I'm interested in the reasoning behind The House Of Crypto's Solana picks more than the price targets. The argument is to follow capital into a network, then examine the exchanges that might benefit when people start trading there. Jupiter and Raydium are the creator's preferred candidates, but the case depends on their expectations for Bitcoin, the economy and Solana activity.
The Solana altcoins thesis
Solana is the underlying blockchain; Jupiter and Raydium are exchange projects built within its ecosystem. The creator expects incoming capital to bring more trading and more exchange fees. They think the associated tokens could offer more upside than Solana's own coin.
I would separate the exchange business from the token at the outset. An application can collect substantial fees without passing an equivalent benefit to token holders. The token's design and the destination of those fees determine whether a successful business supports the investment case.
The supplied visual notes also identify an AI-generated voice, an AI-animated face and modified chart years and dates. Those changes mean the apparent current readings, timing and historical comparisons need independent verification. Changing a calendar can make an old chart setup look current without changing its underlying price history.
The creator says they have been accumulating assets ahead of the rotation they expect. Their aim is to identify an active ecosystem before enthusiasm reaches its application tokens. Hyperliquid and Pump.fun are their examples of assets that have already had strong price moves, rather than the opportunities they want to emphasize now.
I find that method more useful than assuming every token on a popular network will rise together. Capital flows can identify a place to investigate; usage and valuation can help narrow the search. Each needs evidence, though, and a network's popularity cannot substitute for examining the asset itself.
Bitcoin sets the conditions
The creator describes Bitcoin moving to around 87k, then retesting around $82,000. They predict a move toward resistance between $94,000 and $95,000 over the following days or weeks. Resistance identifies an area where selling previously interrupted a rise; returning to an earlier level after a breakout is a retest.
Their forecast allows for sideways trading and a decline to around 78k before the expected advance. The Solana argument therefore does not require prices to rise without interruption. It does, however, depend on their broader bullish scenario surviving those setbacks.
I would use those levels to understand the forecast, not to assign it a probability. A chart can organize possible routes for price, but it cannot establish that Bitcoin must take one of them or measure the downside if the forecast fails.
Bitcoin dominance measures Bitcoin's share of the market capitalization included in a crypto dataset. The creator says that share has risen while Bitcoin's price has stayed relatively flat, which they interpret as pressure on altcoins. Dominance can rise because other assets weaken, so a higher reading does not necessarily mean new money is entering Bitcoin.
The creator sees stronger dominance as compatible with an eventual altcoin rally. Their historical comparison has Bitcoin leading before other assets follow. They associate substantial dominance declines in 2017 and 2021 with broader altcoin seasons and expect another such decline.
That sequence is a forecast, not a rule. I would also distinguish gains against dollars from gains against Bitcoin: an altcoin can rise in dollar terms and still lag Bitcoin. The comparison changes what success means for an ecosystem thesis.
The creator adds a sentiment argument. They describe investors moving from fear into disbelief and expect a shift toward belief to accelerate gains. That is their interpretation of investor psychology. A market-cycle diagram may describe familiar emotions, but it cannot measure the buying power waiting on the sidelines.
The manufacturing argument needs a separate test
The macro case centers on the ISM manufacturing index, which measures conditions in United States manufacturing. The creator compares it with OTHERS/BTC. The supplied visual notes identify that chart as market capitalization excluding the top 10 crypto assets relative to Bitcoin. Their claim is that manufacturing expansion has coincided with stronger relative performance among smaller crypto assets.
They extend the comparison to Bitcoin priced in gold and Ethereum priced in dollars. In their reading, those charts have similar shapes and are rising alongside improving manufacturing conditions. They expect economic expansion to support a stronger altcoin phase than the market has recently experienced.
I see a hypothesis worth testing, but similar chart shapes do not establish that manufacturing activity causes crypto outperformance. Different assets can respond to shared influences. A selected window can also make a relationship look more stable than it is across other periods.
OTHERS/BTC is not a direct measure of Solana exchange tokens. Even if the broad relationship holds, it cannot identify which applications will gain users. A forecast for smaller crypto assets needs more evidence before it becomes a valuation argument for a particular exchange.
The creator associates an ISM reading of 58 with previous declines in Bitcoin dominance. They expect favorable conditions over the coming months and into 2027, while acknowledging that they do not know when that trigger might arrive. I would keep that uncertainty separate from the confidence of the price forecasts.
They also argue that government money creation and an AI-driven expansion support their outlook. These are the creator's macro claims, not independently established conditions in this source. They expect growth first, while recognizing that the expansion could eventually end badly.
A favorable economy may support risk appetite without making a particular token inexpensive. It also says little about whether an application can retain its market position. The macro case and the exchange's business case need to stand on their own evidence.
Follow flows without mistaking them for demand
Artemis is the creator's source for bridged asset net flows. Bridges transfer assets, or representations of them, between blockchains. Subtracting outgoing transfers from incoming ones helps compare where capital is accumulating within the routes covered by the dataset.
The creator first examines the past 3 months and identifies activity involving Robinhood Chain, OP Mainnet and Hyperliquid, with Base and Solana among the networks below them. Over the past month, they describe a different ranking: Robinhood falls toward the bottom, while Hyperliquid, Base and Solana remain prominent.
I find the comparison between windows more useful than either ranking alone. A longer period can still look strong after recent inflows slow. A shorter one can reveal that slowdown, but it can also exaggerate a temporary burst. Looking at both helps test whether a pattern is continuing.
Base is the creator's case study. Aerodrome, a decentralized exchange, or DEX, operates within that ecosystem. They report that it has risen 227% since February and connect its performance with incoming capital and exchange fee activity.
The creator expects Solana to follow Base: incoming capital brings traders, who use exchanges and pay fees, drawing attention to exchange tokens. The comparison gives them a reason to investigate Solana applications rather than focus only on its network coin.
I would check what happens to the transferred capital before accepting that sequence. It could remain idle, enter lending markets or move elsewhere. Even if trading increases, a different application might capture it. A flow ranking cannot identify the eventual winner by itself.
Bridge data also covers only one route into network activity. It is not a complete measure of demand, and an outflow alone does not prove users have abandoned an ecosystem. The destination and purpose of a transfer, along with whether the movement persists, affect its meaning.
Activity gives the thesis something measurable
The creator argues that Solana has a more developed application ecosystem than during its earlier downturn. They point to Pump.fun and other products as evidence of building and usage. Their criticism is directed at investors who support a network like a sports team while overlooking whether people use its applications.
I agree that sustained usage deserves more weight than an unsupported claim of superior technology. Technical capabilities matter when they help people do something they value. A capability on its own does not establish demand for a token.
The supplied Solana screen separates capital in the ecosystem from daily financial activity:
| Metric | Figure shown |
|---|---|
| Total value locked | $6.04b |
| Stablecoins market cap | $16.21b |
| 24h fees | $790,543 |
| 24h revenue | $115,727 |
Total value locked, or TVL, measures assets deposited in the protocols covered by a dataset. It helps describe the scale of committed capital, although asset-price changes can raise or lower it without new deposits. Stablecoin supply gives another view of available on-chain capital, but does not tell you how actively people use it.
I would keep fees and revenue separate when assessing the economics. Fees generally measure what users pay; revenue can mean the portion a protocol retains under the data provider's methodology. Treating them as interchangeable can overstate what the service earns.
The spoken commentary puts current TVL at $6.5 billion and compares it with around $230 million in 2022 and 2023. The spoken figure differs from the screen above. Neither should silently replace the other as a verified current reading.
The creator also reports Pump.fun fees of $5.83 million every single day and describes other applications as generating substantial fees. Application figures should not be casually added to network metrics. Datasets can cover different activities, recipients or time windows, so combining them requires consistent definitions.
For me, these figures identify where closer research is justified. A snapshot does not establish durability. Recurring usage, dependence on temporary excitement and the cost of attracting users all affect how much confidence the fee income deserves.
Jupiter and Raydium are conditional picks
The creator considers Jupiter and Raydium comparatively undervalued and expects a stronger macro backdrop to increase Solana trading. They favor Jupiter's remaining potential partly because Raydium has already made a larger move in the periods they selected.
Those periods differ:
| Project | Gain stated | Period described |
|---|---|---|
| Jupiter | 114% | Last few weeks |
| Raydium | 268% | Last few months |
I would not read a lower valuation into the smaller gain. Unequal windows make the comparison less direct, and recent price performance does not tell us what either token is worth relative to its economic rights. The figures explain the creator's preference, rather than establish that Jupiter is cheaper.
For Jupiter, they point to a descending trendline breakout and retest. A trendline connects chart points to illustrate direction; a breakout means price has moved beyond that drawn boundary. They expect potential upside beyond the earlier high they reference if their broader market thesis works.
For Raydium, the creator describes a possible 4x return to one previous high and an 8x move toward the 2021 high under stronger conditions. They explicitly call these projections speculation. I would treat historical highs as reference points, not prices the market owes investors. The conditions around an earlier peak may not return.
Their scale argument contrasts these exchange tokens with Solana itself. They put Solana's market capitalization at something like $60 billion or more and say a 10x move would imply like 600 plus billion dollars. That is why they look to smaller ecosystem assets for larger potential multiples.
Market capitalization is price multiplied by circulating supply. Smaller size can leave room for a larger percentage change, but can also mean thinner liquidity and greater vulnerability to selling. Size alone does not establish a better risk-to-reward ratio.
I also take the creator's Solana history as a reminder of how severe the path can be. They describe a fall of 97% from an earlier high before a strong recovery, and say they discussed buying near the lows on December 31, 2022. Their point is that a damaged chart does not automatically mean a useless project. Eventual recovery, however, does not make holding through the decline safe.
Where the argument can break
The proposed rotation might never arrive. Bitcoin could remain dominant, macro conditions could disappoint or capital could favor another network. The creator discusses SUI as an ecosystem worth watching, but wants more flows and activity there before giving it greater weight.
They are also enthusiastic about a possible Pump.fun-like application on SUI. It is a prospective catalyst in their account, not an established outcome. I would distinguish anticipation from evidence: the product would still have to arrive, attract users and sustain their interest.
Successful infrastructure can still leave investors with an unsuccessful token. Competition may reduce fee income even as trading volume grows. Token supply can expand while a protocol earns money, and expectations can rise faster than usage, leaving an active project with an expensive asset.
The presentation does not establish how Jupiter or Raydium fees reach token holders or detail their supply schedules. Those mechanics are essential to testing the creator's valuation claim.
I would also keep the operational risks separate from the price thesis. Smart contracts can contain flaws, wallet approvals can expose funds and counterfeit sites can impersonate familiar products. Verify an on-chain product's address before connecting a wallet and understand the permissions a transaction requests. A recognizable token name does not prove an interface is genuine.
Liquidity can make a displayed price misleading. The market may not absorb a meaningful transaction at that price, especially during a sharp move. Slippage is the difference between the expected execution price and the price received; when it worsens, both entry and exit become less predictable.
The creator warns against emotional reactions and chasing assets after large advances. I agree with separating research from urgency, but their suggestion that rising prices always reduce potential is too absolute. Better fundamentals can justify a higher price. The useful comparison is the new valuation against the new evidence, rather than today's price against an earlier low alone.
Test the route between capital and token value
I would use this argument as a research filter, not a deadline for action. The checks need to stay in order so that a strong-looking macro forecast or chart does not stand in for the exchange's economics:
- Start with the actual flow dataset and its observation period. Compare windows to see whether the destination still attracts capital, then establish whether that capital reaches the applications under consideration. A high ranking is a starting point for that work, not its answer.
- Examine the exchange's economics with consistent definitions. Separate user fees from retained revenue and identify who receives the benefit. Explain how application earnings could support the token, rather than relying on its association with a growing network.
- Keep the macro forecast separate. A manufacturing relationship can frame a market scenario, but cannot explain a particular application's competitive position. A breakout describes price behavior; it does not demonstrate that the token is undervalued.
- Check the original dashboard observations and actual chart dates before treating the presentation as a current market signal. Verifying the source is a different task from deciding whether the creator's economic reasoning is persuasive.
The evidence I would want is concrete: incoming capital that reaches the application, usage that produces earnings, and a token design that gives holders an economic benefit. A jump straight from a flow ranking to a return multiple leaves the fee recipient unresolved.
