ETHEUR
Ethereum / Euro
cryptoComposite

> Tom Lee has called for insane prices for years And not just ETH. He was a big bull of IWM for some reason (the bottom 2000 stocks) which was down for 3 years while in that same time, SPX500 returned ~60% and QQQs were ~90% and MAG7 and semis were just stupid crazy. ... and then he just forgot about IWM for some reason and founded his ETH fund.

I agree with you about what he’s said over the years, and what he’s saying now is essentially the exact same type of thing he’s always said about ETH. The difference this time, in fact, is that the man purchased 5% of ETH supply. That’s a TON of supply that otherwise would have needed to be absorbed if ETH actually experiences price appreciation this bull run (if we have one…). People talk of how little ETH there is on exchanges over time, how much is staked, etc. - essentially market supply metrics. 5% is a hell of a lot of market supply that no longer needs to be absorbed by demand. That’s roughly $15 BILLION, at current prices, that no longer needs to be eaten by the little guy’s dollars. Supply and demand dynamics would say that price will have a much larger price % increase compared to if BitMine had not absorbed those units. Sure, bagholders who are annoyed will sell as price goes up, but the wind is at ETH’s back. I don’t know if price will increase - hard to say it will based on history - but I like what I’m seeing for the setup.

The drawdown comparison is more interesting than the headline about AI beating 78% of humans. I'd still want to compare the model against simply holding BTC and ETH over those same periods, especially since the human sample includes leveraged traders. I use Moon for crypto related price speculation and controlling losses seems more valuable to me than having an impressive prediction model.

Imagine a future where most households have solar panels, home batteries and EVs. People could charge their cars at each other’s homes and trade excess electricity directly, with smart contracts handling payments automatically instead of relying on a centralized platform. Could Ethereum or an L2 network become the settlement layer for this kind of decentralized energy market? Especially with vehicle-to-grid  and millions of small transactions, I find this use case interesting. Do you think this is a realistic use case for Ethereum and a potential long-term value driver for ETH, or would traditional systems handle it more efficiently? //translated by AI.

He's complaining about the old days of where you could just send XYZ to an address for payment. Bro would have gotten that address and sent ETH to it instead of USDT and you're saying it's not a skill issue?

I own ETH and BMNR, but Tom Lee lacks credibility. So no, that ain't happening.

Tom Lee’s Ethereum (ETH) predictions for 2025 ranged from $7,500 to $16,000. Throughout 2025, the Fundstrat co-founder and BitMine chairman issued several aggressive targets for the token, framing it as one of the most prominent macro trades of the decade. From capital.com

before trusting the 78% I'd run plain buy-and-hold BTC/ETH spot on each trader's same dates. the AI was long spot majors with no margin through a stretch where that alone made money for most start dates, while the humans traded perps across whatever coins they picked, often on margin. if buy-and-hold also beats three quarters of them with a similar drop, the result is about the asset and the margin, not the engine. two smaller things. was the engine's logic frozen before May 2024, or is this today's production version replayed backwards over the window it was built in? if it's the second, it has already seen the period it's being graded on. and 0.10% fees with no slippage is fine at BTC/ETH spot size, but say it in the post so nobody reads simulated fills as live ones. the 58 of 59 PnL rebuild from fills is a good check, more of these posts should do one.

He is just hyping it up. Even BTC hit its ATH last year, ETH had been failed to reach its former ATH since 2021 bull market year.

**Data** \- Hyperliquid public data: 32,839 accounts that traded $50k+ since May 2024, market makers removed. Random sample of 2,500, of which 1,844 had enough history to measure. \- Bots vs humans, from each account's last 14 active days of fills: 150+ trades a day, a full 2,000-fill page, or trading around the clock counts as a bot. No fills in that window means unknown, and those are left out. Result: 1,076 humans, 340 bots, 428 unknown. \- The history is weekly snapshots, and money moves in and out between them, so time-weighted returns break (one profitable account showed -100%). We used flow-proof measures instead: $ PnL for made money, return = PnL / largest balance, and drop measured from the all-time largest balance. \- Check: we rebuilt PnL from actual fills plus funding for 59 everyday accounts. Made money vs lost money matched for 58 of 59. **The algo side** Botsfolio AI from our production engine: spot BTC and ETH, no leverage, 0.10% fees per trade. Each trader gets a fresh start on their own first active day, measured to their last. It's a simulation, not live fills. **Head to head, 1,076 humans:** \- The AI made more money than 844 of them (78%). \- The AI had the smaller worst drop against 1,007 (94%). \- 26 traders (2.4%) beat it on both return and worst drop. \- Made money: traders 31%, the AI 89%. **What the bots tell you** Bots in the random sample did better than humans, but they weren't magic: 43% made money vs 31%. At the top it flips. Of the top 1% by profit that we could classify, 53 were bots and 25 were humans and those bots made a median of 874 trades a day. **Where AI fits for retail** Two things we found while building this: \- Prediction isn't where it helps. We gave an LLM (Opus 5.5) anonymized chart snapshots of 499 trades and asked which ones to take. The trades it picked weren't measurably better than random picks. \- What separates the humans who made money is drop size. 71% of traders whose worst drop stayed under 25% made money. Of those who lost 90% at some point, 9% did. So the edge for retail may not be finding better entries. It may be having rules that run the same way on the worst day as on the best and keep the drops small. AI can work really good on controlling the drops. **So:** \- Is "don't blow up" an edge? Full report with the method, tables and bot check: [https://botsfolio.com/blog/ai-vs-human-traders](https://botsfolio.com/blog/ai-vs-human-traders)

He said ETH will reach 14k in 26 and 60k at long term. He’s nothing more than a scammer.

Most of the large purchases are bought off market/otc so they have no impact on price. Bitminer has purchased \~25k ETH directly from the Ethereum Foundation for example.

### Botsfolio: AI vs. 1,076 human crypto traders My initial assessment: this is an interesting benchmark, but it does not yet prove that Botsfolio has a durable trading edge. Its most important finding is that a rules-based, unleveraged BTC/ETH strategy may outperform highly leveraged retail traders primarily through risk control, disciplined execution, and avoiding catastrophic losses. The article was published October 16, 2026, and compares Botsfolio's strategy against 1,076 real Hyperliquid trader accounts using matching historical dates. Crucially, the AI results are backtested, not the results of 1,076 live, capital-matched AI accounts. ### 1\. The headline results Botsfolio's published benchmark · May 2024–October 2026 AI windows ending profitable ### 88.6% Human traders: 31.4% Median AI worst drawdown ### 18.3% Human traders: 89.8% AI beat humans on return ### 78.4% 844 of 1,076 accounts Beat AI on return and drawdown ### 2.4% 26 human traders Source: [Botsfolio's published benchmark](https://botsfolio.com/blog/ai-vs-human-traders?utm_source=chatgpt.com). These are the company's reported results, not independently reproduced findings. The distinction that matters most is that the AI traded spot BTC and ETH without leverage, whereas the human sample primarily consisted of leveraged crypto traders trading a broader range of assets. This is a comparison of two different risk-taking approaches, not a controlled experiment isolating the benefit of AI. ### 2\. The most important finding is hidden in their methodology The follow-up methodology page is more informative than the headline article because it discloses tests the strategy failed. Historical drawdown control The company reports that the BTC/ETH Cycle Trader had a median annualized return of 33.6% across monthly starting windows since 2018, versus 24.1% for holding the same assets, with lower drawdowns. Some out-of-sample evidence The company reports holdout tests using settings selected before the evaluation periods. It also reports tests on coins not used to design the strategy. These are useful tests, although they remain company-produced results. The timing edge remains unproven The strategy failed its preregistered random-timing tests for both BTC/ETH and the unseen-coin group. That is material: reducing exposure during bad markets can produce attractive drawdown statistics without establishing that the entry and exit timing itself has predictive value. Live forward validation is still short The company says its current rules were fixed on October 8, 2026, and its live portfolio results are paper-traded. It plans to wait for 180 days of live results before assigning a live grade. The distinction is fundamental: a strategy can be an excellent risk-management system without possessing a statistically robust source of alpha. ### 3\. What I would challenge before trusting the results | Audit question | Why it matters | | ---------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | Does the strategy beat buy-and-hold after all costs? | The correct baseline is holding the same assets over identical periods. | | Does it beat randomized timing rules? | Tests whether the signal adds value beyond exposure management and luck. | | Are the drawdowns measured from intraday equity? | Weekly snapshots can miss substantial losses between observations. | | Are the rules frozen before testing? | Prevents repeated tuning against the evaluation sample. | | Are trades executable at the assumed prices? | Paper trading does not establish real fill quality, market impact or slippage. | | Does it work across distinct market regimes? | A strategy optimized for a weak BTC/ETH period may underperform in a sustained bull market. | | Are results independently reproducible? | A published report is not a substitute for source data, code and independently calculated metrics. | Botsfolio itself acknowledges that weekly drawdown measurements miss intraweek losses, that some design-coin history is in-sample, and that the timing tests failed. ### 4\. What this means for our trading systems The useful takeaway is not to copy Botsfolio's strategy. It is to adopt the strongest parts of its research protocol and apply them to our existing components. A. Separate alpha from risk control Test the signal independently from position sizing, exposure reduction and stop logic. Measure the incremental return attributable to each component, as well as the drawdown reduction and turnover cost. B. Run a matched-baseline gauntlet Compare every candidate against buy-and-hold, simple trend filters, fixed-exposure controls and randomized-timing controls. Match dates, capital, assets, fees, slippage and execution assumptions. C. Measure economic value, not just win rate Prioritize net expectancy, risk-adjusted return, maximum drawdown, tail losses, exposure-adjusted performance and stability across out-of-sample regimes. A high percentage of profitable windows is not enough. D. Reproduce before accepting Obtain the published trade history and rules, independently reconstruct the equity curves, verify fees and timestamp alignment, and rerun the preregistered tests. Treat discrepancies as research findings rather than papering over them. ### My verdict Worth investigating; not yet proven as a deployable source of alpha. The strongest claim supported by the article is that a disciplined, unleveraged BTC/ETH strategy appears to have managed historical risk better than a sample of leveraged retail traders. That is useful evidence for systematic risk management, but it does not establish that AI itself caused the advantage or that the strategy will retain it prospectively. For our own research, I would prioritize the random-timing failure. If the strategy cannot demonstrate that its timing rules outperform simple controls, the appropriate conclusion is to retain any demonstrably valuable exposure-management component and reject unsupported alpha claims. One limitation: I have reviewed the published methodology and claims, not independently downloaded and rerun the underlying trades.

This call has the potential to play out next year (and quickly). ETH/BTC is bottomed out after years of down trend--suggesting massive ETH outperformance if it is going to retest macro resistance areas after holding support. Also, ETH and BTC dont have many levels to get through before price discovery--which means more time this cycle will be spent in uncharted territory and this is where, theoretically, buying manias drive prices to insane levels. 10k by this time next year is aggressive, but I think it's more likely than ETH still being pinned below the current ATH.

It's hardly a shitpost since it identifies the two fundamental drivers of Eth's future success which virtually no one here seems to grasp. I'm trying to get some i formed discussion of FUNDAMENTAL DRIVERS OF ETH here. I don't have those answers, I want to see at least SOME fucking useful discussion of what will actually move the price here.

Come on ETH, you can do it... Give it all back by the end of the day!!

One of the biggest Ethereum bulls here... Can't believe how much adoption is happening without price going up. Admire Vitalik and Drakes Characters and understand their intentions but dont think they should have asked Tom Lee to not go over 5% (not sure if verified) Tom Lee should increase holdings 1% a year until ETH is over 10k.

Tell me about it. The long term hold simply hasn’t paid off for ETH yet.

and? Microstrategy owns less % of BTC’s total supply than Tom Lee does of ETH

In typical fashion, you look away for a second and ETH PUMPS. BOOM

What a bullshit response. Why can't you just use ETH(or any token) and have smart contracts handle the swaps and pay in usdt on the back end if they want to get usdt.

Thanks for the words and the effort to explain all this, but now, just a single GENUINE question! Isn't it only a goal, vision, and plan that XRP sells to their believers like you? Let's go the big facts! Nobody uses the XRP blockchain Stables on the XRP blockchain, the volume is just SMALL! Token XRP is what we call a "governance token" hence hollow!!! All DEFI world happens in ETH, SOL, POL, BASE and other blockchains!!! XRP is just an idea that depends on broad adoption, and you all are counting on it to ever happen. If the adoption ever happens, okay! XRP has a purpose, but now this is just a dream man, while stables are playing the big roles out there. That's why I said you don't understand blockchain at all you just attacked me for nothing. But THERE IS NOTHING WRONG in being a DREAMER :) and HOLD XRP. I don't believe in that, and feel sorry for those who do, but BELIEVING IS NOT WRONG, again! So, good luck, man and again, thanks for the words!

Part agree. He has been iust shooting in the dark though with ETH. Eventually going to hit the target but he has no clue whats going to happen. Just don't understand why he keeps getting this room to keep shooting in dark.

Tom Lee has called for insane prices for years, never hitting once on his predictions. No point in listening to what he say. Also, his company bought 5% of all ETH without it ever pushing the price up. I don't expect fireworks now since they have stopped buying ETH

Tom Lee said this is going to be one of the bigger crypto cycle and ETH will be higher than $10,000 in the next 12 months.