ML

sin94

Well all the layoff you heard are only from the highly successful companies (only Apple being the outlier). The economy pace slow down is getting slower and once projects/budgets dry up smaller and midsize operations are also going to start downsizing. We need more innovation and R&D to spur more growth. Unfortunately AI / ML announcements seem to head towards reducing resources (people) and more on efficiencies.

thesneakysnake

Listen you highly regarded Wendys employees..... Right this second is the Nvidia GTC meeting. It's been going on all week. All you have to do is look at who is attending those meetings and what they're selling. AI/ML and advanced compute. Yea yea, little billy can play the latest AAA game on 8k resolution but that is the small shit to keep you in the dumpster behind Wendys. The real story is who are at these meetings, how many industry leaders and government representatives are there? Spoiler alert, system integrators that develop shipping, defense, geospatial, etc solutions. And all that stuff is only getting better with Machine Learning. Nvidia knows this and that is why they're really selling to people that don't buy a 3060Ti so they can play burger flipping simulator. They sell to people that buy BUILDINGS full of A550s because they need to do crazy deep learning shit. ​ \*Disclaimer - I am a long-term bag holder that is too chicken shit to buy puts on Nvidia. This is not financial advice.... this is a Wendys sir.

BreachlightRiseUp

If we do see a market crash soon they’re one of my largest buys, their CUDA cores are the backbone of basically all ML/AI work in both production and academia

elpresidentedeltoro

For sure, I am the creator - So can answer any question you need answered about the math, data, back test results and what is on deck. Everything has been vetted by ML and DS. Mind some of the clunky UI on the auto scaling issues, as we have been primarily focused on back end and data integrity (which is all there). cheers

enav7

Magic ML,Nets ML, Celtics ML = +601. Ez six bagger

hydrocyanide

>ML isn’t necessarily about patterns. >In this case it’s about weighting probabilities That's what a pattern is.

itskelena

You don’t have to find pattern. And there isn’t one. ML isn’t necessarily about patterns. In this case it’s about weighting probabilities based on market conditions and ML algorithms could be good at this.

10xwannabe

It won't. I don't understand why folks have such a hard time understanding this. The data has been known for about 50 years now. Stock prices are RANDOM. Prices of one day does not influence another day. There is NO PATTERN in returns. That is why it is PURE speculation when you are trying to guess price movements. So AI is not going to help. That is why Wall Street with all its money spent on computers and algos and double masters/phd guys from MIT are not any better then an index investor. There is NO pattern to exploit. If there was an advantage it was high speed trading/ high frequency trading. But tech and regulation evened that out just like efficient market hypothesis said it would. So even if AI somehow did again EMH will even it out as well. You have to have pattern to exploit for ML to be advantageous, like reading CT scans to diagnose breast cancer, for example. SP are random and don't have a pattern. It is like saying can AI predict a shape of a snowflake falling?

The-Unkindness

A 2019 study showed 92% of all FOREX trading were algorithm computers talking to algorithm computers. No humans were involved at all. A 2018 study showed 80+% of stock trades were done by algorithms without human intervention. Today, both are higher. AI has been trading the market for over 20 years. And if you're referring to "AI" as "GPT models", then you're out in left field. LLMs don't trade stocks. That's not their purpose (or even strong suit). Even if you taught one to it would be **insanely** slow compared to the optimized ML models used today.

awwfishsticks

So, just to let you know, Intel’s first generation of graphics cards go toe to toe with Nvidia’s ML chips. Intel A770 vs Nvidia NV A10. Those chips also compete relatively well with the Nvidia 30-series of graphics cards for gaming. AMD can go toe to toe with nvidia for gaming. Apple is designing the best chips for mobile devices and personal computers. I don’t know where you think Nvidia will dominate, but there’s not as much space as you’re hoping.

YngChap

Auburn ML is free money. Houston looks like they have a hardon for getting BULLIED.

stillworkin

I went to the comments for the same question as you; as someone who teaches Data Science/ML etc, I was perplexed by this insufficient graph.

brooklynbest97

Fade Reddit = Furman ML

barbaro1966

AI/ML is the hot space. TRY CDIO OR IBIO> low float uder 25 million shares. good news will make them jump higher

AdMaleficent2789

did you bet the ML

Vast_Cricket

My prior experience is my personal advisor was fired, manager ditto. ML San Jose office was no longer open. My S Barney account lost was excessive. These were all reputable major brokerages. I cashed out all my funds put in Treasury ibond and an annuity account. Today both are actually do well. When you lose faith with investment you move on to something that is safe.

jcoffi

What ML types where this is true?

jcoffi

The only ML where that's the case is symbolic regression. That's black box. The rest, they know. Even with the black box, you can track how the AI came to the conclusion.

Aarschotdachaubucha

That's normal for a lot of experts. The thing that neurologically makes experts what they are is encoding the reflexes and intuitions of their logic and movements into their unconscious neurology. When you have to think consciously about how you're doing something, it slows you down, and inhibits your ability to make a correct decision. That's why we have tropes like, "trusting your gut", because we're really bad at understanding how much our neurology affects our ability to do things. This was a topic of heavy study in the late 1990s the last time AI/ML was a fad. The killer technology then was expert systems, which people thought would replace doctors and other professionals working in diagnostic situations. Turns out that getting experts to relay how they were making decisions so some tech-geek could code it up in a decision tree was near impossible, and even they did explain, they bypassed many of the idiosyncrasies that made their particular diagnostic process successful.

TheKingInTheNorth

With access to employees who work for a Chinese company (that is basically the CCP). There Will Be Blood has an apt metaphor to thinking physical data persistence location makes a difference here. That and access to the data isn’t required to drive consumption recommendations with ML models.

Frosty_Trader_

Starting to build a position in Moneylion (ticker ML). Had phenomenal earnings yesterday

Gassy_Bird

I’m in AB ML personally but it already went up quite a bit on recent positive news.

dafazman

What about BofA/ML? I don't seem them on your list...

Mindless_Aspect2335

Dat python ML doe

jcoffi

First, most of the indicators I use are longer term indicators. With the exception of the MACD-V, for me time is measured in days and not anything smaller. Too much noise and regime change on shorter timescales. I'm always looking for new data. So much time spent on data: Finding data* Estimating potential value* Cleaning/Normalizing data Generating features Extracting the best features Determine actual value Storing the data for later use *Manual processes For indicators (off the top of my head): MACD-V: ATR weighted MACD is more accurate than RSI, PPO, or MACD. That's not my opinion. It's a published study with math to back it. Using the atan of the vix weighted curve of the T10Y3M. It's more accurate than the yield-curve alone for determining the likelihood of a recession as well as where we are in the business cycle. I used genetic symbolic regression to find a formula that uses the measure of the covariance of returns for the price of treasury bonds to determine the markets direction (That's the one I'm talking about earlier in the thread.) But I'm not giving that one out because it comes with the inherent problems that come when using genetic symbolic regression: I don't understand how the formula works. I just know what it does. My cluster needs more power and/or to be more efficient before and I can dig into the equation to understand things before I put it out there. Another thing I'm tempted to do is use ML to read through all the academic/finance papers regarding the market and convert them into python, determine the best means to test them, and then test them. Highest ROC's are saved. But I need to build a rig with two 4090s before I can do that and I don't want to take that money out to do it when I'm already making enough. Also, check my Reddit profile, I mention a few there too.

Travis_Beckmen

I bet 125 bucks on Madgolivishi ML before the fight, or what ever his name is. I knew yan wouldnt pull it off.

FloridaMann_kg

You need a ML program to tell you there’s a low probability for the rus to trade -700 pts in a month? Also you know how to code a ML and best you can come up with is risking very large amounts of money for a very small payoff? Interesting…

Illustrious-Age7342

Intel seems to be turning things around a bit under Gelsinger, but they are not going to recapture node process leadership any time soon. And their competitors are ascending ARM/RISCV > x86 | AMD > Intel | gpus > cpus (for AI/ML) As a technologist there are few companies I am more bearish on. I’m wondering why you, as an investor with a different perspective, would want to own this company

__lawless

The do pretty much have monopoly over AI hardware. But it is not just hardware. Way back in late 2000s they realized the potential that GPUs have for computation and started creating CUDA software toolkit. CUDA empowered developers to create powerful Machine Learning codes that leveraged GPUs, early 2010s. That is when ML caught on fire. So nowadays 90% of ML code infrastructure uses CUDA which only works with NVIDIA hardware. You can see that if a new hardware wants to compete with them has a giant hill to climb. Google tried creating their own hardware TPU, but it did not caught on outside of google. That is not to say the startups that are working on new hardware for AI are doomed, but NVIDIA is way way way ahead.

Bostonparis

I personally like Google more, but realize it is the riskier pick. Also worth noting, this comes from the perspective of someone in their early 20s. So I have a bit of time to let it play out, and have more of a risk appetite. **Pros** One of the reasons I like Google is because, as more of the world comes online for the first time, there's a good chance they'll use a Google product or service. As of January 2023 [64% of the world is online](https://www.statista.com/statistics/617136/digital-population-worldwide/). Meaning there are currently 3+ billion people that do not have internet. You could argue the same thing for Microsoft. Won't more people coming online mean more people buying Windows subscriptions? Yes, that is true. But I would argue that user acquisition is much easier for Google, as nearly all of their services are free. So these new users will become new eyeballs for advertisers. Another reason I like Google, is the subsidiaries that their parent company Alphabet holds. More specifically Waymo and DeepMind. **In my opinion,** driverless cars will be big in the future. Google may not win this race, but they have a headstart. Their driverless taxi is already operating in some locations in the U.S. Still a long way off, but it has the potential to be a large revenue stream in the future. Google Deepmind is also interesting. I'm sure you have heard about ChatGPT and how Microsoft integrating it into Bing will be a Google "killer". While ChatGPT is impressive I don't think it is enough to kill Google (**I could be wrong**). There is quite the A.I. hype right now and Google Deepmind is a good contender. They have made major accomplishments over the last decade, that a lot of people in the ML field did not think was possible for many more decades. Mainly [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo), [AlphaStar](https://en.wikipedia.org/wiki/AlphaStar_(software)) (StarCraft 2), and [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold)(protein folding). **Cons** The biggest con I have with Google is that their advertising revenue makes up nearly 80% of their total revenue. Meaning if something were to happen to their advertising business, such as more regulations being passed, it could severely affect revenue. That is why I am *hoping* that they can diversify their revenue more. Whether that be through Google cloud or one of their subsidiaries above making it big. ​ Sorry about the wall of text. I ended up writing a bit more than I intended. By no means do I know what the right decision is, but hopefully my reasonings aid you in deciding. If anyone agrees or disagrees with me, I would love to discuss.

Hairy-Thought6679

Duh. Gotta make the gains Risk? Yes. More please It’s max $10 profit/trade based on an ML algorithm in trading view. I’m trying to bolster my weak $500 balance while I slowly deposit more each month. No point in going long on anything and I’m not educated enough to yolo any puts just yet. After much thought I’ve determined this to actually be the least risky option until I have something to yolo and post for loss porn next year.

ritholtz76

BofA (ML) has buy rating on this stock. Then this happened.

elpresidentedeltoro

My edge is volatility skew. I call it juice because it makes sense to think of it that what. Juice, meaning, what option has more premium compared to normal. There are a 18 ways to slice it and then is only if you use IV vs Mid. But let me keep it simple first. Let’s say you look at a 1 standard deviation otm option expiring in 5 weeks. What is the mid on that? And let’s say the mid is 1.00, well my question was - is that high or low? Or is it normal? In other words, if I were to have the data on 1 standard deviation options expiring in 5 weeks on stock zyx over the last 2 years - is 1.00 high, low, or normal? The reason I thought this would be helpful is I noticed 5 times when there was unusual large juice in an option series, on the call side. Typically, I like to sell options spreads, but 1 of 10 short options spreads tend to go max loss. I was always trying to study everything - macro, stock specific, earnings, etc and learn when that stock was due for a larger move than average and either turn off my short spreads or sell the other side, but that was much much harder than I thought and there was always something I couldn’t see. The worst part was - I would end up sitting out of a few trades in a row, which means I was missing earning potential on short spreads that’s I could have closed won which in turn hurt my overall p/l. And then I noticed juice - and saw everyonce in a while that juice was so big on one side it was easily recognizable that it was massively skewed. On these events, I decided to stop selling spreads, and follow the juice and do the occasion long, and boy did it pay off big. The problem was it paid off so big, that that is all I wanted to hunt for. So then I would churn my account with low percentage moon shots because I thought I was seeing massive skew, but the fact is, each stock is different, each expirations is different, each level of moneyness is different. And it was too much track it all by hand. So I looked to buy it. The problem was CBOE would sell it for $1700 per month and then I would have to pay to store it, and pay to access it, pay to move it in and out of cold storage. So I built it. It took me about 18 months. I had to rewrite the skew formulas because CBOE uses theoretical inputs to clean the data which smoothed out the anomalies. 14 versions later - I had it, I called it Moon Juice. And then I decided to enhance it, so I brought in a data scientist, a machine learning engineer, a software engineer, and a financial analyst - the project grew legs, now I believe I am on the war path to reinvent analysis for myself and others. I decided to stand up a minor league hedge fund, and built a whole dashboard to include skew by mid, iv, expiration, moneyness, and the Greeks with highest predictive power. Those are gamma and theta, but then I had to do the same thing with those. I wanted to see each Greek alone and compare current values with trailing highs and lows to easily spot anomaly detection. I wanted to be able to look at previous events with extreme volatility - so I had to stand up a data base and figure out how to configure lambdas to keep it fast and allow for multiple securities, up to 89 now, planning to rewrite the whole code base to allow for me to have full market. I also worked with the ML engineer to write something called a strike selector, so everytime I see juice make a lot of noise on a specific slice, I run a binomial engine over black scholes to run theo calcs on full chain and see which strike and expiration would have best % move. I like to look at inflation, yield curve, retail sentiment, so put those on the dash as well. Not much, but I don’t like to actively day trade, I found my edge when I slowed down and analyzed all this every couple days to see if anything is changing.

lotus_bubo

Yeah MSFT is solid all-around and is my favorite. They have great data, and can even use their unsold GPU time to train their own stuff. Apple is so secretive it’s hard to say. The M1 and M2 are surprisingly good at ML computation, so that could be a clue they’ve got some ambitions in the space.

Always_Late_Lately

yeah they have an unreal capture on the AI/ML pipeline from what I understand.

Echo-Possible

Ah I see you have nothing of value to add here. Everything I said about ChatGPT was factual. The ChatGPT model is absolutely massive and costs more per query than can be made from ad placement. It's a losing business until compute and energy become cheaper. That's why task specific models fine tuned from smaller LLMs are a much better proposition than a one size fits all LLM in terms of viable businesses. In fact they're used all over the place already. I actually work in ML and can confirm transformer models have been rampant in NLP applications for like 5 years already. OpenAI just made theirs bigger and used human in the loop reinforcement to make its answers sound more plausible. It still hallucinates and makes things up and is only trained on a pre-2021 corpus. The fact that it's bigger just means it uses more compute and energy, making it economically unviable (today). OpenAI gave up 49% of their business and 75% of all profit until Microsoft recoups their investment in exchange for 10B. Why would anyone agree to that if they were on the verge of upending a multi trillion dollar company. Please feel free to educate yourself and come back with anything useful that may dispute those statements. You obviously haven't done your homework.

BoxGrover

It matters a great deal in banking decisions esp for generating adverse codes and complying with fair lending laws in the US. In other countries all risk models must be validated and audited. Causality and explainability is a must, which is why ML is having a hard time.

wind_dude

No, not sand banging. Google focused on AI/ML that would add to their existing products a bit of an anchoring bias. OpenAI and chatGPT were a little more out of the box, and very much focused on the developer experience, and generated a ton of media buzz. They essentially got first move advantage. Technically the most recent products are pretty much equal, but google is lacking API integration, which is a large over site on their part. OpenAI is exploiting this by offering their chatGPT api at insanely low prices, considering running it on your own or training your own model is not monetarily realistic for any startup.

2-Sigma-Penis

My real car is an ML-350. I bought a Nissan Quest just for Uber purposes.

elpresidentedeltoro

Only took me about 10 years to pull off. Went through 3 “teams” before I found the right group. Found the right software engineer, ML engineer, data scientist, financial analyst and myself as the go between all parties. But I’m damn proud of it. Made some great money trading off of these tools that I was calculating hand for a while. Not enough to retire yet, but enough to consistently make more than a highish paying corporate gig. Last year was entirely focused on building the full dash. It started with something I called moon juice which isn’t that strike selector thing I was talking about, that came later. Moon juice has about 14 versions before we got it right. And then I found that cboe offers the same thing but they charge $1700 per month for it! So that validated all the hard work. Then I built the other tools - strike selector, working on v2 of that, breakevens, full Greek visuals and historicals, and something called Greek moons which is like a Super Greek analyzer tool. Couple other things. I won’t share the code because then some hedge fund can just pretend to be one of us and rip me off, but I can share the dash. I have everything open to the public completely free on SPY. I have all the data for the entire market, but it costs me quite a bit to get the data from cboe, store it, and the lambdas cost money everytime something is clicked on - so I ask for folks to kick in and help cover the expense if you want it for me than the SPY. But SPY I’ll keep free forever and always in the current state. The data points are end of day which means if you’re an intraday trader it won’t help much, but I’ve found I do better when I swing trade. The cost is absurd for intraday deliver and intraday data points, but the way I see that, that’s another game entirely. Maybe I’ll be able to afford that and it’ll be worth it some day, but not now. I have a few folks that have reached out and want my team to build that for them, but I have to make it worth my time and my guys time. This has been a side project that is starting to turn into a full time thing. So just being transparent and cautious not to overstep. Happy to share everything I’ve learned. I figure if I help some of you make a few bucks and this things grows legs, we can (big dream) make a version where we can afford to create trading directly from the dash and if everyone covers their own expense then we don’t have to do the RH PFO bs and seek out people’s interest. I’m rambling now, but I don’t think they intended to do that, I believe they started off with the right belief and wanting to help - I read they, the founders had a thought “if e-mails are free then a trade should be no different.” It’s fair, and is true, but they went a step too far and never realized what could happen when you suddenly give access to millions of people and gamify trading in todays day and age, they eventually all go into the same thing and then the DTCC forced them to basically pick sides. And since the DTCC had the power to shut them down their decision was made. All that to say, I/we have learned from the mistakes of the giants before us and have an idea of how things could be done better or different. How does the proverb go - The first guy through the wall gets bloody. Make no mistake, we are infinitesimally small and might always stay that way. But I also know there is the chance that we could suddenly go viral and next thing you know you have a Keith Gill situation on our hands. I want to stay ethical but true to our roots. The fact is - no retail traders can afford that 1700$ data from cboe, and if it’s that much and exists - it must be worth it - so how can we create a better version of that without stealing their ip - and that’s where we created our own formulas, algos, feeds, and tools, but I don’t feel comfortable open sourcing the code, but I built the tool for anyone who is interested. If not, just know this - I believe the option chain can and sometimes acts as a leading indicator to the stock, so whatever you trade, even if you don’t trade options, I believe, and my research confirms again and again for this to be true, so weave the chain into your analysis, and there’s the rub. What is the best way to do that? All the sudden you realize it’s a behemoth of a task and very quickly it isn’t useful because there is so much data. I’m not claiming it’s 100% or a guarantee, but an edge is just that, and edge, not the whole thing. Anyhow - that and 10 cents will get you a cup of coffee. Cheers - my fellow option enthusiasts 🍻

POWRAXE

I suppose we will see. The real question is how can a 2 trillion dollar company double its market cap? My gues will be through expanding into emerging markets via AI/ML/QC

BullBear9

A question to ask ourselves here is, what differentiates two companies from a qualitative perspective? Once some differentiating features are defined, it is straightforward to feed those features into a recommendation ML model.

Due-Educator5848

I have a few locks. Celtics & Warriors ML tonight for NBA. Arsenal ML tomorrow for soccer.

MarkHathaway1

First people have to have money to buy things. Second, more robots to earn profits for capital. Third continued good relations with the world of workers outside America. Fourth reducing the work force somewhat over time by giving time off and other non-taxable benefits like a 4-day workweek. That may eventually lead to a robot economy with people simply being given an income (see Yang and others who have theorized this for many decades). A lot depends on AI/ML and robotics and other technologies. We don't know what we don't know (and all that).

lotus_bubo

MSFT owns half of OpenAI, who makes ChatGPT. They also have Azure, the second most popular cloud provider, which people use to train AIs with leased GPU time. Their unsold time can be used to train their own AI projects. NVidia makes the GPUs everyone uses for training. CUDA (NVidia's API used for general GPU programming) is standard and optimized for TensorFlow and PyTorch, the two most popular AI platforms. There is support for AMD GPUs but they're just not widely adopted. TSM is the foundry that makes all of NVidia's chips. Enough said. Amazon is the most popular provider of cloud services used to train AI. Google has the third most popular cloud service, but it's a pretty distant third. They had really big gains early on in ML, but have either fallen behind or are tight-lipped about their latest efforts. META has a solid AI team with some top talent, but they haven't revealed what they're really up to. The big advantage they'll have is data they can train with. C3.AI is a piece of shit company that makes AI development tools nobody uses. It's still the wild west in AI. I don't think we've seen the big winners on the software side yet, so VC companies like Andreessen Horowitz might make a fortune betting on the right startup.

lotus_bubo

I'm the most salty hype-averse skeptic, and I spent years shitting on AI because of all the people freaking out over robots taking jobs... ChatGPT is actually revolutionary. So a big thing happened, and it's not well understood by people unfamiliar with how ML works. ChatGPT started out as an attempt to make a Turing testable chatbot, but as it was being trained, an unexpected capability emerged that is a game changing. It developed the ability to perform tasks when directed. A general purpose AI that can perform work based on natural language instructions has been an unreachable holy grail, and this stupid chatbot lucked right into it. There's nothing so special about ChatGPT that can't be duplicated, everyone is now training up their own language model AIs to compete. But we're just seeing the first taste of what these things are capable of, and they can be improved just by throwing more GPUs at it to train it, and by improving the training itself.

VRT333

Drop the ML GitHub expeditiously.

Legalize-Birds

"ML" probably being "Machine Learning"

POWRAXE

ML = Machine Learning I agree. Very unprecedented times. Another factor I think is worth considering, is the amount the US has profited off of the current land war in Eastern Europe. Two of our largest industries are Defense and Energy. Europe is now buying American gas instead of Russian, and we are selling weapons like hot cakes, not just to Ukraine, but to many panicked nations in neighboring regions as well. Call me crazy, but I believe in the possibility of a soft landing in 2023.

MarkHathaway1

What's the "ML" part? I agree that aside from the old stuff like warfare, we are on the verge of some pretty dramatic technological changes. EVs everywhere if batteries come on strong as they must and AI will gradually and then suddenly be huge. Other changes may be in the air, but less obvious or impactful. The combination of things today is much different than other times except perhaps just before or during WW I where there was the industrial revolution and stupid war and only a glimmer of economics understanding.

POWRAXE

I would argue that we are on the precipice of another technological revolution with AI/ML. I know AI is the trendy hype word right now, but I believe the world as we know it, is going to fundamentally change in the next decade, more drastically than any time in recent history, and more significantly than anyone could possibly imagine.

FeelTheFish

I mean, it depends on how well you describe stuff and how you use it. And we are just at some point of this exponential AI growth curve. I remember when I started studying ML, this kind of models felt unachievable. Many papers over the last 10 years have unlocked where we are standing now, and there are many more being worked on rn. I had pretty good results for making all kinds of code with ChatGPT, from implementing backtesting on financial assets (The boilerplate, but It was good enough to just tweak variable values). The most amazing thing I've seen it do is describing that I wanted a sphere in the center, with another sphere rotating at a variable speed, both shaded using lambert functions and a directional light. Got it right in the first result lmao

value_added_bullshit

Igor is awesome. I've read the stuff he did at Deepmind back when they were really pushing the game-ification of ML. Musk was an early investor in OpenAI, he didn't found it. Looks like it could make for a good startup.

dajar88

I'm also very bearish on the "AI revolution," but I'm a Data Scientist who often does machine learning, and I don't completely agree. AI might not be a well-defined term, but I think it's generally understood as "using machines to perform tasks that require intelligence." ML is a subset of AI, and ML goes farther than just doing analysis. Obviously an ML model doesn't "think" like we humans do, but the term learning is quite accurate. A lot of systems don't just memorize analysis, they show a remarkable propensity for absorbing information from data and using this information for other, new tasks. AI, as I've defined it, surely does exist. Most companies are exaggerating when they talk about their AI capabilities, but I've been working in the ML space for a while, and there are plenty of companies actually building real products and doing business by leveraging Machine Learning and AI.

Trostis

Thanks OP. I'm a long time user of portfolio margin (IBKR), and have never seen requierements as low as that...that's why it caught my eye so quickly. Using your example, regular margin would be something like $210K (25% margin on $840k notional); portfolio marging would still be something like $30k ($210k/7). Anyway, good to know TDA is such a good alternative; on their site ([https://www.tdameritrade.com/investment-products/margin-trading/portfolio-margin.html](https://www.tdameritrade.com/investment-products/margin-trading/portfolio-margin.html)) they seem to hint only 3-4 times lower requirement though. Could you, please, elaborate further on my second question? What does the underlying ML algorithm do, other than compare historical performances?

bretskigretzky

60 years and multiple interest rate regimes will spit out nonsense. Way to train your ML with a scope as broad as your balls - you gotta have stones to take on unpaid risk like that and say that the past is a predictor of the future. God speed

Trostis

Out of curiosity, what broker do you use? Only $4140 margin for 100 puts ($840000 notional) is incredibly good. Then, nice write up, I use a similar methodology to determine strikes, but calling it ML might be a stretch…what does your algo do other than comparing historical performances against current? Honest question out of real interest in the subject.

FloridaMann_kg

Lost me at ML and “elidiot wave”

Jstrandt

Yes bubble like the iPhone, home computers, and the internet. I had chatgpt write an employment contract yesterday that was better than the one I bought from rocket-lawyer. After that I had it write a unique song, poem, and business doc. This iteration of AI will exponentially. I work in Cloud focused on this exact AI space and the massive amount of compute and memory needed will create the next large growth round of companies like NVDA, Intel, etc... Cloud has largely been capped by the lack of enterprise ability to adopt it for the true massive resource it can be. Natural language AI with advancements in ML will reshape how we live everyday. Couple this with connectivity to networks like starlink give us the first ever always connected AI based “minds”. The consumer piece of this is much larger than the internet as we know it. Every aspect of your life will benefit from AI especially things like managing your finances, identifying scams and fraud much faster, and many many other “use it everyday” use cases. You can make fun all you want, but as someone inside what currently happening I can tell you I’m all in with investments in AI based products and companies that can fill the very large enterprise and consumer demand coming over the next five years. Anyways, that’s my two cents for anyone that cares.

Jstrandt

Won’t matter. They have more than enough lines of credit to bridge. With the now explosive growth of AL, ML, and soon quantum computing NVDA is one of few companies positioned to fill the demand.

LocalProfessional379

https://www.bestbuy.com/site/lg-27-ips-led-4k-uhd-amd-freesync-monitor-with-hdr-displayport-hdmi-black/6511336.p?skuId=6511336&ref=212&loc=1&extStoreId=802&ref=212&loc=1&&&&&gbraid=0AAAAAD-ORIjUzMIV9w7n5wGNbEFHoshBQ&gbraid=0AAAAAD-ORIjUzMIV9w7n5wGNbEFHoshBQ&gclid=Cj0KCQiA3eGfBhCeARIsACpJNU9DzVQZSlMzH54Il-OMyCn5ML7Po_Oxjn9Vhbs_4yLIuR9WbFUAcjQaAsHrEALw_wcB&gclsrc=aw.ds Hopefully the link works The guy said it wast anything great refresh wise but im not a tryhard anymore

riding_tides

Umm, Android's PlayStore? Maps and ML APIs, like translate, OCR, Lens -- that are in plenty of other apps and software make Google money. They just don't break it down, pretty much Ads vs everything else.

crackcodyscrack

You don't sell AI as an off the shelf product. You have core expertise in a process area for a specific industry and a huge dataset. So, you run a set of ML models, surgically Natural Language models these days, and run unit tests based on your expertise. Conclusion is that AI is not an out the box product and probably will never be, especially not for SME. That's why PLTR is primarily a professional service company focused on defense. You cannot scale that shit beyond consulting hours. That is the reason why this is a fad.

NotTooDistantFuture

Realistically, Metaverse offered no productivity benefit. As much as they pitched ideas of it being used for office work, that just wasn’t going to happen. AI (or ML) has real use cases already proven, and it’s not clear where the ceiling on the usefulness of the tech really is.

elpresidentedeltoro

If you are gonna do it - you need to visualize them all and have a way to bin the greeks, show rate of change, compare different forms of moneyness, all that Jazz. I actually purchased the entire data set for the market. I'm talking every greek, strike, expiration because I wanted to validate or deny what you are talking about. Here is the roadmap of monumental obstacles I faced. If you would have told me all this before I started, I don't know if I would have opted for the voyage. \- The data is damn expensive, up to $31,000 per year direct from the exchange \- You incur additional expenses to store the data and access the data \- You can pipe it into a DB, but that's only helpful if you know SQL, and even then its a bit annoying every time you want to pull something down \- \^\^\^ leads you to creating a front end, but then you have to ask - well, how much data do I need to be able to access at any given time \- The answer is - you want it all, because you have haystacks of information and don't know where you are going to look \- Now you are getting to the point where you want a Data Scientist, or an ML engineer, maybe a Financial analyst, and a software engineer \- Or you can say, piss on it, Ill do it raw, get it free from any broker - but then you can only get point in time, so that would be like trading TA candlesticks and only being able to see one candle at a time \- then you wonder about black-scholes, and alterative pricing models - are they even spitting our the right outputs, is there a better formula, are you the mathematician to figure it out. now you come across MasterClass course instructor - Terrance Tao, Rockstar mathematician - and you think - I just need to slide into his dms for some help \- Maybe you decide you want to buy the data direct, and all the while you are racking up data costs of $300 - 1700 per month depending on the vendor you pick \- Also, every month it takes you to figure out a roadmap - you have data storage costs from a provider like digital ocean, or maybe you opt for Amazon Glacier Buckets, but then you have to pay again to access your data because getting your data out of cold storage glacier buckets is like unfreezing a massive asset, or maybe you keep it somewhere cheap and you have to pay to transport it on a massive oil tanker, metaphorically speaking \- all this to derive a little alpha out of straight Greeks - so then you look, anyone done it before? Sure, a few folks, but what do they want. Some are free, some are paid, some are pretty to look it, some have way too much information, some not enough all this to say, it can be done, but who is doing it, where are you getting it from, what is the integrity of their data? I seemed to have work through all of this, but it is not 100% predictive. It is good, but not perfect, but in a strange way - there is comfort in that and validity that it can have predictive power, but it comes down to all the above being done right, and then creating algorithms to showcase the data in a meaningful way. I have found the most value out of Theta, Gamma, Vega, and volatility skew by tenor with either IV or the actual mid price as the input. I have had trades with SPY, VIX, GME, AMC, NVDA, BBBY, but they don't signal every week. I will say this as my recent win - everyone and their brother was saying BBBY was juiced to squeeze, and by all this greek viz, I could see that retail was putting their money where their mouth was, but the fuel just wasn't there. I didn't short it, but sometimes cash is a position, and not getting sucked into a losing trade can be the best trade I made all year. Nothing is 100% and the longer you or anyone encourages to think is a waste of your time.

HanzJWermhat

This wave of AI is actually the updraft of the first wave of machine learning in 2015-2018. All those Ml startups who couldn’t figure out of to actually implement anything beyond a recommendation algorithm for B2C. It was a fancy regression model but a black box that inter firm and B2C never liked. The current generative AI is much more capable of being used in B2C and B2B services. I see it being able to be a force multiplier in a way ML never could because it can be tasked at less menial and detailed work and targeted at more nuanced business challenges. Like inter-firm communication, helping engineers with rapid prototyping, writing copy, generating designs, collecting requirements, responding to inquiries. The firms that build the specialized tools to do that are going to be the next big startups

Always_Late_Lately

very real and accurate observation. Esp when you consider 'what defines AI?' Machine Learning is basically just stats and live-system optimizations - a medical company I work with was doing that in the 90's to improve surgical outcomes and achieves better results than a university 'AI/ML Research Focus Group' was able to pull off over two years of r&d. So since the core principle is the same (heuristic statistical optimization based on individualized inputs [surgical results on a per-surgeon / center / device / pre-op workup basis] and resulting impact on outcomes) should that company advertise itself as 'The First Surgical Machine Learning and AI Medical Outcomes Optimization Platform'? 🤔 Also highlights that AI/ML isn't needed everywhere, and can even be a step back if improperly applied (which it will be).

norcalnatv

> think the investment in software to support hardware is much lower for ML than for graphics. I think you are confused. Gaming graphics has standards like DirectX and Vulkan. No similar standards exist for machine learning. There was once OpenCL but it never caught on. >Example: [https://github.com/geohot/tinygrad](https://github.com/geohot/tinygrad) Don't know what i'm supposed to be looking at here. >ML is at its core elementary mathematics. I don’t think it would take years to optimize to any hardware. Couple of questions: 1) have you heard anyone at Nvidia speak of ML compilers and how difficult they are to optimize? Ian Buck, Jensen Huang, Collet Kress for example? 2). Do you know why, after products are in the market for say a year, Nvidia (and Google) can show dramatic performance improvement? Like 50% to 80% faster? 3) if software is so simple, how come AMD, Intel, Graphcore, Cerebrus, Sambanova, Grox, Tenstorrent and dozens of other AI startups don't have a presence in the market? AMD's hardware is arguably equivalent to Nvidia's, and they've been working on their ROCm and HIP Software initiatives since 2014. Shouldn't they have a comparable footprint to Nvidia? Or why don't they? >I think that if there is a gold rush for machine learning silicon, the incentives are large enough and barriers to entry not so large, again, you seem misinformed. The empirical data is there are near no competitors despite dozens of start ups and some of the largest technology companies in the world with plenty of money. Nobody has a footprint more than a percentage point or two except for Nvidia. >NVidia will likely stay on top with graphics and gaming where software is much more intricate. Gaming has quickly become nvidia's second biggest business.

DrXaos

I think the investment in software to support hardware is much lower for ML than for graphics. Example: https://github.com/geohot/tinygrad In a year that framework will be plenty to do everything I need. ML is at its core elementary mathematics. I don’t think it would take years to optimize to any hardware. And now meta programming will probably use global search methods which would attempt various optimization strategies and find optimal implementations of micro instructions for any specific hardware version. I think that if there is a gold rush for machine learning silicon, the incentives are large enough and barriers to entry not so large, that competition may limit profit margins. This is a few years out. NVidia will likely stay on top with graphics and gaming where software is much more intricate.

MetaphoricalMouse

wtf i meant to comment on the ML comment now yours. my apologies

EvaUnit343

I think that’s fair. Compute is finally here for large models to be useable *at scale*, although I would push back that this has all of a sudden happened in the past “3 months” lmao. NLP/LLM’s have been around for a decade+, and the ML boom really skyrocketed in my experience in 2016-2019 with accelerated compute on GPUs and popular packages like PyTorch. Even then, people/companies have been using GPUs since the 90’s to do statistical learning or optimization. None of this is new by any measure, which is why I am super wary of the hype these moronic talking heads spew on major financial outlets. Also not every “AI” company is gonna make it, especially those like C3 that IPO’d at peak market hysteria. There only hope is acquisition in my view.

AstockcollapseNow

Last night I advised you to short the Nascrack and set a 2% down day target. [https://i.imgur.com/JePI6ML.png](https://i.imgur.com/JePI6ML.png)

EvaUnit343

I know ML workloads are going to be run more and more, if anything that hurts C3.AI as they are too small to compete with the big boys and improving tools that are already out there. It’s a short term play. C3 has ran up too much based on purely hype. There is no denying this. Improvements in technology != higher revenue. Your thesis is probably right fundamentally on a broad scale, but my play can still work out due to the reasons I delineate.

sQGNXXnkceeEfhm

Large language models might not be the AGI they’re nearly being pitched as, but if you think modern ML workloads aren’t going to continue to be run more and more, you’re a fucking clown. FYI the academic space has been foretelling another AI winter since I was in school. Pretty fucking dumb to imagine the greatest AI breakthroughs in human history are the precursor to this winter (where in the past, the promises were made WITHOUT technology in hand).

DJScrambles

Deep learning trains on GPUs, the vast majority of ML training happens on CPUs so the NVDA thing is moot. Why would big tech have access to more data? Now you're talking about LLMs I think? Every company has their own data they build models on, why would big tech have access to that? I think you have a fundamental misunderstanding of AI

crumbaker

I think AI is the going to change the world, and nothing will ever be the same. Know that Nvidia is going to remain the most used hardware to advance it. With that said it doesn't mean everyone is going to have their own render/ML farm. Their valuation is beyond bonkers.

Left_Boat_3632

Meta has a big AI/ML research division (FAIR I think it's called). I see a lot of papers in the ML world coming from Meta. Definitely doesn't account for a lot of their head count but probably at least 1-2k employees.

XYHopGuy

ah yes, old school ML models. You could paste a messy bash script and ask what it does 10 years ago! Wait

roozter

I'm going back two years to show you the growth of their data center business, and how it will overtake the gaming business, which is what most people known Nvidia for. Most of these people do not understand the potential growth of the data business once more and more AI/ML applications become standard to our everyday life. It doesn't matter if Google or Microsoft or whoever wins in the AI chat wars, because Nvidia wins their cuda(software) and tensor-cores(hardware) monopoly. You can research on more about cuda and tensor-cores on your own if you want to have a better understanding of Nvidia's moat/monopoly. Also, where did I say that I was a sensible investor? I only given you the perspective of someone who is buying/holding NVDA shares, and gave you reasons for doing so. And lastly, as a software engineer, coders don't just switch to another programming language to code because "industry that changes every single day." But then again you wouldn't know this because you have a very basic understanding of what AI/ML is about.

Flordamang

I know a retail trader who was investigated by the SEC after a very lucky trade about a decade ago. There is no way the SEC doesn’t already collect data on transactions. Right now It’s likely only at major price or volatility inflection points. They are probably using ML to catch insider trading now. Quite concerning

Kriegprojekt

The AAA Corporate bond index yield spiked up from 4.65% last night to 4.78% tonight. This usually runs with the 2 YR Treasury yield. That's a really large jump in a day. It hit 4.86% on September 26th when the market took a dump then it hit 4.94% on October 12th. The day the market hit the lows in 2022. The index ran up to 5.13% a week later and stayed elevated for a month. The market hit five smaller dumps between October 12 and November 9 before the AAA Corporate index yield slowly came down around the 4.5-4.7 levels until Feb 1 2023 where it hit a near low in 2023 of 4.23%. Its been a slow, steady run back up to the 4.78% level tonight. I think we would have to see this index hit 5.0-5.15 before the dump given the narrative from the FED about 5.0-5.5% rates to calm inflation. I have been saying since last year I expect a dump back down to 340-360 by end of the month March or early April. If this index yield is any indication of what could happen, we might just see that dump in the next month or two. Add to that the liquidity issues brought up before and Im starting to lean heavily to my predicted outcome. I did buy some 3/17 360 SPY puts the other day but I think Im shy a month to see those do much. https://data.nasdaq.com/data/ML/AAAEY-us-aaa-rated-bond-index-yield

roozter

All you really showing is backward looking data. Whereas, people that are buying/holding NVDA are mostly focusing on the growth of data center, and profit margin. Nvidia's Fiscal 2021: Total revenue: $16.68 bil