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.