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Context:
In 1998 the SEC authorized electronic exchanges; at that exact moment high frequency/algorithmic trading was born. This completely changed traditional trading forever as everyone here knows.
Everyone here who is doing TA is effectively competing against hedge funds / trading firms (cough Jane Street, Renaissance, etc) that hired former MIT math Olympiads to deploy quant models to pattern recognize better than any simple bot you'll find on GitHub can.
This is obvious. I'm not making this post to talk about chart analysis, but rather I think we're going to see a similar shift happen that we saw in 1998 and it will happen that impacts the traditional financial markets. The first firms to benefit from high frequency trading, algorithmic trading bots, and quantitative models had a disproportionate advantage over the general population. Advancements in neural networks are about to make the gap wider than we've ever seen it.
So what's the advancement?
It's not one. It's several.
1- The prerequisites: The major breakthroughs we're about to see are already here, although in a rudimentary state. Those were simply some of the current quantitative models that exist as well as the advancements we've seen in transformer architecture, and as a consequence, language models. Most people I talk to about GPT still don't seem to get it. It scales with compute and there's emergent properties that are completely unexplainable by the researchers that built it. Namely, sentiment analysis was not something these models were trained for but became world class at - more on this later.
2 - Universal Aggregator: The second is pairing a language model with an advanced search engine. The PayPal Mafia quietly took a bet on an AI lab that isn't OpenAI. It's called Mindy. Yu Pan, a co-founder of PayPal and Employee #1 at YouTube cofounded an AI lab backed by Peter Thiel (former CEO of PayPal) and Roelof Botha (former CFO of PayPal). Effectively, the AI is like if ChatGPT and Google had a baby. It's an AI you just email and it has indexed the entire Internet and can answer any question about anything. To give an example that would be relevant to this subreddit, it can give you specific financials on stocks/10ks/etc instead of doing manual research. It's being used by anyone from retail traders to private equity firms already. Admittedly, I currently use this to expedite my DD - and of course, this can be extrapolated to any area of research. But the reason why a tool like this is interesting is because you could hypothetically aggregate all past data with the same underlying technology, which brings me to my third point.
3 - Modeling the entire stock market: No longer will hedge funds be using pure mathematical models for trading with some macro-trends factored in. They will be making their own AI models on the history of the entire stock market using the aforementioned systems. This isn't just quantitative price fluctuations. Firms will create entirely new models where they scrape every piece of news associated with a specific stock, index, and the global economy and perform sentiment analysis on the exact news released on that historical day and tie its sentiment to the exact stock price on that historical day. This allows them to create an entirely wholistic picture of what has occurred in the stock market, and as a consequence, draw future correlations. Does it account for black swans? No. Will it predict our extremely unpredictable market system with complete accuracy? No, but even the most chaotic system must generate alignments; it's the same reason a broken clock is right twice a day. This allows them to look at areas of highly probable winning trades in niche areas of the market that their new model found. This isn't just a matter of markets becoming efficient and competing firms keeping up either, because the firms that win have increasingly more data (and more capital) to put into compute for their models.
Conclusion
To be clear, this does not already exist but all of these advancements are building up to this exact system, and the first group of investors to do it will become unbelievably wealthy. Firms are working on this, so just know when you're tinkering with TA concepts on trading view, the big players are becoming disproportionately more advanced and your models are decaying in efficiency exponentially. This will know exactly when to "Buy the rumor and sell the news". One can even imagine such a system being able to incorporate something as detailed as C suite profiles into their predictions.
TL;DR: Be extremely cautious with short term trading of any kind, even if you're decent at it now - you might not be in 6 months - and it's for very different reasons than people would have said in the past. We're about to witness more advanced analysis than we've ever seen.
submitted by /u/shamefulgallantry10
[link] [comments]
Continue reading...
In 1998 the SEC authorized electronic exchanges; at that exact moment high frequency/algorithmic trading was born. This completely changed traditional trading forever as everyone here knows.
Everyone here who is doing TA is effectively competing against hedge funds / trading firms (cough Jane Street, Renaissance, etc) that hired former MIT math Olympiads to deploy quant models to pattern recognize better than any simple bot you'll find on GitHub can.
This is obvious. I'm not making this post to talk about chart analysis, but rather I think we're going to see a similar shift happen that we saw in 1998 and it will happen that impacts the traditional financial markets. The first firms to benefit from high frequency trading, algorithmic trading bots, and quantitative models had a disproportionate advantage over the general population. Advancements in neural networks are about to make the gap wider than we've ever seen it.
So what's the advancement?
It's not one. It's several.
1- The prerequisites: The major breakthroughs we're about to see are already here, although in a rudimentary state. Those were simply some of the current quantitative models that exist as well as the advancements we've seen in transformer architecture, and as a consequence, language models. Most people I talk to about GPT still don't seem to get it. It scales with compute and there's emergent properties that are completely unexplainable by the researchers that built it. Namely, sentiment analysis was not something these models were trained for but became world class at - more on this later.
2 - Universal Aggregator: The second is pairing a language model with an advanced search engine. The PayPal Mafia quietly took a bet on an AI lab that isn't OpenAI. It's called Mindy. Yu Pan, a co-founder of PayPal and Employee #1 at YouTube cofounded an AI lab backed by Peter Thiel (former CEO of PayPal) and Roelof Botha (former CFO of PayPal). Effectively, the AI is like if ChatGPT and Google had a baby. It's an AI you just email and it has indexed the entire Internet and can answer any question about anything. To give an example that would be relevant to this subreddit, it can give you specific financials on stocks/10ks/etc instead of doing manual research. It's being used by anyone from retail traders to private equity firms already. Admittedly, I currently use this to expedite my DD - and of course, this can be extrapolated to any area of research. But the reason why a tool like this is interesting is because you could hypothetically aggregate all past data with the same underlying technology, which brings me to my third point.
3 - Modeling the entire stock market: No longer will hedge funds be using pure mathematical models for trading with some macro-trends factored in. They will be making their own AI models on the history of the entire stock market using the aforementioned systems. This isn't just quantitative price fluctuations. Firms will create entirely new models where they scrape every piece of news associated with a specific stock, index, and the global economy and perform sentiment analysis on the exact news released on that historical day and tie its sentiment to the exact stock price on that historical day. This allows them to create an entirely wholistic picture of what has occurred in the stock market, and as a consequence, draw future correlations. Does it account for black swans? No. Will it predict our extremely unpredictable market system with complete accuracy? No, but even the most chaotic system must generate alignments; it's the same reason a broken clock is right twice a day. This allows them to look at areas of highly probable winning trades in niche areas of the market that their new model found. This isn't just a matter of markets becoming efficient and competing firms keeping up either, because the firms that win have increasingly more data (and more capital) to put into compute for their models.
Conclusion
To be clear, this does not already exist but all of these advancements are building up to this exact system, and the first group of investors to do it will become unbelievably wealthy. Firms are working on this, so just know when you're tinkering with TA concepts on trading view, the big players are becoming disproportionately more advanced and your models are decaying in efficiency exponentially. This will know exactly when to "Buy the rumor and sell the news". One can even imagine such a system being able to incorporate something as detailed as C suite profiles into their predictions.
TL;DR: Be extremely cautious with short term trading of any kind, even if you're decent at it now - you might not be in 6 months - and it's for very different reasons than people would have said in the past. We're about to witness more advanced analysis than we've ever seen.
submitted by /u/shamefulgallantry10
[link] [comments]
Continue reading...
