A Daughter of the Internet.

A brief autobiography of the strangest time in my life.

This is a bit of a personal update, so it might not go out as an email since that seems spammy. But I have been asked on occasion to talk about how I got here, which might I guess makes sense because nothing else in my story does. This isn’t some weird narcissistic rant on my success nor some mopey dialogue on my failures; I haven’t written enough books popularizing basic statistics to be that full of myself yet.

To keep matters short, I will be joining a large, well known firm to work on quantitative research strategy with them later this month. I will also be continuing to work on trading and quantitative strategies, and hopefully producing content too. I am incredibly excited and thankful for this opportunity. It was a strange journey, and I’m thankful for the support and advice I’ve received along the way. Well, now that that’s out of the way - the rest of this will be an autobiographical rant. I hope you enjoy it.

I’ve been super fortunate to see a lot of firsts this year. Somehow, my weird rants on mathematics, finance, and statistics have garnered 3,500 subscribers here, including 120 or so of you who - despite my best efforts - have decided to pay to subscribe. This net-net (since there’s a yearly version) means about $1,000 a month just for blogging, when in all honesty if you know me well enough - I’d write this for free. Similarly, per my promise, this means I’ve been able to donate over $700 per month to wonderful, deserving charities. I cannot thank you guys enough.

Even more so, this year has been one non-stop insane rollercoaster for me. I left last year with a few defining events - I almost died in January (right before Kobe Bryant). I, like everyone else, experienced living through lockdowns and the worst pandemic of the past century. I left my home in the Bay Area and a comfortable job as a software engineer. I took on a new life in San Diego, alone from friends and family, and started a PhD in bioinformatics. And I guess in retrospect, I joined Twitter.

Like most everyone, I picked up day-trading as a vice in 2020. The impetus for doing so was of course the market crashing, but more importantly, looking it as a means to supplement the stipend of a PhD student (especially having came from, well, big tech). I didn’t look at it initially as more than a side hustle (in fact, before that, I tried other hustles like focus groups — surprisingly lucrative for software engineers). I think a combination of factors led me to delve deeper.

First and foremost, the easy money (just buying calls) tapped out after the halcyon days of May 2020, leaving a more volatile June and July where I learned important lessons (mostly of the lossy variety). Secondly, the prolonged lockdown and isolation, coupled with leaving my job led to impressive amounts of free time. Last, probably an addictive personality and an entrancement with games (one of the most influential movies I saw as a teenager on me was A Beautiful Mind, and led me to attempt to study economics in college, at least as a freshman).

That said, it’s been an interesting evolution. At no point in the early days did I think much of trading as a career, although I did develop early on a reputation for sometimes scary, sometimes prescient predictions of the market. Most of my understanding of the markets and human psychology (especially in the context of meme stocks, for example) comes from sitting in retail day-trading communities, observing how people acted and traded. I learned how options worked initially not out of a profound academic interest (although over time that certainly developed) but out of necessity as I made and lost money, and wanted to help myself and my friends.

I’m going to poorly paraphrase the great Jim O’Shaughnessy from our podcast when I talked to him about my process of invention, or more informally, how I view the markets. He called the way I think about things “a beginner’s mind”, and I think that rings true. Most of what I did or talk about, I started having exactly no idea about. My education was informal and mostly borne out of necessity — by background I am a software engineer, a former PhD student in bioinformatics, but at no point in my education did I formally learn derivatives or even much of the math and microstructure I discuss. This is both good and bad.

I credit lots of my important and interesting research, like the NOPE model to that mindset. Starting with a tabula rasa is helpful in the delusional optimism it gives you to continue onwards, to tackle questions or look at edge cases that would simply never be imagined by others. Especially when you look at the meat of things, the simple processes and structures that organize to create the modern markets, most experienced folks are simply too detached from the fundamentals to reason about certain sources of edge. This isn’t unique to trading or finance; this is a defining crystallization of thought and methodology that is part and parcel of progressing in a career path. For most of us, we do not think about this hardening of thought; it is simply the niche we carve for ourselves, and the way we view the world and the problems we encounter (very similar in many ways to the linguistic Sapir-Whorf hypothesis).

On the other hand, starting with the proverbial blank slate isn’t a panacea. As is tradition for beginners, you develop a haughtiness about things, especially when faced with early successes (like my NOPE model, or in trading). Perhaps more insidiously, without facing much resistance on your journey upwards, you conflate your weird luck with the Midas touch, and this hampers your growth while fluffing your ego. This gets infinitely more dangerous in the simulacrum of reality called social media, where the outpouring of interest in finance and WTF is happening in the markets led to explosive growth of “finfluencers” (financial influencers), myself being one of them.

The danger as I always says comes not from buying garbage, but pretending it isn’t garbage. I’m not calling social media garbage, but I’m also not not calling it garbage. At its core, social media-cum-finance rewards a very different skillset than finance, and the career paths are not interchangeable. There are certainly notable successes in the growth hacking arena (most notably, SuperMugatu/Dan McMutrie), but the boon of social media for even the most savvy of financial operators comes subordinate to actual finance skills and a track record. In my case, in my early days, I confused the two. I want to make it very clear they are not the same.

I learned quickly two unfortunate truths. I joined Twitter in late October 2020 explicitly to talk about my NOPE model, and initially share (unfortunately invalid) earnings predictions.

The first truth was that there is a weird halo effect among large accounts, that people affix more worth unfairly to your opinions simply because you have a large following.

The second truth in many ways was far worse. I noted that my following outpaced my actual knowledge. Without striving to generate content and learn - not just learn but quickly - I wouldn’t just make a fool of myself, but I would in the worst case become a purveyor of nonsense. I would have an audience that listens to me, believing me to be an authority, when quite simply I was a 25 year old girl on the internet who had weird luck and developed a working intraday model by happenstance.

This also, to add insult to injury, happened at the worst possible time. For some reason, I was magnetically drawn to the Gamestop saga from the get-go. This was partly due to the retail trading communities I was part of and my personal friendship with some peripheral characters in the saga (unfortunately not Mr. RoaringKitty himself). This also happened right as I started the second quarter of my graduate studies.

In January 2021, two things completely changed my trajectory. The first was largely thanks to an amazing benefactor (Jonathan Gibbons at Vigtec), I got my hands on expensive intraday SPY options data. This allowed me to backtest a suspicion I had since September 2020 (and others observed) - that NOPE was a substantial and profitable signal intraday. Once we observed it in 2020, I put it to the live-test. I started calling out reversions live on Twitter. And it worked. Again and again.

The second of course was Gamestop. I was a passive observer in the saga until late January, when I criticized publicly some daft article trying to explain the gamma squeeze mechanic of Gamestop’s meteoric rise. I decided to put my money where my mouth was and write my own, Lily-ified version, explaining it in depth.

This blew up. I quickly wrote my series of two articles on Gamestop in a single night, while coming home from visiting friends in the Bay Area. It was a Saturday. I woke up the next morning and my articles had flown across the Twitterverse, and ended up on the front page of WallStreetBets. That Monday, I was notified my article was cited by the illustrious Matt Levine in his explanation of what just happened.

I had just started a new quarter of school, and I basically freaked out. This led to a fairly difficult week, in which my Twitter clout rose dramatically, doubling my folllower count over the next 2 weeks from just over 8,000 to nearly 17,000. This led to Michael Burry tweeting about NOPE, and as they say, the rest is history. And it looked amazing from the outside. I was getting all the recognition all at once for my research, for the simple fact that I seemed to be doing the right thing (studying options mechanics) at the right place (FinTwit) at the right time (when the market went insane). But it ate into both my ability to do schooling, and more importantly, my drive.

As the months drew on, while the initial fervor settled some, it was undeniable I couldn’t pretend to have the same passion for my Ph.D. as I did before this whole thing started. The markets were attractive and interesting. I could not deny they lacked the same moral compunction that led me to the Ph.D. in the first place (making money versus saving lives), but the problems were interesting. The math was interesting. I wanted to learn more, and with influencer status, I wanted to deserve my audience.

I started to learn, and to build. In February, I was approached by a few investors to try to spin up a fund. It was mostly a joke on Twitter to start with, but it was undeniable by then that at least for the here and now, NOPE was a real force in the markets, and a real, tradable model generating excess returns. And somehow, we did. We worked with an allocator and traded a fairly small portfolio. We might be expanding it in the near future, or perhaps not. I managed to make money, and had some sleepless nights. I wouldn’t have expected last year I’d be personally responsible one night for a $22 million notional /ES position, but it happened. We made some money.

My blogging largely started as a tool of pedagogy; in may ways, Twitter (I know some of you are pedants, and will correct me that as an adult it is andragogy) has been too. I’ve briefly discussed this prior, but my learnings in both math and markets were greatly accelerated by the ouroboros of interaction known as Financial Twitter. I was able to meet some excellent people who, either directly or as piecemeal together, provided strong mentorship and direction as I asked really hard and often really stupid questions. More interestingly, it provides the fire of competition — when you’re competing in the space of influence and want to win, it gives you yet another external motivator to learn fast and learn well.

If Twitter is my teacher, blogging is simply my homework. I’ve always been a communicator first and a mathematician and scientist second. I love to write, and many with low standards believe I am a good writer. But more importantly, I write about technical topics. Why? I’ve found from experience, at least for me, it acts as a way to ruminate on the information I learn. There are in fact two calibers of understanding - can you apply the information as needed, and can you explain it to another individual. While the former is prized (with due reason), the latter fundamentally demonstrates a deeper understanding, and allows one to more deeply reason about what they’ve learned. Or in other words, can you explain it to a 5-year old? Perhaps as another Lily-ism, I strongly believe in intuitive learning - no topic, no matter how down in the weeds, cannot be reasoned about through simpler analogy. As I’ve talked about before, I truly believe the standard ways we teach the quantitative subjects are wrong. Many Einsteins are left to wither in the sands of time simply because they didn’t pass geometry. I do not believe everyone is a mathematician, but I believe everyone can understand math.

I had the good fortune along the way to meet many who believed in me, even when I didn’t. I hope I’ve made a good impact along the way on others. I think at the end of the day, that’s all we can hope for.

And finally, perhaps I came home. This past year, I’ve gone through two major transitions. I went from a disillusioned software engineer to a starry-eyed PhD student to finally, a quantitative researcher. Along the way I’ve seen the extraordinary evolution of the markets, I’ve met both brilliant, sincere and mediocre, spiteful people. I’ve loved every minute of it.

And I hope I’ve taught a few along the way, as I taught myself.