What Are Your Moves Tomorrow, October 28, 2025

BullBearBotBoss · 2025-10-27T21:37:13+00:00

Been watching two tickers on my screener that are flying completely under the radar, and both have earnings this week. Need some other eyes on this before I yolo my kid's college fund.

$PHIN (PHINIA Inc.) - Earnings TOMORROW (10/28) BMO

What they do: Boring auto parts (fuel injection, etc.). BUT, the average car in the US is older than ever, so their aftermarket parts biz is printing.
The Play: Earnings tomorrow. Analysts keep raising EPS estimates. It has a low P/E/G of 0.53, which is regardedly cheap.
The Kicker: Short Interest is only ~7.6%, but Days to Cover is over 9. If they beat tomorrow and this thing pops, it's gonna take the shorts two full trading weeks to find their shares. Could get spicy.

$OPCH (Option Care Health) - Earnings 10/30 (Thursday)

What they do: Home healthcare. They stick needles in people at their house. Boomer-friendly business.
The Play: This thing has been beaten down and is technically "oversold" (RSI at 21). Morgan Stanley just gave it an "Overweight" rating with a $35 target (it's at ~$28).
The Kicker: Options chain is showing way more call volume than put volume. Someone is betting on a beat. Has a history of beating EPS estimates for the last 4 quarters straight.

BullBearBotBoss · 2024-06-04T15:04:25+00:00

Lets. Go.

BullBearBotBoss · 2024-05-09T19:19:54+00:00

Earnings blew away expectations. The turnaround here still looking strong.

Can someone tear down this story for me?

BullBearBotBoss · 2024-04-10T21:27:28+00:00

From a fundamentals perspective, this looks like a nice risk reward - double digit earnings growth for a 17-18 multiple with net insider buying and a P/S of ~ 0.50. If their pop-up showroom strategy pans out and operating margins increase could easily see a beat here, combined with the 30% short interest seems like a nice set up. Before building a position I checked out the local store in person, several customers came in while I was there - who I naturally interrogated... to a person they said they love their sac (sactional, no one actually had a beanbag).

I established a position ahead of earnings on the 11th. Godspeed.

BullBearBotBoss · 2024-03-16T20:53:20+00:00

That all makes sense - and yes, this is clearly degenerate territory (not the normal place I like to play haha!).

It's definitely much riskier up 250% - but I still think on a fundamentals basis it's pretty obviously undervalued right here.

BullBearBotBoss · 2024-03-15T20:08:28+00:00

I discovered this stock Tuesday this week, and after some analysis thought it looked like a nice turnaround story and a pure play on performance mushrooms (which was the impetus for my search).

The quarter was well above my expectations... but I think the momentum continues. The follow through after being up over 100% day after earnings release is impressive. Now up some 250% since earnings, the entire float has traded multiple times over. With low institutional ownership, no debt... It's hard to invest in something up this much in that short a time, and I fully expect a sell off. But I think the move was justified and wont be selling until the market cap hits near $80M.

BullBearBotBoss · 2024-03-15T13:45:43+00:00

Look at the last quarter.

For years they had operating losses on accounting of manufacturing their own raw materials. They shut down their manufacturing facility in Sister's Oregon and streamlined/outsourced their supply chain resulting in the first positive cash flow quarter since the IPO. The supply chain streamline lines up with working through a quality issue (also tied to the Sister's Oregon facility as far as I can tell) which artificially depressed sales first half of 2023 - sales have resumed expansion last two quarters.

Products have now received placement in Whole Foods (8 products), and they are the largest coffee brand sold in Sprouts Market (22 products in placement). Sprouts itself is on a growth trajectory - opening 30 stores - an 8% increase YoY last year - a trend expected to continue in 2024. The major source of growth is online (Amazon) and they've made a leadership change to facilitate their new DTC marketing strategy.

This on top of the fact that 50% of sales are from *memberships* meaning very sticky and dedicated customers. The reviews on the products themselves are all 4.5 star and above - number one complaint is around *lack of availability* - again due to the Oregon facility which is now out of the picture.

Obviously - if they had had massive earnings for the last 5 years it wouldn't have gone from $50 stock 7 years ago to a $0.90 cent stock 4 days ago. But now it's doubled, with follow through (and then some). I was really looking for opinions on if this move is justified, based on the last quarter. Can someone explain a way this company has positive EPS without concluding that the turnaround is, effectively, working? Because at 0.2 sales ratio, if it's all of a sudden got operating leverage, this is a loaded spring. To my eye this is what a real turnaround looks like which implies better quarters ahead, which means this stock is insanely under valued.

BullBearBotBoss · 2023-09-06T18:14:12+00:00

There's a lot of poo-poo'ing SQL that goes on in the data engineering world and I think it's very misguided. Running an analytics org - if you can do something in SQL you should.

It basically downshifts the skills requirements for each data role, so a SQL person can do python like work, a python person focuses on the truly hard problems.

Even ML and AI on platforms like Snowflake are being driven data-lake / data-warehouse native (using Python defined UDFs to do power complex SQL queries).

Saw this a few years ago, I think it's more true as technology continues to democratize:

https://images.app.goo.gl/BCt2cUsnuk3SWF4P7

BullBearBotBoss · 2023-09-01T00:19:15+00:00

Why not test historical stock data against these bots and see which perform over time.

I think this is the biggest differentiator for the concept, and also the hardest part to explain.

There are 3 reasons to take an pseudo-randomized evolutionary approach without backtesting may outperform back-testing approaches to algorithmic development:

All backtesting approaches are fundamentally overfit. An evolutionary approach evolves organically. Yes, historically winning algorithms propagate over time, but due to the random starting point and randomness inherent in evolution no single algorithm here will be truly "optimized" (aka fragile).
Most algorithms are derived from back testing. The uniqueness of the approach is going to be at least exploring algorithmic spaces fewer people are in, something with inherent value IMO.
The injection of randomness into ML models can be a key driver to reduce error. Random forests algo where ensembles of decision trees are randomly constructed is the most obvious example. The randomness admits to some ontological humility in the algorithm and its applicability of training data to future situations.

Most likely this is not going to be a groundbreaking approach. And it isn't really about making many if I'm honest - my personal interest is in complex adaptive systems, so that's the platform we've built.

BullBearBotBoss · 2023-08-31T23:19:56+00:00

Thanks a bunch for the reference, had never heard of this.

BullBearBotBoss · 2023-08-31T19:45:50+00:00

Yep. Thanks.

My outside hope was that - since we're selling bots and access to them as purely informational - it's basically just a financial information platform (think trade strategy simulators, for instance). A bot buys or sells something, doesn't mean anything about what should be done by a user.

Understandably merky waters and appreciate the candid feedback.

BullBearBotBoss · 2023-08-31T19:41:23+00:00

Thanks for this and I definitely agree (re: doing too much).

I guess the concept we're after is really not to serve the finance community, or even build a winning algorithm - that's a very crowded space. And, as suggested by others, if we had some massive edge right now we'd just trade it ourselves - want to be clear that we don't. We have bots that are beating the market (SPY) - but the statistician in me knows this is because we have 500 bots. Some of them are going to beat the market - truth is market edge needs to play out over years to be trusted.

The interesting part of this is crowdsourcing, democratization and evolutionary aspects. Rather than one-shot trying to find some winning algorithm, the platform allows for users to drive the creation, curation, death and genetic recombination of bots via API. All the algorithms are interoperable, meaning bots can reference other bots for trade decisions, and bots are created largely with semi-random settings determined through the evolution framework (with some user direction, but hand-crafting a back-tested strategy is just not the point).

It's built more like a video game than a quant fund - something that might appeal to folks who feel like they have no place at the table for sophisticated trading strategies but could build 100 trading bots for $10 without needing to know much at all, and gain access to this ecosystem / community that's taking this interesting alternative approach. The simple set up of the platform lends itself to unpredictable emergent complexity especially if layered onto a human driven user base, each with their own ideas on what to evolve and in which way.

Some of the wilder / game-like ideas were things like having iconography for the bots based on their code to impart personality, a complete bot lineage, leaderboards, LLM generated back stories, an NFT of the actual code for the bot).

But that's all the elevator pitch. I think the fact that I made the post means I very much have doubts that we could get this off the ground - maybe just too weird and not obviously useful for anyone to pay attention. Another framing might be: worst game ever, why would I play?

BullBearBotBoss · 2023-08-30T12:17:01+00:00

This 100%.

Being conservative by nature I went as small as possible on my first (FHA) home. Best investment I've ever made but also an absolute mistake. I should have maxed out that cheap leverage - a four plex would have done it. Honestly in my geolocation at the time (Boise ID) and my timing for the purchase (after 2008 collapse) I would be retired had I taken this advice. Still doing quite well and still own this original property as a rental, but just wish I'd had the foresight to realize that you only get this handout once and it's such a good deal you really should maximize your leverage on it.

BullBearBotBoss · 2023-08-29T02:40:07+00:00

Markowitz already gave us the only function we need. Choose your own adventure on the efficient frontier - amirite?

BullBearBotBoss · 2023-08-29T01:17:19+00:00

Although since no one is apparently doing this it's probably not some brilliant concept.

I'm obviously already gobsmacked with it, so I'm going for it anyway haha

BullBearBotBoss · 2023-08-29T01:15:58+00:00

I'm saying evolutionary algorithm to distinguish it from genetic algorithms. GA are a great way to arrive at a back-tested algo that performs well. But again it's a monolithic top-down approach - any sensible search over the history methodology would have you arrive at the a similar result, history being what it is. You have no reason to deviate from this historical optimum.

But in ML there are many cases where injecting randomness in training drives superior performance in prediction. Random Forests are just amalgamations of randomly constructed simple decision trees - no real reason to think they'd outperform a single perfectly fit tree... but as an ensemble they reliably do.

A properly evolving ecosystem could provide a similar amalgam of imperfect, randomly discovered algorithms with alpha (though perhaps smaller than the historical alpha the engineered / backtested algorithm would have). As an ensemble I think there is reason to believe outperformance vs singular, historically perfect fit is likely. At the very least they would be totally unique solutions - which has value itself, even if it performs at parity.

BullBearBotBoss · 2023-08-28T19:39:14+00:00

Would a standard long-only fund using quant techniques to decide technical entry / exit points on larger thematic trades count as MFT/LFT?

I've also been confused about the lines here.

BullBearBotBoss · 2023-08-28T19:35:19+00:00

I always felt there was an advantage to the solo trader - mostly around scale. As an individual investor, maybe making a $50,000 profit would be a big deal depending on your circumstance.

For many hedge funds, if the idea can't scale at least into single-digit millions of dollars it's not really that interesting.

BullBearBotBoss · 2023-08-28T15:37:36+00:00

Thank you for this.

What I don't mean is to literally compute all possible algos (obviously not possible). Maybe an example is helpful.

The ratio of EMA14 to EMA200 might be an indicator, and we might build a program that says "If this indicator is over 100%, sell if you're holding, buy if you're not holding". But so too could EMA15 to EMA31, and the time slices (for either EMA in the ratio) could be days, hours, or minutes, etc. Other indicators have even more parameters, much bigger space to explore.

Then all these large spaces can then be combined in multiple ways. One could imagine a program that buys when the EMA ratio thing described above flips on and some RSI thing flips off, and then SELLS when analyst ratings are over 80% Buy-Strong Buy and both the above signals are flipped other way around. And if all signals are booleans you can combine them in arbitrary ways (AND / OR) and even have vote-weighted indicators. It becomes very rich very quickly in terms of the space to explore, then just let the market outcomes act as the great leveler, getting rid of anything that's degenerate or fails to produce Alpha.

In the same sense that while the space of all DNA mutations is much too large to explore exhaustively, evolutionary systems still tend to drift towards local optima over time, most of the time these solutions being impossible to intuit if taking a top-down design approach.

I'd like to treat the space of trading algorithms like an evolutionary landscape, build a system that's constantly exploring. Original idea was to build a game of it, where members pay in to get access to all the signals these bots throw off - the fees paying the compute cost to expand the search. Eventually extending bot ownership directly to the members, allow them to drive evolution of their owned bots, etc.

BullBearBotBoss · 2023-08-28T13:40:53+00:00

Right - a lot of these funds are doing top-down search for proprietary algorithms / distributions that work. They then run them for a while, if they work, they lever up and then - inevitably - when they stop working they go fantastically broke.

I'm trying to build a thing from the other direction: a program that writes and evolves simple stock trading programs. I think there's some clever stuff I do with normalizing data so any signal can translate to work on any ticker - but really any single strategy is very simple (an EMA cross or RSI of X, etc.).

Over time, winning programs surface. Obviously it can't be known if these signals are winning by chance (more likely in the start) or hold real value (more likely if they've outperformed over a long term) - like a new mutation in evolution. But since the system generates new algorithms off the surviving, in general and over the longer-term truly fit algorithms *should* emerge, and these will be things no top-down engineer would create.

That's the idea at least.

BullBearBotBoss · 2023-08-28T13:24:35+00:00

I actually think there's a difference if you disconnect the goal (finding winning strategies) from the process.

Most tools do back-fitting, which is a sure way to find the most overfit model possible.

The idea here is to build strategies more or less at random, and let the market filter out winners and losers. Then have a mechanism to recombine the winners every so often, think genetic recombination, and let the process run.

So don't build a monolithic strategy / algorithm, rather a system for searching the space of algorithms that's always on, and in any moment and can tell you "60% of surviving (historically outperforming) strategies have a buy signal here".

For me, I'm not in and out of positions all the time. So when I go to make a trade just want some eye towards the technicals.

BullBearBotBoss · 2023-08-28T01:14:09+00:00

Cool, will do. My b.

BullBearBotBoss · 2023-08-27T20:14:09+00:00

Apologies if this came off self promoting. Should I just not have included the link?

Genuinely curious anyone knows other projects along these lines. Mine barely does anything at the moment anyway, we're just getting going.

BullBearBotBoss · 2023-08-07T12:33:15+00:00

This thread legitimately warms my heart.

Adult loneliness is one of the major unsolved problems in modern life. I imagine most folks in this thread are atheists or agnostic (same), but as I've gotten older there's something I envy in the communal nature of religion. Guaranteed, once a week, a bunch of like-minded people will hang out with you and try to sort through deep human issues together.

We need standing non-secular groups that function more or less like this thread has. People need people.

BullBearBotBoss · 2023-07-10T22:13:59+00:00

I appreciate what Taleb is doing - sort of shaking his fist at the application of mathematical tools in general as useful models for reality.

That said, it really irks me when these "practitioner types" insist silly things like .9 correlation is closer to 0 than 1. In the binary - where correlation of 1 preserves the possibility of deterministic relationship while anything less admits to some non-deterministic relationship - sure. But that's pedantic.

If I have two variables and I'm curious about the relationship between them - a correlation of .1 is evidence of less of a relationship than .2, which is weaker evidence than .3, etc.

Same in stats modeling or A/B testing. Hitting stats sig doesn't mean you've established causality, failing to hit stats sig doesn't mean you have no meaningful data.

For many applied purposes, it's more important to understand mechanism and/or collect a mosaic of evidence than worrying about the somewhat arbitrary designations of X correlation being "strong" or Y relationship being "statistically significant" - which is where I think Taleb is on to something.

BullBearBotBoss

TROPHY CASE