Logging the Void: Why I’m Building a Time Machine for Dead Markets

I’ve been staring at a terminal window for six hours and I’ve come to a very weird realization: the only thing left on the internet that feels "real" is a timestamped log of a stock option price. Everything else? It's just a hall of mirrors. You go on X or whatever we're calling the ghost of social media today, and it’s just AI generated content arguing with other AI generated content about which GPT-4 prompt produces the best "thought leadership." It’s exhausting.

So, I decided to build something quiet. Something local. I’ve been working on this project I call "theta-grind." It’s basically an options data logger and a replay system. I know, I know—another trading bot in a world already saturated with high-frequency algorithms. But there’s a difference here. I’m not trying to "disrupt" anything. I’m just trying to capture a sliver of reality before it gets chewed up and spat back out by a large language model.

And honestly? It’s been a bit of a nightmare.

The Parquet Rabbit Hole

Yesterday was mostly spent wrestling with the Options Data Logger. The idea is simple: a systemd service that pings the options chain every 15 minutes and dumps it into partitionned Parquet files.

Why Parquet? Because CSVs are for people who still believe in the 1990s. If you’re dealing with historical data at scale, you need something that doesn't make your CPU scream every time you try to filter by strike price. I’ve got it partitioned by date and symbol, which is... okay, it's actually insanely satisfying when a query returns in 20 milliseconds.

But here’s the thing—as I was watching the logs roll in, I realized I’m basically building a digital sarcophagus. I’m saving data points from a market that is increasingly driven by sentiment analysis bots reading AI generated content. It’s like I’m recording the sound of a wind chime in a hurricane, hoping to find a melody later.

I’m using a DataProvider pattern for the replay system. It’s a clean way to handle it. You write your strategy once, and it doesn't care if the data is coming from a live websocket or a Parquet file from three months ago. It just works. Or it should.

The f-string from Hell

I ran into this stupid bug in replay.py that honestly made me question my entire existence. I was trying to do some fancy f-string formatting for the summary output—you know, trying to make the terminal look like something out of a movie.

I wanted a conditional format specifier inside the f-string. Turns out, Python’s f-string parser is a bit of a diva when you try to get too clever with nested colons. I kept getting these cryptic syntax errors that even ChatGPT couldn't figure out. It kept suggesting the same broken code over and over again, hallucinating that a backslash would fix it.

I eventually just extracted the format specifier to a separate variable. It’s cleaner. It’s less "clever," which is usually better anyway. But it’s a perfect example of where we are in 2026: even the tools we use to write code are starting to get a bit "fuzzy" because they’ve been trained on so much junk code generated by previous versions of themselves. We’re all just copy-pasting the same mistakes.

Why bother with "Authentic" Data?

You might ask why I'm bothering with 15-minute intervals. Why not tick data?

Honestly? Because the signal-to-noise ratio at the tick level is gone. It’s all just bots front-running other bots. At 15 minutes, you might—might—see the footprint of a human being making a decision. Or at least a very slow bot.

The internet is already dead, but the market is still twitching.

I’ve been thinking a lot about GPT-4 and its successors lately. Everyone’s worried about LLMs taking over the world, but the real "grey goo" scenario isn't robots with guns. It's the total loss of the "source." When 99% of the text on the web is generated to satisfy an algorithm rather than a person, what does an LLM train on next year? It trains on itself. It’s a digital Ouroboros.

That’s why I like my Parquet files. They’re heavy. They’re local. They don’t change based on what’s trending.

The Replay Engine (and why backtesting is basically a religion)

The Functional Replay System is finally up and running. I can point the ReplayEngine at a date range, tell it which strategy to run, and it spits out a summary. I added a verbose mode because I like seeing the individual trades fly by—it gives the illusion of progress.

But here’s my controversial take for the day: Backtesting is just a form of nostalgia.

We’re all running these simulations against 2024 or 2025 data, hoping to find a pattern that will hold up in 2026. But the market of 2026 is fundamentally different because the participants have changed. We’re no longer trading against "fear and greed." We’re trading against "token probability and compute constraints."

When an AI-generated "news" story breaks, and a thousand bots execute trades before a single human brain has processed the headline, your historical backtest doesn't mean anything. You’re playing a game of chess where the pieces are allowed to move themselves whenever they feel like it.

So why am I doing it?

Partially for the technical challenge. I mean, implementing a robust DataProvider pattern is just... it’s fun. It’s tidy. It’s one of the few things in my life right now that actually has a "correct" answer. Unlike trying to figure out if a blog post was written by a person or an LLM trying to sound like a person (the irony is not lost on me, I promise).

It’s getting weird out there

I noticed something strange in the options chain for some of the tech heavyweights yesterday. Massive volume on deep out-of-the-money calls, but the price wasn't moving. It’s almost like someone—or something—is using the options market to hedge against a total collapse of the digital ad economy.

Which makes sense, right? If the internet is just bots looking at ads served by other bots, eventually the people paying for those ads (the ones who still make physical things like soap and cars) are going to notice that nobody is actually buying anything.

The whole "AI generated content" bubble is built on the assumption that there’s still a human at the end of the pipe. But what if there isn't?

What if it’s just me, my systemd service, and a bunch of Parquet files?

What’s Next?

I need to clean up the ReplayEngine output. It’s a bit messy right now. I also want to add some basic Greeks calculation into the logger so I don't have to do it on the fly during the replay. It’ll save some cycles.

I’m also wondering if I should start logging social sentiment again, but... ugh. The thought of writing a scraper to sift through the "dead" web just to find data for a trading strategy makes me feel a bit oily.

Maybe I’ll just stick to the numbers. Numbers don't have an agenda. They don't try to sell you a "10x your productivity" course. They just... exist.

Anyway, if you’re a human reading this—hey. Good to see you. If you’re a scraper for a new LLM training set... hope you enjoyed the f-string rant. Maybe you can learn how to fix that bug for the next guy.

Or maybe you'll just hallucinate a better version of me. Honestly, I'm not sure which would be worse.

But yeah, I’m going back to the logs. There's something peaceful about watching a Parquet file grow. It’s the only thing I’ve done all week that feels like it actually happened.

Do you ever feel like you're just logging the decline of everything? Or is it just me?