Okay, the crypto market. It feels like it moves
at the speed of light, doesn't it? Oh, absolutely.
Thousands of projects out there. Prices jumping
all over the place and just this, like, constant
wall of noise online. Yeah, it's overwhelming.
Trying to research the old way. Forget it. Weeks
reading dense white papers. Yeah. You just end
up stuck, right? Unable to decide. Totally. Analysis
paralysis is real. But what if there was a shortcut?
What if you could use a tool, maybe one you already
use? like chat GPT, and use it to cut through
that chaos. Analyze things like a pro strategist.
OK, I'm listening. Welcome to the deep dive.
Today, that's our mission. We're going to try
and pull out a clear six -step system. from the
sources we looked at. A system for smarter crypto
decisions using AI. Sounds good. Yeah. Covering
the big things, finding real value, figuring
out timing, checking the market mood. And maybe
discovering those hidden gems, too. Exactly.
And we're starting right at the beginning with
the fundamentals. Right. So finding true value
in crypto. First thing we've got to admit, fundamental
analysis here is different. Different how? It's
not like looking at traditional stocks. You know,
you can't really pull up quarterly profits or
sales reports. Because they often just don't
exist for these projects. Pretty much. So you
need different metrics. OK. So if we're not tracking
profits, what are those essential crypto native
things we should be asking the AI about? Well,
the focus areas are pretty specific. You need
to look hard at tokenomics. Tokenomics. Yeah.
That's how the coin is made, shared, used, all
that. Exactly. How it works. Then on -chain data.
which is the actual activity happening on the
blockchain itself, right? Stuff you can measure.
That's it. Plus, you know, the strength of the
actual team behind it and the community support,
that matters a lot in crypto. Okay. So if we
take a big one like Ethereum, we're not just
asking chat GPT for, like, a basic summary. Right.
We need it to synthesize its ecosystem health.
You know, how active are the DeFi apps? What's
happening with NFTs on there? And the hard numbers,
too. Definitely on chain metrics like what's
the trend in daily active addresses how much
ETH is being staked transaction volume That kind
of stuff. This is where specific crypto knowledge
Really helps I guess especially with tokenomics
the source mentioned the EIP 1559 update for
aetherium. Why is that so important for its value?
Ah, yeah, that's kind of the aha moment for understanding
supply and crypto. Okay, so after EIP 1559 part
of every transaction fee on Ethereum gets burned.
Burned meaning destroyed, like gone forever.
Exactly. Taken out of circulation permanently.
Now, if the amount getting burned is consistently
more than the amount of new Ether being created...
Then the total supply actually goes down. Precisely.
It shrinks. This creates a deflationary pressure.
Inflationary pressure, okay. Yeah. Think of it
less like regular money and maybe more like a
limited edition collectible that just gets a
tiny bit rarer every time someone uses the network.
Interesting analogy. And that potential scarcity...
Well, according to the research, that's a huge
part of its long -term investment case. But even
a project with strong fundamentals like that
must have weaknesses, right, risks. What should
the AI analysis be flagging there? Absolutely.
A good analysis has to point out the risks. Three
main areas usually come up for something like
Ethereum. OK, what are they? One, intense competition.
You know, faster chains like Solana are always
nipping at its heels. Right. Two, scalability
issues. Historically, Ethereum has had problems
with high network fees when it gets busy. Yeah,
I've felt that pain. And three, just the general
legal and regulatory uncertainty hanging over
crypto globally. If the AI doesn't mention weaknesses,
you know, the analysis just isn't complete. It's
biased. OK, so if traditional metrics like profit
are out and we have these crypto specific things
like token burning and on -sheen data, how do
we actually judge the quality? the long -term
potential. Well, that's where the second step
comes in. You check it against proven principles
from successful investors, specifically in this
asset class, not just importing rules from the
stock market. Ah, okay. Step two, principle -based
analysis. Yep. This step is crucial. We can't
just apply, like, Warren Buffett's rules directly.
They often don't translate well to decentralized
stuff. So what are we doing instead? We get the
AI to run a kind of pass -fail checklist based
on the thinking of people who've successfully
held crypto long term. And these principles are
like... The non -negotiables for seeing if it
has real long -term legs. Pretty much. The analysis
needs to confirm, first, does it have a clear
use case? Meaning, does it actually solve a real
problem, digital or otherwise? Exactly. Is it
useful? Second, does it show a strong network
effect? Where it gets more valuable as more people
use it or build on it. You got it. Think Facebook,
but for crypto protocols. And third, quickly,
decentralization. How spread out is the control?
Is it really run by lots of different people,
or could one group just pull the plug? That's
the core question. So the Ethereum example we
used, it generally passes these checks because
of its huge DeFi and NFT ecosystem, that network
effect. And its massive global network of validators
keeping it running. Right. Distributed control.
OK, so steps one and two give us fundamental
strength. That helps figure out what might be
a good investment. But then there's the million
dollar question. When? Exactly. That's when we
shift gears. We move from the fundamental what's
to market timing, and that brings us to technical
analysis. Step three, technical analysis, or
TA. This is about trying to predict shorter term
price moves by reading charts. Right. Looking
at past patterns. That's the idea. And you often
source these charts from platforms like TradingView.
And you mentioned needing the PayChat GPT version
plus to actually upload a chart image. for analysis.
Yeah, that's the practical bit. You feed it the
image. And what does the AI do with it? The model
gets to work. Your prompt tells it to find key
things, mainly support levels. Price floors,
where buying tends to kick in. Correct. Psychological
or actual levels where prices bounced before.
And resistance levels. The ceilings, where selling
pressure usually takes over. Yep. where price
has struggled to break through in the past. The
powerful part seems to be the scenario planning
the AI can do based on this. Precisely. It basically
gives you a potential roadmap. It might say,
OK, here's the bullish case. If the price breaks
firmly above, say, $3 ,500 resistance. that could
signal upward momentum towards a higher target.
In the flip side. The bearish case. If it breaks
below a key support level, maybe $3 ,000, that
could indicate more potential downside. So it
helps you set entry and exit rules beforehand,
before emotions get involved. That's the goal.
Take some of the emotion out of it by having
a plan based on the chart's history. Okay, but
here's a thought. If the AI is just looking at
historical chart data to find these levels, how
reliable is TA? Especially when the crypto market
can just get totally rocked by sudden news, like
a regulatory announcement or some crazy hype
cycle. That's a really good point and a fair
challenge. TA is definitely only one piece of
this puzzle. Right. It shows you the historical
patterns, the structure. But yeah, it absolutely
cannot predict sudden external news events. No.
What else do we need? This is exactly why we
must layer in the current market mood. Which
means, step four, sentiment analysis. Ah, okay,
sentiment analysis. Because hype and fear really
drive those short -term swings, don't they? They're
huge drivers. So step four tries to quantify
that mood. We use the AI to basically scan recent
news, maybe the last week or so. And social media
too, like X, Reddit. Yeah, measure the buzz,
the feeling out there. Is it generally positive,
negative, or just kind of neutral? So it helps
cut through the noise and actually categorize
what's driving the feeling. That's the idea.
We ask the AI to clearly separate things out,
like, OK, what are the bullish factors right
now? Maybe a successful network upgrade just
happened, like the Denken upgrade aiming to lower
fees. Or maybe an ETF application looks promising.
Right. And then, critically, what are the bearish
factors? Is there an ongoing SEC investigation?
Was a major security flaw just found? And it
puts it all together. Ideally, yeah. Then the
AI tries to assign an overall sentiment score.
You know, maybe it spits out a 7 out of 10 towards
bullish and explains why. It gives you the emotional
temperature. That seems crucial for timing, layering
it on top of the TA. It really is. But, uh...
Vulnerable admission, I'll admit. I still wrestle
with prompt drift myself sometimes when I ask
the AI for something subjective, like a sentiment
score. Prompt drift, meaning it doesn't always
give you what you expect. Yeah, getting consistent,
reliable numbers for something as fuzzy as sentiment
takes constant tweaking of the prompts. But I
think it's still a necessary check against relying
purely on charts. That makes sense. That prompt
drift issue really highlights that the human
overlay is still vital, isn't it? You're checking
the AI's reading against your own understanding.
Absolutely. You can't just blindly follow the
output. Do sex silence. OK, let's talk efficiency.
Step five is about automation, right? Fighting
that burnout because the crypto market is genuinely
24 to 7. It never sleeps. So yeah, we need ways
to manage the firehose of information without
going crazy. How can the AI help there? We need
to automate how we digest info. Probably the
most valuable prompt here is one that creates
a short, sharp daily briefing. What goes in the
briefing? Key stuff. Overall market cap change
in the last 24 hours. Who are the top movers,
up or down? Any specific news about projects
you're tracking, like our Ethereum example. That
sounds useful. How do we get that automatically,
say, every morning? Well, for true automation,
you probably need ChatGPT Plus again for its
scheduled sends feature. Or you could link it
up with external tools, like Zapier, to push
those updates out automatically. OK. And one
crucial bit of data that must be in that daily
briefing is the overall crypto fear and greed
index. Ah, the index that measures market emotion
extremes. Yeah, it's like a quick gut check.
Are we in extreme fear or extreme greed? Helps
contextualize everything else. The system seems
great for keeping tabs on coins we already know,
like Ethereum. But let's be honest, a lot of
people are looking for discovery. How do we use
AI to find that? potential big thing. Right.
Moving past the usual suspects, that's where
the AI can become a really powerful coin screener.
This is how we scale the analysis. Okay, step
six. Using chat GPT as a coin screener. Filtering
the thousands down to a manageable few. Exactly.
Finding potentially undervalued projects that
might have serious growth potential. But you
need very strict rule -based criteria for the
AI to follow. And those rules have to be super
specific, right? What kind of filters are we
talking about? Yeah, precision is key. For instance,
maybe you target small to mid cap projects. So
you set the market cap filter, must be between,
say, $50 million and $500 million. Not too big,
not too tiny. OK, what else? You'd also want
to narrow the focus to specific sectors that
seem to have high potential or a strong narrative.
The sources highlighted three possibilities.
Which were? Artificial intelligence, so AI related
crypto projects. Real world assets, RWA. That's
crypto representing tangible stuff like real
estate or bonds. Correct. Tokenizing real world
things. And third, gaming or GameFi finance models
built into online games. You'd tell the AI to
look only within one of those chosen sectors.
And we're definitely looking for real utility,
not just hype. Oh, absolutely. That's maybe the
most crucial filter. Any project the AI suggests
must meet our basic fundamental checks from step
one and two. So active team, clear use case,
good tokenomics. Yep. And the big one, it cannot
be a meme coin. No Doge clones. It must have
a demonstrable real purpose. Okay, setting those
strict rules seems vital. Moment of wonder. Seriously
though, whoa. Just imagine the hours of manual
research this saves. You're basically scaling
your initial analysis across maybe hundreds of
projects instantly. Yeah. Then your human brain
power focuses only on the most promising candidates
that pass the AI's filter. That scaling is definitely
the game changer. Quick advanced tip from the
sources, though. Don't just rely on one AI run,
right? No way. Always cross -reference what chat
GPT spits out. Check it against trusted data
platforms like Glassnode or new sources like
CoinDesk. Give them multiple perspectives. And
keep the analysis updated frequently? Constantly.
Things change fast. And the final, final word
always has to be about risk management. Never
invest more than you can afford to lose. Never.
And have your rules set up before things get
crazy to avoid FOMO, that fear of missing out
when the market gets volatile and emotional.
So... The big idea here really is integration,
this whole six -step system. It transforms ChatGPT
into this incredibly high -powered research assistant.
But not a financial advisor telling you exactly
what to buy or sell. Absolutely not. It's a structured
process. You start with fundamental value, steps
one and two. Then you look at technical timing,
step three. Check the market mood with sentiment,
step four. Automate your updates, step five.
And finally, use the screener for focused discovery,
step six. Okay, so building on that system, let's
think about real world conflicts. What happens
when a project looks fundamentally strong? Maybe
it's got great RWA adoption, solid tokenomics,
but the AI sentiment analysis, step four, shows
overwhelming negative feelings, perhaps due to
some external legal threat it surfaced. Yeah,
that's a tough scenario. Fundamentals good, sentiment
bad. So how should long -term investors think
about that? Do you prioritize, say, the coin's
strong community and its track record of development
over that short -term price sphere the AI is
picking up? Where's the balance? That's the complex
decision point, isn't it? There's no single right
answer. It depends on your risk tolerance and
time frame. Hmm. Definitely something to think
about. Yeah. Well, the best way to get a feel
for this is probably just to try it. Maybe take
that fundamental analysis prompt, step one, and
run it on one crypto project you're curious about
today. See what comes back. Good call to action.
Get hands on. We'll be back next time for another
deep dive.