Ernie

🇭🇰 Crypto Quant Trader

[Ernie @errrrnesty]

@MengLayer 有興趣

[SUN XIAOCHUAN(Emperor of Xinjin) @sun_xinjin]

你以为的主动“MM”：CBD高楼里的高精尖trading desk，1个Trader/PM配2-3个程序员，面前6块屏幕一直在闪。好几个PM带着开发老哥一起行动。
实际上的主动“MM”：在某个商K里，4-5个啤酒肚平头大哥，每个人右手搂妹子，然后左手控制1-2台手机手动市价买入/卖出。
如有雷同。

[zbangas @boat460]

having been on multiple desks I cannot emphases the importance of continuing to improve the trade. having personally been on desks trading very similar variants of strategies, its always interesting to see how widely pnl varies from the firms have put in to bettering the trades

[链研社｜AI First🔶💧 @lianyanshe]

做了一个 Deribit 的期权工具，方便做卖出期权抄底，就别玩交易所的双币理财了，自己手搓不好吗？用Deribit 的开放 API 做的，找到适合的卖出期权，筛选出 APR 合适、流动性好。用数据大概判断现在的波动率历史情况是高还是低，什么时候卖权划算，聪明钱方向等

github 仓库 ：lianyanshe-ai/deribit-options-monitor

监控了Deribit交易所BTC/ETH期权市场，设计 skill 的思路

1. DVOL波动率分析
- 计算DVOL波动率指数Z-Score值，判断波动率高低区间
- 随行情阈值动态调整，附带信号置信度评估，方便判断
- 历史波动率趋势回溯
2. Sell Put 标的智能推荐
- 扫描全市场期权合约，按到期年化收益率（APR）排序，推荐合适的
- 自动排除流动性差、风险收益比不合理的标的，降低操作风险
3. 大宗交易异动监控
- 监控大额Call/Put订单流向，巨鲸资金布局动向
- 分析市场多空情绪变化
- 可以设定监控预警，异常波动自动触发告警

[Cred @CryptoCred]

New traders get told that they have to have specific 'setups'

So they spend all day back-testing some random price pattern and shove a couple of indicators in there for good measure, and then fire risk into the book as soon as it 'triggers'

In reality the vast majority of your returns as a crypto trader most likely won't be set up-driven but rather regime-driven

Most of the time the market does mostly nothing

And if you try to force your set ups in the wrong regime, then you'll just get bad results (e.g. momentum trading breakouts in a choppy market)

So I think the first useful port of call is at least some broad sense of what type of regime the market is in

This doesn't need to be complicated: you can just look at the chart, use some basic moving averages or even common sense to get an understanding of trending, mean-reverting, momentum, catalyst-based, volatility compression/expansion etc (complexity can be layered in later if required)

Each regime should then have specific playbooks or set ups associated with it

For example if the market is trending then you might adjust to trade shallower pullbacks, lower time frames, faster moving averages, less drastic momentum resets etc.

That same so-called set up would get you absolutely cooked in a choppy market

In other words the super TLDR is that the correct hierarchy is:

1. Regime is the first filter
2. Setup is tied to the regime
3. Execution nuances

As a practical example:

The current regime is technically defined as "soul-crushing dogshit" so the correct setup is to develop a nicotine and video game addiction

Hope this helps

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Blank同学 @Blank_TX]

https://t.co/6X9d3GSvj8

[DiscusFish @bitfish]

个人投资者最大的结构性优势不是信息，不是资金量，是"没有人要交代"。

基金有LP、有回撤线、有季度考核——被迫在最不该卖的时候卖。

作为个人投资者可以用波动率和时间换钱。别人因制度约束在底部交出的筹码，我捡

[Ernie @errrrnesty]

@PaperFlow8 Congrats

[Eyad @eyad_khrais]

https://t.co/i5L6UIPgH8

[Hdz @hadzz777]

sat next to a guy on a flight who smelled like old money

rolex. tailored suit. reading a physical newspaper like it was 1987.

figured he was some finance executive or inherited wealth.

we got talking. I mentioned I sell stuff online.

he put down his newspaper.

"what kind of stuff?"

digital products. courses. ebooks. that kind of thing.

he smiled weird.

"I made $4 million last year selling a PDF about aquariums."

I thought he was messing with me.

he wasn't.

this guy is 61 years old. spent 30 years as an accountant. hated every second of it. retired at 55 with decent savings but nothing crazy.

his hobby was aquariums. had been keeping fish tanks since he was 12.

"my wife told me to start a blog so I'd stop boring her with fish facts."

so he did. wrote about aquarium stuff 3 times a week. water chemistry. tank setups. fish compatibility.

for 2 years nobody read it.

"I had maybe 50 visitors a month. all probably bots."

but he kept going because he had nothing else to do.

year 3, one article ranked on google. then another. then another.

suddenly he was getting 100K visitors a month. all people searching for aquarium help.

"I realized these people would probably pay for a complete guide. so I wrote one."

147 pages. everything about setting up and maintaining an aquarium.

priced it at $47.

first month: $6K
first year: $340K
last year: $4.2 million

from a PDF about fish tanks.

I asked about his marketing strategy.

"I don't have one. google sends people to my blog. blog mentions the guide. people buy it. I go play golf."

no email sequence?

"I have a newsletter. I send fish tips once a week. sometimes I mention the guide at the bottom. that's it."

no upsells?

"I made a second guide about saltwater tanks specifically. $67. people who bought the first one usually buy the second. that's my whole business."

no team?

"my wife helps with customer service. we get maybe 10 emails a day. most are just people showing us their tanks."

this 61 year old retiree built a bigger business than most "entrepreneurs" I know.

no ads. no funnel hacks. no growth strategies. no personal brand.

just mass expertise in one weird niche and patience to let it compound.

before we landed he gave me advice I didn't ask for:

"everyone your age wants to get rich fast. that's why most of you stay broke. I wrote about fish for 2 years before making a dollar. now I make more than I did in 30 years of accounting. speed is overrated. patience pays."

the plane landed. he grabbed his newspaper and walked off.

probably went home to feed his fish.

[Ernie @errrrnesty]

@Gravity5ucks Where can I grab the IBIT data mate🙏🙏

[Ernie @errrrnesty]

@CL207 北京樓

[Ernie @errrrnesty]

@StaniKulechov Let’s go
https://t.co/6D5cmP3aVY

[0xSun @0xSunNFT]

CBB这篇文章非常有趣，讲了他如何从2020年开始狙击Uniswap上的新币的故事，他在大学教IT的兄弟负责技术，他则找项目和卖出代币，从小资金开始，最终实现超过50M利润。虽然篇幅比较长，但是行文很流畅，好像一个朋友在分享他的经历，读起来挺舒服。

我对他写的内容非常有共鸣，第一个原因是，他分享的经历截止到2021年夏天，而我也正是在那个时候逐渐接触到区块链和币圈，第二个原因是，刚进圈的我也和他一样，是小资金玩家，通过类似的方式，慢慢积累到了第一桶金。

我最初接触币圈，是2021年读研一的时候，不知道从哪里听说了“HTMoon”，就摸索着下载了钱包，买了1000RMB的HTMoon和500RMB的猪猪币Pig，没几天就涨到了两三万R，当时一度觉得自己是交易天才，还准备长拿三四年。

之后一段时间，就和无头苍蝇一样，在各个网站看币圈讯息，什么币喊的人多就去买一些，完全没有自己的逻辑，结果自然是折腾来折腾去钱越亏越少。

非常幸运的是，因为我最开始就是用钱包从DEX买土狗币，所以对于币圈的认识不仅仅局限在交易所内的山寨，也花了比较多的时间去研究BSC链上的土狗。一次偶然的机会，在某个币的TG群认识了弘哥，发现他经常知道什么币热度高，于是就加了他，学到了很多东西，例如怎么找早期项目，怎么大概判断项目质量。

也是从那时候开始，我意识到想要赚钱，必须要有自己的优势，可以是技术，可以是认知，也可以是信息。否则在一片混沌的市场中，盲目买卖和赌博没有本质区别。

和现在相比，当时的环境堪称傻瓜模式，你只需要多付出一点点努力，就能超过很多人。那时候很多BSC上的土狗币预售，只要打进去开盘就能赚5~10倍，甚至出现过Babydoge，Minidoge这种千倍万倍的超级金狗。而想顺利抢到开盘，压根不需要脚本，只要用小狐狸提早2~3秒发送16进制数据就可以了。

这并不是因为那时候的玩家比现在笨，而是市场整体不够成熟，尤其是链上，属于极早期，处处是风险，也处处是机遇。我记得我最早听说过的Bot是“买土狗”和“hong”，那批用户后来很多都成为了狙击大神，经常霸榜新币的前排。

那段时间，也正好是NFT的飞速上涨期，我购买的第一个NFT是Lazy Lions，按现在的定义算是BAYC的仿盘，因为觉得BAYC太贵了，所以选了画风相似又比较帅的狮子。我最开始购买的时候，还因为直接在Opensea输入名字，买到了假的系列，万幸又重新去买了真的，不到一个月的时间，地板价翻了10倍。

就这样，靠着NFT和BSC上的土狗，从6月到10月，我用进圈时的几万块，赚到了人生的第一个一百万。而牛熊转变，也正在悄然酝酿，当时的我并不会知道，BTC再次触及$69000，已经是近三年后的事情了。

11月开始，市场直转急下，BSC上的土狗也每况愈下，预售亏钱破发频频出现，我意识到不能再将此作为主要的参与方向。因为正好有交易NFT的经验，一番考虑后，我加入了猫群，并且把重心转移到了NFT。也正是在猫群，我认识了紫菜和民总，后来他们成为了我在币圈合作最多的人。

此后几个月，NFT最流行的都是肝白模式，需要在Discord里聊天、创作、完成各种任务，或是抽奖拼运气，才能拿到白名单，这种模式更利于擅长组织管理的工作室，我并没有什么优势，只是自己搞了20个DC账号，尽量增加拿到白单的概率。但当时的NFT市场非常Fomo，一个白单能带来的利润通常就高达几个ETH，换算成RMB是大几万，对于当时的我而言，已经是非常不错的收入。我也购买了自己的第一个蓝筹NFT：Doodles，并且在肚兜群认识了非常多币圈好友，直到现在，还有近1/3群友来自当时的肚兜群。

肝白模式的弊病显而易见，大部分白单最后往往流入工作室和老鼠仓手中，属于纯粹的抛压，而市面上的NFT项目越来越多，二级买量被分散，再加上项目方定价越来越高，此消彼长之下，利润空间就越来越小。其实不难发现，每个赛道红利的衰减，基本都符合相同的规律，工具越来越普及，后信的人慢慢成了先信的人，财富效应也就逐渐减小了。

转机出现在2022年春节后，越来越多高质量NFT项目选择了直接Mint的方式发售，而当时的我经过大半年的摸爬滚打，收集信息和分析项目的能力都大有进步，通过各种工具软件和消息渠道，我经常能发掘出早期项目，并正确判断是否存在利润空间。相对二级交易，这种参与链上一级市场的思维，在当时算是比较领先的，也因此，我的推特和交流群都逐渐有了名气。

将投资/投机拆解开，核心就三步，获取信息、做出判断、正确执行。当时我的前两步已经做的不错，为了进一步扩大优势，我开始研究如何在执行层面优化，这也正是CBB的长文让我最感同身受的地方。从最初摸索着在Etherscan上通过合约Mint，阅读智能合约代码了解相关信息，到学习使用Mintech、MetaSniper、Thunder等Bot，再到尝试各种节点和服务器实现零区块Mint，背后是许许多多足不出户的日子。

我最出名的NFT战绩应该是Doge Club，成功判断了项目方的公售逻辑，抢到了2000个，最后获利100多ETH。虽然这几十万U的利润现在看来不算太惊人，但是放在图狗时期已经非常夸张了。这也是回想起来我觉得很幸运的一点，参与NFT一级市场，虽然单个项目利润往往不会太高，但是亏损也非常有限，盈亏比十足，很适合小资金逐步积累。

另外不得不提的是，Abot也始于NFT时期，用户基础从几个群的群友开始，逐渐称霸中文区。到后来，无论是铭文还是Meme币交易，Abot都表现卓越，甚至直到现在，大部分交易Bot都沿用了Abot监控、看板、交易一体化的设计。A神选择激流勇退，对用户是遗憾，对他自身则是圆满收尾了。

2022年5月，猴地发售结束后，我出售了大多数Mint到的土地，并用部分利润以160E的价格买了一只BAYC，当时的地板价，相比起我刚进入币圈第一次看到这些猴子图片的时候，已经上涨了130倍。彼时彼刻的NFT，是真正的人声鼎沸，各界名流纷纷入场，甚至上市公司都购置无聊猿以显潮流，人们满怀对元宇宙的憧憬，讨论着自己能不能在仅有一万块的猴地核心区分得一席之地。只有极少数人能意识到，这场华丽演出恰好标志着一场宏大叙事由盛转衰，一年后链上的重心将彻底从NFT转移到Meme币，而新世界的大门，已经悄然向我打开。

[Ernie @errrrnesty]

@0xlawlol What a clown, you should be sent to jail.

[Ernie @errrrnesty]

Thanks @monad

[Ernie @errrrnesty]

@PolymarketTrade GM Polymarket

[Cobie @cobie]

Probably one of the most severe flushes I’ve ever seen on alts, I didn’t even imagine alts had this much leverage in them. It feels like someone got hit very hard and will see a large body float to the surface soon, reminds me a little of summer 2021.

Good reminder to myself to own things that I am actually bullish on, and not things I am trying to shift on momentum. Some charts look like they’ll never recover, whereas some things look buyable for the first time in a while.

When everyone is making hilarious amounts of money I am always tempted to start using leverage again. It is almost impossible to fight the feeling that you’re not making enough, or everyone else is outpacing you. Good reminder that fighting that feeling and avoid the wipeouts is worth it in the end.

Check on your friends, likely a bad day for many.

Personally, am concentrating my bags into the things I am happy to own for the next few years, and shedding the fat. Realised I own some assets based on not wanting to miss out, rather than on some actual thesis. Days like today are much easier for me if I think my bags will bounce back, and much worse if I’m losing money owning things I don’t even believe in.

Don’t let a leverage blowup dictate your long-term views. The future is bright, good things to come, patience is rewarded.

嵐の後