NAZMUL BNB-

Bitcoin plunged to $75,000 over the weekend, marking its worst performance since the 2022 crypto winter. The cryptocurrency erased roughly $800 billion in market value from its October peak above $126,000, dropping it out of the global top 10 assets for the first time in years.
The selloff forced nearly $2.5 billion in leveraged positions to close and saw bitcoin fall behind traditional heavyweights like Tesla and Saudi Aramco in market rankings. This wasn't an isolated event. Gold dropped 9% to $4,900, while silver suffered a historic 26% crash to $85.30 in the same period.
What Triggered Bitcoin's Weekend Collapse?
The immediate catalyst came from escalating military tensions between the U.S. and Iran on Saturday. When geopolitical risks spike, investors typically shift capital into the U.S. Dollar through what traders call a "flight to safety." Bitcoin, operating 24/7, became the market's first casualty during thin weekend liquidity. The dollar's strength was amplified by Kevin Warsh's nomination to lead the Federal Reserve. This nomination drove a massive rally in the U.S. Dollar, making dollar-priced assets like bitcoin, gold, and silver more expensive for international buyers. The result was a coordinated selloff across all hard assets, debunking the narrative that crypto alone was facing pressure.
Weekend liquidity remained unusually thin following an October 10 crash that many traders attribute to issues at major exchanges. Market depth, which measures the capital available to absorb large trades, is still more than 30% below its October peak according to market Data. Order books have not fully rebuilt, and the spread between buy and sell prices remains wider than normal.
How Technical Breakdown Accelerated The Decline
Bitcoin's price action on Saturday revealed a market structure under severe stress. The cryptocurrency failed to hold support at $82,500, a level that technical analysts had identified as critical. This breakdown triggered additional selling as algorithmic trading systems and manual traders alike recognized the breach.
Bitcoin price chart (Image: TradingView)
The price broke through an ascending trendline that had been in place since late December. More importantly, bitcoin fell below its 50-day exponential moving average, currently near $75,500. This moving average now acts as resistance rather than support, a bearish development that typically signals further downside pressure.
For the first time since October 2023, bitcoin lost its realized market value of $80,700. This metric represents the average cost basis for all bitcoin currently held, essentially the collective "break-even" point for bitcoin holders. Trading below this level puts the majority of market participants underwater on their positions, increasing the likelihood of panic selling.
Did Michael Saylor's Strategy Position Worsen The Selloff?
Strategy's bitcoin holdings became a focal point when prices briefly dropped below the company's average cost basis of $76,037. The company holds over 700,000 bitcoin, making it the largest institutional holder. Panic spread across social media that Saylor might be forced to sell, which would have devastated an already fragile market.
However, none of Strategy's bitcoin is pledged as collateral, meaning there is no forced selling scenario. The real impact is on the company's ability to raise cheap capital for additional purchases. Strategy's stock price has fallen nearly 70% from its July 2024 high of $455 to current levels around $143, making it more expensive to issue new shares or debt.
This situation matters because Strategy has been one of the market's most consistent buyers. Without this institutional demand, the market loses a significant source of buying pressure, leaving it vulnerable to further liquidations and profit-taking.
What's Next For Bitcoin Price?
Technical analysts are watching the low to mid-$70,000 range as the next major support zone.
Historical patterns suggest extended recovery periods. After the 2021 peak, bitcoin took 28 months to recover. Following the 2017 initial coin offering boom, the recovery lasted nearly three years. Laurens Fraussen, an analyst at Kaiko, notes that exchange volume contractions during the 2017-2019 period saw 60% to 70% declines, while the 2021-2023 drawdown was more moderate at 30% to 40%.
Conclusion
Bitcoin's crash to $75,000 represents a convergence of geopolitical stress, structural market weaknesses, and forced liquidations during historically thin weekend trading. The selloff wiped out $800 billion in market value and exposed persistent liquidity problems that have plagued crypto markets since October.
Note: This article is market commentary based on publicly available information and does not constitute financial advice.

When people talk about AI economies, they usually start with speed. Faster inference. More agents. Bigger throughput numbers. It sounds logical at first. After all, if AI systems can think and act faster, value should move faster too. But when you look closer at how real systems behave over time, something important is missing. Memory. Not file storage or logs, but lived memory. The kind that lets systems remember past interactions, adjust behavior, and build trust through repetition. Without that, AI economies feel temporary. They spike, reset, and spike again. Like a busy train station with no one actually living there. This is not just a product issue. It is an economic one. When AI agents forget, users start from zero every time. Work is repeated. Context is lost. Small inefficiencies stack up. Over time, those inefficiencies break the loop that keeps people coming back. Economies depend on continuity. Humans expect it instinctively. We remember who we trust. We remember what worked before. AI systems that lack memory behave like new hires who never learn from yesterday. That is fine for demos. It fails in production. As AI systems move from tools to participants, this gap becomes harder to ignore.
This problem became visible in early AI platforms long before most people noticed it. Many systems processed millions of requests a day. On paper, usage looked strong. But underneath, retention told a different story. Agents did not carry context forward. Each interaction was isolated. Businesses integrating these systems had to rebuild workflows again and again. That kind of friction quietly kills adoption. It also creates cost problems. When systems forget, they repeat the same inference steps. They ask the same questions. They recompute what could have been remembered. During periods of high demand, inference costs spiked across the industry. Stateless systems absorbed those shocks poorly. They reacted instead of adapting. Systems with memory behaved differently. They smoothed demand by learning patterns over time. They avoided unnecessary calls. The difference was not dramatic in the short term. But it was consistent. And consistency is what economies are built on. This is where the conversation shifts from technology to structure. Memory is not a feature. It is infrastructure. Without it, AI activity can exist, but it cannot compound.
This is why Vanar Chain stands out when you look past the surface. While many projects focused on speed and marketing narratives, Vanar quietly prioritized memory as a native layer. From the start, the chain was designed to let AI agents retain context over time. Not just raw data, but meaning. In simple terms, this allows an AI agent to remember what happened before and use that understanding in future decisions. Mistakes leave traces. Successful actions are reinforced. Over time, behavior improves instead of looping. This changes how AI systems feel to users. Interactions become familiar. Responses improve naturally. Trust builds because the system remembers you. From a business perspective, this also changes cost dynamics. When agents remember, they do less unnecessary work. That efficiency shows up slowly, then all at once. Vanar’s approach does not rely on bold promises. It relies on structure. Memory lives on-chain, which means it is persistent, verifiable, and shared across the system. That makes AI behavior more predictable and easier to integrate into real workflows.
Zooming out, the bigger shift is already underway. AI is no longer just assisting humans. It is starting to operate alongside them in economic roles. Negotiating. Managing resources. Executing tasks over long periods. Participants like that need history. Markets without memory collapse into noise. Every interaction becomes a one-off. Coordination breaks down. Chains that treat AI as just another workload may host activity, but they will struggle to host economies. The difference matters. Hosting activity is about volume. Hosting economies is about continuity. Memory is what connects yesterday to tomorrow. Without it, automation becomes fragile. With it, systems become resilient. Vanar’s design reflects an understanding of this transition. It does not assume AI value comes from speed alone. It assumes value comes from learning, adjustment, and long-term behavior. That framing is subtle, but it changes everything.
Right now, the market still responds to short-term signals. Announcements drive attention. Demos drive engagement. Tokens react quickly. But underneath, quieter indicators are starting to matter more. Retention. Cost stability. Behavioral improvement over time. Systems that retain context are beginning to outperform in these areas by small margins. Not enough to make headlines yet. Enough to compound. That is usually how real infrastructure wins. Slowly, then decisively. Memory does not feel exciting because it works in the background. But economies depend on it. As AI systems continue to evolve, the ones that remember will feel less like tools and more like partners. The rest will keep restarting from zero. Vanar saw this early. Not as a slogan, but as a design choice. And in AI-driven economies, design choices made early tend to matter the most.
@Vanarchain #vanar $VANRY

There is a specific kind of frustration that only shows up when money is supposed to move quietly. It usually happens late, when markets slow down and attention fades. A transfer should be simple. Instead, it stalls. You wait for confirmations, watch the clock, and wonder whether the delay itself has created a problem. Did someone notice the transaction? Will compliance questions surface later? Will the settlement finalize cleanly, or will it hang just long enough to introduce doubt? These moments are not dramatic, but they shape behavior. Over time, they make people cautious. They make traders hesitate. And they expose a gap between what crypto finance promises and how it often feels in practice. Dusk was designed around this exact gap, not by adding more features, but by focusing on how financial actions actually unfold when privacy, speed, and trust all matter at the same time.
At its core, Dusk Network positions itself as a settlement network for situations where visibility is a liability rather than a benefit. The system uses privacy-preserving cryptography to keep transaction details confidential, while still allowing regulated participants to verify what needs to be verified. This balance is deliberate. Total anonymity does not work for real financial markets, especially when tokenized bonds, equities, or other real-world assets are involved. At the same time, full transparency exposes strategies, positions, and intent. Dusk sits in the middle. Transactions can remain private by default, but they are still provable. That means compliance checks can happen without turning every trade into public information. For users, this feels closer to traditional finance workflows, where confidentiality is expected, not questioned.
Speed plays an equally important role. Many blockchains are fast on paper, but uncertain in practice. A trade might be included quickly, yet finality can still feel distant. Dusk approaches this differently by aiming for deterministic settlement. Blocks are produced at a steady rhythm, and once a transaction is finalized, it is not meant to be revisited. For someone moving assets between platforms, this removes a layer of mental overhead. You do not need to wait and wonder whether a reorganization will undo your action. You do not need to price in the risk of front-running while confirmations stack up. Settlement becomes something you can rely on, not something you monitor nervously. This is especially relevant for tokenized securities, where timing and certainty matter as much as price.
The role of the DUSK token reflects this infrastructure-first mindset. It is not framed as a speculative object or a narrative asset. Its purpose is functional. DUSK is used to pay for network activity and to secure the chain through staking. Validators lock tokens to participate in block production, and rewards are distributed in a way that supports long-term network health. Part of the rewards go to those maintaining the system, part supports governance, and part is reserved for ongoing development. The supply schedule is intentionally slow, stretching emissions over decades rather than years. This reduces sudden shocks and aligns incentives with patience. For users, the takeaway is simple. The token exists to keep the system running smoothly, not to distract from what the system is meant to do.
Where this design becomes tangible is in Dusk’s focus on real-world assets and regulated trading environments. Through platforms like Dusk Trade, the network is being positioned as a place where tokenized securities can settle privately and efficiently, without forcing users to hand custody to intermediaries. This matters because custody is often where risk concentrates. Reducing the number of handovers reduces the number of failure points. Partnerships with regulated venues and infrastructure providers aim to bring traditional assets on-chain in a way that feels familiar to institutional users. The goal is not to reinvent finance, but to remove friction where it no longer makes sense. Interoperability tools allow these assets to connect with other ecosystems, so liquidity is not trapped in one place. This gradual integration is less visible than headline-grabbing launches, but it is more aligned with how financial systems actually grow.
In the short term, markets will continue to focus on price movement. Tokens will spike, pull back, and generate noise. That is unavoidable. The longer-term signal for Dusk is behavioral. If users complete a private settlement once and it works as expected, they are more likely to do it again. Trust forms quietly. Infrastructure earns its place not through excitement, but through repetition. Dusk’s approach suggests a belief that the future of crypto finance will not be built on constant attention, but on reliability during unremarkable moments. The times when nothing goes wrong. The times when a trade settles, custody stays intact, and no one else needs to know. That is not flashy. But in finance, that is often the point.
@Dusk #dusk $DUSK

Most people still associate blockchains with trading, speculation, or experimental apps. In everyday life, money works very differently. It needs to be predictable. It needs to move fast. And most importantly, it needs to feel boring in the best possible way. Plasma starts from that reality. Instead of asking what else a blockchain can do, it asks a simpler question: what would blockchain infrastructure look like if stablecoins were treated as real money, not just tokens riding on someone else’s network?
The core observation behind Plasma is straightforward. Stablecoins already move billions of dollars every day, yet they mostly run on chains that were never designed for high-volume payments. On networks like Ethereum or Tron, users must hold a separate native token just to send money. Fees fluctuate. Congestion appears without warning. Simple transfers can feel unpredictable, especially for small payments. That setup works for traders and power users, but it breaks down quickly for merchants, payroll, remittances, or everyday transfers. Plasma treats this as a design failure, not a user problem. Its answer is to build a chain where stablecoins sit at the center of the system, not at the edge.
This design choice shapes everything. Plasma does not try to be a general-purpose blockchain that later adds payments as a feature. It positions itself as digital money infrastructure first. Stablecoins are handled as first-class economic units, meaning the network is optimized around how people actually use them. One of the clearest examples is gas abstraction. On Plasma, users can send stablecoins like USDT without holding the native token just to pay fees. The protocol handles gas in the background through a separate model. For users, this feels closer to how money already works. You spend what you have. You do not need to manage an extra asset just to make a payment. This may sound small, but in practice it removes one of the biggest friction points in crypto payments.
Under the hood, Plasma is built to support this payment-first philosophy at scale. It uses a Byzantine Fault Tolerant consensus system designed for speed and reliability, aiming for sub-second finality and high transaction throughput. That matters because payments behave differently from trades. They are frequent, often small, and sensitive to delays. A coffee purchase or a merchant settlement cannot wait minutes for confirmation, nor can it tolerate fees that change from one hour to the next. Plasma’s architecture reflects this reality. The goal is not maximum decentralization on paper, but dependable performance under real-world load. This is the kind of infrastructure thinking that payments demand, even if it is less flashy than launching new features every week.
At the same time, Plasma does not isolate itself from the broader crypto ecosystem. It runs an Ethereum-compatible execution environment, which means developers can use familiar tools and languages without learning something entirely new. Wallets, smart contracts, and existing workflows carry over with minimal friction. This makes Plasma more than a simple transfer network. It becomes programmable money. Stablecoins can move quickly, but they can also interact with applications, logic, and financial tools built on top. Cross-chain integration plays a role here as well. By connecting to broader liquidity systems, Plasma positions itself as a settlement layer inside a much larger web of chains and assets, rather than a closed island.
The economic layer of Plasma follows the same pragmatic logic. The native token, XPL, exists to secure the network, support staking, and fund long-term ecosystem development. It is not forced into every user interaction. This separation is intentional. Most people who want to use stablecoins do not want exposure to volatility or token management. Plasma acknowledges that reality instead of fighting it. At the same time, the network still needs incentives, security, and governance. XPL fills that role in the background, while stablecoins remain the foreground experience. Vesting schedules and structured allocations are designed to align long-term participation rather than short-term extraction, which is essential for infrastructure meant to last.
What Plasma ultimately represents is a shift in priorities. For years, blockchains have been built as multipurpose platforms first and financial infrastructure second. Plasma flips that order. It assumes that stablecoins are already one of crypto’s most successful products and asks how to support them properly. The early signals, including strong initial liquidity and rapid integration into cross-chain flows, suggest real demand for this approach. Still, the true test will come over time. Payment rails are judged not by launch metrics, but by consistency. Can the network remain reliable under pressure? Can it handle regulation, scale, and real-world usage without degrading the user experience?
Plasma does not promise to reinvent money. It does something quieter and arguably more important. It tries to make digital money behave the way people already expect money to behave. Fast. Predictable. Easy to use. If stablecoins are going to move from trading desks to everyday life, infrastructure like this will matter far more than the next speculative trend.
@Plasma #Plasma $XPL

In crypto, price usually speaks louder than progress. When a token runs hot, people assume something important is happening. When it drifts or drops, attention moves elsewhere. Walrus sits in that uncomfortable middle ground right now. It is not collapsing, but it is not being celebrated either. That alone makes it interesting. As of early February 2026, WAL trades far below its previous highs, with steady volume and a market cap that suggests hesitation rather than abandonment. This kind of market behavior often points to uncertainty, not failure. People are unsure how to value what is being built. Walrus is not positioning itself as a flashy new narrative. It is trying to solve a problem most users never think about until something breaks: reliable, affordable data storage that applications can actually depend on over time.
At a simple level, Walrus is about storing data. But that description misses the point. The real challenge in decentralized storage is not whether data can be saved. It is whether it can be retrieved reliably without costs spiraling out of control. In many systems, reliability comes from heavy replication. Data is copied again and again across nodes, just in case something fails. That works, but it is expensive. Over time, bandwidth and storage overhead quietly eat into the system. Walrus approaches this differently through its encoding design, often referred to as Red Stuff. Instead of endlessly copying full datasets, the network breaks data into pieces in a structured way. If parts go missing, the system can recover only what was lost, rather than rebuilding everything. A simple way to think about it is repairing a torn page instead of reprinting the whole book. This design choice is not glamorous, but it directly targets the cost of long-term reliability, which is where many storage networks struggle.
This technical choice shapes how Walrus behaves economically. Storage networks live and die by incentives. Users want predictable pricing. Node operators want stable rewards. Tokens, however, are volatile by nature. When prices surge, storage suddenly becomes expensive. When prices fall, operators lose motivation and leave. Walrus tries to smooth this tension by using prepaid storage fees that are paid in WAL and distributed over time to storage nodes and stakers. The goal is not to eliminate volatility, which is unrealistic, but to soften its impact on the people actually keeping the network alive. For users, this means storage costs that do not swing wildly from one month to the next. For operators, it means income that reflects real usage rather than short-term market noise. This kind of design rarely excites traders at first, but it matters for building something that lasts.
Of course, none of this exists in a vacuum. Token supply dynamics still matter, and Walrus is no exception. A large portion of the total supply is not yet circulating, which introduces future sell pressure as unlocks occur. This is not unique to Walrus, but it does create real calendar risk. Long periods of calm can be followed by sudden increases in available supply. If demand for storage and staking does not grow alongside those unlocks, the token price can remain under pressure regardless of technical progress. This is why usage metrics matter more than marketing. Growth in paid storage, consistent renewals, and active node participation are the signals that show whether the system is earning its place. Without them, even the best-designed infrastructure struggles to gain market trust.
What makes Walrus stand out is not a promise of dominance, but a realistic path to usefulness. If the network works as intended, applications can treat storage as a dependable layer rather than a constant risk. Developers do not need to rebuild complex offchain systems just to ensure their data survives. They can assume availability, verify integrity, and move forward. That shift is subtle but important. It turns storage from a background service into a core part of how apps function. Over time, this kind of quiet reliability tends to compound. It does not generate hype overnight, but it builds trust through consistent performance. In that sense, Walrus resembles infrastructure we rely on every day. No one praises it when it works. Everyone notices when it fails.
The current market disagreement around WAL reflects this tension between visible price action and invisible progress. Traders look for momentum. Builders look for systems that do not break under pressure. Walrus is clearly optimizing for the second group. Whether that choice pays off depends on execution and adoption, not slogans. If usage grows and the economic model continues to support both users and operators, the network earns the right to be considered core infrastructure. If not, it risks becoming another well-intentioned experiment. For now, Walrus sits in that in-between state where the outcome is still open. That uncertainty is exactly why it deserves careful attention, not blind optimism or quick dismissal.
@Walrus 🦭/acc #walrus $WAL

I was you a few years ago.
I was glued to the 1-minute chart, refreshing constantly, watching every tick, and taking far too many trades. I'd take 10–15 trades a day and still end most weeks red.
I was chasing moves that looked good in the moment. I'd cut winners early because I was scared to give back gains, then hold losers because I convinced myself they'd come back.
The worst part? I knew exactly what I was doing wrong, but I couldn't stop.
Placing trades felt productive. It felt like progress. In reality, I wasn't trading with a plan. I was gambling with extra steps.
The Strategy Nobody Wants to Hear
This is the entire strategy:
When price is above the 200-day moving average, I look for longs. When price is below the 200-day, I look for shorts. In both cases, I wait for a break and retest before entering.
That's it.
No complex indicators. No secret settings. No system that needs constant tweaking. Just the 200-day moving average and patience.
Why the 200-Day Matters
The 200-day moving average isn't magic. It works because of psychology and participation.
Institutions watch it. Algorithms react to it. It's one of the clearest lines separating a stock that's trending from one that's in trouble.
When price is above the 200-day, dips are more likely to get bought and upside continuation makes sense. When price is below it, rallies tend to fail and downside pressure is higher.
It's simple, but simple doesn't mean ineffective. Pull up enough charts and the pattern becomes obvious.
The Break and Retest
This is where most traders get impatient.
They see a stock break a key level and immediately chase the move. Then price pulls back, emotions kick in, and they sell for a loss.
Instead of chasing, I wait for confirmation. The stock breaks resistance, pulls back to retest that level, and old resistance acts as new support. That retest is the entry.
Waiting for the retest confirms the breakout, filters out false moves, and gives you a better entry with defined risk. More importantly, it keeps emotion out of the trade.
Why This Feels Uncomfortable
This approach will make you feel like you're missing out.
You'll watch stocks move without you because they never give a clean retest. Other traders will post gains while you're sitting in cash.
That's the cost of discipline.
What you don't see are the revenge trades, the forced entries, and the slow bleed that comes from overtrading. Missing a move is frustrating, but forcing a bad trade is far worse.
The market will always offer another opportunity. Your capital won't always survive poor decisions.
If you need action every day, this probably isn't for you.
If sitting on your hands feels impossible, this will be uncomfortable. And if trading has to feel exciting, you'll hate this approach.
But if you're tired of the emotional roller coaster—the wins turning into losses, the constant overtrading, the stress that follows you after hours—then simplifying your process matters.
Zoom out. Respect the 200-day. Wait for the retest. Let the trade come to you.
It's not flashy, but it's repeatable.
The Levels Don't Lie
I post my levels before the move happens. The structure is always the same: break, retest, continuation.
Stop overcomplicating trading.
Stop searching for the perfect indicator.
Stop chasing price.
Just follow the levels.
(This is market commentary for educational purposes only, not financial advice.)

Bitcoin (BTC) fell below $80,000 as US spot ETF outflows and fading Fed rate-cut bets weakened demand.US BTC-spot ETFs recorded $1.61bn in January outflows, extending a three-month streak of institutional selling.Despite short-term losses, Fed easing prospects and market structure reform support a cautiously bullish outlook.
Bitcoin (BTC) breaches key support at $80,000. ETF outflows, Trump’s Fed Chair nomination, and US economic data impact buying interest in risk assets.
The US BTC-spot ETF market extended its weekly outflow streak, signaling a slump in institutional demand for BTC.
US economic indicators tempered expectations of an H1 2026 Fed rate cut, adding to the negative sentiment.
Meanwhile, President Trump nominated a more hawkish-than-expected Fed Chair to replace Powell, spooking the global markets.
Despite January’s losses, a dovish Fed rate path and the progress of the Market Structure Bill continue to support a cautiously bullish medium-term price outlook.
Below, I consider the key drivers behind recent price trends, the short-term outlook, the medium-term trajectory, and the key technical levels traders should watch.
US Spot-ETF Market Wraps Up a Grim January
The US BTC-spot ETF market saw $1.49 billion in net outflows in the reporting week ending January 30. Following outflows of $1.32 billion in the week prior, the US BTC-spot ETF market reported total net outflows of $1.61 billion in January 2026 after December’s outflows of $1.09 billion.
According to Farside Investors, key flows for the week included:
iShares Bitcoin Trust (IBIT) had net outflows of $947.2 million.Fidelity Wise Origin Bitcoin Fund (FBTC) reported net outflows of $191.5 million.In total, eight ETF issuers reported weekly net outflows.
Crucially, the US BTC-spot ETF market extended its monthly outflow streak to three months, tilting the supply-demand balance in the bears’ favor. Meanwhile, BTC extended its monthly losing streak to five months, plunging from October’s all-time high of $125,761 to a January 31 low of $75,678 before steadying. BTC last sat below $75,000 in April 2025.
Flow trends and BTC’s price action reflected the effect of US tariff policies, the US government shutdown, central bank policy stances, and regulatory developments on sentiment.

BTCUSD – Daily Chart – 010226 – The Reversal
President Trump Nominates Fed Chair Powell’s Replacement
While ETF outflows weighed heavily on sentiment, Fed Chair Powell’s replacement is a potential boon for the Bitcoin bulls over the longer-term.
This week, President Trump nominated Kevin Warsh for the Fed’s top job, signaling a shift in monetary policy and attitudes toward BTC.
Markets reacted adversely to the nomination. Economists view Kevin Warsh as more hawkish than the other potential nominees. While supporting lower interest rates, the consensus is that he would be less aggressive in cutting rates.
However, if appointed Fed Chair, his pro-Bitcoin stance may fuel speculation about BTC becoming a US strategic reserve asset. A US Strategic Bitcoin Reserve would tilt the supply-demand balance firmly in BTC’s favor, supporting a bullish medium-term outlook. Kevin Warsh previously signaled his support for Bitcoin as a store of value, placing it in the same category as gold.
In 2025, Senator Cynthia Lummis introduced the Bitcoin Act, proposing that the US government acquire one million BTC over five years, with a mandatory 20-year holding period.
For context, Congress, the US Treasury, and the Fed Chair would need to approve BTC as a US strategic reserve asset.
Crucially, renewed speculation that BTC could become a Strategic Reserve Asset would counter spot ETF outflows, supporting a bullish medium- to long-term price outlook.
Bitcoin and the US Economic Calendar: US Services PMI and Jobs Report in Focus
Looking at the week ahead, US services sector data, the jobs report, and Fed chatter will influence Fed rate cut bets and risk sentiment.
Slower services sector activity and softer labor market conditions would support a more dovish Fed rate path. Rising expectations of an H1 2026 Fed rate cut would boost demand for risk assets such as BTC.
Recent US economic indicators, including labor market and inflation data, and Fed Chair Powell, signaled a more hawkish Fed rate path. Shifting sentiment toward the Fed’s policy stance contributed to spot ETF outflows and BTC’s drop below $80,000. Nevertheless, hopes for a rate cut linger.
According to the CME FedWatch Tool, the probability of a March cut fell from 50.9% on December 30 to 13.4% on January 30. Meanwhile, the chances of a June cut dropped from 84.5% on December 30 to 61.8% on January 30.
Bitcoin Fear & Greed Index Plunges Deep into Extreme Fear
BTC-spot ETF outflows and BTC’s stumble below $80,000 sent the Bitcoin Fear & Greed Index deep into the Extreme Fear zone. The Fear & Greed Index dropped from 20 on January 31 to 14 on February 1, indicating oversold conditions and a potential rebound.
BTC Fear and Greed Index – 010226
Downside Risks: Central Banks and ETF Flows
While fundamentals support a constructive medium-term bias, downside risks remain, including:
The BoJ signals a higher neutral interest rate (potentially 1.5%-2%), indicating multiple rate hikes. BoJ rate hikes and Fed rate cuts would narrow rate differentials, potentially triggering a yen carry trade unwind.US economic data and the Fed support a more hawkish policy stance.BTC-spot ETFs face renewed outflows.
These factors would likely send BTC below $70,000, exposing the November 2024 low of $66,832.
In summary, the short-term outlook remains bearish as fundamentals align with technicals. However, the medium- to longer-term outlook is constructive, based on favorable fundamentals developing. These dynamics include the prospects of Fed rate cuts, the potential for BTC becoming a strategic reserve asset, and the progress of the Market Structure Bill on Capitol Hill.
Technical Analysis
The weekly losses left BTC trading below its 50-day and 200-day Exponential Moving Averages (EMAs), indicating bearish momentum. However, improving fundamentals suggest a rebound from the current levels, countering the negative technicals.
A break above the 50-day EMA would bring $95,000 and the 200-day EMA into play. A sustained move through the 50-day and 200-day EMAs would signal a bullish trend reversal, paving the way toward $100,000. Notably, a sustained move through the 200-day EMA would reaffirm the bullish medium-term price outlook.

BTCUSD – Daily Chart – 010226 – EMAs
Bearish Structure Validated: What Happens if BTC Reclaims $85,000?
Avoiding a sustained fall below $75,000 would support a recovery to $85,000. A breakout above $85,000 and the upper trendline would invalidate the bearish structure, affirming the bullish short-term (1-4 weeks) target of $100,000 and the medium-term (4-8 weeks) target of $115,000.
However, a sustained drop below $75,000 would expose the November 22 low of $66,832 and validate the bearish structure.
BTCUSD – Daily Chart – 010226 – Bearish Structure
Outlook: $75,000 Support Key to Medium-Term Bullish Outlook
US economic indicators, the Fed, the BoJ, and US BTC-spot ETFs will influence demand for BTC.
The US jobs report (February 6) will be the main event of the coming week. Weaker labor market conditions and rising bets on a March Fed rate cut would likely lift sentiment. Kevin Warsh’s policy stance will also be crucial, given last week’s price action.
Considering current market dynamics, the medium- and long-term outlook remains constructive, with a 6-12 month price target of $150,000. The US Senate’s passing the Market Structure Bill would add to the bullish outlook.
(Readers should conduct their own research and risk assessment)

Dusk has a way of finishing things early. The state closes. The math checks out. The committees are done. From a technical point of view, the work is complete. If correctness were the only measure that mattered, this would be the end of the story. But in real organizations, the moment after finality is often where tension starts, not where it ends. The system has reached a clear outcome, yet the room hesitates. Screens stay unshared. Messages stay unsent. People glance at the same empty line in a release template, searching for words that feel safe enough to exist outside the system. This is where Dusk quietly reveals a problem most blockchains never surface: finishing computation is not the same as earning the right to speak.
At the core of Dusk’s design is a simple promise. Transactions can be correct, final, and verifiable without becoming public in a way that leaks sensitive information. Views are scoped. Proofs are valid, but not portable by default. This is a feature, not a flaw. In regulated or privacy-sensitive environments, showing everything to everyone is not progress. It is risk. Dusk respects that reality. The result is a chain where auditability exists, but only within defined boundaries. A verifier can confirm that something happened without gaining the ability to explain it freely to others. That separation feels clean at the protocol level. In practice, it creates a new kind of pause. The system knows the truth, but people still need permission to describe it.
This is where the authority gap appears. After finality, someone has to translate a closed state into a sentence that can travel. Not a proof. Not a hash. A sentence that survives review, legal scrutiny, and internal politics. Most teams discover that this sentence is harder to produce than the transaction itself. Say too much, and you widen the scope of disclosure by accident. Say too little, and the message becomes meaningless. So people default to silence. They forward links instead of explanations. They write “cleared within scope” and move on. The outcome ships, but the meaning stays locked inside the system. Everyone feels the absence, even if no one names it.
Over time, organizations adapt. They stop trusting the idea that they will “explain it later.” Later is exactly when explanation becomes dangerous. Once something is finished, words carry weight. A careless phrase can create obligations, expectations, or visibility that were never intended. So teams start speaking earlier. Not publicly, but internally. Before execution, they quietly agree on who is allowed to say what, where it can be said, and what shape it can take. These are not grand policy documents. They are small, practical understandings. If this closes cleanly, we will say this and nothing more. If it needs to travel, this person signs. If it stays internal, it looks like this. The chain keeps moving fast. The organization learns to choreograph speech with the same care it gives to execution.
This shift changes how people experience speed. Dusk’s finality remains quick. States still close without drama. But communication becomes the slower layer. Not because the system is inefficient, but because language is irreversible. Once a sentence leaves the room, you cannot pull it back into confidentiality and pretend it never existed. In traditional systems, this risk is hidden behind paperwork and delay. Dusk removes that buffer. It delivers certainty early, which forces organizations to confront their own comfort with disclosure. The friction does not come from the protocol. It comes from human caution.
What emerges is a new pattern of trust. Instead of long explanations, teams rely on minimal signals. A timestamp. A reference. A statement that something cleared, without saying why. For outsiders, this can feel thin. For insiders, it feels responsible. The proof exists for those who are entitled to see it. The sentence exists only in its safest form. This is not about secrecy for its own sake. It is about respecting boundaries that matter. In many environments, the ability to prove without explaining is exactly what allows work to happen at all. Dusk does not create this reality. It simply refuses to blur it.
The deeper insight is that Dusk is not just a technical system. It is a mirror. It shows organizations where their authority actually lives. Not in code, but in roles, signatures, and agreed language. When those are unclear, progress stalls even after finality. When they are defined, the system feels smooth. The chain closes its windows. Reviews pass. Releases ship. Yet even then, something remains unsaid. The perfect sentence, the one everyone wanted, never appears. And that is the point. In a world where privacy and correctness coexist, silence is not a failure. It is often the most accurate statement available.
@Dusk #dusk $DUSK

When people talk about blockchain, they often start with ideals. Open. Permissionless. Fully decentralized from day one. It sounds good, and it looks good on a website. But when these systems are pushed into the real world, something usually breaks. Payments need to go through on time. Systems need to stay online. Businesses need to explain decisions to regulators, partners, and users. Under that pressure, many networks quietly step away from their early promises. Vanar Chain begins from a different place. Instead of treating decentralization as a switch that must be turned on immediately, it treats it as something that grows with trust. The idea is simple and familiar. People adopt systems that work first. Only then do they feel safe letting go of control. This is how the internet scaled. It is how cloud services gained adoption. And it is how most financial infrastructure earned confidence over time. Vanar’s approach may feel less dramatic, but it is grounded in how real systems survive outside of theory.
At the core of this approach is what Vanar describes as a trust ladder. The network begins with a limited number of known and tested participants distributed across regions. These participants are not chosen at random. They go through evaluation, monitoring, and real usage. As they build a track record, the network gradually opens to more operators and contributors. This is not decentralization by declaration. It is decentralization by evidence. Many projects talk about progressive decentralization, but often leave it vague, pushed into an undefined future. Vanar puts it directly into its design logic and documentation. The message is clear. Stability comes first, because without it, nothing else matters. This matters especially for systems that touch payments, data, and compliance-heavy workflows. A network that fails under early stress does not get a second chance. By treating trust as something that is earned and expanded, Vanar is aligning its structure with how organizations and users actually behave when real value is involved.
Another important choice sits slightly beneath the surface, but it may be more impactful than the governance model. Vanar is not built around a gamble on staking economics alone. Instead, it is built around developer time. In practice, the largest cost in Web3 is not gas or hardware. It is the hours developers lose rewriting systems just to make them compatible with a new chain. Many technically strong networks fail here. They demand new tools, new languages, and new mental models, then wonder why adoption stalls. Vanar takes a compatibility-first position. The idea is to let teams bring what they already use, ship products faster, and adopt new features gradually. This is not framed as a shortcut. It is framed as respect for how builders work. When developers can move without throwing away years of effort, ecosystems form naturally. In the short term, this compatibility is what opens the door. In the long term, it creates space for deeper features to matter, because teams are already there, already building, already invested.
That long-term differentiation shows up most clearly in Vanar’s work around data and AI, especially through what it calls Neutron. On the surface, Neutron is described as a way to compress large files into much smaller on-chain representations while preserving meaning. The numbers are attention-grabbing, but the real idea is simpler and more practical. Instead of forcing all data to live fully on-chain, Neutron allows structured “seeds” to exist off-chain for performance and flexibility, while still being anchored on-chain for verification, ownership, and integrity. Think of it like keeping a full document in a secure archive, while the fingerprint and key facts are recorded in a ledger that cannot be altered. This balance matters. It keeps systems fast and flexible, without losing the ability to prove what happened and when. Over time, these seeds can carry not just data, but logic and reasoning layers that software agents can read and act on in a predictable way.
This leads to the deeper problem Vanar is trying to address, which is trust at the level of explanation. Crypto has a trust problem. AI has a trust problem. When the two meet, that problem doubles. In real-world systems, it is not enough for an action to be correct. It must be explainable. Why was a payment approved. Why did a rule trigger. Why was a document accepted as valid. These questions are routine in finance, logistics, and enterprise software. They are also where many blockchain demos stop being usable. Vanar’s vision points toward infrastructure that fades into the background. Systems that feel less like experiments and more like utilities. Predictable validation. Readable data. Logic that can be inspected and justified. This is not about hype or bold promises. It is a quiet bet that the next phase of Web3 growth will look less exciting on the surface, but far more durable underneath. If that bet is right, Vanar’s trust ladder, compatibility focus, and emphasis on explainable data may matter not because they are revolutionary, but because they are normal enough to work.
@Vanarchain #vanar $VANRY