, and the other thing." And over time, you know, through working through all of these examples and like getting this feedback, you've learned to make better decisions in these contexts. How long do you feel like it took until you were able to make

思考一会儿后，我会给出答案，而他会说：“但我们实际上采取了另一种做法。”随后，他会和我一起梳理：“你还应该把这个、那个以及其他因素考虑进去。”随着时间推移，通过逐一分析这些例子并获得反馈，你会逐渐学会在这些情境下作出更好的决策。你觉得自己花了多久，才开始能够提出建议？

positive suggestions that like actually would influence the trading that we do?

这些建议不仅真正具有建设性，而且确实会影响我们实际的交易方式。

I don't know, a couple.

不知道，大概两三个吧。

I mean, I suppose like in the small I think in the small like pretty quickly. Um because it's just stuff that like other people don't have time to pay attention to and you can you don't have to like better than everybody else to add value.

如果从小处着手，我觉得很快就能做到。这些事情只是其他人没有时间关注，而你不必比所有人都强，也能创造价值。

you can become, you know, Jane Street's expert on this one ETF and its trading dynamics or like think really hard about something that um no one else at the firm has yet and have some interesting suggestions and over time like the scope of what you are comfortable doing on your own increases. At first it's like hey I think we should make this small change. What do you think? And you'd get comfortable with doing that. and

例如，你可以成为 Jane Street 某一只 ETF 及其交易动态方面的专家，或者深入思考公司里其他人尚未仔细考虑过的问题，并提出有意思的建议。随着时间推移，你有把握独立处理的事情会逐渐增多。一开始可能是：“我觉得我们应该做这个小改动，你觉得怎么样？”之后，你会逐渐习惯这样做。

eventually get more comfortable like making the bigger decisions on your own, but like reaching out and gathering more input when you weren't sure.

最终，你会更有把握独立作出更重大的决定；但如果拿不准，仍会主动征求更多意见。

What you you said that you you could become like James Street's expert on like some particular corner. Can you give me like an example of like some little corner of the world that at some point you became like the local expert in?

你刚才提到，一个人可以成为 Jane Street 在某个细分领域的专家。你能否举个例子，谈谈自己曾在什么阶段成为公司内部某个小众领域的专家？

Oh man, we're going back a lot of years. Um, I think at the time our trading systems were pretty slow and we were rolling out a new generation of trading systems that were considerably faster by like two orders of magnitude or something and but still slower than the frontier and slower than we are today. Someone had to think about like how to optimize our use of these systems and analyze like where

那要追溯到很多年前。当时，我们的交易系统相当慢，而公司正在推出新一代交易系统，速度大概提升了两个数量级，但仍然落后于行业前沿，也比我们现在的系统慢。总得有人思考如何优化这些系统的使用，并分析速度因素的具体影响。

we were gaining from speed or what opportunities we were missing. And I don't know who should do that. Eric knew he has time. [laughter] I think another thing is like I spent a lot of time thinking about like futures and their multipliers and the risk that you have when you trade a future and how that's different from like when you trade a stock. Yeah, this seems like a dumb corner of the world, but also one that

我们需要判断速度在哪些方面带来了优势，又让我们错过了哪些机会。至于该由谁来负责，我也不知道。埃里克知道自己有时间。我还花了很多时间思考期货及其乘数、交易期货时承担的风险，以及这些风险与股票交易有何不同。这似乎是个很不起眼的小领域，但也是

I've spent a weirdly large amount of time over the years thinking about where it's like you get used to trading equities and it's like this is great when I want to think about the risk of the trade I can, you know, a starting point is the cash flow. What's the cash flow of the trade? It's just the product of like the price and the size. This is so nice and then like turns out not so much in futures,

这些年来，我莫名其妙地投入大量时间研究的领域。习惯股票交易后，你会觉得这种方式非常便利：当我想评估一笔交易的风险时，可以从它的现金流入手。这笔交易的现金流是什么？其实就是价格乘以数量。这非常直观，但事实证明，在期货中却没有那么简单。

right?

确实如此。

Right. Maybe we're saying what is a future? A future is like an agreement in the future, hence the name to exchange some money, right? based on some other like benchmark that this thing is tied to. So like you know the S&P 500 futures are tied to like the closing value of the basket of S&P 500 stocks at a particular point in time I think or is it No, I think it's actually the opening

也许我们应该先解释什么是期货。期货就是约定在未来进行交易，因此才叫“期货”，其中会交换一笔资金。具体金额取决于这份合约所挂钩的某个基准。例如，S&P 500 期货挂钩的是一篮子 S&P 500 股票在某个特定时间点的收盘价值——我想应该是这样。不对，我想其实是开盘价。

the opening.

开盘价。

Okay.

好的。

On what I know [laughter] uh Friday of the month.

据我所知……[笑声]是当月的某个星期五。

Amazing.

很厉害。

Um but on which exchange? [laughter] Yeah. In when you buy a like uh crude oil future, you are uh you agree to pay some money for either like the value of some you know index related to oil prices or you know a thousand barrels of oil delivered to you in Cushing, Oklahoma. Um and so-called physical delivery. Yeah, physical delivery or like you know live cattle where actually sometimes the

不过，这是在哪家交易所进行的？[笑声]比如，当你买入一份原油期货合约时，你就同意支付一笔钱，换取某个与油价相关的指数价值，或者由他人将一千桶原油交付到Cushing, Oklahoma。这就是所谓的实物交割。再比如活牛期货，实际交付的牛有时

cattle are delivered um dead but uh you know tens of thousands of pounds of beef. We you know were generally financial players who did not want to take delivery of a thousand barrels of oil in pushing Oklahoma. [laughter] Um I've never been there. Um, and like I don't know where I would store, you know, the 20,000 lbs of beef. Um when like when you buy a future like you put up some

是死的，也就是数万磅牛肉。我们通常都是金融市场参与者，并不想实际接收交付到Cushing, Oklahoma的一千桶原油。[笑声]我从未去过那里，也不知道该把20,000 lbs牛肉存放在哪里。当你买入期货合约时，需要向

margin with the exchange and you know if you are long and the future goes up um the exchange credits some money to you.

交易所缴纳一些保证金。如果你持有多头，而期货价格上涨，交易所就会将一笔钱记入你的账户。

When the future goes down, the exchange removes some of your money and possibly asks you to put up some more margin so that they know that you'll be able to pay off losing bets.

当期货价格下跌时，交易所会从你的账户中扣除一些钱，还可能要求你追加保证金，以确保你有能力赔付亏损的头寸。

Mhm.

是的。

Another thing you can trade is like the spread of two futures. Um, you get long one and short another. So like the CL um U6 CL V6 spread uh if you buy that on the CME you get longer crude oil futures deliverable in September and shorter crude oil deliverable in October. And if you trade like S&P 500 future spreads you have the opposite sign convention.

另一种可以交易的产品是两种期货之间的价差，即做多一种，同时做空另一种。例如 CL U6 CL V6 价差，如果你在CME买入它，就会做多9月交割的原油期货，同时做空10月交割的原油期货。如果你交易的是 S&P 500 期货价差，其正负号约定则恰好相反。

But uh in either case like your position, sorry, the thing you trade like decomposes into um two different positions rather than like the one thing you bought, right? And I guess the point here is that all of this is like one level more abstract than like the underlying things are being traded. So like you know instead of trading the outright thing, you're trading this future on the thing.

不过，无论是哪种情况，你交易的产品都会拆分为两个不同的头寸，而不是你买入的那一个产品。这里的重点是，所有这些都比实际交易的底层标的更抽象一层。你不是直接交易标的本身，而是交易以它为标的的期货。

And then there's a bunch of conventions and some of the conventions are about like it has a name that says what the two legs of the spread are but like which one is positive and which one is negative depends on the future right and you can't just like look at the notation for sure and know the answer. And then where we started this other crazy thing called the multiplier which like actually tells you the real size of

此外，还有大量相关约定。其中一些约定涉及名称，会标明价差的两个腿分别是什么；但哪一腿为正、哪一腿为负，则取决于具体期货，无法仅凭表示法确定。还有刚才提到的另一个特殊概念，叫作乘数，它实际上会告诉你这笔交易的真实规模。

this, right? Like when you pull up the ticker for some future say crude oil, uh you'll see a price for like one barrel of crude oil or one pound of live cattle. But actually like when you buy the contract and take delivery, you get a,000 barrels. Um, and the like notional value is a thousand times of like the coded price. And different sources will depending on where you're getting your pricing from, you will get different

例如，你调出某种期货的行情代码，假设是原油期货，就会看到一桶原油或一磅活牛的价格。但实际上，当你买入合约并接收交割时，会拿到,000桶。其名义价值是所标价格的一千倍。不同的数据源会提供不同的

prices and different multipliers and sometimes like different currencies. Uh, like someone will quote in uh dollars and another person will quote it in cents. It's a small matter of a factor of 100.

价格和乘数，有时甚至使用不同的货币单位，具体取决于你从哪里获取定价。例如，有人会用美元报价，另一个人则会用美分报价，两者之间相差100倍。

Yeah. And as long as you always um keep track of which prices correspond to which multipliers and never mix up the two. You won't accidentally trade 100 times less or um much worse 100 times more than you intended.

是的。只要始终弄清楚哪些价格对应哪些乘数，绝不将两者混淆，就不会不小心少交易100倍；更糟糕的情况则是，交易规模达到原本计划的100倍。

Right? And so this matters kind of from a risk perspective. Like ideally we'd like to have relatively simple systems that are kind of like stepped back from the details of the trading strategy which kind of put a kind of outside envelope on the risk that the systems are taking. And that's like way easier if you actually know what's going on.

因此，从风险角度来看，这一点非常重要。理想情况下，我们希望系统相对简单，能够从交易策略的细节中抽离出来，并从外部为系统承担的风险设定边界。如果确实了解正在发生什么，事情就会容易得多。

But this is what you're pointing to is this all this whole like what we call a metadata problem of like actually understanding the details of how these contracts actually work. And it's Jade, it's just made much more complicated by the fact that like the world is full of huge amounts of unnecessary complexity of like different conventions and different details of how these things are quoted in different contexts.

但你指出的正是我们所说的整个元数据问题，即如何真正理解这些合约究竟如何运作。这个问题之所以变得复杂得多，是因为现实世界中充斥着大量不必要的复杂性，例如不同的惯例，以及这些产品在不同情境下采用不同报价方式的各种细节。

Yeah. And like you have assumptions that are baked into your um trading and risk systems like this price will always be positive that turn out to not be true.

是的。而且你的交易和风险系统中会内置一些假设，例如价格永远为正数，但事实证明并非如此。

on April 20th, 2020, the price of the like almost expiring uh crude oil future on the CME um settled at a negative price. Um and you know, you'd always think like, well, goods are good. They're not they're worth something.

2020年4月20日，CME上一份临近到期的原油期货合约以负价格结算。人们通常认为，商品是有价值的好东西。

They're worth something. They're not bad. You should [laughter] pay a positive amount of money for them.

商品是有价值的，不是坏东西。[笑声]人们应该为它们支付正数金额。

And that breaks a lot of assumptions.

而负价格会打破很多假设。

Um, but how can it happen? How can a crude oil future be worth negative? Like goods normally are good. Like what happens?

但这种情况是如何发生的？一份原油期货怎么会是负值？商品通常都是好东西，究竟发生了什么？

Yeah. So that crude oil contract was for oil delivered to a particular place at a particular time. And um at the time in K Cushing, Oklahoma storage was getting full. There wasn't much place to put the crude. Supply of oil everywhere was high and demand um for obvious reasons was uh considerably lower [snorts] than usual.

那份原油合约规定，石油要在特定时间运到特定地点。当时，Oklahoma州Cushing的储油设施快要满了，几乎没有地方可以存放原油。各地的石油供应量都很高，而需求由于显而易见的原因，远低于[哼笑]往常。

If you remember, if you remember, April 2020. Yeah. And that added up to like well if you were long, what would you uh do with it? And this isn't quite true because the like for one day the settlement price was a negative number and the like final settlement price of this contract was positive. Like in the end people were willing to pay positive dollars for

如果你还记得，那是2020年4月。所有这些因素加在一起，就意味着，如果你持有多头，该如何处理这些原油？不过，这种说法并不完全准确，因为有一天的结算价是负数，而这份合约的最终结算价却是正数。也就是说，到最后，人们愿意花正数金额购买

crude.

原油。

But on April 20th, there just were not enough people willing to step seven and say, "I will pay uh money for this thing."

但在4月20日，根本没有足够多的人愿意站出来表示：“我愿意花钱买这个东西。”

Right? And when all of your systems had assumed this price was positive for a long time, yes, maybe your trading systems do not have a great day, right? Maybe the people who would normally provide to these moves weren't able to send orders because like their order entry system didn't know what to do with a negative 100% return.

如果你的所有系统长期以来都假设这个价格是正数，那么你的交易系统当天可能会表现不佳。那些通常会为这类行情提供流动性的人，可能无法发送订单，因为他们的订单录入系统不知道该如何处理负100%的回报率。

[laughter] Um, also like if you were modeling other products based on the price of crude oil on that day, how are you modeling oil companies or everything else? Should you be using like that negative crude future? I'm not sure.

[笑声]另外，如果当天要根据原油价格为其他产品建模，又该如何为石油公司或其他一切事物建模？是否应该使用那个价格为负的原油期货？我也不确定。

Right. Seems I guess this like zooming out for a second. This like points to like a thing about trading which is some of trading is thinking hard about the technology stack and the math and the modeling and some of it is just diving into a bunch of groy detail of like how does the world actually work what are the mechanics of the markets and the systems and like the amount of storage space there is in Cushing Oklahoma and like

我们不妨把视角拉远一些。这个例子揭示了交易的一个特点：交易工作一方面需要深入思考技术栈、数学和建模，另一方面也需要深入研究大量繁琐的细节，例如现实世界究竟如何运作，市场和各类系统的机制是什么，以及Cushing Oklahoma究竟有多少仓储空间；

all of those things can play in and this is like a general fact about trading is like lots of different kinds of things about the world can flow into the trading process. It's not like I think sometimes people who hear about James Street think it's all about like you know high-tech high performance smart math models and stuff and like that stuff is all real and part of it but there's also a lot of detailed

所有这些因素都可能影响交易。这也是交易普遍存在的一个特点：现实世界中许多不同类型的因素都能进入交易流程。有时，人们听说Jane Street后，会以为这里的一切都围绕着高科技、高性能和精妙的数学模型。当然，这些确实存在，也是交易工作的一部分，但要成为一名出色的交易员，

engagement with how the world actually works that also plays into being an effective trader right and I think that's true across uh like all of our businesses but it is very true in commodities where like the things that you are buying and selling are connected to the physical world. Oil and wheat and things that like people actually uh physically produce and consume and occasionally deliver to you

还需要深入了解现实世界究竟如何运作，这一点同样至关重要。我认为，我们所有的业务基本上都是如此，但在大宗商品领域尤其明显，因为买卖的东西都与现实世界息息相关。石油和小麦等商品，都是人们实际生产和消费的东西，偶尔还会实物交割给你。

and occasionally deliver to you.

偶尔还会实物交割给你。

Yeah.

是的。

So in addition to thinking about all of these kind of trading focused concepts on the desk, you also spend a bunch of time thinking about technology on the desk. Can you say a little bit more about what that was like?

因此，除了在交易台思考所有这些以交易为核心的概念之外，你还会花很多时间思考交易台的技术问题。能否再详细谈谈当时的情况？

Right. At the time, I think how Jane Street approached developing technology for uh trading was having a bit of a phase shift. We went from having developers who primarily worked on like the core infrastructure that undergurted our trading, the trading systems, the market data, bookings pipeline, that sort of thing. while like traders like people on our commodities desk and our domestic ETFs desk uh would plug in to

当时，我认为Jane Street开发交易技术的方式正在经历一次阶段性转变。过去，开发人员主要负责支撑交易业务的核心基础设施，包括交易系统、市场数据和记账管道等；大宗商品交易台和美国国内ETF交易台的交易员则会接入

those systems but do like less systems development themselves. And at that time, I guess we finally felt that we had enough breathing room to say like, well, what if we built commodity specific systems to help address the unique problems with like trading commodities? And this is like a thing that happened not like all at once, but like at different desks at different points of time. Some desks were more

这些系统，但自己较少从事系统开发。那时，我们终于觉得自己有了一些余力，可以开始考虑：如果构建大宗商品专用系统，用来解决大宗商品交易特有的问题，结果会怎样？这种转变并非一下子全面发生，而是在不同交易台、不同时间点逐步展开。有些交易台的

technical in their needs, some were less. And so this kind of like desk dev phenomenon like unrolled like you know over a period of years as we kind of got enough capacity and now like every desk across the firm has embedded desk devs because you just kind of need it everywhere.

技术需求较高，有些则较低。因此，随着我们逐渐拥有足够的资源，这种交易台开发模式在数年间逐步铺开。如今，公司的每个交易台都有嵌入其中的交易台开发人员，因为各处确实都需要他们。

Mhm. Right. But at the time it felt like well this is working here we should try this elsewhere too. And when we were starting to build out the commodities desk dev team we needed someone from trading to help think about it. and I was relatively technical having done my tech internship for one of my free summers. Um, and having learned o camel and o camel boot camp, I uh got involved with that and that involved things like

当时我们的看法是，既然这种模式在这里行之有效，也应该到其他地方尝试。开始组建大宗商品交易台开发团队时，我们需要一位交易人员协助规划。我的技术能力相对较强，因为我曾利用一个空闲的暑假做过技术实习。此外，我还学过OCaml，参加过OCaml训练营，因此参与了这项工作，其中包括

thinking about how we like keep track of our risk or exposure to different commodities. If you're looking at your positions in energy products, like you could look at it at a very high level or you could look at just oil and related products. Um, you could look at like West Texas Intermediate crude oil alone or you could look at like the, you know, November contract of this one commodity and you need to be able to like shift

思考如何跟踪我们面临的各种大宗商品风险或敞口。查看能源产品头寸时，既可以从非常宏观的层面观察，也可以只关注石油及相关产品；还可以只看西得克萨斯中质原油，或者只看某种大宗商品的11月合约。你需要能够随时

from level to level all at once and be able to dig into exactly where your exposure to these was coming from. And we've been doing this before. It was in large Microsoft Excel spreadsheet, US commodities risk.xlsm. XLSM and you'd like press a button and it would pull in, you know, data about our our trades and um positions and crunch some numbers. One, it sometimes it was slow. Um, two, it it was something that

在不同层级之间切换，并且深入探究这些敞口究竟来自哪里。而我们以前就是这么做的。当时用的是一个很大的 Microsoft Excel 电子表格，叫 US commodities risk.xlsm。XLSM然后按一下按钮，它就会导入我们的交易和头寸数据，再进行一些计算。第一，它有时会很慢。第二，这套东西

had built up over time and was built by people who, you know, were very good at using Microsoft Excel and very knowledgeable about commodities but were not by and large like software engineers and kind of like building it up again from first principles and making sure that you got the foundation right to something that we could uh really trust let us build, you know, more and more ambitious things on top of that and we

是逐渐积累起来的。构建它的人虽然非常擅长使用 Microsoft Excel，也非常了解大宗商品，但总体而言并不是软件工程师。因此，我们从基本原理出发重新构建了它，并确保打好基础，使其成为一个真正值得信赖的系统。这样，我们就能以此为基础，构建越来越多更具雄心的功能，超越

were able to build when it was an Excel spreadsheet that ran slowly and worked except occasionally when it didn't, right? I feel like people who don't use Excel much probably underestimate how good it is and then like when you've used Excel a lot, you have a really good sense of like oh my god, what the limitations are. Like Excel in many ways is kind of great. like it's like gives you great ways of visually inspecting

以前使用 Excel 电子表格时所能构建的东西。那个表格运行缓慢，通常可以使用，只是偶尔会失灵。我认为，不常使用Excel的人可能会低估它有多好；而大量使用过 Excel 的人，又会非常清楚它的局限性有多么令人抓狂。Excel 在很多方面其实很出色，提供了直观检查

and understanding the data. It's extremely flexible. It's actually quite easy to debug in important ways. Um but yeah, there are like profound performance limitations. Like what what else actually was like bad about doing it in Excel? What were the downsides?

和理解数据的良好方式。它极其灵活，而且在一些重要方面相当容易调试。不过，它确实存在严重的性能限制。除此之外，使用Excel完成这项工作还有哪些实际问题？它有哪些缺点？

Yeah, I should say before all of this that I love Microsoft Excel and I don't use it very much anymore. Um but you know, we like functional programming here. Excel formulas, spreadsheet formulas are, I think, the world's most widely used functional programming language.

在谈这些问题之前，我得先说明，我很喜欢 Microsoft Excel，只是现在已经不太使用了。我们很喜欢函数式编程。Excel 公式，也就是电子表格公式，在我看来是全世界使用最广泛的函数式编程语言。

For sure.

当然。

I think Excel has lambdas now.

我记得 Excel 现在也有 lambda 了。

It does. And let bindings.

是的，还有 let 绑定。

And let bindings. Yeah. Um I think thanks to Simon Payton Jones and some other people at Microsoft who helped build it.

还有 let 绑定。我想这要感谢 Simon Payton Jones 和Microsoft 的其他一些人，是他们协助开发了这些功能。

Yeah. When you write like Python code, usually you have the like code in front of you, the logic in front of you, but the the data is like hidden away until you like write something that like visualizes it for you. And with Excel, like the data is front and center and the logic um is hidden in the back. And like that's pretty useful. people are making decisions about what to do based on the data that they see. Uh so what

例如，编写 Python 代码时，代码通常就在眼前，逻辑也清楚地呈现在面前，但数据却隐藏起来，直到你编写一些内容将其可视化。在 Excel 中，数据处于最显眼的位置，逻辑则隐藏在背后。这其实很有用。人们会根据自己看到的数据决定如何行动。那么，

were the problems here? Performance uh was sometimes fine uh sometimes at critical moments quite bad.

这里存在什么问题？性能有时尚可，但有时会在关键时刻变得很糟糕。

You aren't just like writing the cell formulas yourself. You're writing Visual Basic to construct the spreadsheet that you use, you know, right? This is what this is like cell meta programming. You write a program that writes a program that's like laid out in this in the sheet. You press a button that like runs a nest of Visual Basic macros that like initialize um a sheet with all of our um positions

你不仅要亲自编写单元格公式，还要编写 Visual Basic 来构建所需的电子表格。这有点像单元格层面的元编程：先编写一个程序，让它生成另一个程序，再把后者铺设在工作表中。按下按钮后，系统就会运行一整套嵌套的 Visual Basic 宏，用于初始化一张包含我们所有头寸

and trades and the code the uh code on top to analyze them. And Visual Basic is okay. [laughter] No, I mean it like it was impressive what we were able to do with it, but having a uh type- safe language where the compiler can catch a lot of your mistakes where you can write robust tests and interfaces is an advantage that many programming languages have that is like hard to do in Visual Basic.

和交易的工作表，以及用于分析这些内容的上层代码。Visual Basic 还算可用。我们确实能用它完成这些工作，这很了不起；但如果能使用一种类型安全的语言，让编译器捕捉许多错误，并支持编写可靠的测试和接口，就会更有优势。许多编程语言都具备这些优势，但在 Visual Basic 中很难做到。

Um, also like version control. Yeah.

此外，还有版本控制问题。

Um, hard to do version control in a spreadsheet.

电子表格很难进行版本控制。

Um, Excel is not optimized for that. I once wrote an Excel GP tool that yeah was just like a firm utility for searching through spreadsheets because like opening them up one at a time and in Excel and control fing um first through the cell formulas and then through the visual basic is impractical.

Excel 并未针对这方面进行优化。我以前写过一个 Excel GP 工具，也就是公司内部用于搜索电子表格的实用工具。逐个用 Excel 打开电子表格，再按 Ctrl+F 搜索，先逐一查看单元格公式，然后查看 Visual Basic，是不切实际的。

No way to live.

这种工作方式根本无法维持。

Yeah. And like if you need to do like large migrations of many spreadsheets at once, which we sometimes did, it helped to be able to search them. Maybe related to the lack of version control is that like things just build up. Uh someone like put some throwaway cell formulas for analysis in like one corner of one sheet and then it's just there forever.

如果需要一次性大规模迁移许多电子表格——我们有时确实会这样做——能够搜索这些表格就很有帮助。这可能也与缺乏版本控制有关，各种内容会不断积累。有人随手编写几个临时的单元格公式用于分析，把它们放在某个工作表的一角，之后这些公式就永远留在了那里。

Yep. So, you get to a point where like no one fully understands what it's doing, though some people know a lot and are pretty sure. And it's hard to like rebuild that model of how the spreadsheet works and like become like confident that there are no issues. Um, so that itself I feel like makes the process of replacing it hard, right?

发展到一定程度后，便没有人能够完全理解电子表格究竟在做什么。虽然有些人了解很多，也相当有把握，但要重新建立起对电子表格运行方式的完整认知，并确信其中没有问题，仍然十分困难。因此，我认为仅这一点就会使替换电子表格的过程变得非常困难。

spreadsheet, it's an artifact, it has a lot of embedded like no human in trying to help bridge the gap between like the spreadsheet that did a thing and then like the piece of software that we were going to build to replace it.

电子表格是一件成品，其中嵌入了大量任何个人都无法完整掌握的内容。我们必须设法弥合两者之间的差距：一边是能够完成某项工作的电子表格，另一边是我们打算构建来取代它的软件。

Right. So a lot of what I did was translating between the domain understanding that like uh the other traders and I had and a description of the domain and the problem that like a software engineer can work with and like build a robust system around rather than someone saying well I want this and it should do that and that and the other thing like distilling that down into like what we're fundamentally trying to

因此，我的很多工作都是在两者之间进行转化：一边是其他交易员和我对这个领域的理解，另一边是对该领域及相关问题的描述，使软件工程师能够据此开展工作，并围绕它构建一个稳健的系统。我们不能只是由某个人提出，他想要某个东西，要求它完成这个、那个以及其他任务，而是要把这些要求提炼为我们从根本上想要

do which is you know model the risk exposures of our uh commodities desk.

做的事情，也就是对我们大宗商品交易台的风险敞口进行建模。

Mhm. and how [snorts] we are trying to do it. This the input sources and how they should fit together, what the basic atoms are. And we built up model of the thing we're building, the flowchart of how all this data like fit together and turned into like the end result. And then people were able to just confidently build um some part of it knowing that it would fit together into like the hole correctly.

此外，还要明确如何实现这一目标，包括输入源有哪些、应当如何组合，以及基本元素是什么。随后，我们逐步建立起一个关于所构建内容的模型，并绘制一张流程图，说明所有数据如何组合在一起并转化为最终结果。这样一来，大家就可以放心地构建其中某个部分，因为他们知道这个部分能够正确融入整体。

I imagine it's like much better for the software engineers if they don't just like have like a spec a narrow speck of a thing to build, but also they like understand the background of the domain in a way that's like a little more kind of actionable and fills out their understanding of the world.

我认为，这种方式对软件工程师要好得多。如果他们不只是拿到一份范围很窄的需求说明，并按照说明开发某个东西，而是还能理解这个领域的背景，以更具实用价值的方式完善自己对整个世界的理解，就会做得更好。

Yeah. And information flows the other way as well. Um I think the domain experts like who are not themselves software engineers like don't understand how good things can be if we build the right software for it. Um the like I have suffered for years like doing things in this way because there weren't tools to help me do it better. And I've gotten so used to it that I don't realize I need something better.

信息也会向另一个方向流动。我认为，那些本身并非软件工程师的领域专家并不了解，如果我们为此开发合适的软件，情况可以得到多大改善。他们可能会想：多年来，我一直忍受着以这种方式工作，因为以前没有能够帮助我把事情做得更好的工具。我已经习以为常，甚至没有意识到自己需要更好的工具。

Right? Like one of the characteristics of many James Street traders is high pain thresholds, right? People are like willing to deal with like pretty messy things and pretty unpleasant things if like you know I have to like you know you know knock my head on the table three times and slot my cheek in order to like cause money to come out of the machine. They'll just like do that all over over and over.

Jane Street 的许多交易员都有一个显著特点：忍痛能力很强。只要某种方法能够奏效，他们就愿意应付相当混乱、相当令人不快的事情。例如，如果必须拿头撞桌子三次，再扇自己一耳光，才能让钱从机器里出来，他们就会一遍又一遍地这样做。

Yeah. And someone who makes a good desk dev at Jean Street can say like, "No, there's no way you should be doing it that way. [laughter] If we, you know, built this tool, it would be much easier to do these studies that are either difficult to do now or that no one would bother doing now because we don't have the right language for working with them.

而一名优秀的 Jane Street 交易台开发人员会说：“你们绝对不应该这样做。如果我们开发出这个工具，这些研究就会容易得多。由于没有合适的语言来处理这些研究，它们现在要么很难开展，要么根本没人愿意做。”

Right? Right. So part of what you need to convey to the traders is like a better cost model. Like understanding what's possible and understanding that like some improvements are like maybe way cheaper than they imagine and some are more expensive than they imagine.

因此，你需要向交易员传达的一部分内容，是一个更准确的成本模型：既要了解哪些事情可以实现，也要明白有些改进的成本可能远低于他们的想象，而另一些则高于他们的想象。

It's not just the costs. You can have like great ideas for what we as a like desk or as a company should be doing differently. It's like I am someone with like this particular set of skills and knowledge and my job is to help Jane Street, you know, do the best trades.

这不只是成本问题。你还可能就我们这个交易台或公司应当在哪些方面改变做法，提出很好的想法。换句话说，我拥有这样一套特定的技能和知识，而我的工作就是帮助 Jane Street 做出最好的交易。

All right. So, you're no longer on a trading desk. Today, you help lead our alt data effort.

你现在已经不再隶属于某个交易台。如今，你协助领导我们的另类数据业务。

Um, can you say more about like like what is alt data and what is it at Gain Street? Yeah. So, out in the world on Wall Street, um, alternative data is like data that you use for the investment process that isn't normally used in the investment process.

你能否进一步说明什么是另类数据，以及 Jane Street 的另类数据是什么？可以。在华尔街，另类数据是指用于投资流程、但通常不会在投资流程中使用的数据。

It's an unstable naming convention, right? Like if enough people use it, does it like drop its alt like moniker?

这种命名方式并不稳定。如果使用某类数据的人足够多，它是否就不再被称为“另类”数据了？

The canonical example of alternative data is like a satellite photo of a Walmart parking lot. You take a photo of the parking lot. You see how many cars are there. If there are a lot of cars, they have a lot of customers and you know they're doing well. If they parking lot's empty, um that's a bad sign. And in contrast, there's like traditional data, orders and trades on exchanges, basic like information about like

另类数据的经典例子是 Walmart 停车场的卫星照片。你可以拍一张停车场的照片，查看那里有多少辆车。如果车辆很多，就说明顾客很多，由此可以判断 Walmart 经营得不错；如果停车场空荡荡的，那就是一个不好的信号。与之相对的是传统数据，例如交易所中的订单和成交数据，以及有关

instruments, stocks. Imagine like Warren Buffett like reading an annual report and thinking about the balance sheet and income statement. That's traditional data.

金融工具和股票的基本信息。可以想象 Warren Buffett 阅读年报，分析资产负债表和利润表，这些都属于传统数据。

Okay.

明白了。

And at Jane Street, a lot of data is alternative. The the satellites are alternative. the like company filings with the SEC are alternative. Um the things that are traditional are like the like real-time market data from exchanges, you know, the basic instrument metadata that we need in order to like run our like trading and risk systems and booking and so on.

在 Jane Street，很多数据都属于另类数据。卫星数据是另类数据，公司向 SEC 提交的文件也是另类数据。传统数据则包括来自交易所的实时市场数据，以及我们运行交易、风险和记账等系统所需的基本金融工具元数据。

Right. Back to the multipliers.

对，又回到乘数了。

Yeah. And it's based on, you know, the path dependent evolution of Jane Street's trading, you know, starting as a market maker and then expanding um into adjacent areas, right? In fact, it's reflected in like team structure. We have a market data team that does that kind of like live data from exchanges and from other places and we have a metadata team and then we have this alt data team which in

是的。这取决于Jane Street交易业务具有路径依赖性的演变过程。Jane Street最初是一家做市商，后来拓展到相邻领域。事实上，这也体现在团队结构上：我们有一个市场数据团队，负责处理来自交易所和其他来源的实时数据；还有一个元数据团队，以及这个另类数据团队。某种意义上，

some sense is like kind of picking up the rest of that Yeah. of that of that kind of value that you get from other sources of information.

另类数据团队负责发掘其余信息来源的价值。

Mhm. [clears throat] And then how did you get involved in this space? [sighs] How did I get involved in this space?

嗯。[清嗓子]那你后来是怎么进入这个领域的？[叹气]我是怎么进入这个领域的？

[laughter] Um, so at Dain Street, you can do whatever you want as long as it's the right thing to do. Your job is not to maintain this one system or to like trade this one product. your job is to, you know, help Jane Street like maximize its P&L um over long term. What that means is that if you see a problem that you think is important to solve, what's stopping you from solving it? I mean, probably you should talk to other people

在Jane Street，只要一件事是正确的，你就可以去做。你的工作并不是维护某一个系统，也不是只交易某一种产品，而是帮助Jane Street实现长期P&L最大化。这意味着，如果你发现一个重要且需要解决的问题，就没有什么能阻止你去解决。当然，你或许应该先和其他人讨论，

and make sure that like this is a real problem and that it is worth fixing and it is worth your time fixing.

确认这确实是一个真实的问题，值得解决，也值得你投入时间。

Mhm. And you know, at some point I was thinking about like a bunch of different data related things at Jane Street and realized that like we were failing to find trades because our data was too hard to work with because like external data specifically was too difficult to work with. like our infrastructure for streaming in real time and um surfacing for like historical studies of like the real-time market data was great and had

后来有一段时间，我一直在思考Jane Street内部各种与数据相关的问题，并意识到，我们之所以没能发现一些交易机会，是因为数据太难处理，尤其是外部数据。我们已经建立了用于实时流式传输数据，以及呈现数据以供历史研究使用的基础设施。实时市场数据方面的工作做得很好，也有

lots of engineers and specialized infrastructure. While the data infrastructure for all that other data was kind of weaker, it was annoying to just get data in the walls of street both like from a contractual perspective.

大量工程师和专门的基础设施。相比之下，其他所有数据的基础设施都比较薄弱。即使只是把数据引入Jane Street内部也很麻烦，其中还包括合同方面的问题。

There was no alt data team at there was no alt data team.

当时没有另类数据团队，没有另类数据团队。

So how did it happen? Like where was the work done? Who was doing the work? Like what was the shape of all that? Yeah, I I think when we started it was like me and you know a small number of devs including Jacob who was on uh the podcast earlier.

那么，这项工作当时是如何开展的？在哪里完成？由谁负责？整体情况如何？我记得刚开始时，大概只有我和少数几位开发人员，其中包括之前上过这档播客的Jacob。

Mhm.

嗯。

And uh a very small number of data engineers and we had three areas we were focusing on. Wait, but I think you're going ahead. I thought like I was sort of thinking like like the the early version of this I assume was just like kind of crowdsourced by the death before like how sure we were using external data um before we had an alt data team and the work of building the like data pipelines and integrating that data

另外还有极少数数据工程师，我们当时主要关注三个领域。不过，这已经是后来的情况了。你问的应该是更早期的版本：在另类数据团队成立之前，我们如何使用外部数据，以及数据管道和数据整合工作是否由开发人员群策群力完成。

would be done by whoever was around and available at the time. So someone on the uh commodities desk would say like we want this data set and our data vendor management team would go out and buy that data set and a dev or a trader or a trading desk operations engineer would like bring in the data and put it somewhere maybe in some corner of a large NFS tree um or shared file system maybe in a database but which

构建数据管道和整合数据，通常由当时正好有空的人负责。例如，大宗商品交易台的人会提出需要某个数据集，随后由数据供应商管理团队购买，再由开发人员、交易员或交易台运营工程师将数据导入并存放在某处。它可能位于大型NFS目录树的某个角落或共享文件系统中，也可能被放进某个数据库，但具体位置并不确定。

Um and like the result would be I don't know like 60% as good as it could be like and I guess this process is happening like some on the commodities desk domestic ETF desk some whatever like various different desks reinventing the wheel doing slightly different versions of it by people whose job was not primarily like thinking about how to build data pipelines well who were not thinking

最终结果如何，我也说不准，可能只能达到理想水平的60%。我猜，这套流程也会出现在大宗商品交易台、美国国内ETF交易台等不同部门。也就是说，不同交易台都在重复造轮子，各自构建略有差异的版本。负责这些工作的人，其本职工作并不是思考如何完善数据管道，因此也不会系统地考虑

about what could be shared between those processes who sometimes but not always thinking about like how should people around the firm not just like in the use case that I'm thinking about right now be using this data.

这些流程之间有哪些内容可以共享。他们有时会考虑公司里的其他人应该如何使用这些数据，但并非总是如此，更多时候只会关注自己眼下正在处理的用例。

Mhm.

嗯。

And thesis was that we could have a centralized team try to do that. think about a centralized team to source um some of these weirder alternative data sets to build better tools for building these data pipelines and to maintain some of these data pipelines especially like ones that kind of had formide impact that would be used in a lot of different places. So how did you go from like the recognition of like man this is kind of

因此，当时的设想是组建一个集中式团队来负责这项工作。这个团队可以寻找和采购一些较为特殊的另类数据集，开发更好的数据管道构建工具，并维护部分数据管道，尤其是那些会影响整个公司、在许多不同场景中使用的数据管道。那么，你是如何从意识到这种局面十分

a mess like people are generating data sets in messy ways don't have great ways of distributing them don't have good technology for serving the data don't have good ways of cleaning the data don't have good shared practices and so like we have less good data that is like just harder to convert into valuable trades and is therefore worth less money so like that seems like a key observation how did you go from that

混乱——大家以杂乱无章的方式生成数据集，却没有良好的分发方式、数据服务技术、数据清洗方法和共享实践，导致数据质量较差，也更难转化为有价值的交易，因而价值更低——这一关键发现，

observation to like actually doing something about it so like sure you've identified a problem and many people at the firm would agree that it was bad in all these ways but like why there are lots of things we can make better like why this one part of it was like we'd experiment we'd do something on a small scale and the results would work well so we do it more we'd like find one particularly

走到真正采取行动这一步的？你已经发现了一个公司里许多人都会认同的问题，但可以改进的地方明明很多，为什么偏偏选择这一部分？原因在于我们会先开展小范围实验，如果效果不错，再扩大规模。例如，我们会找到一个非常

basic but annoyingly difficult to use data set and make it easier to use, like think very hard about it. And people would start using that new thing. And lo and behold, like there were effects that they like were not able to find when the data was really difficult to work with that they were suddenly able to find when the data was uh easy to work with and when someone had you know thought really hard about those edge cases and

基础却很难使用的数据集，让它变得更易用，并深入思考其中的问题。随后，大家会开始使用这个新成果。正如预期的那样，他们发现了一些过去因数据难以处理而无法发现的规律。如今，这些规律突然变得可以发现，因为数据更容易处理，也有人认真考虑过各种边界情况，

like put in the work of understanding like the data model and so on. So basically incrementally go do some things, make it better, demonstrate that it's worth money. You can sort of see the P&L coming out the other side. And after a while it just kind of becomes clear to everyone that like, oh, we could use like a lot more of this.

并投入精力理解数据模型等内容。因此，我们基本上是逐步改进数据，使其变得更好，再证明这些改进确实能够带来经济价值。你可以清楚地看到另一端产生的损益。久而久之，所有人自然都会意识到，我们还需要开展更多这样的工作。

We could use a lot more of this. And so like um we hired our first data engineer at Jane Street ever in 2023 and the feedback has been like, "Oh, we could really use a lot more data engineering help." Actually the more people work with data engineers and see like the results from good data engineering work the more they are excited to like apply that toolkit to other problems I guess by informing people about how uh much

我们确实需要开展更多这样的工作。因此，我们在2023年聘请了Jane Street有史以来第一位数据工程师。此后，大家一直反馈非常需要更多数据工程方面的帮助。事实上，人们与数据工程师合作得越多，越能看到优秀的数据工程工作带来的成果，也就越有兴趣将这套工具应用于其他问题。我认为，这是因为大家逐渐意识到，

easier it is to like build good quality data sets than it was before and be like results that we're able to get in doing so they want to do it more.

现在构建高质量数据集比以前容易得多；看到我们由此取得的成果后，他们也更愿意采用这种方式。

So induced demand induced demand. [laughter] Yeah. So what just to like step back so like we went in 2023 from like having our first data engineer like how many do we have now?

所以，这是一种诱导需求。退一步看，2023年时我们才有第一位数据工程师，现在有多少人了？

Let's say 20 something.

大概二十多人。

Got it. So like a much bigger effort like as as it's grown out like I'm kind of curious like what's the structure of the team? Right. You mentioned data engineers, right? But I think I don't think all data is just just data engineers. So like how does the team break down into different kinds of people with different kinds of expertise?

明白了，现在的投入力度大得多。随着团队不断壮大，我很好奇团队采用怎样的架构。你刚才提到了数据工程师，但从事数据工作的并不只有数据工程师。这个团队如何划分不同类别、具备不同专业能力的人员？

One group is uh data strategy and that's not engineering at all. Um but their job is to understand what data gain street uh needs and what data is out there in the world and then if there's a match uh like you know get the data uh for us so that we can do good data engineering to it and so internally that looks like talking to our different trading desks and understanding like what problems they are trying to solve and what

其中一个小组是数据策略组，完全不从事工程工作。他们负责了解Jane Street需要哪些数据，以及外界有哪些数据。如果两者恰好匹配，他们就会为我们获取这些数据，以便我们进行数据工程处理。对内，这项工作主要是与不同的交易台沟通，了解他们正在尝试解决哪些问题，以及在获得更好的

problems they could solve with better data. The external part involves getting out there and showing up and but it's like another case where you have to understand like grate details of the real world.

数据后，他们能够解决哪些问题。对外则需要走出去，亲自到场。不过，这项工作也要求从业者了解现实世界中的各种具体细节。

Yeah. We go to conferences where we'll like talk to data vendors. Um so there are these like data catalog companies one example of which is new data and the like data catalog companies like they have analysts who learn about the different kinds of data that are being offered to um the financial industry and like document them somewhere and um data consumers like us you know pay for a subscription to that catalog and they'll

我们会参加一些会议，与数据供应商交流。有一些数据目录公司，Neudata就是其中之一。这些公司会聘请分析师，了解金融行业中正在出售的各类数据，并将其整理记录下来。像我们这样的数据消费者会付费订阅这类目录，而这些公司

also like organize these conferences um where data you know data buyers show up, data sellers show up. They'll have like speed dating between the buyers and sellers where you'll like read all these profiles and you'll say like, "I want to talk to these ones and then they match you and you have like 10 uh 15 minute uh dates in a row where they explain their product to you and you ask the questions

还会组织由数据买家和数据卖家共同参加的会议，并为买卖双方安排“快速约会”。你会先浏览所有资料，然后表示想与哪些人交谈。接着，他们会为你配对，让你连续进行10场、每场15分钟的“约会”。卖方会介绍自己的产品，你也会提出问题，

that you have and try to figure out whether like this is some data that would make sense for do you sit there with like a glass of Chardonnay as your uh [laughter] yeah uh usually it's drinks after but and you know you go there and some of them you say well this was interesting but I don't think there's a match here and [laughter]

以判断这些数据是否适合我们。你会坐在那里，手里还拿着一杯霞多丽，作为你的……通常都是活动结束后才喝酒。到场之后，有些供应商会让你觉得：“很有意思，但我们可能并不匹配。”[笑声]

other others you say like we should talk more can I get you know your number your business card [laughter] so that's one way of two-sided market, right? There's like buyers and sellers.

还有些供应商会让你觉得双方应该进一步交流，并询问对方的号码或索要名片。[笑声]因此，可以把它看作一个由买方和卖方构成的双边市场。

Like are who are the who are they? Like first of all, who are the buyers? Like it's obviously people like us, trading firms. Is it all trading firms? Who are the buyers?

这些买卖双方分别是谁？首先，买方是谁？显然，其中包括我们这样的交易公司。所有交易公司都是买方吗？究竟哪些机构属于买方？

Yeah. So I think there are a lot of trading firms that don't look like us.

我认为，许多交易公司与我们并不相同。

There are like hedge funds whether like big multistrats or small um asset managers, banks, um participants who want data for their like investment decisions and analysis or research. Some of them are expanding into like other uh kinds of buyers uh like private equity or whatever the sellers vary. Um there are satellite providers say you know who have satellite data. Um so you can see the Walmart parking lot.

例如，对冲基金——无论是大型多策略基金，还是小型资产管理公司——以及银行等市场参与者，都希望获得数据，用于投资决策、分析或研究。有些数据提供商也在拓展其他类型的买方，例如私募股权公司。卖方则各不相同。例如，有些卫星服务提供商掌握卫星数据。因此，你可以看到Walmart的停车场。

So you can see the Walmart parking lot.

是的，你可以看到Walmart的停车场。

So there are like big companies that own lots of data sets over lots of verticals. the like Bloomberg, S&P, Elsag refinatives of the world. There are companies that like focus on a specific kind of data, consumer transaction data. Mhm.

还有一些大型公司拥有覆盖众多垂直领域的大量数据集，例如Bloomberg、S&P，以及LSEG、Refinitiv之类的公司。另一些公司则专注于某种特定类型的数据，例如消费者交易数据。

Um there uh are companies there are companies that like happen to have data from their business either like you know they're a satellite company and they they have satellites or they're like a company that like provides goods or services and they have exhaust data and they think hm like maybe someone would want to buy this. Do you actually have a sense of like how much of this big market for data is has

还有一些公司只是在经营过程中产生了数据。例如，它本身是一家拥有卫星的卫星公司；或者是一家提供商品或服务的公司，掌握作为业务副产品产生的数据，于是想到可能有人愿意购买这些数据。你是否大致了解，在这个庞大的数据市场中，有多大一部分

trading of various forms or financial companies for various forms on the buying side and how much is other stuff?

是由各类交易公司或金融公司作为买方，又有多大一部分被用于其他用途？

Some of this data is probably also used for like targeting ads.

其中一些数据也可能用于定向广告。

Sure. Um, and like one important difference between the trading use case and the ad use case is that we don't want to know like who the individual consumers are, right? We want to know like oh like people are spending more at Lululemon, people are you know buying more Ritz crackers but like apart from you know demographic information that might help us weight the data or maybe information that would let us see like oh people who

没错。交易场景与广告场景之间有一个重要区别：我们并不想知道每位消费者的具体身份。我们想知道的是，人们在Lululemon的消费增加了，或者人们购买了更多Ritz饼干。除此之外，我们可能还需要有助于对数据进行加权的人口统计信息，或者能够显示购买某种商品的人

buy this also buy that. We don't want to know who the actual person is. Well, with advertising, I think it's the point, right?

也会购买另一种商品的信息。我们并不想知道他们具体是谁，但在广告领域，识别具体个人恰恰可能是重点。

For so for at least if you're doing this kind of like micro targeting.

至少在进行这种微定向投放时是如此。

Yeah, if you're doing the micro targeting.

是的，如果进行的是微定向投放。

Okay. So, let's take a step back. So, we just said like one part of this is this data strategy which is kind of thinking about the sources and the syncs understanding places where data can come from and places where data can be used internally and doing this like kind of internal and external kind of matchmaking process.

我们退一步来看。刚才提到，团队的一部分工作是数据战略，即思考数据的来源和去向，了解数据可以来自哪些地方、能够在内部哪些地方使用，并在内部需求与外部数据之间进行匹配。

Yeah. Like some of it is through that uh through cataloges, data sets that are um advertised and exist and some of it is from thinking you know from first principles like what question am I trying to answer and what data could help me answer that question and who might have that data whether they are selling it right now or not.

其中一部分工作是通过数据目录寻找已经存在并被公开推介的数据集；另一部分则是从第一性原理出发，思考自己想回答什么问题、哪些数据可以帮助回答这个问题，以及谁可能掌握这些数据，无论对方目前是否在出售这些数据。

Um some I guess there's like someone who you think might have data who isn't selling it but like you can go talk to them and maybe there's a product they can make.

也就是说，你认为某个人可能掌握某种数据。虽然对方并未出售这些数据，但你可以与其洽谈，对方或许能够据此开发出相应的产品。

Yes. In addition to like data sourcing like we have our you know data infrastructure the like uh tools for ingesting and transforming data for like monitoring our data pipelines the building blocks that people who are building data pipelines actually use.

除数据寻源之外，我们还有自己的数据基础设施，包括用于摄取和转换数据的工具、用于监控数据管道的工具，以及供数据管道构建人员实际使用的基础组件。

Got it. And this is what looks maybe more like a kind of straight ahead software engineering role. Yeah, that is more like software engineering. Your product is like software and systems and services and the people who do that are largely people we hired through our you know standard software engineering pipelines. And then we have uh data engineering where like they're also engineers um but the product is like a good data

明白了。这类岗位可能更像常规的软件工程岗位，也确实更接近软件工程。其产品包括软件、系统和服务。从事这类工作的人，大多通过我们的标准软件工程师招聘流程加入。除此之外，我们还有数据工程师。他们同样是工程师，但其产品是高质量的

set. What they do is like they build uh data pipelines. They bring data into chain streets walls and they transform it into the like form that we need in order to like use it in research and trading. Um but they also have to learn a lot when doing so. They have to understand like how does this data set work? How does this data domain work?

数据集。他们负责构建数据管道，将数据引入Jane Street内部，再将其转换成我们需要的形式，以供研究和交易使用。在此过程中，他们还需要了解许多相关知识，必须弄清楚数据集的运作方式，以及相应数据领域的具体情况。

like what what is this data actually modeling? Um they have to like understand how we're going to use the data, understand how the data is produced and synthesizing all of that like figure out like okay what should Jane Street be doing with this data. So you said a lot about what they need to understand, but I'd love to hear a little more about what they actually do, right? Part of what you said is writing

例如，这些数据实际在对什么进行建模？他们必须了解我们将如何使用这些数据、数据是如何生成的，并综合所有信息，判断Jane Street究竟应当如何利用这些数据。你刚才谈到了许多他们需要了解的内容，但我还想进一步了解他们实际会做哪些工作。你提到其中一项工作是编写

a pipeline, and a pipeline itself is just a kind of program, right? You some program that like brings in and transforms and regularizes the data into the shape that you need. But like to what degree is that just like a mechanical matter of like parsing and understanding, you know, you know, what is the shape in which the the bits have been laid out? And to what degree do you need stuff that's like more kind of in

数据管道。数据管道本身就是一种程序，即能够引入、转换和规范化数据，将数据处理成所需形式的程序。但这项工作在多大程度上只是机械性地解析数据，弄清楚这些比特的排列结构？又在多大程度上需要更偏向

the domain and like I what's the shape of that work that you need to do that involves understanding more about the data?

领域知识的内容？这种需要进一步了解数据的工作，具体是什么样的？

Some of it is mechanical but like we build tools to automate as much of that as possible.

有些工作确实是机械性的，但我们会构建工具，尽可能将其自动化。

Um part of it is thinking about what does this field actually mean? Maybe like we could go into an example like what's an example of like some data source that has some like stuff that is on its base hard to interpret and then you go out and like understand more about the domain and can produce an easier to use version of that data.

另一部分工作是思考某个字段究竟意味着什么。或许可以举一个例子：是否存在这样一种数据来源，其中某些内容本身很难解读，但在进一步了解相关领域后，就能生成更易于使用的数据版本？

So company financials are like not alternative at all. They're they're the opposite of alternative like you know in the US um at the moment companies um public companies publish their financial statements every quarter and you know they it's published via like filings with the SEC um maybe a snippet is shared via press release at around the same time and you have all of this data about you know from like lines from a

公司财务数据完全算不上另类数据，恰恰相反，它们是最传统的数据。例如，目前美国上市公司每个季度都会发布财务报表。这些报表通过向SEC提交文件来发布，其中某些片段可能在大致相同的时间通过新闻稿发布。随后，你会获得所有这些数据，例如来自

company's balance sheet or income statement and you want to turn that into some like structured form that we can do like quantitative studies on top of.

公司资产负债表或利润表的各个项目，并希望将其转换成某种结构化形式，以便我们在此基础上开展定量研究。

So that's like rough when you say structure versus unstructured there's like like a PDF or a big pile of text and you want to convert that into like morally like rows in a table.

因此，你所说的结构化与非结构化，大致是指手中有一个 PDF 或大量文本，希望将其转换成类似表格中逐行排列的数据。

Yeah. And you know there are companies that will do this um that sell uh like tabular versions of um company financials. Let's start off with what even is a company? How do you know like what a company is? How do you know which financial statements are about the same company or about the same like financial period of a company? Um, some companies like uh have these like weird structures. is a I think they're

对。也有一些公司从事这类业务，即出售表格化的公司财务数据。我们先从最基本的问题开始：公司究竟是什么？如何判断某个实体是否算一家公司？如何确定一家公司的身份？又如何判断哪些财务报表属于同一家公司，或者对应同一公司的同一个财务期间？有些公司的结构很特殊，我想它们

called like dual listed companies where there's company A and there's company B and neither one is a uh parent company of the other but like they have the same management the same board often um and there is some I don't know intercomp agreement that they have such that like owning them is economically equivalent okay and like so you have two things are like formally different companies

叫作双重上市公司。也就是说，存在公司 A 和公司 B，两者都不是对方的母公司，却拥有相同的管理层，通常还有相同的董事会。两家公司之间还存在某种协议，使得持有其中任何一家公司在经济上都是等价的。明白。因此，这两个实体在形式上是不同的公司。

different companies practice economically like are the same thing.

它们是不同的公司，但在经济意义上实际上是同一个实体。

Yeah. And so you you think about edge cases uh like that and how do you like you receive data about something? Is it one company? Is it both companies together? What is it exactly? And because you're trading on this, you associate it with like securities. Um does it apply to all the securities of like both companies?

对。因此，你必须考虑这类边缘情况：当你收到与某个对象有关的数据时，它对应的是一家公司，还是两家公司的整体？这个对象究竟是什么？而且，由于你需要依据这些数据进行交易，还必须将其与证券关联起来。这些数据是否适用于两家公司的所有证券？

Right. And this just kind of goes back into the metadata problem of like there are all sorts of weird company structures and different names for things and different naming conventions and sometimes you talk about the is which is like the name of the thing internationally and sometimes there's the seedall which differs depending on which clearing system you're in and which um like does the data apply to

对。这又回到了元数据问题。公司结构千奇百怪，各类事物的名称和命名惯例也各不相同。有时人们会使用 ISIN，也就是国际通用的标识名称；有时则会使用因不同市场而异的 SEDOL。你使用的是哪个清算系统？这些数据适用于哪个层级？

the company or like a share class um or a specific or a specific listing like this is a million ways of expressing the names of things and there's a bunch of like complex structural stuff that like is kind of real but also maybe you want to collapse thinking about it and then companies publish multiple um versions of their financial statements.

是公司、某个股票类别，还是某个特定标的或特定上市证券？同一事物的名称有无数种表达方式，其中还涉及许多复杂的结构性问题。这些问题确实存在，但你也可能希望将它们合并起来考虑。此外，公司还会发布多个版本的财务报表。

they'll put out uh one and then they might like revise it or they'll like change their you know fiscal calendar or accounting in ways that make like a not completely comparable to b throughout this you have to think through like what data was knowable at what point in time and like what features do I actually need for our trading are there other like the I mean the kind of metadata one is one that kind of come

公司会先发布一个版本，之后可能进行修订，或者更改财务年度安排或会计处理方式，导致A和B无法完全比较。在整个过程中，你都必须仔细考虑各个时间点上哪些数据在当时可以获知，以及我们的交易究竟需要哪些特征。还有其他例子吗？刚才谈到的元数据是这次对话中

up a lot in this conversation. Are there are there like good non-metadated examples where you have to like you know you get some data source it has a bunch of numbers in it but like what actually do these numbers mean and like how do you go from like oh yeah they told you some stuff to like understanding like where there might be errors in the numbers or where like you know it might you know mean different things for

反复出现的一个话题。是否有一些不涉及元数据的典型例子？例如，你获得了某个包含大量数字的数据源，但这些数字究竟意味着什么？在对方提供了一些信息的基础上，你又该如何进一步理解这些数字中可能存在哪些错误，或者对于不同公司、不同记录

different companies or different records or different examples or whatever like what's like just to get a sense of like how the kind of domain specific stuff shows up like beyond the kind of traditional metadata problems and into like the wilder world of the different kinds of data you encounter.

或不同案例而言，这些数字可能具有哪些不同含义？我想了解的是，除了传统的元数据问题之外，这些特定领域的问题会如何出现在你们接触的纷繁复杂的各类数据中。

So, like we talked about the satellite photos of uh Walmart parking lots and and by the way that's like an old it's like such an old example like people used to talk about when people talk about like the classic example of like does it count as insider trading if like you're flying in a plane and you look down and you see like you know I guess people you know that you you see like you know something like

例如，我们刚才谈到了Walmart停车场的卫星照片。顺便说一句，这已经是一个非常老的例子。过去人们谈到那个经典问题时，总会举这个例子：如果你坐飞机飞过某地，低头亲眼看到了这类信息，

this like does that does that one is that actually a good one?

这是否算内幕交易？这个例子真的合适吗？

I don't think so. [laughter] Um, I'm might get like a bunch of LinkedIn messages from vendors after this saying like, "You're totally wrong. I have these great photos of parking lots that I'd like to sell you." Um, one problem is that I don't know, it's kind of annoying. You have to get a take a lot of photos of a lot of parking lots. It's noisy. Maybe you get not that many photos per store per, you know, day or

我认为不太合适。[笑声]我可能会在 LinkedIn 上收到许多供应商发来的消息，说：“你完全错了。我这里有很棒的停车场照片，想卖给你。”其中一个问题是，这件事有些麻烦。你必须拍摄大量停车场的许多照片，而且数据噪声很大。每家门店每天或每周能够拍到的照片可能并不多。

week. Walmart has 20% of it its shopping online. Other companies have more and parking lots are not going to help you when uh you know measuring the behaviors of New York City shoppers. And it's also just like really indirect. Like the thing you're getting at is like how many people showed up which is different from how much they're spending.

Walmart 有 20% 的购物发生在线上，其他公司的比例更高。如果你要衡量纽约市消费者的行为，停车场数据也无法提供帮助。而且，这类数据其实非常间接。你真正获得的只是到访人数，而这与他们花了多少钱并不是一回事。

Mhm.

是的。

And like you kind of think from first principles like who knows what people are spending at Walmart stores. Well, Walmart does, but they're not going to tell you uh until the end of the quarter um than uh the consumers do. So, I don't know. You could ask them.

如果从第一性原理出发，谁知道人们在 Walmart 门店花了多少钱？Walmart 知道，但他们要到季度结束时才会告诉你。消费者也知道。因此，你可以去问他们。

Although, one by one, it's going to take a while.

不过，如果逐个询问，恐怕需要很长时间。

Yeah, it's going to [laughter] take a while. Um but like you know their bank knows um the person the the bank that gave them that credit card or you know the Visa or Mastercard network or like the company that provides the you know plumbing to a host of like credit unions or for their like uh credit and debit card programs like they know and maybe they're willing to share like anonymized or aggregated um

对，确实需要[笑声]很长时间。但他们的银行知道，也就是向他们发行信用卡的银行；Visa 或 Mastercard 网络也知道；为许多信用合作社的信用卡和借记卡项目提供底层基础设施的公司同样知道。而且，他们或许愿意分享经过匿名化或汇总的

statistics about like what spend they're seeing. And so you get some information about the transactions that some panel of cards uh are making and then where do you go from there? It's not a random sample all consumer transaction in the United States.

消费统计数据能提供一些信息，帮助你了解某个银行卡样本群体所进行的交易。接下来应该如何处理？这并不是美国所有消费者交易的随机样本。

Mhm.

是的。

Transactions belonging to like one credit card program. Maybe the people who shop uh and have cards from like uh credit unions are different from the transactions from people who like have a Chase Sapphire Reserve. Maybe like the demographics of the panel are just not the same as the broader United States.

它只是某个信用卡项目中的交易。购物时使用信用合作社银行卡的人，其交易或许不同于使用Chase Sapphire Reserve的人。这个样本群体的人口结构也可能与全美国的人口结构不同。

Maybe people uh like drop in or drop out of the panel. And you know, if there are more consumers on the sample, does that mean that people are spending more at Walmart or does it mean there are more consumers in the sample? Um, and so like there are interesting modeling questions about like well we have this information and how do we like make good predictions based on it adjusting for all of those factors.

还可能有人加入或退出样本面板。例如，如果样本中的消费者数量增加，这究竟意味着人们在Walmart的消费增加了，还是仅仅意味着样本中的消费者增多了？其中存在一些很有意思的建模问题：获得这些信息后，应该如何调整所有这些因素，并据此做出准确预测？

And is that like trying to kind of model out and adjust and maybe like cross compare different sources? Is that part of the data engineering work or is that part of what happens like on the trading desk after you get like the cleaned up data put in front of you?

这是否意味着需要通过建模剔除并调整这些因素，或许还要对不同的数据源进行交叉比较？这是数据工程工作的一部分，还是要等交易台拿到清洗后的数据再处理？

It's a mix of the two. I think the but like some of this like really just does happen. um on the trading desk. But I think the groundwork for doing all of this comes from like a deep understanding of like how these data sets work and what information they provide you when which the data engineer typically like builds up a good understanding of.

两方面都有。其中一些工作确实是在交易台完成的。不过，完成这些工作的基础，是深入理解这些数据集的运作方式及其能够提供的信息，而数据工程师通常会逐步建立起这种深入理解。

One thing I' I'd worry about you mentioned there's all this like aggregation and anonymization that's done by the upstream vendors. You can imagine like that could be done wrong or just done in ways that introduces weird biases into the data that's hard for you to see. Like is that a problem that you guys end up having to wrestle with?

有一点令我担忧。你提到，上游供应商会进行大量汇总和匿名化处理。这些处理可能会出错，或者以某种方式给数据引入难以察觉的异常偏差。这是否是你们最终必须应对的问题？

Oh, I mean so yes, vendors like do weird things with their data all the time. Um sometimes they'll uh like you know have data missing. Um sometimes they they will say uh we compute this column one way and then later tell you whoops made a mistake. All those values were wrong.

是的，供应商总会对数据进行一些奇怪的处理。有时数据会出现缺失。有时他们会说：“我们是这样计算这一列的”，之后又通知你：“计算有误，那些值全都不对。

Here's what you should have used instead.

应该改用这些值。”

um sometimes you know sometimes associating like a transaction with the like stock of the I don't know the merchant is difficult and they improve these taggings over time. So sometimes they'll like say you know these transactions that you know we said were one thing are actually like from this other company or other brand.

还有一些时候，很难将一笔交易与相应商户的股票关联起来，而供应商会逐步改进这些标注。因此，他们有时会表示，此前被归入某一类别的交易，其实来自另一家公司或另一个品牌。

Mhm. And actually, one thing that like shows up if you have this issue where like they're going back and fixing stuff in the past is there's all these causality questions. Right.

这种情况下会出现一个问题：如果供应商回头修正过去的数据，就会牵涉各种因果关系问题。

Right. On one hand, you know, isn't that helpful? They're fixing the data set.

是的。一方面，这种修正很有帮助，因为他们正在修正数据集。

Yeah. It was wrong now.

之前的数据是错误的，现在已经得到修正。

It was wrong. Now it's right. On the other hand, if you are like doing a study at a firm like Jane Street and you're trying to figure out like if I had been subscribing to this data set, like would I be able to do good trades?

之前的数据有误，现在改正了。另一方面，如果你在Jane Street这样的公司从事研究，就需要弄清楚：如果自己当时订阅了这个数据集，能否据此完成不错的交易？

Mhm.

是的。

Um should I purchase the data set? If you're trying to make that decision and you are looking at the data that is not actually what you would have received, but instead you know the corrected more accurate version later, you're not actually studying like what trades you would have done if you'd been subscribing to the data set. You're uh simulating what you would have done if you had a time machine,

我是否应该购买这个数据集？如果要做出这一决定，查看的却不是自己当时实际会收到的数据，而是后来经过修正、更准确的版本，那么所研究的就不是当时订阅这个数据集后会进行哪些交易，而是在模拟拥有一台时光机时会如何交易。

right?

是这样吗？

And although it's subtle, right? It depends on the nature of the correction, right?

而且这个问题非常微妙，具体还取决于修正的性质。

is sort of there's a question of like in some sense what could have been known at the time and also like what does it mean for like what could have been known in the future. There could be some data set where like the underlying data like has real information and then they correct a completely mechanical thing and then it's okay. But like it's very easy when you're doing that thing to smuggle information into the past and like it

从某种意义上说，这里涉及一个问题：当时究竟可能知道哪些信息，以及这些信息对未来可能知道的内容意味着什么。例如，如果某个数据集的底层数据确实包含真实信息，而供应商修正的只是一个纯粹的机械性问题，那么这种修正就没有关系。但在修正过程中，人们很容易在不知不觉间把信息偷渡到过去。

becomes very subtle. Whereas if you just like time stamp what you had at the time then you know well at least it was definitely possible to have it at the time because at the time I had it.

这样一来，问题就会变得非常微妙。相反，如果只是给自己当时拥有的数据加上时间戳，至少可以确定这些数据在当时肯定能够获得，因为自己当时确实收到了。

Yes. Um so uh what we love is when vendors say like this is exactly what I delivered at this moment in time and you know here is the corrected version that I delivered at this other point in time and not everyone does that and so what we try to do is save all the data that we have access to along with the like time we received it so that no matter what transformations we do later we can always reconstruct like what we would

因此，我们最希望供应商明确说明：“这是我在这个时间点交付的数据；这是修正后的版本，是我在另一个时间点交付的数据。”但并非所有供应商都会这样做。因此，我们会尽量保存自己能够获取的所有数据，并记录收到数据的时间。这样，无论之后进行了何种转换，我们都能还原原本可见的数据。

have known when, right? And no matter what the vendor does with their time stamps, we at least always capture our time stamps of when we got it, which by the way is a very close analogy what we do with market data, right? Market data also comes with time stamps from the exchange and we look at them with a certain amount of like suspicion because like all sorts of weird stuff can happen. You can have

由此可以确定，我们是在什么时间知道什么。无论供应商如何处理自己的时间戳，我们至少始终会记录收到数据时的时间戳。这与我们处理市场数据的方式非常相似。市场数据也带有交易所提供的时间戳，但我们会以一定程度的怀疑看待这些时间戳，因为可能发生各种异常情况。例如，

some weird clock synchronization. The times can be off in various ways, but we capture stuff when it lands on our network. And again like we sort of there's still information in their timestamps but like there's a way in which we trust ours more than we trust theirs.

时钟同步可能出现异常，时间也可能由于各种原因产生偏差。不过，我们会在数据抵达自己的网络时记录时间。当然，对方的时间戳仍然包含信息，但从某种意义上说，我们更信任自己的时间戳。

Yeah. And knowing like okay I received this packet at this nancond in time gives us more confidence you know making decisions based on our historical studies than like uh I received this packet at about this moment in time.

是的。明确知道“我是在这一纳秒收到这个数据包的”，会让我们在依据历史研究做决策时更有信心，而不是只知道“我大概是在这个时刻收到这个数据包的”。

Right.

是的。

Yeah. So to switch gears for a sec, yeah. Um, one thing that has changed a lot in the last handful of years, in fact, you know, a lot of that change over overlapped with a period of, you know, building up the alt data team is boy golly has AI moved a lot.

好，我们暂时换个话题。过去几年里，有一件事发生了巨大变化，而且这种变化在很大程度上与我们逐步组建另类数据团队的时期重合，那就是 AI 的发展速度非常快。

Um, and I think it's kind of there's kind of two different ways within the J Street context for change. One is we have a lot more ML models, neural net models that are being used as part of the trading process for consuming data and transforming it. And then also we have these like super smart LLMs that are like useful productivity tools. And I feel like both of these have lots of potential to change the alt data

我认为，在 Jane Street 的情境下，这种变化大致体现在两个方面。一方面，我们有了更多 ML 模型和神经网络模型，用于交易流程，对数据进行处理和转换。另一方面，我们也有了非常智能的 LLM，可以将其作为实用的生产力工具。这两方面都有很大潜力改变另类数据的

process. And I'm kind of curious how the impact of those has landed along the last few years. So uh I think there are two kinds of impacts of AI here. One is that like we have better models for consuming this data and making predictions about the world, finding patterns and you know that means that like the value of all of our data of good quality data sets has just like gone up. And with LMS in particular, like it's easier to make use

处理流程。我很好奇，过去几年里，它们究竟产生了怎样的影响。我认为，AI 在这里带来了两类影响。一类是我们有了更好的模型，可以处理这些数据、预测现实世界并从中发现规律。这意味着，所有这些数据的价值都提高了，我们拥有的高质量数据集数量也大幅增加。尤其是在有了 LLM 之后，利用文本数据变得更加容易，

of text data to like extract information or features from text that like would have been manual, annoying, impractical, a nest of reaxes, right, uh beforehand.

可以直接从文本数据中提取信息或特征。以前，这类工作需要手动完成，不仅麻烦且不切实际，还必须使用大量杂乱的正则表达式。过去的情况确实如此。

Yeah. They're like an amazing tool for extracting structured data from unstructured text.

它们是非常实用的工具，能够从非结构化文本中提取结构化数据。

Yeah. Although also there's like a huge causality problem, right? Because if you use like a really up-to-date LLM to like that knows stuff about old data and trying to, you know, use it to featurize some old data, like you're going to get very confusing things happen because the LLM is smart.

不过，这里也存在一个很大的因果关系问题。如果使用一个非常新的 LLM，它知道旧数据之后发生了什么，而你又试图用它从某些旧数据中提取特征，就会出现一些非常令人困惑的情况，因为 LLM 很聪明。

It knows things about the future. Then yeah, the LM knows what happened to like Enron. um [laughter] the LLM training data like maybe it includes the financial statement or a future one right uh so that presents some difficulties so then there's like AI as a tool where like a lot of the same trends and limitations that you see in software engineering also high to data engineering. They uh make it easier

它知道未来发生了什么。例如，LLM 知道 Enron 后来发生了什么。LLM 的训练数据中可能包含那份财务报表，甚至包括后来发布的财务报表，因此会带来一些困难。此外，还有作为工具使用的 AI；在这方面，软件工程中出现的许多趋势和局限同样适用于数据工程。它们可以让人更轻松地

to produce code to do analyses and in the hands of like good data engineers who know what they're trying to get from these tools and can tell whether the results are good or bad. They're really helpful and you know our data engineers um use LLMs also. Oh, they make it easier than ever to generate like bad code, right? [laughter] Um, never has the gap between merely doing

编写用于分析的代码。如果使用者是一名优秀的数据工程师，清楚自己希望从这些工具中得到什么，也能够判断结果的好坏，那么这些工具就非常有用。我们的数据工程师也会使用 LLM。当然，它们也让人比以往任何时候都更容易生成糟糕的代码。单纯把事情做出来，与

something and doing something well been larger in terms of cost, right? It's so easy to just do something, but like sometimes the results are very bad.

把事情做好之间的成本差距从未像现在这样大。如今，随便做出一些东西非常容易，但结果有时会非常糟糕。

Yeah. And it's easy to do something and think I have solved the problem when in fact you have like failed to understand the problem.

而且，人们很容易做出一些东西后便以为自己已经解决了问题，但实际上甚至根本没有理解这个问题。

Mhm.

是的。

Right. So you have the normal ups and downs of using AI tools like they're they're amazing and also they have lots of pitfalls.

因此，使用 AI 工具通常有利也有弊：它们非常强大，但也存在许多陷阱。

Um and then you talked about AI as a kind of feature extraction but there's also like neural nets as models right as new ways of extracting value out of the data that you get. Like has that changed the process? Has it changed like the value of having alternative data? Has it changed the pitfalls of using it? It certainly changed the value of having data alternative of or otherwise like it's generally more valuable. Um maybe

你刚才谈到了使用 AI 进行某种特征提取，但除此之外，还有将神经网络用作模型，以及从所获得的数据中提取价值的新方法。这改变了整个流程吗？它是否改变了另类数据的价值，或者改变了使用另类数据的隐患？这无疑提升了拥有另类数据或其他数据的价值。总体而言，数据变得更有价值了。也许

like some shapes and quantities of data are like more amunable to these models than others.

某些形式和规模的数据比其他数据更适合这些模型。

So one thing I'd wonder about is like the value of some of the kind of what in other contexts we'd call feature engineering.

因此，我想了解以下工作的价值，也就是在其他场景中所谓的特征工程。

Like I think one of the there's the the whole like bitter lesson idea that lots of ways that you try and like express priors into the data itself or the shape of the model or whatever maybe become less useful as the models get bigger and more powerful and stuff because some of the kind of regularization and featurization can kind of be done inside of the model. Um, so there's like that and then there's also the question of

我想到的一点是，存在这样一种“苦涩的教训”：许多试图将先验知识融入数据本身、模型结构或其他部分的方法，可能会变得不再那么有用，因为模型正在变得更大、更强。毕竟，一部分正则化和特征化工作可以直接在模型内部完成。这是一方面；另一方面还有一个问题：

like how just in general how much like data cleaning and data preparation like is that like more useful in the in a neural net context is it less I think the value of cleaning the data is greater I think like I don't know if your data is not point in time and secretly leaking information from the future into data that should be in the past these stronger models are going to certain kinds of like outliers and weird

总体而言，在神经网络场景下，数据清洗和数据准备是变得更有用，还是不再那么有用？我认为，数据清洗的价值反而更高。例如，如果数据没有按照时间点准确对齐，无意中将未来的信息泄露到本应属于过去的数据中，这些更强大的模型就会利用这些泄露的信息。至于某些异常值和奇怪的

patches of data that like some models like handle well and other models like handle poorly in their training process and getting the quality right uh is you know more important um for those models I so like internally that models will be able to do more. You will have to like handcraft features less and it will be better at figuring out like which ones are um important or not. But like the value of getting the

数据片段，有些模型能够很好地处理，另一些模型则在训练过程中处理得很差。因此，对这些模型而言，确保数据质量更为重要。我在内部的看法是，模型能够完成的事情会越来越多，需要手工设计的特征会越来越少，而模型会更善于判断哪些特征重要、哪些不重要。但是，正确处理输入模型的

data that you do pass in right is pretty high. And I think then it becomes like, well, are LLMs with the skills and harness and whatever we build about them going to be able to completely automate that? I think that's similar to will we replace all software engineers with LLMs. Um, right too, right? the current horizon and at least what you can see immediately. It seems like an enormous amount of like human

数据仍然具有很高的价值。接下来的问题是：LLMs 加上相关技能、配套框架以及我们围绕它们构建的各种东西，能否将这项工作完全自动化？我认为，这类似于讨论我们是否会用 LLM 替换所有软件工程师。至少就目前能够看到的情况而言，人的判断力似乎占据了极大的比重，

judgment is incredibly important.

而且重要得不可思议。

Yeah.

是的。

Like in some ways it feels like human judgment just gets more important as like it becomes the thing that unlocks like you can like do all the stuff really fast if you can figure out if it's good. So like the verification bottleneck becomes really important and there's a kind of very critical human part of that at the moment.

从某种程度上看，人的判断力反而变得越来越重要，因为它成了关键的解锁条件：只要能够判断结果究竟是好是坏，就可以极快地完成所有事情。因此，验证成了一个非常重要的瓶颈，而目前其中仍有一个非常关键、必须由人来完成的环节。

Taste for what should be built. Well absolutely.

也就是判断该做什么的品位。完全正确。

Yeah. Speaking of what should be built, um I feel like the alt data world here has been like, you know, a kind of Jane Street flavored software engineering adventure in that like boy, we've made a lot of our own exciting custom stuff. Um and we've also used some like external outside world stuff. And I'm I'm curious like how you guys have thought about and navigated the question of like where does it make sense to buy some external

说到该做什么，我认为这里的另类数据领域一直有些像一场带有 Jane Street 风格的软件工程冒险，因为我们确实自行开发了许多令人兴奋的定制工具，同时也使用过一些外部的现成工具。我很好奇，你们如何思考和处理这个问题：在什么情况下，购买某种外部

product for like helping to manage this?

产品来协助管理这些工作更为合理？

After all, we're not the only people who manage these kind of alter data pipelines. Um, and where we have decided to make our own things and like has that been good, has it been bad, like how do you think about the choices there?

毕竟，并非只有我们在管理这类另类数据管道。那么，我们会在哪些情况下决定自行开发工具？这些决定的效果如何？你如何看待其中的取舍？

Yeah, some of our software stack like is very much pulled from the rest of the world. Like a lot of data engineers outside Jane Street use dbt which stands for data build tool. Mhm.

我们软件栈的一部分在很大程度上借鉴了外界的方案。Jane Street 之外的许多数据工程师都使用 dbt，它是 data build tool 的缩写。

Um a data build tool is like a tool for like describing and orchestrating um transformations like in your data warehouse usually like using SQL queries but in like a code reviewable testable composable good software engineering practices way.

data build tool 是一种用于描述和编排数据转换的工具，通常使用 SQL 查询处理数据仓库中的数据，同时采用便于代码审查、测试和组合的方式，遵循良好的软件工程实践。

Sounds great. Um so I have seen 300 line postres um views and functions uh in my time at Jane Street and we don't want any of that like dbtf lets you break down the data transformation problem into smaller components and like test the properties of the data at each stage so that you can like rely on it.

这听起来很不错。我在 Jane Street 工作期间，见过长达 300 行的 postres 视图和函数，而我们并不希望出现这种情况。dbtf 可以将数据转换问题拆分成更小的组件，并在每个阶段测试数据属性，从而让使用者能够信赖处理结果。

Mhm. Our like data warehouses query engine is Trino. There are lots of Python tools for like processing tabular data. But other things, you know, we do build ourselves where like if we were starting from scratch and we're entirely cloud-based. Um maybe we would do something more offtheshelf. Um, but one thing that is pretty cool is that Jane Street is like good at building and operating like on-prem data centers and

我们的数据仓库查询引擎是 Trino，此外还有许多用于处理表格数据的 Python 工具。不过，有些工具确实是我们自行开发的。如果从零起步并完全基于云构建，我们或许会采用更多现成方案。Jane Street 的一项优势是非常擅长建设和运营本地数据中心。

we had to be in order to like run low latency trading systems before we were doing any of that alter alternative data stuff.

为了运行低延迟交易系统，我们早在涉足另类数据之前就必须具备这种能力。

That's right. Right. We need and we need our own data centers in part for like the kind of physical location thing where you need to like have your boxes close to the exchange and also just some of the like underlying abstractions like clouds no good at giving you multiccast right there like you can't easily deploy FPGAAS there's all sorts of things that you want in terms of like control of the physical

我们过去和现在都需要自己的数据中心，部分原因在于物理位置方面的要求，例如必须让服务器靠近交易所。此外，云服务也不擅长提供 multicast 等底层抽象能力，FPGAAS 也很难在云上轻松部署。总之，我们需要具备多种能力，尤其需要控制物理层面。

architecture that you get in your own data center that you just kind of can't get in the cloud and like the cloud gives you a bunch of other benefits and these days we do a lot of both right if you do have the ability to do things well on prem solution that works is maybe not the one that uh you would use if you were like just using BigQuery or Snowflake or um what have you, right? And I guess part of this is like

在架构方面，自有数据中心能够提供一些在云上基本无法获得的能力。当然，云也能带来许多其他好处，如今我们同时大量采用这两种方案。如果有能力做好本地部署，适用的解决方案可能会不同于只使用 BigQuery 或 Snowflake 等服务时采用的方案。我认为，这在一定程度上类似于

you know came up in the previous uh one of these conversations was we built our own data warehouse, right? So we have supertore and we didn't build it for alt data or at least not primarily for alt data but like that's like a tool that alt data now uses.

我们之前一次对话中提到的情况：我们建立了自己的数据仓库，也拥有 supertore。它并非为另类数据而建，至少主要目的不是处理另类数据，但如今已经成为另类数据团队使用的工具。

Yes. And uh it's great. It's great having all of our data in one place and being able to handle the volumes of trading data and other data that we use.

这个工具非常出色。我们可以将所有数据集中在一个地方，并有能力处理海量交易数据以及所使用的其他数据，这一点确实非常有价值。

It's useful for being able to join it to all of our other services um internally.

这也便于我们将这些数据与内部的其他所有服务连接起来。

And the cost model is different. I don't want to imagine how much we would spend in credits or slots if all of the queries that we are uh running now were like build in like a cloud uh warehouse.

它的成本模式也有所不同。我难以想象，如果目前运行的所有查询都需要按 credits 或 slots 计费，例如在云端搭建数据仓库，我们需要支付多少费用。

So, so why is that like what how is the how is the cloud cost model different from the kind of cost model that we get from this thing that we built ourselves?

为什么会这样？云服务的成本模式与我们自行搭建的系统有何不同？

So like the cloud uh cost model will be based on you know either the amount of data you read or the amount of like time on like in their units of compute.

云服务的成本通常取决于读取的数据量，或者使用其计算资源的时长。

Mhm. um they use. And what we can do is just think about like the cost of acquiring the hardware and operating the hardware which all in all is like significantly lower like they have like large uh curse margins. Um is it is it that the overall margins are are large or is it more that like the incremental cost model? You're also not thinking about like the cost of like trying this one new thing, this one new query.

而我们只需考虑购置和运行硬件的成本。总体计算下来，这种方式的成本低得多，因为云服务商的毛利率很高。这里的关键是整体利润率较高，还是增量成本模式？此外，我们也不必考虑尝试一项新事物或执行一条新查询的成本。

And if you just sort of take the things that you get from cloud vendors and say let's let's look at those prices and then apply it to our workloads, you'd be like, wow, I could get a lot of hardware for that cost. So just like the way the math in practice works out is that like it would incentivize us to spend a lot of time figuring how to optimize and do less work on the system than it does when we actually go out and buy the

如果直接查看云服务商提供的服务价格，并将这些价格套用到我们的工作负载上，就会发现同样的资金可以买到大量硬件。实际结果是，这种定价模式会促使我们投入大量时间研究如何优化，尽量减少系统的工作量；而当我们真正购买

physical hardware. So like economic theory and like architecture aside, it's just like that's how the pricing model seems to work.

实体硬件时，则不会这样做。因此，抛开经济理论和架构不谈，定价模式在实践中似乎就是这样发挥作用的。

Yeah. If we were using one of these like uh other products, we would spend a tremendous amount of time like optimizing our spend and and like trying to shape our workloads to like fit their cost model. And we do think about the performance of Trino and our data warehouse. we end up spending less time on that, right? Because because overall the costs in fact are lower.

如果使用这类外部产品，我们就会投入大量时间优化支出，并设法调整工作负载的形态，以适应其成本模式。我们确实也会考虑 Trino 和自有数据仓库的性能，但最终在这方面投入的时间更少，因为总体成本实际上更低。

Yeah, makes sense.

确实如此，这很有道理。

So another thing I think is maybe interesting to talk about is like you know we went from in 23 one data engineer to now more than 20 data engineers. Um and it sounds like we're continue to be very hungry and are eagerly hiring love to hire more.

另一个值得讨论的话题是，我们从23年只有1名数据工程师，发展到现在拥有20多名数据工程师。我们对人才的需求依然非常旺盛，目前正在积极招聘，希望吸纳更多人才。

Um and so I'm curious like what makes a good data engineer? like what are you looking for when you're trying to hire someone and then how do we find them?

那么，什么样的人才算是优秀的数据工程师？你们在招聘时看重哪些方面，又该如何找到这样的人？

Yeah. So the most important um characteristics I think are like curiosity and a good investigative process. As part of the job, you'll be learning about some new domain, some industry, some kind of data, some kind of trading, and also you will have like messy, unfamiliar data that is useful, but also has lots of embedded problems in it. And

我认为，最重要的特质是好奇心和良好的调查研究方法。在工作中，你需要了解一个全新的领域、行业、数据类别或交易类型，也会面对杂乱、陌生但有用的数据，其中隐藏着许多问题。

I think to succeed, you kind of have to enjoy learning about all these new things.

我认为，要想做好这份工作，至少需要喜欢了解这些新事物。

And you have to enjoy learning about these new things. You have to enjoy like getting into the details. You have to be careful and think about like what you know and what you don't yet know and how you would support going from point A to point B. I think people with science or social science backgrounds who do work with like messy real world data and try to make sense of it are well suited to this. We also you know look for

你必须乐于了解新事物，也必须喜欢钻研细节。你需要谨慎思考自己已经知道什么、尚不知道什么，以及如何用证据支持从A点推导到B点的过程。我认为，具备自然科学或社会科学背景、处理过杂乱的现实世界数据并尝试从中理出头绪的人，非常适合这份工作。我们也会考察

engineering skills. I think the systems complexity of what data engineers are doing is you know lower than much of what we ask our software engineers to do. But like the data and business complexity is still very high.

工程能力。总体而言，数据工程师所处理的系统复杂度低于我们对大多数软件工程师的要求，但数据和业务的复杂度依然非常高。

Um data pipelines are code. Um you still want them to be good.

数据管道也是代码，因此你仍然希望它们成为优秀的软件。

Software. You want them to be good code.

换言之，它们应当是优秀的代码。

You want it to be like uh you know clear, correct, maintainable.

你希望代码清晰、正确且易于维护。

Mhm. [clears throat] And we look for that too. Um and where do we find uh data engineers? Some of them have worked in the financial industry before. Some of them are people who were like the data person at their like small or midsized startup where they like had to like kind of understand the data and the business context and deal with its messiness because there were only so many other people around to do do that

这也是我们看重的特质。那么，我们从哪里寻找数据工程师？有些人以前曾在金融行业工作，另一些人则曾担任中小型初创公司的数据负责人。他们必须了解数据和业务背景，并应对其中的混乱，因为身边能够处理这些事情的人很少。

who also you know have developed like good software engineering skills.

同时，他们也培养出了扎实的软件工程能力。

And so so are you mostly looking for people who essentially kind of have data wrangling experience as kind of a thing they've done already professionally?

所以，你们主要是在寻找那些拥有数据整理经验，并且此前从事过相关工作的人吗？

Usually though like that doesn't have to be like you know at a company it can be like as part of like research of doing science or some other way. Mhm.

通常如此，但相关经验不一定来自公司，也可以通过科学研究或其他方式积累。

Um, all of the people we've hired so far have been experienced hires who, you know, were doing some thing beforehand. Next year, we're hoping to venture into hiring like interns. Yeah, we started talking in 2025 about like interviewing in 2026 for an internship that runs in the summer of 2027. for people who start in 2028 after graduating from school probably and you know are really ramped up and um doing

目前为止，我们招聘的所有人都有工作经验，此前都做过一些相关工作。明年，我们希望尝试招聘实习生。我们从2025年就开始讨论在2026年进行面试，招募参加2027年暑期实习的人。他们毕业后可能会在2028年正式入职，随后还需要经过充分的适应和培养。

useful work in 2029 um right which is long-term planning and I think we're we're looking for people who like care about the data first and you know maybe they have good engineering skills now maybe they like have the potential to after a bunch of training.

直到2029年，他们才能真正开展有价值的工作。这确实是一项长期规划。我们要找的是真正关心数据本身的人。他们现在或许已经具备出色的工程技能，也可能需要经过大量培训，才有潜力达到这一水平。

Mhm. Right. Right. But like the kind of excitement about digging into the data is like the primary thing you're looking for.

不过，你们最看重的还是深入钻研数据的热情，是吗？

Yeah. While on the other hand, there are people who work with a lot of data and are like I am excited about building you know fancy models and neural networks or about like building scalable systems the ML or the software engineering parts of it. And you know we want those people at Jane Street too but not for this role.

是的。另一方面，有些人虽然会处理大量数据，但真正感兴趣的是构建复杂的模型和神经网络，或者构建可扩展的系统，也就是ML或软件工程方面的工作。当然，Jane Street也需要这样的人才，但并不是招来担任这个职位。

Got it. And then just at the interview side like you want you know to find people who are excited about and have good taste about how to think about and dig into data like how do you use an interview to sus that out?

明白了。再谈面试：你们希望找到对数据充满热情，并且知道如何思考和深入研究数据的人。那么，你们如何通过面试看出这些特质？

It's hard. Um we uh have you know a few data investigation interviews [clears throat] or data wrangling interviews um where we give them some unfamiliar data set and they investigate and build some model of how it works and like do something with it. seeing how someone approaches that problem. Are they careful and detail- oriented and do they like think about what they know? Um what assumptions

这很难。我们会安排几轮数据探索面试或数据整理面试。我们会给应聘者一个陌生的数据集，让他们进行研究、建立模型、解释数据的运作方式，并利用数据完成一些任务。我们会观察应聘者如何处理这个问题：他们是否认真、注重细节、做事有条理，是否会审视自己掌握的信息以及所作的假设。

they're making, how they would test those assumptions or do they like write a bunch of code and say hopefully this works but I'm not sure, right? Right. And once again, kind of rounding back to the like, you know, kind of engaging with like the real world underneath the data and trying to actually think about what's going on and seeing if if that affects what you should be doing.

我们还会观察他们如何验证这些假设，还是直接写出大量代码，然后表示但愿这些代码能够奏效，但自己也不确定。这里再次回到刚才那一点：应聘者需要真正关注数据背后的现实世界，认真思考究竟发生了什么，并判断这是否会影响自己应当采取的做法。

Yeah.

是的。

Awesome. All right. Well, maybe that's a good place to end it.

很好，那就到这里吧。

Yeah.

好的。

Thanks for joining me.

谢谢你来参加节目。

Thanks, Ron.

谢谢你，罗恩。

You'll find a complete transcript of the episode along with show notes and links at signalsandthreads.com.

本期节目的完整文字稿、节目说明和相关链接可在signalsandthreads.com查看。