巨头章鱼 - 管理一个中频加密资产交易台（第六季第五集）

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，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.

好了。

Alright.

你准备好了吗？

Are you ready?

嗯。

Yeah.

当然。

Absolutely.

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，不应作为投资决策的依据。

This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

Newfound Research的客户可能持有本播客中讨论的证券。

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.

在本期节目中，我与匿名推特用户Macrocephalopod进行了对话。

In this episode, I speak with the anonymous Twitter user Macrocephalopod.

我们对话的脉络跟随了他的职业轨迹，从对冲基金中的低频风格溢价，到建立交易中高频加密策略的自营交易台。

The arc of our conversation follows the arc of his career, beginning with slow frequency style premium in a hedge fund to building a prop desk that trades mid to high frequency strategies in crypto.

对话的很大一部分可以从研究、运营和风险管理的角度来比较和对比不同角色。

A large part of the conversation can be characterized as comparing and contrasting the roles through the lenses of research, operations, and risk management.

例如，多空股票与多空加密货币在哪些方面有实质性的不同？

For example, in what ways is long short equity meaningfully different than long short crypto?

或者，在中频与低频策略中，市场冲击、成交比率和借券失败等问题有多重要？

Or how important are topics like market impact, fill ratios, and borrow fails in mid versus slow frequency strategies?

尽管加密货币是讨论的背景，但我相信本集所传递的智慧适用于所有市场。

While crypto is the venue, I believe the wisdom imparted in this episode spans all markets.

请欣赏我与Macrocephalopod的对话。

Please enjoy my conversation with Macrocephalopod.

Macrocephalopod。

Macrocephalopod.

嗨，科里。

Hi, Corey.

很高兴能

Great to be

在播客上。

on the podcast.

谢谢你来参加。

Thank you for coming.

非常期待这次对话。

Really excited to have this one.

这将是一场有趣的对话，不过我得小心应对，以确保尊重你希望保持匿名的意愿。

It's gonna be a fun conversation though I have to dance a little nimbly here to make sure that we're we're keeping you anonymous as you like to be.

所以我不能像往常那样问你的背景。

So I can't do the usual what's your background starter.

那我们从一些不同的问题开始吧。

So let's start with something a little different.

是什么让你加入了Twitter？

What made you join Twitter?

如果你愿意透露的话，'macrocephalopod'这个用户名是怎么来的？

And if you can disclose, where did the macrocephalopod handle come from?

当然。

Sure.

所以我是在2020年底加入Twitter的，但显然在此之前我已经使用Twitter很久了。

So I joined Twitter in late twenty twenty, but obviously, I've been using Twitter for a long time before that.

对吧？

Right?

Twitter刚推出不久，我就注册了一个实名账户，之后很长一段时间都用它，主要是潜水，偶尔才会向别人提个问题。

So I had a real name account from very shortly after Twitter was first founded and you could make an account, and I used that for a very long time, mostly to lurk, very occasionally to ask a question of someone.

到了2020年，疫情期间，显然大家都居家办公。

And then during 2020, during the pandemic, obviously there was a lot of work from home.

我发现自己多了些空闲时间，想找个乐子。

I found myself with more time on my hands and wanted some entertainment.

而且我越来越觉得，Twitter上有很多我尊重、重视其观点的人。

Also I more and more felt like there were people on Twitter who I respect, whose opinion I value.

我希望能更直接地与他们交流，讨论我自己的想法，尤其是关于金融的想法，也包括一些闲聊。

I wanted to be able to speak to them more and discuss ideas that I had, especially ideas around finance, but also general banter.

因此，由于我的工作性质，我觉得以匿名账号的方式来做这件事是最好的。

So because of my job, where I work, I felt the best way to do that was with an anonymous account.

显然，你可以看到，Twitter 上有很多人在金融领域使用匿名账号，原因多种多样。

Obviously you can see that there are many, many people on Twitter using anonymous accounts in finance for a variety of different reasons.

但对我来说，主要是我想比用真名时更自由地表达，因为我的工作限制了我。

But for me, was mainly that I wanted to be able to speak a little bit more freely than I thought I'd be able to under my real name because of my job.

于是我创建了一个账号，开始发布一些金融内容、交易内容和量化金融内容，有几个人似乎对此很感兴趣。

So I created an account, started posting a little bit of finance content, trading content, quantitative finance, which a few people seem to find interesting.

然后，事情就这样发展下去了。

And yeah, it went from there.

我在那里找到了一个很棒的社群，很多人发布非常有趣的内容，而且至少有一半时间也对我的观点感兴趣。

Found a really great community of people there, a lot of people posting really interesting content and who seemed to be at least half the time interested in what I had to say as well.

至于用户名，背后并没有什么特别的故事。

And the username, there's no great story behind it.

我看到很多人都使用动物主题的账号。

I saw a lot of people had animal themed accounts.

我想，嘿，这太棒了。

I thought, hey, that's great.

我喜欢什么动物？

What animals do I like?

章鱼还挺酷的。

Octopuses are kind of cool.

而大头足类，当时我主要交易期货和货币，所以我想在cephalopod前面加上macro，因为cephalopod已经被用了。

And macrocephalopod, well, I was trading mostly futures and currencies at the time, so I thought I'd stick a macro in front of it as cephalopod was already taken.

很遗憾，你的用户名没有一个激动人心的故事。

So sadly, no exciting story for you about the handle.

好吧，至少我理解了。

Well, I understand it at least.

那我们聊聊你创建Twitter账号之前在做什么吧。

So let's go pre what you were doing when you launched the Twitter account.

你提到在交易一些期货。

You're talking about trading some futures.

我们稍后会谈到这个。

We'll get to that.

我知道在2000年左右，根据你的背景，你当时实际上在执行一些较长周期的期货策略，很多都属于风格溢价类型。

I know earlier in the 2,000 from your background, you were actually implementing longer frequency future strategies, a lot of them in the vein of style premia.

在我们之前的对话中，你提到回过头来看，这些策略非常天真，你很惊讶它们当初居然能奏效。

In our prior conversations, one of the things you said was that looking back, they were very naive strategies, and you're surprised that they worked in the first place.

你认为它们为什么会奏效？为什么自那以后阿尔法收益会衰减得如此严重？

Why do you think they worked, and why do you think the alpha has decayed so much since then?

是的。

Yeah.

好问题。

Good question.

我想我应该澄清一下，我们确实用这些策略赚了钱，而我所说的策略主要是日频策略。

So I guess I should clarify that we did make money with these strategies, and the kind of strategies I'm talking about are primarily daily strategies.

所以你每天重新平衡一次投资组合。

So you you rebalance your portfolio once per day.

你通常会持有几周到几个月。

You typically hold it for weeks to months at a time.

你主要交易的是流动性很强的期货和货币市场、股票指数、债券和利率、大宗商品，你并不会对市场的细节和微观结构进行大量建模，某种程度上，你根本不太去想这些。

You're mostly trading very liquid futures and currency markets, stock indices, bonds and interest rates, commodities, and you are not doing a whole ton of modeling around the details and the microstructure of the market and, you know, you're, to some extent, not thinking about it that much.

那么，为什么这些策略会有效呢？

So why does some of this stuff work?

我可以给你介绍一下我曾经考虑过的那种策略的大概样子。

And I can give you a little flavor of the kind of strategies that I was thinking about.

CTA趋势跟踪就是一个例子。

So CTA trend following is one example.

我早期职业生涯的相当一部分时间都在作为量化研究员研究这类策略。

I spent quite a bit of my early career as a quant researcher working on that kind of strategy.

套利策略，也就是现在人们喜欢称之为宏观动量或经济动量的策略。

Carry strategies, strategies which now people like to call macro momentum or economic momentum.

关注经济数据发布的节奏，以及它们是超预期上涨还是下跌，并围绕这些想法构建多空投资组合。

So looking at the cadence of economic data releases and whether they're surprising to the upside or the downside, and trying to build long short portfolios around these ideas.

那么，为什么我认为它们最初会有效呢？

So why do I think they worked in the first place?

你知道，关于这类策略有大量的学术文献。

Well, you know, there's a big academic literature on these kinds of strategies.

我认为，至少从长期来看，如果你回溯三十年，它们至少在回测中是有效的。

I think there's pretty compelling evidence that at least over the long term, if you're looking back thirty years, they have worked at least in backtest.

而且，还有很长一段时间，人们实际交易这些策略并赚到了钱。

And there's also quite a long out of sample period of people live trading these strategies, and they have made money.

你可以看到许多拥有三十年业绩记录的CTA。

You can look at many, many CTAs with thirty year track records.

你可以看看期货交易公司，比如AQR和Winton，它们已经存在很久，并通过这类策略赚钱。

You can look at futures trading firms, AQR, Winton, who've been around a long time and making money with these kinds of strategies.

我认为它们有效有两个原因，其中一个是因为它们确实存在真实的风险溢价。

And I think they work for two reasons, one of which is that many of them have a genuine risk premia there.

你因为承担了其他人不愿承担的某种风险而获得补偿。

You are being compensated for taking some kind of risk that other people don't like to take.

这方面的经典例子是利差策略，你可能会在几乎所有尝试实施利差策略的资产类别中遭遇严重的左尾崩溃，尤其是在外汇市场，但利率市场也同样如此。

The classic example of this is a carry strategy where you are just subject to big left tail crashes in essentially every asset class where you try and implement carry, particularly in FX, but definitely in rates as well.

但我也认为，历史上这些策略还包含大量低效成分。

But also, I think historically, these strategies had a big component of inefficiency in them as well.

这种低效源于这些策略存在很高的准入壁垒。

And that inefficiency came about because there were large barriers to access for these strategies.

过去，要获取进行研究所需的数据以判断这类系统性策略是否有效，要困难得多。

So it was much more difficult in the past to just get the data you needed to even do the research to see if these kinds of systematic strategies worked.

即使你能获得数据，进入市场也更困难，而且当时没有为非机构投资者提供便捷的策略接入工具。

Even if you could get the data, it was more difficult to access the market, and there were no nice wrappers for non institutional players to access the strategies.

如果你是个体投资者，想参与某种动量策略，基本上根本没有选择，对吧？

If you are an individual who wanted to access some momentum strategy, you basically have zero options, right?

你只能自己尝试操作，但这非常困难，仅此而已。

You could either try and do it yourself, which is hard, and that was it.

当时没有ETF为你打包动量策略，或提供价值策略之类的投资渠道。

There was no ETF which wrapped up on momentum strategy for you or gave you exposure to value or something like that.

由于这些市场准入的困难，人们没有足够关注这样一个事实：如果你做多高利率货币、做空低利率货币，那么在长达十到十五年的时间里，你都能获得大约1.5到2的夏普比率，而这种状况直到2008年金融危机才告终。

And because of those market access difficulties, it meant that people were not paying as close attention as they should have been to the fact that if you just went long currencies with high interest rates and shorter currencies with low interest rates, then you made money with a sharp of like one and a half to two for ten to fifteen year period, which really only came to an end during the financial crisis in 2008.

这在一定程度上解释了为什么这些策略如今表现得不如从前了。

And this kind of explains why these strategies perform less well now, I think.

如今，数据和市场的获取渠道都好了很多。

And now there is much better access to data, access to markets.

这些策略现在的收益已经与所承担的风险相匹配，而效率低下的部分已经有所消退。

These strategies now have the kind of returns which are commensurate with the risks that are being taken, and the kind of inefficiency component has gone away a little.

因此，我认为我们当时运气不错。

And so I think we were a little lucky.

我们或许赶上了效率尚未完全消失的末期，同时也恰逢2016年、2017年左右所谓的Smart Beta、流动性另类投资的爆发期。

We maybe caught the tail end of some of the period where there was still an inefficiency here, but also coincided with this big explosion of whatever you want to call it, smart beta, liquid alternatives around 2016, 'seventeen.

突然间，大量资本涌入这些策略，竞争稀释了大部分效率洼地，留下的只是风险溢价。

Suddenly a lot more capital came flooding into these strategies and competed away a lot of the inefficiencies, and what you're left with is the risk premia.

风险溢价是不错的。

A risk premia is good.

很多人应该投资于风险溢价，而且可能应该比目前拥有更多的风险溢价敞口。

A lot of people should invest in risk premia and should probably have more risk premia exposure than they currently have.

但确实，我认为在过去十到十五年里，这些策略的盈利能力明显下降了。

But yeah, there is a notable decline, I think, in the profitability of these strategies over the last ten to fifteen years.

不过，我想跟你之前对我说的另一句话做个对比：尽管某些在期货市场中对应的策略可能失效了，但一些长期股票策略似乎仍然有效。

So I wanna contrast that though with another comment you made to me, which was that some of the longer term strategies in equities still seem to work despite the fact that maybe their parallel concept strategy applied in the futures space doesn't.

我很想知道，为什么你觉得这些策略在股票市场中仍能保持优势，而在期货市场中却没有。

I'm curious as to why you think they've maintained their edge in equities but not in futures.

是的。

Yeah.

我想首先需要确认，这个说法本身是否成立？

So I guess the first thing is, is that statement true at all?

要明确断言这一点是真还是假非常困难，但如果你观察一下大型多策略对冲基金的做法，它们通常会将更多资本或风险投入股票市场，而非期货市场，这暗示它们认为股票市场存在更大的机会。

And it's very difficult to make any concrete claims that it is true or not true, but if you look at what big multi strategy hedge funds are doing, they are generally putting more of their capital or their risk to work in equity markets than they are in futures markets, implying they think there's a greater opportunity there.

如果你有能力获取数据并进行模拟和回测，你会发现，一些市场中性量化策略在股票市场中的表现，比在宏观资产类别中的表现更为稳定。

And if you're in the position to have access to data and run simulations and run back tests, you can see that the performance has held up for some of these market neutral quant strategies and equities more than it has in the macro asset classes.

我认为，其中一部分原因是股票交易在技术上比期货交易更复杂。

And part of that, I think, is that equity trading is a technically more difficult problem than futures trading.

因此，如果你想大规模开展，就需要获得杠杆，这意味着你需要融资和主经纪商服务。

So if you want to do it at scale, you need access to leverage, which means you need financing and prime brokerage.

你需要能够进行互换交易，并且能够处理在相当复杂且分散的市场中执行交易，尤其是在美国。

You need to be able to do stuff on swap, and you need to be able to handle executing in, you know, what is a pretty complex dispersed market, especially in The US.

而期货交易，如果你只是想持有一些头寸几周或几个月，那比在股票市场中要简单得多。

Whereas futures trading, if what you're trying to do is take some positions and hold them for a few weeks or months, that is much more straightforward than in equities.

但另一个原因，我认为是股票市场可交易的标的范围要广泛得多。

But then another reason, I think, is that there's a much broader range of names to trade in equities.

所以，你知道，数据点要多得多。

So, you know, many more data points.

而且，通常来说，在股票市场交易的成本要高得多，我想稍微详细说明一下这些点。

And also, it's generally much more expensive to trade in equities, and I wanna elaborate on those a little bit.

所以，如果你面对的是一个标的数量很少的市场，比如G10货币，你基本上只有九对货币可以交易，而且交易成本非常低。

So if you've got a market which has a small number of names, let's talk about currencies like G10 currencies, you've basically got nine pairs you can trade, and it's very cheap to trade.

因此，通常交易GCN货币对的点差低于一个基点，且交易量巨大——几乎每个GCN货币对都是如此，你几乎不可能在这样的市场中获得大量超额收益，因为进入门槛太低，你可以轻易投入巨额资金，交易成本又极低，任何发现阿尔法的人都会全力投入，最终竞争会消除这些优势。

So typically, spreads of less than a basis point to trade GCN currencies, and there's a lot of volume, which there is in essentially every GCN currency pair, then you're really never going to expect to see a lot of alpha in that market, because it's so easy to access it and you can put on so much size and it's so cheap to trade that anyone who can find alpha is going to deploy as much capacity into that as they can, and it's going to compete away the advantages.

而股票市场则不同，尤其是在中盘和小盘股板块，点差通常是几个基点，成交量普遍较低，除了少数几个最大市值的股票外。

Whereas equities, you know, typically, for at least the other mid and small cap segment of the market, multiple basis points, bit off a spread, generally pretty low volumes, you know, except for a handful of the biggest names.

也许某只股票的日成交量仅占其市值的1%，而标普500期货的日成交量则可能是其未平仓合约的数倍。

Maybe something trades 1% of its market cap per day, whereas S and P 500 futures, like, probably trade multiples of their open interest every day.

这种低流动性与高交易成本的结合，这一点比较微妙。

And this combination of lower liquidity and higher transaction costs means that and this is this is kind of subtle.

如果你暂时忽略交易成本，就能在股票市场中发现不错的交易优势。

You can find good edges in equities if you temporarily suspend reality and get to ignore trading costs.

你还能发现强大且持续存在的预测性优势，能够预测某只股票价格的走势。

And you can find really strong, persistent, predictive edges where you can predict how the price of some stock is gonna move.

由于股票标的范围广泛，你能获得大量数据来验证这些策略。

And because there's such a broad universe of names, you've got a lot of data you can use to validate this.

你可以真正做一些事情，比如先用你标的池的一半进行研究，然后看看这些结论在另一半标的中是否依然成立。

You know, you can really do stuff like taking half your universe, doing your research there, and then saying, does this still hold in the other half of the universe?

如果你交易货币，就根本做不到这一点。

It's something that you really can't do if you're trading currencies.

因此，你拥有更多工具来避免发现自己陷入虚假效应，比如那些实际上无效、只是数据挖掘出来的策略。

So you've got a lot more tools to prevent yourself from finding fake effects, like strategies which don't really work, they're just data mined.

而且交易成本很高，这为你提供了一种缓冲，让你更容易发现可预测性。

And you've got this kind of cushion of the trading cost is very high, so so you can more easily find predictability.

股票市场的游戏就是如何将这种可预测性变现，对吧？

The game in equities is to then monetize that predictability, right?

所以，光说‘太好了，我能预测每当X发生时，这只股票平均会移动五个基点’是不够的。

So, you know, it's not enough to say, great, I can predict that this stock is going move on an average of five basis points whenever X happens.

实际上，如果交易这只股票要花费你六个基点的成本，那根本就没有任何利润可言。

Actually, if that stock costs you six basis points to trade, then there's no P and L to be had there.

但通过组合大量这样的微弱信号——这些信号可能仅能在买卖价差范围内预测——你可以在股票市场中获得交易成本之后仍表现良好的策略；而在货币市场中，你的微弱信号，比如试图预测数周或数月内的走势，实际上非常非常弱，弱到你很容易自我欺骗，误以为发现了并不存在的效应。

But by combining lots and lots of these weak edges, which maybe only predict within the bid offer spread, you can get something which looks good after transaction costs in equities, whereas in currencies, your weak edges, you know, if you're trying to predict something over the course of weeks or months, they're very, very weak indeed, and they're so weak that actually it's it becomes much easier to fool yourself and just find effects that are not really there.

这场对话开始涉及模拟、研究和测试的领域。

This conversation starts to lean into the realm of simulations and research and testing.

对吧？

Right?

交易成本和股票市场这一关键特征，可能是限制套利、导致某些可预测性持续存在的原因。

That concept of transaction costs and equities being a key feature as to maybe the limits of arbitrage as to why some predictability continues to exist and persist.

我知道你对这个领域思考了很多，不仅仅是交易成本，还包括回测、研究和模拟整体。

This is an area I know you've thought a lot about, not just the transaction cost, but just backtesting and research and simulation in general.

你在推特上写了很多相关内容。

You've written a lot about it on Twitter.

你还专门做了一个名为“24天回测错误”的系列，非常出色。

You even did a whole series called the twenty four days of backtest errors, which was a great series.

我强烈推荐大家去查阅一下。

I highly recommend people look it up.

我猜我们完全可以做一期完整的播客，专门讨论回测问题。

My guess is we could do an entire podcast just about backtesting.

也许我们下个季度可以这么做。

Maybe we'll do that, you know, next season.

鉴于你对此写过这么多内容，假设你首先认为这是一种有益的练习，你认为在研究过程中最恰当的使用方式是什么？大多数人又在哪里犯了错误？

Given how much you've written about it, presuming you think it is a useful exercise in the first place, how do you think it's best employed within the research process, and where do you think most folks go wrong?

是的，绝对如此。

Yeah, absolutely.

我当然认为回测是一种有用的工具。

I obviously do think backtesting is a useful tool.

我想说的是，我并不把回测看作一种研究工具。

And I guess what I would say is that I don't think of backtesting as a research tool so much.

它对我来说并不是研究过程中的重要部分。

Like it's not something which is important to me in the research process.

对我来说，研究是在运行回测之前所做的工作。

For me, the research is what you do before you run the backtest.

然后你进行回测，以验证这个想法，并了解表现会是什么样子。

And then you do the backtest to validate the idea and get some ideas about, well, what is performance going to look like?

回撤会是什么样子？

What are drawdowns going to look like?

也许你想测试一些不同的投资组合构建思路，并使用回测来实现这一点。

Maybe you want to test some different portfolio construction ideas and use a backtest to do that.

但我所做的大部分研究，以及我认为这个行业中许多专业人士所做的研究，都是花大量时间观察数据、构建模型、检验假设，并试图在考虑任何模拟之前就理解这些效应。

But the majority of the research that I do, and that I think a lot of people working professionally in this industry do, is that you spend a lot of time looking at data and building models and testing hypotheses and trying to understand effects before you even think about simulating at all.

因为第一个问题是：这里是否存在可预测性？

Because the first question is, is there something predictable here?

你可以通过构建特征并进行一些线性回归，得出预测结果，然后问：我的预测与未来两天收益的相关性如何？

And you can go a long way with buildings or features and doing some linear regressions and getting a prediction out and saying, great, what is the correlation to my prediction with the return over the next two days?

只要把这些事情做好，就有无数种方式会在进行线性回归时出错，更不用说使用现代非线性机器学习工具时可能犯的错误了。

And as long as you do those things well, there are a million ways to mess up doing a linear regression before we even get onto how can you mess things up if you use some very modern nonlinear machine learning tool.

但在进行回测之前，你可以做很多工作，包括哪些特征会是有用的。

But you can do a lot before you get to the backtest, including which features are going be useful.

当我构建了一个特征后，我是否需要先对它进行转换才能用于预测？

Once I build a feature, do I need to transform that feature before it's useful in a prediction?

我该如何将多个特征组合成最终的阿尔法信号？

How do I combine multiple features into a final alpha?

我该如何预测我交易的资产的波动率和相关性？

How do I make forecasts for volatility and correlations of the assets that I'm trading?

仓位规模或市场贝塔暴露的合理限制应该是多少？

What are sensible limits to have on position sizes or market beta exposure?

所有这些工作你都可以在考虑回测之前完成。

All this is stuff you can do before you ever think about your backtest.

一旦你有了这些输入——你的阿尔法、波动率预测、风险模型和交易约束——就可以将它们输入回测，看看用我创建的这个模型能预期获得怎样的表现。

And then once you've got that, you then take all these inputs that you've created, your alphas, your volatility forecast, your risk model, your training constraints, and then you put those through a backtest to say, okay, what kind of performance can I expect with this model that I've created?

你的回测结果总是会过于乐观，对吧？

Your backtest is always going to be optimistic, right?

即使你的回测夏普比率是2，你也绝对不能指望实际交易时夏普比率也能达到2。

Just because your backtest has a sharp of two, you absolutely do not expect to have a sharp of two when you start trading this thing.

除了你进行的数据挖掘外，你的回测中很可能对很多因素进行了错误建模。

Apart from all the data mining you've done, you've probably mis modeled a lot of things in your backtest.

而这些错误几乎总是对你不利，对吧？

And almost always, these errors go against you, right?

你几乎不可能在回测中犯错，然后实际交易时表现得比预期更好。

It's very rare that you make an error in your backtest, and then it turns out when you start trading, you do better than you expected.

因此，我使用回测的方式，正如我所说，是作为最终检查，验证策略在一组假设下是否有效，我会用它来比较不同的模拟假设。

So the kind of things I would use a backtest for, like I said, this final check that the strategy works given a set of assumptions, I would use it to compare different simulation assumptions.

比如，我对市场冲击、成交率、借券可用性有不同的假设时。

So if I have different assumptions around what is my market impact, what are my fill rates, What is my borrower availability?

我为借券支付多少费用？

How much do I pay for my borrowers?

或者我的延迟是多少？

Or what is my latency?

但你可以用回测来，例如，绘制出我的盈亏如何随延迟变化的图表。

But you can use a backtest to, for example, make a chart of how does my P and L vary as a function of my latency?

假设你其他所有部分建模都正确，你可以改变延迟假设，看看你的策略对延迟有多敏感。

Assuming you've modeled everything else correctly, you can change your latency assumption and see how latency sensitive you are.

我认为回测的用处之一就是这个，或者可能用于比较同一策略的两个不同版本。

That's something I think a back test is useful for, or potentially to compare two different variants of the same strategy.

所以有两种不同的方式来构建你的阿尔法。

So two different ways of constructing your alpha.

你有一组给定的特征。

So you've got a given set of features.

我为从这些特征中得出最终的阿尔法构建了两个不同的模型。

I built two different models for getting a final alpha out of those features.

然后我让这两个模型都通过相同的回测。

And then I run them both through the same backtest.

你希望即使你的回测中有一些假设在现实中并不完全准确，至少这两个回测之间的差异是具有意义的。

And you hope that even if your backtest has some assumptions which are not quite true in reality, at at least the delta between those two backtests is gonna be something which is meaningful.

最后，我们使用回测的一个非常重要但可能被低估的用途是：模拟交易与实盘交易的对账。

And then finally, a very important thing which we use backtesting for, which I think is a little underappreciated is reconciliation versus live trading.

所以在每一天、每周、每月或你所用频率的末尾，你会对刚刚交易过的那一天进行模拟交易，并问：我的回测结果与现实有多接近？

So at the end of every day or week or month or whatever your frequency is, you run your simulated trading over the day that you just traded, and you say, how well does my backtest match reality?

如果存在差异，你必须深入理解这些差异。

And if there are differences, you really wanna understand those differences.

这是我们大量使用回测的一个方面。

That's that's something we use backtesting for a lot.

你还写过很多关于因子模型和股票因子对冲的内容。

Another area you've written quite a bit about is factor models and equity factor hedging.

你能谈谈为什么因子对冲在多经理股票对冲基金中如此关键，以及这些概念如何可能延伸到宏观因子的对冲吗？

Can you talk a little bit about why factor hedging is so critical in a multi manager equity hedge fund and how these types of concepts might actually extend to hedging macro factors?

嗯。

Yeah.

当然。

Absolutely.

我想我可以假设你的听众已经了解什么是股票因子模型以及什么是股票因子。

I guess I'm gonna be able to take it as an assumption that your audience understands what an equity factor model is and then what equity factors are.

我知道你经常提到这一点，所以。

I know you talk about it a lot, so.

如果我们不清楚，可以暂停，去阅读相关资料，然后再回来。

We'll assume that if you don't understand, hit pause, go read about it, come back.

很好。

Great.

因此，股票因子模型的概念包含两个部分。

So the idea really of an equity factor model, there are two parts to it.

一部分用于描述风险，另一部分用于建模收益。

There's a part that helps you describe risk, and there's a part that helps you model returns.

这两部分都很重要。

And they're both important.

量化股票经理很可能会在某个地方使用因子模型，即使他们的策略并非完全基于因子模型，其中也会包含一些因子假设或因子建模，并将其同时用于收益建模和风险建模过程。

And a QuantEquity manager will quite likely have a factor model somewhere, even if their strategy is not entirely built as a factor model, there'll be some factor assumptions or factor modeling somewhere within it, and they'll use that both as part of the return modeling process and as part of the risk modeling process.

但值得一提的是，几年前我曾在推特上发过一个帖子，讨论了即使你不是量化股票中性基金经理，这种模型也有用处。

But yay, I had a Twitter thread, I think some years ago now, where I talked about how this can be useful even if you're not a quant equity market neutral manager.

在这种情况下，假设你是一家大型多策略对冲基金，拥有大量股票选择者，一些交易团队可能是行业专家或国家专家等。

And the situation here is assume you're a big multi manager hedge fund, and you've got a large number of stock pickers who work for you, some trading teams who maybe are sector specialists or country specialists or something like that.

他们的决策相当主观。

And they're quite discretionary.

你正在试图通过所有内部股票选择者捕捉一些主观的阿尔法收益。

There's some discretionary alpha you're trying to capture with all your in house stock pickers.

他们确实带来了真正的阿尔法。

And they've put genuine alpha.

你可以看看一些大型多管理人对冲基金的收益，你会发现那里确实存在真正的阿尔法。

You can look at the returns of some of the big multi manager funds, and you can see there is genuine alpha there.

但同时也存在问题，即他们往往对某些特定类型的股票存在偏见。

But there's a problem as well, which is that they tend to have, for example, biases to particular kinds of stocks.

典型的例子是，对冲基金中的主观股票选择者喜欢动量股，喜欢优质股，喜欢盈利强劲、销售增长快的股票。

So the classic thing are that discretionary stock pickers at hedge funds like momentum names, they like quality names, they like strong earnings, they like sales growth.

我不会说他们喜欢高融券量的股票，但实证表明，他们倾向于选择那些融券量很高的股票放入他们的空头头寸，部分原因是这些股票之所以有高融券量，正是因为对冲基金的交易员做空了它们。

I wouldn't say they like short interest, but empirically, they tend to pick stocks that have a lot of short interest to be in their short book, partly because that's how stocks get a lot of short interest, that pods at hedge funds short them.

因此，作为基金的管理者，你最终会承担大量不必要的风险敞口。

And because of this, as the manager running the fund, you end up with a lot of incidental exposures that you don't necessarily want.

比如大规模的行业、货币或国家敞口，市场敞口，因为交易员在没有约束的情况下，会倾向于选择高贝塔股票，并简单地用一比一的股指空头对冲。

So big sector or currency or country exposures, market exposures, because pod manager left to their own devices will go along some high beta name and just do a simple one for one equity index short against it.

因此，在他们进行对冲之后，你实际上仍然获得了正的贝塔值，再加上动量、收益增长等因素带来的各种敞口——也许你希望保留一部分这些因素，但你并不希望从个股选择中获得如此多的敞口。

So you actually got positive beta after their rough hedge, plus all the factor exposures from momentum and earnings growth and things that maybe you want some of that, but you don't want as much as you're getting from the individual stock picks.

因此，你可以在其上叠加一个量化模型，说：好吧，我目前有哪些因子敞口？

And so you can put a quant model on top of this and say, okay, what factor exposures do I have?

根据我的敞口情况以及我希望达到的目标，我该如何找到一些低成本的对冲工具来抵消我不想要的敞口？

And given my exposures and where I'd like to be, how can I find some cheap hedges for this exposure that I don't want?

当我提到你不想拥有这些时，我具体指的是什么？

And what I mean when I say you don't want it?

这里一个非常重要的点是，如果你有大约100个不同的投资小组，你真的会关心它们之间的相关性。

Well, a really important thing here is that if you've got, say, a 100 different pods, you really care about how correlated they are.

举个例子来说，假设有100位不同的基金经理在构建一个投资组合，他们的夏普比率都是1，而且彼此之间的相关性为20%，因为他们有一些相似的敞口或持仓重叠。

And to give a kind of example of this, say you've got a 100 different managers building a portfolio and they've all got a Sharpe of one, say, and they've all got a 20% correlation with each other because they have some similar exposures or similar stocks in their portfolio.

20%的相关性听起来似乎不高，但正是这20%的相关性意味着，当你在这些100位经理之间进行分散投资时，你所能达到的最佳效果，也只是将夏普比率从1提升到2。

And 20% doesn't sound like very much, but that 20% correlation means that if you're diversifying among those 100 managers, the best you can do is double your Sharpe from one to two by doing that.

这里使用的模型非常简化。

This is a very simplistic model here.

因此，如果你能通过对冲掉一些导致它们相关联的因素来降低这种相关性。

And so say you could reduce that correlation by hedging away some of the factors which are making them correlated.

所以你可以将这种相关性降低到10%。

So you can reduce that correlation down to 10%.

那么，你就能将夏普比率从翻倍提升到三倍，对吧？

Then instead of being able to double the Sharpe, you can triple the Sharpe, right?

只需消除部分相关性，再稍微加一点杠杆即可。

Just by getting rid of some of the correlation and then leveraging up a bit.

因此，通过运用一些聪明的对冲策略，你的夏普比率就能提升1.5倍。

So that's a, you know, a 1.5 X boost to your Sharpe from being able to apply some smart hedging.

而且，如果你能把相关性降到非常低，比如5%，你就能将投资组合中单个经理的夏普比率提升四倍。

And, you know, if you can get that correlation down really low, you could get it down to 5%, you can quadruple the sharper of a single manager in the portfolio.

如果你能将相关性降到零，那么整个投资组合的夏普比率就是单个经理的十倍。

And if you can get it down to zero, then the sharper your portfolio is 10 X, sharper of the individuals.

现实中，你永远无法将相关性降到零，但这个例子让你明白，即使只是略微降低你两个不同交易团队之间的相关性，也能显著提升你的业绩。

Reality, you can never get this down to zero, but this gives you an idea of even just being able to reduce a correlation between two different trading groups that you have a little bit can be a real boost to your performance.

你最后问到这如何应用于对冲宏观因素，我说这很困难。

You asked then at the end about how does this apply to hedging macro factors, and I said that's difficult.

理想情况下，你希望能够对冲掉你对GDP增长或下一次通胀数据的暴露。

You know, ideally, you would like to be able to hedge, say, your exposure to GDP growth or your exposure to the next inflation print.

如果你尝试用经典的量化方法来做，就会获取经济数据发布和意外值的时间序列，这还不错，很好。

And if you try and do this in a classic quant way, then you'll get the time series of economic data releases and best surprises, and it's okay, great.

当CPI意外上涨时，哪些股票波动很大？

Which stocks move a lot when CPI surprises to the upside?

当GDP价格意外上涨时，哪些股票波动很大？并尝试计算它们对这些因素的贝塔值？

Which stocks move a lot when GDP price surprises to the upside and try and compute some betas to that?

这之所以极其困难，是因为数据点实在太少了。

This is an incredibly difficult problem just because there are so few data points.

我想GDP是季度数据，CPI是月度数据。

I guess GDP is quarterly, CPI is monthly.

因此，你根本得不到足够的数据来估算这些关系。

So you're really not getting enough data to try and estimate this.

所以，我认为更好的方法是寻找这些宏观因素的替代指标，这些指标具有更高的频率。

So a better approach, I think, is to try and find proxies for these macro factors, which are higher frequency.

比如美元、十年期收益率、原油价格，然后观察它们的贝塔值，这样你可以更精确地进行估计。

So things like the US dollar, the yield on the ten year, the price of crude oil, and look at betas to these, and this is something you can estimate with a lot more fidelity.

然后你可以应用类似的想法。

And then you can apply a similar idea.

如果你不希望你的能源经理只是承担大量的正向原油贝塔敞口，你可以尝试对冲掉这些因素的敞口。

You try and hedge out your exposure to these factors if you don't want your energy managers just to be taking a lot of positive oil beta, for example.

在你职业生涯的某个阶段，你从对冲基金转到了自营交易公司。

At one point in your career, you moved from operating in a hedge fund to operating in a prop shop.

我们通常认为这两个领域很相似，但我认为它们之间存在一些明显的差异，我很想听听你的看法。

We often think of those as similar worlds, but I think there are some distinct differences that I'd love for you to talk about.

具体来说，对冲基金和自营交易公司在结构上的差异如何影响公司内部人员的激励机制？

Specifically, how does the change in the structure of a hedge fund versus a prop shop affect the incentives of the people within the firm?

这种激励机制的变化又如何影响基础设施的建设或研究的方向？

And how does that change in incentives affect things like how infrastructure is built or what sort of research goes on?

是的。

Yeah.

所以我认为最大的区别在于，自营交易公司对交易收入的使用有更大的自由度。

So the biggest difference, I think, is that in a prop trading firm, you have more freedom in how trading revenues can be used.

所以如果你把这些公司看作是对流入的交易收入拥有某种索取权的实体。

So if you think of these firms as having some claims on that stream of trading revenue that comes in.

你需要支付基本成本，比如租金、基础设施和数据费用、法律费用，无论是什么类型的公司，这些都必须支付。

So you've got your baseline costs, you pay rent, you have infrastructure and data costs, have legal costs, and they've all got to be paid for no matter what kind of firm you are.

然后你还有员工，你的交易员可能期望获得一部分利润分成之类的报酬。

And then you have staff, have your traders who probably expect a percent of P and L or something like that.

但你还有很多非交易岗位的员工，他们也需要获得有竞争力的薪酬，并且也期望分享公司业绩的回报。

But then you have a lot of non trading staff as well, who also need to be paid competitively, and who also expect some beta to the firm's performance.

然后你还有资本提供者。

And then you've got the providers of capital.

在对冲基金中，那就是投资者，他们期望获得投资回报。

So in a hedge fund, that would be the investors who expect a return on their investment.

显然，这正是他们最初投资的原因。

Obviously, that's why they're invested in the first place.

然后你还有公司的所有者和管理者，他们希望通过分红或最终出售公司等方式实现利润。

And then you've got the ownersmanagers of the firm, who are trying to realize some profit, either by a dividend or because they're eventually going to sell the firm or something like that.

你有这些各种权益主张，尤其是这些主张之间存在紧张关系，因为可供分配的蛋糕是固定的。

And you've got all these claims, and in particular, there's tension between all of these because there's only a fixed pie to go around.

而如果你是一家自营交易公司而非对冲基金，一个关键的解决方式是：资本提供者和公司所有者通常是同一群人。

And one big thing that you resolve if you're a prop firm rather than a hedge fund is that the providers of capital and the owners of the firm are generally the same people.

因此，许多关于如何支付交易员、或多少交易收入应再投资于技术或基础设施改进等方面的争论或矛盾就会消失，因为这两方是同一个人。

So a lot of the arguments or tensions that would happen around stuff like how well do we pay our traders, or how much of our trading revenue do we reinvest into tech or infrastructure improvements go away because those two groups are the same person.

例如，如果你说：我们今年想大幅增加技术投入，因为我们想购买大量GPU集群，或在云计算上投入大量资金。

So for example, if you say, we want to have a big tech spend this year because we want to buy a lot of GPU clusters, or we want to spend a lot on cloud compute or something like that.

这些投入 presumably 只会在很多年以后才带来收益。

This is work that's presumably only going to benefit many years down the line.

而如果你是一家对冲基金，你的现有投资者根本无法确定十年后他们是否还持有份额，从而享受到这些收益。

And if you're a hedge fund, your current investors have no real idea whether they're still going be invested ten years down the line in time to see these benefits.

因此，他们更希望你减少这种再投资，将更多利润作为回报分配给基金。

And so they're going to prefer you to do less of that reinvestment and for you to pay out more of the profits as return to the fund.

这就是最大的区别。

So that's the biggest difference.

这在公司内部创造了真正的文化变革，因为我认为，在自营交易公司中，每个人都会对公司的运营产生更强的归属感，并更倾向于从长期角度看待问题，这是其中一个差异。

It creates a real culture change inside the firm because I think you end up with everyone feeling a lot more ownership in what's going on inside a prop firm and viewing things a little bit more for the long term is one difference.

其次，如果你是一家对冲基金，你会被激励着始终考虑可扩展性。

And then a second difference is that if you're a hedge fund, you are incentivized a little bit to always think about scalability.

你可能拥有几种不同的收入来源。

You've probably got a couple of different revenue sources.

你有管理费，也有业绩报酬。

You've your management fee and you've got your performance fee.

如果你将基金规模扩大两倍，你的业绩表现可能不会那么好，因为你规模变大了，市场冲击更强，建仓也更困难。

And if you make the fund two x figure, okay, your performance is probably not gonna be quite as good because you're larger and you have market impact and harder to put the positions on your need.

但如果你的基金规模扩大一倍，你的管理费就会翻倍。

But if your fund gets twice as big, your management fee is just gonna double.

如果你是对冲基金的经理，这还不错。

And that's nice if you're the the manager of a hedge fund.

因此，你总是有动力去思考：我怎样才能让事情变得更大？

So you're always incentivized to think about how can I make things bigger?

我能筹集更多资金吗？

Can I raise more money?

这意味着对于在那里工作的人而言，他们不断被推动去想：你如何把你目前做的事情扩大五倍？

And that means that for the people working there, they're constantly being pushed to say, right, how can you do what you're currently doing, but five times bigger?

即使这会牺牲利润与亏损的质量。

Even if it sacrifices the quality of the P and L for that.

这可能不错，因为可能带来更大的收益，但作为策略的管理者，操作一个Sharp One策略和操作一个Sharp Two策略的感觉截然不同，尤其是在回撤规模和你对策略持续有效性的信心方面。

And that can be nice because that can be bigger paydays, but operating a sharp one strategy is a very different feeling to operating a sharp two strategy if you're the person managing that strategy, right, in terms of the size of your drawdowns and your level of confidence that the strategy is continuing to work.

因此，如果你是一家自营交易公司，并且更注重建立高质量的收入流，比如专注于一些高质量的策略，更关注长期发展，我认为这能为你的交易员提供一个更良好的工作环境。

And so if you're a prop firm and you have more of an emphasis on, okay, let's build a quality stream of revenues, you know, some high quality strategies, focus more on the long term, I think it gives a nicer environment for your traders to work in.

你没有提到的一点是授权灵活性，我认为这将很好地引导我们进入我想继续讨论的下一部分。

One of the things you didn't talk about was mandate flexibility, which is I think gonna lead us nicely into the next part of the conversation I wanna go into.

在自营交易公司，你们在2021年决定做的一件事是协助建立一个加密货币交易台，而在一家有明确投资范围的大型对冲基金里，你肯定没有这样的机会。

Because at the prop shop, one of the things that you decided to do in 2021 was help set up a CryptoDesk, which if you're operating within a large hedge fund with a defined mandate is certainly not something you're gonna have the opportunity to do.

所以谈到加密货币交易台，我想深入探讨一下细节。

So talking about the CryptoDesk, I wanna dive into the weeds there.

我想了解，当你考虑设立像加密货币交易台这样的新业务时，加密货币在基础设施和运营风险方面与股票和期货市场有何具体不同？

I wanna understand when you're thinking about setting up something new like a Cryptodesk, how does the infrastructure and operational risk of crypto specifically differ from the equities and futures markets?

我很想听听，总的来说，从零开始搭建这个交易台的过程中，你们学到了哪些经验？

I'd love to know, like, just generally speaking, what lessons did you learn building this desk from scratch?

是的。

Yeah.

你说得对。

So you're right.

我们是在2021年开始搭建我们的加密货币交易台，正好赶上了2021年的大崩盘，这挺不错的。

We we started building our crypto desk in the 2021, just in time to see a big crash in 2021, which was great.

当然，也正好赶上了2022年加密货币市场接二连三的更多崩盘。

And, obviously, in time to see many, many more crashes in crypto in 2022.

但没错，我们仍然对此保持承诺。

But, yeah, this is still something which we're still committed to.

我的意思是，我今天仍在运营一个加密货币交易台。

I mean, I'm continuing to run a crypto desk today.

因此，加密市场与传统金融期货和股票市场的运作方式存在巨大差异。

So there is a huge difference in how crypto markets operate versus how TradFi futures and equity markets operate.

我认为最大的区别在于，传统市场比加密市场中介化程度高得多。

The biggest one, I think, is that traditional markets are much more intermediated than crypto markets are.

你经常会发现，交易所、清算所和主经纪商都是不同的公司。

And you will frequently find that you'll have the exchange and clearing house and the prime broker will all be different firms.

而在加密市场中，通常这些职能都由同一家公司承担。

Whereas in crypto, typically, this would all be the same firm.

这曾经，且在某种程度上至今仍被宣传为加密的优势：加密交易所同时充当你的交易对手、清算所、主经纪商和融资提供方。

And this was, and to some extent, still is sold as an advantage of crypto, that the crypto exchange is your counterparty, the clearinghouse, and your prime broker, and your financing provider all at the same time.

现在人们普遍认识到，这种模式会带来你并不一定想要的相互关联性和脆弱性。

I think it is now recognized that that creates interconnectedness and fragility that you don't necessarily want.

我认为交易所仍然希望提供所有这些服务，但我现在看到越来越多的呼声要求增加中介环节。

I think the exchanges would still love to be providing all these facilities, but I'm now seeing more of a push to have more intermediation here.

另一个重大区别是，加密货币市场更加实时。

Another big difference is that crypto is much more real time.

所以，如果你的投资组合出现亏损，不会像传统金融那样，在收盘前有经纪商打电话给你，说‘你得在明天开盘前追加保证金’。

So if you are taking some losses in your portfolio, there's no prime broker who calls you up toward the end of the day and says, Hey, you need to top up your account with some more margin before they open tomorrow.

在加密货币市场中，你会被直接清算。

You just get liquidated straight away in crypto.

因此，你需要更加密切关注这一点。

So that's something you need to be much more on top of.

这意味着，对于传统金融交易公司来说属于中台职能的资产负债管理，在加密货币领域则更贴近交易台，因为这里存在所谓的‘运营阿尔法’——即能够高效管理任何时候资产的分布，从而最大化资本回报。

And it means that your treasury management, which for a TradFi trading firm is a kind of middle office role, becomes much closer to the trading desk in crypto because there's real edge, we call it operational alpha, in being able to efficiently manage where your assets are at any point in time to essentially maximize your return on capital.

另一个区别是，获取融资在加密货币领域更加困难，尤其是在FTX事件之后。

Another difference is that accessing financing in crypto is much harder, particularly post FTX.

而且，在加密货币交易所持有资产的内部资本成本，远高于在经纪商处持有资本的成本。

And your internal cost of capital for holding assets on a crypto exchange is much, much higher than it is for holding capital at a prime broker, for example.

你会非常担心存放在加密货币交易所的资产。

And you really get concerned about assets which are held at a crypto exchange.

因此，这也会影响你想执行的策略类型。

So it affects the kind of strategies you want to run as well.

你只想做那些资本回报率高的交易，因为你担心这些资本。

You really only want to run stuff which is high return on capital because you're worried about that capital.

从基础设施角度来看，加密货币交易所——我认为这是好事——通常设计得比传统货币市场更便于小型交易者使用。

And then infrastructure wise, crypto exchanges, and I think this is a good thing, typically designed to be accessible for much smaller traders than treasury markets are.

因此，完全有可能，而且有很多例子表明，一个人坐在卧室里编写做市算法，然后在币安、YBTI、OKX或其他平台交易并赚钱。

So it is absolutely possible, and there are many examples of this, of one person sitting in their bedroom, coding up a market making algorithm and going to Trader on Binance or YBTI or OKX or something and and making money.

但这类公司后来扩展成了大型团队，成为加密货币领域的重要参与者，而这种情况在传统市场中已不再常见。

But then firms like this have expanded into big teams and become really significant players in crypto in a way which doesn't really happen in traditional markets anymore.

但正因为如此，加密货币交易所的技术架构看起来非常不同。

But as a result of that, the tech stack on the crypto exchanges looks very different.

例如，交易所通常运行在云端，而不是某个数据中心里。

So for example, the exchange typically runs in the cloud rather than in a data center somewhere.

它们分发市场数据的方法是通过 WebSocket 发送，以文本格式编码，这显然是一种极其低效的市场数据编码方式。

Their method of distributing market data is to send it over a WebSocket, encode it as text, basically, which is an extremely inefficient way to encode market data.

由于这种方式，延迟非常高。

There's very high latency because of this.

这种做法还导致延迟中存在大量随机性，也就是我们所说的抖动。

There's lots of randomness and what we call jitter in the latency because of this.

因此，如果你在机构工作，关心延迟低或订单执行时间相对可预测，你就需要做大量工作——这些工作在传统市场中并不需要，或者与传统市场所需的工作性质完全不同，才能获得可接受的交易体验。

So if you are working in an institution you care about stuff like our latency being low or having relatively predictable order entry times, you do a lot of work, which you don't really have to do in, or is a different kind of work to the work that you have to do in traditional markets to get an acceptable trading experience.

因此，存在一套非常独特的技能组合，与传统市场的交易软件开发者的技能集截然不同。

So there's a very particular skill set which looks extremely different from the skill set of a trading software developer in traditional markets.

你提到的许多观点都集中在风险上，我非常想更深入地探讨这个领域。

A lot of those points you touched upon were very much centered around risk, and I'd I'd love to dive into that area a little bit more.

你提到，例如，要考虑在不同交易所的资金及其资本成本。

You mentioned, for example, that thinking about money on different exchanges with a cost of capital.

我听说过一些公司明确地将这笔资金视为贷款来对待。

I've I've heard of firms very explicitly having to think about it as a loan.

因此，无论他们把多少钱转移到交易所，都必须基于对交易所的某种风险假设，达到一个隐含的最低回报门槛。

So whatever money they move to an exchange, there's an implicit hurdle rate of that they have to exceed based upon some sort of risk assumption about that exchange.

我非常希望你能更多地谈谈风险管理，或许可以对比一下加密货币市场与传统市场的差异，深入探讨一下交易所风险之类的问题。

I'd love for you to just talk about risk management more, maybe contrast it within crypto to more traditional markets, touching on things a little bit more deeply like exchange risk.

另一个让我想到的是管理交易项目中的肥尾风险。

And then another one that comes to mind for me is sort of that managing the fat tail of line items.

加密货币领域不断有新的币种和衍生品上市，因此需要考虑如何管理这种肥尾风险。

Crypto is a space where you get all of these new currencies and derivatives coming to market all the time thinking about managing that fat tail.

我非常期待能更多地了解这一点。

I'd be certainly interested in hearing more about.

当然可以。

Yeah, sure.

我想首先要说的是，自2022年11月FTX崩盘以来，每个人、每一家机构——至少我认为所有人都现在对交易所风险更加重视了。

So I think the first thing to say is that since November 2022 and the FTX blowup, everyone, every institution, at least I think just everyone, takes exchange risk much more seriously now.

这之前人们可能只是隐约意识到，但在FTX事件之前并没有太当回事，而现在它已成为一个真正首要的关注点。

So I think this is something where people kind of had it at the back of their mind, but maybe didn't think about it that much before FTX, and now it's a real primary concern.