LightSpringFox - 加密货币做市（第五季第二集）

嘿，大家好。

Hey, everyone.

我是科里。

Corey here.

感谢收听《与模特约会》的另一期节目。

Thanks for tuning into another episode of flirting with models.

如果你喜欢这个节目，我非常希望你能花点时间评分、评论，最重要的是，分享给朋友。

If you're enjoying the show, I'd greatly appreciate it if you take a moment to rate, review, and most importantly, with a friend.

口碑传播是这个播客成长的方式。

Word-of-mouth is how this podcast grows.

如果你想了解更多关于Newfound的收益叠加型共同基金、ETF和模型投资组合的信息，请访问returnstacks.com。

And if you'd like to learn more about Newfound's platform of return stacked mutual funds, ETFs, and model portfolios, head over to returnstacks.com.

现在，继续我们的节目。

Now on with the show.

好的。

Okay.

你准备好了吗？

Are you ready?

准备好了。

Yep.

好的。

Alright.

三、二、一。

Three, two, one.

我们开始吧。

Let's do it.

大家好，欢迎各位。

Hello, and welcome, everyone.

我是科里·霍夫斯坦，这里是《与模型调情》，这档播客将揭开面纱，探寻量化策略背后的人性因素。

I'm Corey Hofstein, and this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

科里·霍夫斯坦是Newfound Research的联合创始人兼首席投资官。

Corey Hofstein is the cofounder and chief investment officer of Newfound Research.

由于行业监管规定，他不会在本播客中讨论Newfound Research的任何基金。

Due to industry regulations, he will not discuss any of Newfound Research's funds on this podcast.

播客参与者表达的所有观点均为其个人意见，不代表Newfound Research的观点。

All opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of Newfound Research.

本播客仅用于信息目的，不应作为投资依据。Newfound Research的客户可能持有本播客中讨论的证券。

This podcast is for informational purposes only and should not be relied upon as a basis for investment Clients of Newfound Research may maintain positions and securities discussed in this podcast.

如需更多信息，请访问thinknewfound.com。

For more information, visit thinknewfound.com.

如果你喜欢这个播客，我们非常感谢你能在你最喜欢的播客平台上为我们留下评分或评论。

If you enjoy this podcast, we'd greatly appreciate it if you could leave us a rating or review on your favorite podcast platform.

同时，请关注我们的赞助商。

And check out our sponsor.

这一季，嗯，就是我自己。

This season, it's well, it's me.

人们经常问我，Corey，你到底做些什么？

People ask me all the time, Corey, what do you actually do?

嗯，早在2008年，我联合创立了Newfound Research。

Well, back in 2008, I co founded Newfound Research.

我们是一家量化投资与研究公司，致力于帮助投资者通过更全面的多元化策略主动应对投资风险。

We're a quantitative investment and research firm dedicated to helping investors proactively navigate the risks of investing through more holistic diversification.

无论通过我们管理的基金、我们支持的交易所交易产品，还是我们构建的完整投资组合解决方案，比如结构性阿尔法模型投资组合系列，我们都为金融顾问和机构提供多种解决方案。

Whether through the funds we manage, the exchange traded products we power, or the total portfolio solutions we construct, like the structural alpha model portfolio series, we offer a variety of solutions to financial advisers and institutions.

请访问www.thinknewfound.com了解更多信息。

Check us out at www.thinknewfound.com.

好了，现在进入正题。

And now on with the show.

这是首次与模型进行互动，本期嘉宾是匿名的，仅在Twitter上使用用户名LightSpringFox。

In a first for flirting with models, my guest this episode is anonymous, going only by the handle LightSpringFox on Twitter.

Fox先生是一名量化交易员，在MG和R公司从事加密货币做市业务，但他最初的职业生涯既不在加密货币领域，也不在做市领域。

Mister Fox is a quantitative trader who works in crypto market making at MG and R, but mister Fox did not begin his career in crypto nor even in market making.

相反，他的背景是传统股票因子投资，因此我们花了大量时间比较和对比低频与高频领域的差异。

Rather, his background is in traditional equity factor investing, and so we spend a good deal of time comparing and contrasting the low and high frequency domains.

我们还讨论了做市优势的本质、高频交易的独特风险，加密货币与传统金融做市的差异，以及福克斯先生认为的。

We also discussed the nature of market making edges, the unique risks of high frequency, how crypto and traditional finance market making deviate, and what Mr.

高频交易中最具挑战性的问题。

Fox considers the, quote, hardest problem in HFT.

闲话少说，请欣赏我与LightSpring Fox的对话。

Without further ado, please enjoy my conversation with LightSpring Fox.

这对我来说是第一次，有一位真正匿名的嘉宾，LightSpring Fox。

Well, this is a first for me, a truly anonymous guest, LightSpring Fox.

我可以叫你福克斯先生吗？

Can I can I call you mister Fox?

可以。

Yeah.

这样再好不过了。

That's perfect.

福克斯先生，欢迎来到本节目。

Mister Fox, welcome to the show.

欢迎来到《与模型调情》。

Welcome to flirting with models.

这将是一期有趣的节目。

This is gonna be a fun episode.

我们先从一个显而易见的问题开始吧。

Let's just start with the obvious question.

LightSpring Fox 这个名字是怎么来的？

Where did the name LightSpring Fox come from?

首先，谢谢您，科里。

Well, first of all, thanks, Corey.

感谢您邀请我做客。

I appreciate you having me on.

能来到这里真是太好了。

It's great to be here.

关于 LightSpring Fox 这个名字，几年前，对于那些在加密货币领域已久的老听众来说，BitMEX 几乎是最大的衍生品交易平台。

So the LightSpring Fox name, a couple years back, for your listeners who have been in crypto for a while, BitMEX was kind of the biggest derivatives venue.

他们有一个公开的排行榜，你可以选择自己的昵称，或者系统会随机生成三个单词的组合。

And they had this public leaderboard where you could either choose your handle or it would be like randomly generated three words.

当时，排行榜榜首是一个随机生成的三词组合：Heavy Autumn Wolf，我忘了他们的盈亏是多少，但肯定有数千万美元。

And at the time, the top of the leaderboard was this randomly generated three word combination, heavy autumn wolf, And I forgot what their P and L was, but it was, like, must have been in the tens of millions in dollars.

那时我就想，哇，这真是个真正的专业人士。

And at the time, I was just thinking, wow, this is, like, a real professional.

这里真的能赚到大钱，我也想达到接近这个水平。

There's real money to be made here, and I wanna approach a level that's somewhere near this.

但我不一定是那个‘Heavy Autumn Wolf’，不过我很乐意做‘Light Spring Fox’。

But, like, I might not be the heavy autumn wolf, but I'm happy to be the light spring fox.

我喜欢这个。

I love it.

我喜欢这个。

I love it.

但你并不是一直都在加密货币领域。

But you weren't always in crypto.

在我们深入加密货币世界以及你所从事的量化策略之前，我们先从你的背景说起。

So before we dive into the world of crypto and sort of the quantitative strategies that you do work on, let's actually start with your background.

你实际上来自一个更传统的量化金融领域。

You actually come from a more traditional quantitative finance world.

是的。

Yeah.

我觉得我的起点非常传统。

I would say I've had a pretty traditional start.

我学的是工程专业，2018年毕业，所以还不算职业生涯太晚。

I studied engineering and I graduated in 2018, so I'm still not super late career.

毕业后，我在一家量化股票管理公司工作了两年。

And out of school, I worked at a quantitative equities manager for two years.

想象一下像AQR那样的公司，虽然不是AQR，但策略类似。

Picture like an AQR, but it wasn't AQR, but similar strategies.

所以是长期持有、低频调仓，但依然非常量化和自动化。

So long horizon, infrequent rebalances, but still very quantitative and automated in their approach.

所以在那里我写了大量的代码并进行了很多数据分析。

So was writing a lot of code and doing a lot of data analysis there.

你现在就职于一家加密货币领域的量化投资公司，我必须问一下，你为什么还要保持匿名？

Now you are currently employed at a quantitative investment firm in the crypto space, I have to ask, why continue to remain anonymous?

嗯，据我在我所处领域内所见，加密货币似乎是最容易被偷的东西，这确实是当前行业一个不幸的现实：如果有人盗用你的银行账户或刷你的信用卡，很多时候这些行为是有保险的，可以被撤销，你联系相关人士就能拿回钱。

Well, you know, to me crypto, just based on what I've seen in my time in the space, it seems like a super optimal thing to steal, and that's an unfortunate kind of truth of the state of the current industry where if someone gets into your bank or charges your credit card, a lot of the times that's somehow insured, it can somehow be reversed, you call the right people and you get your money back here.

但如果有人入侵了你的加密货币钱包，几乎不可能追回资金，而且已经发生过一些高调的针对性黑客攻击。

If someone gets into your crypto wallet, there's very little shot of getting that money back, and there's been some high profile targeted hacking attacks.

对我来说，这纯粹是一个期望值问题。

And to me, it's like it's just an expected value problem.

我并不是那种极端偏执、谁都不认识的人。

Like, I'm not I'm not the super paranoid guy where no one knows who I am.

如果你在推特上私信我，而我信任你，我会和你见面，让你知道我是谁。

Like, if you DM me on Twitter and I trust you, I'll meet up with you and you'll know who I am.

但对我来说，把我的真实姓名公之于众的潜在收益，与因别人知道我管理大量加密货币而遭遇针对性攻击的概率相比，这根本不是一个值得承担的合理风险。

But just the expected value of having my real name out there versus the probability of a specific targeted attack occurring because someone knows that I handle large quantities of crypto doesn't seem like an reasonable risk to be taking to me.

你认为随着加密领域的演变，操作安全也会随之进步，使得这种匿名不再必要吗？

Do you think that as the crypto space evolves, operation security will evolve in such a way that that will no longer be necessary?

比如，对你来说，保持匿名的这种正期望值决策可能不再必要了？

Like that sort of positive EV decision for you to remain anonymous may not be necessary anymore?

还是说，这是加密本身固有的特性，这种匿名可能永远都是必要的？

Or do you think it's in the nature of crypto itself that that's sort of always going to be necessary potentially?

不。

No.

我不希望如此。

I would hope not.

从大规模采用的角度来看，人们不敢公开自己的名字，这并不是一个理想的状态。

Like, from a mass adoption standpoint, it's not really a great state of affairs that you're afraid to have your name out there.

明确地说，我认为通过正确的操作安全措施和设置，你的风险可以非常低。

To be clear, I think you can have the right operational security and the right setup where your risk is very low.

我的意思是，现在有很多公众人物在加密领域公开露面，使用他们的真实姓名。

I mean, there's a number of public figures out there who are in the crypto space very publicly with their real name out there.

他们有完善的安全措施，并投入了大量时间在这上面，因此并不担心。

And they have the security practices, and they put a lot of time into those things where they're not worried.

但对我来说，因为我主要参与订单簿交易，而不是做风投之类的业务，所以我并不想一直维持极高的操作安全级别。

But for me, because I'm, like, interacting with order books and not really doing venture deals and stuff, I just don't wanna bother with having to maintain super high levels of operational security all the time.

让我们深入探讨一下细节吧，因为你的职业转型不仅仅是从传统金融转向加密货币，还从长期的股权因子策略转向了更高频的做市策略。

So let's dive into the nitty gritty a little bit because your career transition hasn't just been from traditional finance to crypto, it's also been from these more long horizon sort of equity factor strategies to higher frequency market making type strategies.

我觉得如果你能对比一下短期和长期量化交易之间的差异，会非常有趣。

I thought it'd be really interesting if you could compare and contrast maybe the difference between that short horizon and long horizon quantitative trading.

是的。

Yeah.

当然。

Absolutely.

我认为一个不错的切入点是，列出高频交易的优势和劣势。

I guess a good way to frame this is as, like, a set of reasons why higher frequency horizons are better and reasons why they're worse.

那我们先从优点说起吧。

So I think let's start with the good.

在高频交易中，数据极其丰富。

In high frequency, data is super abundant.

我们的系统一天生成的数据量，轻松超过所有美国股票整个基本面资产负债表历史的总和。

Our systems will generate more data in a day than easily than the entire, like, fundamental balance sheet history of all US equities ever.

从这个角度看，这是一个非常理想的建模环境，你不必过于担心过拟合的问题。

And from that standpoint, it's very nice modeling environment where you don't have to worry about overfitting so much.

变化在生产环境中能迅速达到统计显著性。

Changes are statistically significant in production very fast.

所以如果你做了调整，就像是即时获得回报。

So if you make a change, it's, like instant gratification.

你不必等待数月或数年才能确定它是否产生了影响。

You don't have to wait months or years to be certain that it made a difference.

波动性更低，而随之而来的另一个优势是，如果你做得好，最终得到的实时收益曲线会非常出色。

The variance is just lower, and another pro that comes with that is the live equity curve you end up with is very impressive if you do this well.

如果我把我们实时的收益曲线给以前工作的同事看，他们会说：‘你在这次回测中犯了什么错误？怎么买的是那些上涨的股票？’

If I showed, our live equity curve to someone from my old job, they would say, what mistake did you make on this backtest that you're buying the stocks that went up?

这看起来就像是前瞻偏差。

It would just look like look ahead bias.

但随之而来的缺点是，这些交易中存在非常严重的逆向选择，每个人都想要这条实时权益曲线。

Now the cons that come with that is there's very, like, vicious adverse selection in these trades, and everyone wants that live equity curve.

而你能参与的这些最诱人的交易机会数量是有限的。

And there's only a limited capacity for the number of those juiciest trades that you can take.

因此，你正在与全球各地的聪明团队竞争，他们也在追逐同样的机会。

So you're competing with smart teams all around the world who are essentially going for the same opportunities.

结果，阿尔法衰减的速度比在较长周期下快得多，且容量通常也更低。

And as a result, the alpha decay is much faster than at longer horizons, and the capacities tend to run lower.

你不能只是脱离市场，长时间地进行某种统计研究——这可能是我之前工作时的做法。

You can't just abstract out the market and work on some statistical research for long periods of time, which I may have done in my previous role.

你必须时刻关注执行细节，因为在这里，优势很大程度上就体现在执行上。

You really have to be thinking about execution every step of the way because it's so much of where the edge stands here.

那我们来谈谈优势吧。

Well, let's talk about edges for a second.

迈克尔·莫比森曾将优势大致分为四类。

So Michael Mobison sort of famously classified edges into four different categories.

行为优势是指利用他人的非理性行为获利。

There's the behavioral edge where you're taking advantage of the misbehavior of others.

分析优势是指你拥有和别人相同的信息，但能以独特的方式分析，从而挖掘出独特的洞察。

There's the analytical edge where you're, you have the same information as everyone else, but you're able to analyze it in a unique way to dig out maybe a unique insight.

信息优势是指你实际上掌握着其他参与者不知道的信息，而技术优势则可能体现在你能比竞争对手更高效、更低冲击或更快地执行交易。

There's an informational edge that you literally know different things than other participants and then there's a technical edge which might be something like you can execute more efficiently or with less impact or more quickly than your competitors.

我想知道，在加密货币做市中，你认为真正的优势究竟在哪里？

I'm curious where you think the edge really lies in crypto market making.

没错。

Right.

我认为优势主要体现在这四类之中，我认为优势主要来自分析和技术层面。

So I think that edges are largely to operate within those four categories, I think that edges are largely analytical and technical.

尤其是在高频交易的时间尺度上，微观结构至关重要，问题本质上会收缩为更少的变异来源。

Because especially at the high frequency horizons where microstructure is really what matters, the problem kinda collapses into fewer sources of variance.

这些差异来源并不是秘密。

And those sources of variance aren't a secret.

你有公开的订单簿和一堆相关工具。

You have public order books and a bunch of correlated instruments.

你还有通过这些工具流动的订单流。

You have the order flow going through those.

因此，在微观结构层面，你其实并没有发现某种信息优势——即你掌握着别人不知道的、能在下一秒推动市场的信息。

So there's really at the microstructure horizon, you're not really finding an informational edge where there's something that you know moves the market in the next one second that no one else knows well.

你更关注的是利用所有现有信息，进行更优的分析。

You're more focused on taking all the existing information and analyzing it better.

与此相关的是，有一个很强的技术成分，你需要非常快速地执行，因为即使你的模型更优、更准确，你仍然会与其他参与同一游戏的参与者高度相关，而正是在这种情况下，执行优势显现出来——你需要极其迅速，无论是跨不同地点传递信息，还是在单一地点内处理时间。

To go with that, there's a strong technical component where you need to execute on this very fast because to some extent, your model can be better and and more accurate, but you're still gonna be correlated with all the other participants playing the same game, and that's where that execution edge comes in where you just need to be very, very fast, whether that's communicating information between different places or you're processing time within one place.

至于行为层面的优势，我认为随着市场的成熟，这种优势已经消失了，至少作为唯一的盈利来源已经不复存在。

And as for the behavioral component, I think maybe as the market has matured, this has disappeared, at least as a sole source of profitability.

因为大概一两年前，你还可以在流动性好的工具上报出很宽的价差，偶尔会有一些疯狂的交易者进来，直接支付，我们称之为‘支付通过’。

Because maybe a year or two ago, you could, for example, just quote really wide in liquid instruments, and every once in a while, some maniac would come in and just pay, like, well, we call it a pay through.

所以他们直接往订单簿里砸上一亿美元，你就能赚到100个基点之类的。

So they just blast, like, a $100,000,000 into that book, and you'd catch a 100 bips or whatever.

但现在，市场正变得越来越高效。

But now increasingly, like, the markets have gotten more efficient.

流动性变得更深了，这种不良行为可能偶尔才会发生一次，但你不能把它当作你唯一的竞争优势。

Liquidity has gotten deeper, and that sort of bad behavior Maybe it happens once in a blue moon, but you can't really rely on it as your sole source of edge.

我想深入探讨一下技术层面，因为我知道在传统金融中，高频交易领域存在着一场关于机房临近和深入硬件的激烈竞争，但在加密货币领域则不同。

I wanna push into that technical side a little bit because I do know that in traditional finance, right, there's a big arms race in high frequency trading around colocation and getting down to the metal versus in crypto.

许多中心化交易所都托管在亚马逊云服务（AWS）全球的服务器上，这场竞争似乎略有不同。

A lot of the centralized exchanges are hosted in AWS, Amazon cloud servers around the world and the arms race seems to be a little bit different.

有一些AWS网络知识可能有助于你接近极限，但似乎在机房临近能力和最终可实现的网络速度方面存在局限。

There's some AWS networking knowledge that can help you maybe get close, but it does seem like there's limits to the colocation ability and ultimately just sort of the network speed that's achievable.

而对于那些可能在去中心化端交易的人来说，你最终会受到区块速度的限制。

And then for folks who maybe do trade on the decentralized side, right, you're ultimately limited by block speed.

因此，我想知道，当你思考分析与技术之间的权衡时，加密货币中中心化交易所的设计是否存在某些限制因素，导致无法像传统金融那样高度重视技术层面？

So I'm curious as to when you think about that trade off between analytical and technical, are there limiting factors around the way centralized exchanges are designed in crypto that prohibit really the same emphasis on the technical as you tend to see in traditional finance?

所以我会说，对技术的重视同样强烈，但风格不同。

So I would say the emphasis on the technical is equally strong, but it's a different flavor.

它更富有创造性，我甚至可以说它更像一门艺术，而不是科学。

It's a slightly more creative and I would almost call it more of an art than a science.

在传统金融中，高频交易是一种非常精细的学科，优化空间非常有限，因为每个人都拥有到撮合引擎相同长度的电缆。

So in traditional finance, it's a very surgical discipline and a very constrained optimization as far as HFT goes because you have this situation where everyone has the same length cable to the matching engine.

撮合引擎是非常确定性的。

The matching engine is very deterministic.

大家都听过那些故事，结果就是你必须用硬件实现整个交易策略，只争纳秒，任务非常明确。

Everyone has heard those stories, and what ends up happening is you're implementing in hardware your entire trading strategy and just counting nanoseconds, and the task is, like, very defined.

每争取到一纳秒，就能让我们排到第一位，至于赢多少并不重要。

It's like every nanosecond we gain here is a nanosecond that puts us into first place, and it doesn't matter how much you win by.

而在加密货币领域，我们称之为‘抖动’，这意味着处理时间缺乏确定性。

While in crypto, there's a lot of what we call jitter is the jargon, which basically means there's a lack of determinism in how long things take take to process.

所以即使你稍微领先，当所有流程完成、撮合引擎处理完毕、订单进入订单簿时，你可能反而落后了。

So even if you're, let's say, slightly ahead, you might end up behind by the time all is said and done and the matching engine processes and the orders hit the books.

因此，在这里更重要的是理解这些独特行为、了解每个交易所API的特性，以及如何快速地在不同的AWS区域之间传输信息，而许多这类系统并非为这种使用场景而设计。

So understanding those unique behaviors, understanding the quirks of each exchange's API, understanding how to get information from one AWS region to another fast is more of the game here, and a lot of these systems weren't built for, like, this use case.

在传统金融中，当你在数据中心与FPGA或其他设备同机部署时，该链条的每个组件都是专门为快速向撮合引擎提交订单而构建的。

So in traditional finance, when you're colocated in a data center with your FPGA or whatever, every component of that pipeline has been purpose built for the purpose of submitting orders to a matching engine fast.

而AWS的设计目标是适用于大多数场景，它适合托管Instagram，也适合在云端进行大规模机器学习任务，但在构建这些交易所时，没人真正考虑过这种需求。

While AWS is built to be good for most things, it's good for hosting Instagram, it's good for doing big machine learning tasks in the cloud, but no one was really thinking about this when they built these exchanges.

在AWS上快速扩展某项服务的最简单方式，尤其是当你不确定它会增长到多大规模，又不想提前投入大量定制硬件时。

It's just the easiest way to scale something fast on AWS, especially if you don't know how big it's gonna get and you don't wanna invest in tons of bespoke hardware upfront.

所有这些因素共同体现了一种强烈的技术导向，但其优化方式是一种截然不同的创造性思路——围绕一个并非专为高竞争性交易设计的系统进行调整。

And all of those factors summarize to a large technical emphasis, but a very different flavor of kind of creative optimization working around a system that wasn't necessarily built for the purpose of highly competitive trading.

你在谈论背景时忽略了一点：在你从传统金融行业转向目前就职的公司之前，你曾独自创业，从事过一些独立的加密货币做市业务。

One of the things you skipped over in discussing your background is that you actually struck out on your own for a bit doing some independent market making in crypto between your sort of career in traditional finance and and working at the firm you're working at now.

我想知道，在那些早期的日子里，你最具教育意义的经历是什么？

I'm curious in those early days, what was your most educational experience?

我认为最有教育意义的事情，就是把你的东西放到市场中，看看会发生什么。

I think the most educational thing you can do is to just put something out into the market and see what happens.

对我们来说，我们有一个模拟器，很快就意识到模拟器或模型与生产环境之间的每一个差距都会以最不利的方式显现出来。

And for us, that was like we had a simulator, and we just very quickly realized that every single gap between the simulator or a model and production would just realize in a maximally adverse fashion.

我的意思是，当你拟合一个模型时，你期望误差项以零均值为中心，有时你会赚得比预期多。

And by that, I mean when you fit a model, you expect the error terms to be centered with mean zero, and sometimes you'll make more money than you expected.

有时你会赚得少。

Sometimes you make less.

但在这种情况下，任何不确定性来源都会带来极大的负面后果，这是一个非常艰难且令人沮丧的教训，但它真正让我们明白了在独立交易时我们所参与的游戏的本质。

In this case, any source of uncertainty was, like, maximally negative, and that's, like, a very hard and frustrating lesson, but it really taught us about the game we were playing when we were independent.

我还想说的是，这个行业里有一种近乎福音般代代相传的启发式方法、技巧和多年积累的经验，这些内容很难在公开渠道找到，而我很幸运地与一位曾经激烈从事这一行、如今已转投其他事业的人保持了友谊。

And the other thing I would say is there's kind of a almost like a gospel passed down through generations in this industry of various heuristics and tricks and just things that are learned over the years that are very hard to find publicly, and I was lucky enough to kinda maintain a friendship with someone who used to do this very competitively and has since moved on to other ventures.

因此，他更愿意分享他过去所做事情的性质。

And as a result, they were a little more comfortable sharing the nature of the kinds of things they did.

我经常和这个人进行很长的邮件往来，我会分享说：好吧。

And I would just have very long email chains with this person where I would just share, like, okay.

我们遇到了完全相同的问题。

So we have this exact problem.

这是背后的数据。

Here's the data behind it.

他们会说，当然了。

And they'd say, oh, of course.

这都是很标准的东西。

That's, like, very standard stuff.

但对我们来说，我们通常都是通过艰难的方式学习的。

But for us, we're just learning usually the hard way.

所以这极大地加速了我们的成长曲线。

So that kinda, like, really sped up the curve for us.

所以，是的，我非常感谢那个人。

So, yeah, I owe a lot to that person.

如果你在听，你知道你是谁。

If if you're listening, you know who you are.

如果我今天跟你一起工作，加密市场做市商的一天究竟是什么样的？

If I were to shadow you for a day, what does the day of a crypto market maker actually look like?

总的来说，我会把这份工作归类为分心的编程。

Broadly, I would categorize the job as distracted coding.

我一直在改进系统。

I'm always working on improvements to the system.

我面前总是有代码，无论是Jupyter笔记本还是更偏向生产基础设施的代码。

I always have a code in front of me, whether that's like Jupyter Notebook or just more production infrastructure code.

同时，我还要监控这些系统及其运行状态。

And then I'm also kinda monitoring the systems and what they're doing.

我们有几个用户界面，可以让我们清楚地了解正在发生的事情。大多数时候，系统都会平稳运行，你不需要像盯着鹰一样时刻关注，但偶尔会出现一些有趣的情况，这时你就会深入查看。

We have a number of UIs that give us transparency as to what's going on, and most of the time, everything will run-in a manner where you don't really have to be watching it like a hawk, But every once in a while, an interesting event will come up, and you'll zoom in on that.

想想发生了什么。

Think about what happened.

我举个更具体的例子，假设市场在一秒钟内波动了100个基点，这在加密货币市场中很常见。

I guess, to give a more specific example, let's say the market moves a 100 bips in, like, one second, which happens frequently in crypto.

对吧？

Right?

你会看一下，确认是否完成了所有想做的交易，看看在某些情况下是否有人更快，然后可能进行更广泛的数据分析，因为单个事件更多只是个例。

And you would just kinda look at that and see if you made all the trades that you wanted to, see if someone was faster in certain cases, and then maybe follow-up with a more broad data analysis because any single event is more of an anecdote.

你不希望基于这些情况做出更改。

You don't wanna introduce changes based on that.

所以你会查看历史遥测数据和历史交易所数据，了解过去发生了什么。

So then you would look at the historical telemetry, the historical exchange data, and see what's been going on in the past.

一旦你确认存在某种模式，并且可能有可以改进的地方，你就会开始在生产环境中引入新功能、模型的新元素，或一些能让我们更快的改动。

And once you affirm that there's a pattern and like maybe a meaningful improvement you can make, you would then start to introduce changes to production with a new feature, a new element of the model, something to make us faster.

这种事件分析多常导致生产环境的变更？

How frequently does that event analysis lead to a change in production?

这是个好问题。

That's a good question.

我认为你通常需要观察几次才好。

I think you'd usually want to observe something a few times.

所以可能大约30%的事件会促使你做出某些改动，而不是仅仅因为市场波动，一切如预期般发生了。

So maybe, like, 30% of all events constitute some change that you want to make as opposed to just the market moved and everything kinda happened as expected.

然后，你可能需要多次观察同一个问题。

And then from there, you'd probably wanna observe that same issue a few times.

但其实还有很多可以改进的地方，因为你总能做得更好。

But it's a lot really because you can always do better.

即使你在某种情况下赚了钱，你本可以赚得更多。

Even if you made money in a situation, you could have always made more.

你本可以更快一些。

You could have always been faster.

你本可以更准确，而你的竞争对手一直在这样做。

You could have always been more accurate, and your competition is doing this the whole time also.

所以，如果你让系统停滞不前，不根据观察到的交易数据进行这些改进，那么几个月后，你的超额收益就会逐渐衰减至零，进而变为负值，因为所有其他参与这个游戏的人都在做同样的事。

So if you just let the system sit stagnant and don't make these improvements based on the observed trading data, what's gonna happen is over the course of months, your alpha is just gonna decay to zero and then it's gonna decay to negative because everyone else is doing the same thing who's playing this game.

那么，在不泄露超额收益的情况下，你能谈谈这些改进可能是什么样子的吗？

So without leaking any alpha, can you talk a little bit about what these changes might look like?

比如，这只是参数调整，还是在构建全新的算法？

Like, this just parameter tuning or is this building whole new algorithms?

那么，在你进行这种事件分析之后，你到底在改变什么？

Like, what are you what are you actually changing after you do this event analysis?

是的。

Yeah.

所以我不会把这称为仅仅是调整参数。

So I wouldn't call it so much as tweaking parameters.

那种情况你可能在期权交易台更常见，那里你会不断微调波动率曲面，试图让系统了解你对市场的看法。

That's something you would find more unlike an options desk maybe where you're constantly nudging the wall surface and trying to inform your system as to, like, your view on the market.

我们更多是寻找那些系统反应与我们经济直觉不符的地方。

We're more just kind of finding places where the system doesn't react in a way that we would expect based on, like, our economic intuition.

有时候，这可能简单到只是输入了错误的数据。

And sometimes that might be as simple as even bad data coming in.

比如说，某个交易场所发布了一笔巨额场外大宗交易，但数据是滞后的。

So let's say some venue publishes an over the counter block trade for massive size, but the data is stale.

因为它们记录这种特定类型大宗交易的方式，时间戳比实际交易时间晚了十五分钟。

It's time stamped fifteen minutes after the actual transaction occurs because that's how they choose to catalog this particular type of block trade.

所以，如果您的系统无法识别这些数据是滞后的，它就会捕捉到所有它认为正在实时发生的订单流，并认为比特币的价值高出五个基点，无论具体情况如何，然后基于此进行大量交易。

So what's going to happen is if your system has no way of identifying that this data is stale, it's going to pick up all this order flow that it thinks is happening right now and consider a Bitcoin to be worth five bps more, whatever the case may be, and do a bunch of trades based on that.

因此，在事件分析中，您会回溯并查看这个数据来源。

So in an event analysis, you would go back and look at this source of input.

您会将其确定为真正影响您结果的罪魁祸首，并找到更好的方式来处理这部分数据，或者干脆忽略这些滞后的数据。

You would isolate it as the culprit for what really moved things for you and figure out a better way to handle that piece of data or maybe ignore the stale data.

或者情况可能相反：您看到一笔交易，根据您的经济直觉，本应推动市场变动，或者您看到订单簿发生了本应影响市场的变化，而您意识到这正是市场变化的首个信号。

Or it could be the opposite where you see a trade that, based on your economic intuition, should have moved the market or you see a change in the order books that should have moved the market, and you see that this was the first sign of change in the market.

您会疑惑为什么您的模型没有捕捉到这一点，或者也许它捕捉到了，但赋予的权重不够。

And you're wondering why your model isn't picking it up, or maybe it's picking it up but it's not weighing it heavily enough.

因此，该模型在方法上仍然是高度自动化的，但您可能会微调它对某些输入的估值，或试图弄清楚为何某些输入被赋予了这样的权重。

So the model is still very automated in its approach, but you might be nudging how it values certain inputs or trying to figure out why certain inputs were valued the way they were.

而很多这类工作归根结底是为了更快地响应。

And a lot of this just comes down to being faster also.

澄清一下，当市场变动时，本质上就是调整您报价中价格错误的部分，并撤除他人价格错误的报价。

To clarify, like, when the market moves, you're it really comes down to changing your quotes that are the wrong price and removing other people's quotes that are the wrong price.

所以，例如，如果你试图更改你的报价但速度不够快，基于这种事件分析，你可能会问：竞争对手是如何做到更快的？

So if, for example, you tried to change your quotes but you weren't fast enough, a question you might ask based on that event study is what is the competition doing to be faster?

他们是否采用了某些优化措施？

Are there certain optimizations they have in place?

我们觉得这些优化可能是什么样子，以及我们如何获得它们，或者获得更好的方法？

What do we think those optimizations might look like and how can we obtain them ourselves or obtain something something better.

你提到，你们并不像那些根据波动率曲面调整参数的高频期权做市商那样进行参数调优。

You said that you don't necessarily do parameter tuning like someone who does a high frequency options market making desk that might nudge parameters based on their view of the vol surface.

但你似乎确实提到，你们可能会调整其他参数。

But you did sort of seem to say that you might nudge other parameters.

我对参数调优这个概念很感兴趣，因为当你与做市领域的从业者交流时，这经常被提及。

I'm curious about this idea of of parameter tuning because it is something that comes up a lot when you talk to people in the market making space.

你能稍微详细解释一下什么是参数调优吗？你们是否做参数调优？如果做了，原因是什么？

Can you maybe elaborate a little bit on what parameter tuning is and whether you guys do it or not and maybe why it's done if it is?

对。

Right.

正如我之前所说，我认为我们的系统相当自动化。

As I stated earlier, I think our system leans quite automated.

我们可以好几天不管它，它也能正常运行。

We could leave it alone for days at a time, and it would be just fine.

但参数调整在高频交易中如此普遍的原因，再次是我提到的样本量问题。

But the reason changing parameters is such a popular thing in high frequency is, again, this sample size thing I alluded to.

样本量积累得非常快。

Like, sample size accumulates really fast.

如果你的多空股票组合表现良好一周，你不会因此跑去借钱加仓这个策略。

If your long short equity portfolio has a good week, you're not gonna, like, go out and borrow money to put into that strategy.

那只是波动性。

That's variance.

对吧？

Right?

而在高频交易中，这实际上可能是一个合理的建议。

While in in high frequency, that might actually be a reasonable thing to suggest.

因此，我们进行的大量参数调整并不是像你提到的那样基于估值水平或期权联动。

So a lot of the parameter tuning we do isn't valuation level or linking options, as you mentioned.

它更像是，好吧。

It's more like, okay.

这个策略表现不错。

This strategy is doing well.

我们在市场上拥有良好的优势。

We have a good edge on the market.

我们需要在拥有优势时充分利用它。

We need to push that advantage while we have it.

因此，我们可能会开始大幅增加仓位。

So we might start to size up quite aggressively.

我们可能会开始接受更低的收益空间，仅仅因为我们对自己的公允价值判断非常有信心，以至于愿意缩小置信区间。

We might start to command lower edge just because we think our idea of fair value is so accurate that we're willing to have a tighter confidence interval.

因此，即使在最自动化的系统中，很多参数调整仍然涉及像‘我想要交易多大的规模？’这样的问题。

So a lot of parameter tuning, even in the most automated system, you're still gonna have those parameters like how big do I wanna trade?

然后，我想从另一个角度来说，如果你觉得没有太多优势，你可能会减少交易。

And then I guess to kinda hit the opposite side of that equation, if there's a scenario where you don't think you have a lot of edge, you might dial things back.

例如，如果即将有重大新闻发布，而你不确定你的系统会如何应对，你可能会撤回部分订单，降低整体风险承受能力，让这三十秒的新闻发布过去，然后再回到市场。

So for example, if there's a big news event coming out and you're not sure how your system's gonna respond to that, you might pull some of your orders, dial back the overall risk tolerance, and just let that thirty second news release happen and then get back into the market.

在我们为这次采访做预通话准备时，你曾随口对我说过，高频交易中最难的问题是：‘在确保成交的前提下，你是否仍具有优势？’

When we did a pre call preparing for this interview, one of the things you said to me offhanded was that the hardest problem in high frequency trading is quote, conditioned on getting filled, do you still have an edge?

这个问题是你所做一切的核心。

That that question was sort of at the core of everything you look to do.

你这话是什么意思？

What do you mean by that?

没错。

Right.

我认为一个很好的方式是引入‘模型优势’这个概念。

I think a good way to frame this is to introduce this term of model edge.

在较慢的因子股票策略中，如果你对股票收益的R平方很高，那你已经完成了80%的工作。

In like a slower factor equity strategy, if you have a high r squared on stock returns, you're like 80% of the way there.

对吧？

Right?

你只需要将这些输入到投资组合优化器中，做多预期回报高的资产，做空预期回报低的资产，联系你的主经纪商，每周、每月或每季度重新平衡这些头寸，只要流动性足够好，你的市场冲击就不会太大。

You just need to put this in a portfolio optimizer long the higher return expectations, short the bad return expectations, call your prime broker, and rebalance those positions every week, month, or quarter, and hopefully, they're liquid enough that your market impact isn't too big.

但在高频交易中，模型优势几乎对你的盈亏没有影响，我用一个例子来说明。

Well, in high frequency, Model Edge can have almost zero bearing on your p and l, And I'll motivate this with an example.

所以，订单簿压力是一个公开的秘密。

So there's this open secret of book pressure.

对吧？

Right?

你只需根据最佳买价的挂单量和最佳卖价的挂单量来构建一个输入变量。

So you just formulate an input based on the quantity on the best bid and the quantity on the best ask.

如果你对这个订单簿不平衡量与下一跳价格方向进行回归，在大多数交易品种中，你得到的R平方值会让股票因子量化交易员觉得不可思议。

And if you were to do a regression of this booking balance on the direction of the next tick, in most instruments, you would get an r squared that an equity factor quant would think is incredible.

但问题是，前提是能成交。

But the problem is conditional on getting filled.

在这种情况下，你实际上没有任何优势，因为仅凭这个信号不足以跨越买卖价差来支付价差和流动性提取的费用差异。

You actually have no edge in that situation because what's gonna happen is this signal alone won't be enough to cross the spread to pay both the spread and the fee differential of removing liquidity.

但如果你试图被动地交易这个信号，你会等待这种失衡累积，因为根据定义，这就是你的信号运作方式。

But if you try to trade the signal passively, you're gonna wait for this imbalance to build up because by definition, that's how your signal works.

在正向失衡的情况下，你会将订单放在最佳买价上，并且会排在最佳买价队列的末尾。

You're gonna add your order on the best bid in the case of a positive imbalance, and you're gonna be at the back of the queue on the best bid.

因此，你只有在对下一笔价格变动判断错误时才会成交，因为排在你前面的所有最佳买价订单都必须先成交。

So the only time you're gonna get filled is that subset of times that you're wrong about the next tick because everyone on the best bid in front of you has to get filled first.

这是一个激励性的例子，说明你可能拥有非常高的R平方值，但成交条件下的优势绝对是负的。

So that's like a motivating example of how you could have a very high r squared, but your edge conditional on getting filled is definitely negative with this.

在低延迟领域，当系统崩溃时，通常只是个麻烦。

In the low latency space when systems go down, it's usually just a headache.

你提到了季度再平衡的股票因子投资组合。

You talked about quarterly rebalanced equity factor portfolios.

如果系统崩溃，你通常有数小时，最坏情况下甚至有数天或数周的时间来恢复系统并重建投资组合。

If your systems go down, you normally have hours, if not days and weeks in the worst case to get them back up and running and and get that portfolio rebuilt.

在高频交易领域，这些系统错误可能导致类似几年前凯特资本集团的情况——他们在推送某些代码错误后的四十五分钟内，损失了超过4亿美元。

In the high frequency space, those system errors can lead to situations like we saw a couple of years ago where Knight Capital Group burnt through over $400,000,000 in just forty five minutes from when they pushed some code errors.

实际上，我认为从技术上讲，问题是他们的服务器未能更新，而三台服务器运行的是新代码，只有一台服务器仍在运行旧代码。

Actually, I think it was a technically, their server failed to update was the problem and yet three servers running on new code and one server running on old code.

不过，这跟我们现在讨论的主题无关。

Anyway, neither here nor there.

我真正想问的是，你如何思考管理技术风险？

The the real question I'm getting at here is how do you think about managing technology risk?

这涉及几个不同的方面。

It's a few different things.

我认为，对于凯特资本公司，你所描述的这种情形，如果你去阅读美国证券交易委员会对该事件的分析报告，就会发现那里的工程师花了四十五分钟才弄清楚出了什么问题。

I think one with KCG, with Knight Capital, the scenario you described, if you go and read the SEC breakdown of that event is the engineers over there were trying to figure out what was wrong for forty five minutes.

也许我对这个情况的理解并不完全准确，所以我不想对此妄加评论。

Maybe I don't fully understand the situation, so I don't wanna talk too badly about it.

但我们的核心理念始终是：只要有任何异常迹象，立即关闭系统。

But, really, our philosophy all the time is if anything looks off, just shut it down immediately.

所以先关掉，再问问题。

So turn it off first, ask questions later.

有时候这会通过一些合理性检查来自动化实现。

And some of the time that will be automated with, like, sanity checks.

因此，你可能会放弃某些非常奇怪的机会，因为它们触发了合理性检查。

So you might even give up some subset of, like, really bizarre opportunities because they trip a sanity check.

你会觉得这里的市场数据出错的概率太高了，干脆直接关闭所有订单，等待人工检查。

You're gonna say there's too high of a probability that my market data here is wrong that I'm not even gonna I'm just gonna turn off, cancel all my orders, and wait for a human being to check on it.

另一端的情况是，我们总是有人在角落里留意着。

The other end of that is if we always have someone watching from the corner of their eye.

所以，如果有什么东西逃过了所有的合理性检查，你会根据我们构建的界面，自然而然地判断什么是正常、什么是异常，因为你整天都能看到这种视觉反馈。

So if there's something that gets past all of our sanity checks and you're almost conditioned based on the UI we have built out to as to what's normal and what's not because you have that visual feedback all day.

你立刻关掉它，然后再去弄清楚发生了什么。

You just shut it down right away and then figure out what's going on after.

显然，希望事情永远不要发展到这两种情况——无论是自动关闭还是手动关闭。

Obviously, hopefully, things never get to either of this point, either the automated turning off or the manual turning off.

所以另一部分是严格的代码审查流程，有时甚至会让人觉得枯燥。

So the other piece of that is a really rigorous code review process where at times it can even feel like tedious.

人们会对某些细节吹毛求疵，但事实上，这完全是好事。

People will be nitpicky over certain points, but in reality, it's all a really good thing.

它让每个人都能成为更好的程序员。

It makes everyone a better programmer.

它确保所有进入生产环境的代码都具有可读性。

It makes sure that all the code that gets into production is readable.

它达到了很高的质量标准。

It's done to a high standard of quality.

因此，每当有代码要上线时，都需要团队中多位成员的签字确认，我认为这常常会引发许多富有成效的讨论，以提升系统的安全性。

So you have sign off from several members of the team, basically, whenever something goes into production, and I think a lot of productive discussions come out of that as to making the system safer.

当你将传统金融领域与加密货币领域进行对比时，另一个独特的差异是：在传统金融中，证券在多个交易所上市通常是一种例外，而非常态。

Another unique compare and contrast when you look at the traditional finance space versus the crypto space is that in traditional finance, having security listed on multiple exchanges tends to be more of an exception than the rule.

而在加密货币领域，这反而更常见，是常态而非例外。

Whereas in crypto it tends to be more the rule than the exception.

你会看到比特币，例如，在所有主要的中心化交易所和去中心化交易所上交易。

You tend to find Bitcoin for example, trading on every major centralized exchange and decentralized exchange.

你认为这对加密货币领域的做市商带来了哪些机遇和挑战？

How do you think this presents both opportunities as well as challenges for market makers in the crypto space?

我认为碎片化带来了明显的机遇，因为如果你能考虑到不同交易平台、不同工具之间虽然本质上都是对同一事物的敞口，但存在的各种独特差异，就能从中找到优势。

I think the fragmentation presents obvious opportunity in the sense that if you can account for all the unique differences between the different venues, between the different instruments that are essentially exposure to the same thing, There's edge to be had there.

对吧？

Right?

因为这就像一个复杂的分布式系统问题。

Because it's like a hard distributed systems problem to solve.

我们在工程上花了大量时间，确保系统平稳运行，并在不同地方之间传递信息。

We spend a lot of time in engineering on keeping it running smoothly and getting information from one place to another.

所以，如果你能正确解决这个问题，就会存在机遇，因为这是一个很难解决的问题。

So if you solve the problem right, there's opportunity because it's a hard problem to solve.

我认为挑战在于，这些资产并不是完全可替代的。

I guess the challenges are in the sense that these things aren't fungible.

它们实际上是不同的工具。

They're actually different instruments.

所以即使是像永续合约这种最流行的流动性期货，交易所的融资费率也会略有不同。

So even for something like the perpetual swap, the most popular kind of liquid future, what happens is exchanges will have slightly different funding rates.

它们的融资间隔也会略有不同。

They'll have slightly different funding intervals.

合约所依据的指数也会有所不同。

The contract will have a different index that the swap is priced on.

因此，你需要以一种通用的方式考虑所有这些差异，绝对不能手动处理，因为这类东西有成百上千种。

So you need to account for all of these differences in a generalized manner, definitely not by hand because there's, like, hundreds and hundreds of these things.

所以，构建一个能很好地处理这些问题的系统很有挑战性，你总是在不断排查边缘情况，比如：这个交易所真的决定这样做了吗？

So building a system that does that well is challenging, and you're always kinda hunting down the edge cases of like, really, did this exchange decide to do it this way?

我们之前有一个在其他所有地方都有效的系统，但这个交易所却完全不遵循标准，选择了不同的做法，但这里的套利空间太大，我们不能不交易。

And we we had something that worked everything everywhere else, and there's complete lack of standardization where this one venue decided to do it a different way, but there's too much edge there for us not to trade.

所以我们必须重新通用化，以覆盖这个边缘情况。

So we're gonna have to regeneralize to cover that edge case.

所以我认为，很多挑战就来源于此。

So I think that's where a lot of the challenge comes from.

我们正看到越来越多的高频交易公司进入加密领域。

We're starting to see a lot more high frequency trading firms enter the crypto arena.

你认为从传统金融中有哪些领域知识是可以迁移的？当这些公司进入这个领域时，又有哪些方面会让他们感到意外？

What domain knowledge do you think is portable from traditional finance and what do you think will end up surprising these firms as they sort of make their foray?

对。

Right.

所以我认为，事情是以一种有趣的方式发生的：在过去一年里，交易量急剧上升，所有人都开始构建这些系统，并可能在2021年底或2022年初上线运行。

So I I think it kinda happened in in an interesting way where over the past year, like, volumes really skyrocketed and everyone started building these things out and probably going live into the later portion of 2021 or early twenty twenty two.

而现在，我们正处于一个交易量实际上有所下降、套利空间缩小、市场竞争更加激烈的环境中。

And now we're in an environment where volumes have kinda gone down actually, and edge has gone down and the market has gotten more competitive.

我认为，首先，人们会惊讶于他们在2021年初所选择的游戏难度有多高——那时，直到最后一位律师签署完最后一份文件、所有审批都通过为止。

I think, first of all, people will just be surprised of how hard of a game they will set out to play at the start of 2021, which is by the time the last lawyer signs the last paper and the last approvals come through.

但现在的市场已经完全不同了，因为如今每一家主要的自营交易公司至少都在关注这个领域，很可能已经在采取行动了。

It's a very different market because now every single major proprietary trading firm is at least looking at this and probably doing something.

所以我认为这是最大的意外之一。

So I think that's one of the biggest surprises.

我想另一个惊喜会与我之前提到的技术优势有关，即在约束较少的优化中，速度固然重要，但优化方式会略有不同。

I guess the other surprise would go along with what I mentioned earlier on the technical edge where the less constrained optimization, where being fast is important, but you optimize for it in slightly different ways.

如果你习惯于在像CME这样高度确定性的市场交易，可能需要一点时间来适应。

And if you're used to trading on, like, super deterministic CME, it might take a little getting used to.

但除此之外，我认为许多传统金融基金会觉得这里很熟悉，因为订单簿就是订单簿。

But aside from that, I think a lot of TradFi funds will find themselves at home here just because an order book is an order book.

说实话，我认为所谓的加密原生自营交易公司是个误称。

To be honest, I think the crypto native prop firm is kind of a misnomer.

你仔细看看许多加密原生公司，里面大部分都是来自传统高频交易领域的人。

You look inside a lot of crypto native firms, and it's mostly people who are, like, extraditional HFT.

因此，就这一点而言，我认为市场本身是相似的。

So as far as that goes, I think the markets themselves are are similar.

你能详细谈谈加密货币中心化交易所的格局在过去几年是如何演变的，以及这对做市商产生了怎样的影响吗？

Can you expand a bit on how the landscape has evolved in centralized exchanges in crypto and maybe how that's impacted the market makers over the last couple of years?

当然。

Sure.

所以我认为首先，交易所变得更好了。

So I think first of all, the exchanges have gotten better.

而且它们和顶级传统金融交易所相比仍有很大差距，但交易所确实进步了，这使得更复杂的系统得以建立。

So and there's still a far cry from the super high performing TradFi exchanges, but the exchanges have gotten better and it's allowed for more complex systems to be in place.

你不再需要等待五秒钟才能确认订单。

You're not waiting five seconds for your order to confirm.

因此，在这方面，我认为市场一直在变得更快，随着交易所改进和竞争加剧，它还会继续提速。

So in that sense, I think the market is always getting faster, and it will continue to get faster as the exchanges get better competition increases.

我认为明显的套利机会变得越来越难找了。

I think the obvious arbitrages are kinda harder to come by.

大家都谈论阿拉梅达是如何起步的，我相信是通过日本的交易，还是韩国？

Everyone talks about how Alameda got their start, I believe, with this Japan trade, or was it Korea?

无论如何，价格差异就是有10%。

Either way, there's just, a 10% price difference.

对吧？

Right?

我认为，如果当时做这个会很棒，但随着这个市场受到越来越多的关注，现在这样的机会非常难得了。

And I think that it it would have been great to be doing this at that time, but I think those opportunities are very hard to come by now with the attention that's come to this market.

但话说回来，我认为在交易量持续低迷的时期，交易所可能会出现一些整合。

But, you know, in a sense, I think there may also be some consolidation in exchanges, especially if we continue to go through this period of lower volumes.

一些较小的交易所可能会并入更大的交易所，但这只是我个人的推测。

Some of the smaller exchanges might roll up into the bigger ones, but that's just speculation on my part.

我不知道是否已经发生了许多这样的交易。

I don't know if a lot of deals like that have gone down yet.

高频交易在传统金融领域历来是一个出人意料的两极分化话题。

High frequency trading has historically been a surprisingly polarizing topic in traditional finance.

你认为关于高频做市，最大的误解是什么？

What do you think the greatest misperceptions are about high frequency market making?

我认为最大的误解——至少对于你们的受众这类量化背景的人来说——是这些策略依赖于在一些非常愚蠢的交易上占据执行优势。

I think the biggest misperception, at least among quantitatively oriented people since that's who your audience is, is probably that these strategies consist of execution edge on, like, really dumb trades.