Some of the leading privacy coins are defying the gravity of the digital asset market, signaling growing investor demand for privacy-focused cryptocurrencies.
Zcash (ZEC) surged to an over eight-year high of $388 on Friday after rising 7.6% in the last 24 hours, according to data aggregator CoinMarketCap. With a $6.2 billion market capitalization at the time of writing, ZEC flipped Monero (XMR) to become the most valuable privacy-focused cryptocurrency.
ZEC’s 45% weekly rise bucked the broader crypto market downturn, which continued its consolidation after a much-awaited tariff deal between the US and China failed to materialize on Thursday.
The rally suggested renewed investor appetite for privacy-focused coins, which are designed to conceal user transaction details.
Top 10 privacy cryptocurrencies. Source: Cointelegraph
Privacy coins such as Zcash and Monero obscure sender, receiver and transaction details, offering greater anonymity than pseudonymous cryptocurrencies like Bitcoin (BTC). While Bitcoin transactions are traceable onchain, privacy tokens are designed to mask wallet addresses and transaction histories.
The rise to the eight-year high came days after BitMEX co-founder Arthur Hayes predicted a ZEC token rally to $10,000, further bolstering buzz around the token.
Zcash rallied from $272 to a peak of $355 in the hours after Hayes’s bullish prediction on Sunday, Cointelegraph reported.
While demand for the token is steadily increasing, whales — large holders of cryptocurrency — have been offloading the Zcash token.
ZEC tokenholders rose by 63% to 1,968 over the past week, but whale wallets have sold a net total of $702,000 in ZEC tokens, according to crypto intelligence platform Nansen.
ZEC/USD, 1-year chart. Source: Nansen
“Crazy to see how $ZEC has pulled a 10x in just two months, completely decoupling from the market and ignoring overall sentiment,” according to Simon Dedic, founder and managing partner at Moonrock Capital.
“I want to emphasize that this isn’t some shitcoin, it’s a multibillion-dollar asset. That makes this kind of performance even more remarkable,” he wrote in a Friday X post.
Australian police cracked a coded cryptocurrency wallet backup containing 9 million Australian dollars ($5.9 million).
Australian Federal Police (AFP) Commissioner Krissy Barrett described the effort as “miraculous work” during a Wednesday speech, crediting a data scientist who has become known within the agency as a “crypto safe cracker.”
During an investigation into a purported “well-connected alleged criminal” who stockpiled cryptocurrency by selling “a tech-type product to alleged criminals,” the AFP came across password-protected notes on his mobile phone. Upon further examination, law enforcement also identified an image containing random numbers and words, Barrett said.
Barrett said the numbers were divided into six groups with over 50 combinations, and the AFP digital forensics team “determined it could be related to a crypto wallet.” The suspect allegedly refused to hand over the keys to his crypto wallet, an act that carries a 10-year penalty in Australia.
“We knew if we couldn’t open the crypto wallet, and if the alleged offender was sentenced, upon release, he would leave prison a multi-millionaire, all from the profits of organized crime,” Barrett said. “For our members, that was not an acceptable outcome.”
How the code was cracked
One of AFP’s data scientists realized that the alleged criminal “tried to create a crypto booby prize in how the numbers were presented.” To decode the 24-word seed phrase, he had to remove the first number from each sequence.
The data scientist explained that “some of the number strings felt wrong and they looked like they were not computer-generated.” He added that those strings “looked like a human had modified the sequence by adding numbers to the front of some sequences.”
This wasn’t the first crypto recovery for the AFP’s digital forensics team. In a separate case, the same unidentified data scientist helped recover more than $3 million in digital assets using another decoding technique.
In both cases, the crypto was seized by the AFP-led Criminal Assets Confiscation Taskforce. If the court orders the funds to be confiscated, the money will end up in a commonwealth account and redistributed by Home Affairs Minister Tony Burke to fund crime prevention.
ChatGPT functions best as a risk detection tool, identifying patterns and anomalies that often emerge before sharp market drawdowns.
In October 2025, a liquidation cascade followed tariff-related headlines, wiping out billions of dollars in leveraged positions. AI can flag the buildup of risk but cannot time the exact market break.
An effective workflow integrates onchain metrics, derivatives data and community sentiment into a unified risk dashboard that updates continuously.
ChatGPT can summarize social and financial narratives, but every conclusion must be verified with primary data sources.
AI-assisted forecasting enhances awareness yet never replaces human judgment or execution discipline.
Language models such as ChatGPT are increasingly being integrated into crypto-industry analytical workflows. Many trading desks, funds and research teams deploy large language models (LLMs) to process large volumes of headlines, summarize onchain metrics and track community sentiment. However, when markets start getting frothy, one recurring question is: Can ChatGPT actually predict the next crash?
The October 2025 liquidation wave was a live stress test. Within about 24 hours, more than $19 billion in leveraged positions was wiped out as global markets reacted to a surprise US tariff announcement. Bitcoin (BTC) plunged from above $126,000 to around $104,000, marking one of its sharpest single-day drops in recent history. Implied volatility in Bitcoin options spiked and has stayed high, while the equity market’s CBOE Volatility Index (VIX), often called Wall Street’s “fear gauge,” has cooled in comparison.
This mix of macro shocks, structural leverage and emotional panic creates the kind of environment where ChatGPT’s analytical strengths become useful. It may not forecast the exact day of a meltdown, but it can assemble early warning signals that are hiding in plain sight — if the workflow is set up properly.
Lessons from October 2025
Leverage saturation preceded the collapse: Open interest on major exchanges hit record highs, while funding rates turned negative — both signs of overcrowded long positions.
Macro catalysts mattered: The tariff escalation and export restrictions on Chinese technology firms acted as an external shock, amplifying systemic fragility across crypto derivatives markets.
Volatility divergence signaled stress: Bitcoin’s implied volatility stayed high while equity volatility declined, suggesting that crypto-specific risks were building independently of traditional markets.
Community sentiment shifted abruptly: The Fear and Greed Index dropped from “greed” to “extreme fear” in less than two days. Discussions on crypto markets and cryptocurrency subreddits shifted from jokes about “Uptober” to warnings of a “liquidation season.”
Liquidity vanished: As cascading liquidations triggered auto-deleveraging, spreads widened and bid depth thinned, amplifying the sell-off.
These indicators weren’t hidden. The real challenge lies in interpreting them together and weighing their importance, a task that language models can automate far more efficiently than humans.
What can ChatGPT realistically achieve?
Synthesizing narratives and sentiment
ChatGPT can process thousands of posts and headlines to identify shifts in market narrative. When optimism fades and anxiety-driven terms such as “liquidation,” “margin” or “sell-off” begin to dominate, the model can quantify that change in tone.
Prompt example:
“Act as a crypto market analyst. In concise, data-driven language, summarize the dominant sentiment themes across crypto-related Reddit discussions and major news headlines over the past 72 hours. Quantify changes in negative or risk-related terms (e.g., ‘sell-off,’ ‘liquidation,’ ‘volatility,’ ‘regulation’) compared with the previous week. Highlight shifts in trader mood, headline tone and community focus that may signal increasing or decreasing market risk.”
The resulting summary forms a sentiment index that tracks whether fear or greed is increasing.
Correlating textual and quantitative data
By linking text trends with numerical indicators such as funding rates, open interest and volatility, ChatGPT can help estimate probability ranges for different market risk conditions. For instance:
“Act as a crypto risk analyst. Correlate sentiment signals from Reddit, X and headlines with funding rates, open interest and volatility. If open interest is in the 90th percentile, funding turns negative, and mentions of ‘margin call’ or ‘liquidation’ rise 200% week-over-week, classify market risk as High.”
Such contextual reasoning generates qualitative alerts that align closely with market data.
Generating conditional risk scenarios
Instead of attempting direct prediction, ChatGPT can outline conditional if-then relationships, describing how specific market signals may interact under different scenarios.
“Act as a crypto strategist. Produce concise if-then risk scenarios using market and sentiment data.
Example: If implied volatility exceeds its 180-day average and exchange inflows surge amid weak macro sentiment, assign a 15%-25% probability of a short-term drawdown.”
Scenario language keeps the analysis grounded and falsifiable.
Post-event analysis
After volatility subsides, ChatGPT can review pre-crash signals to evaluate which indicators proved most reliable. This kind of retrospective insight helps refine analytical workflows instead of repeating past assumptions.
Steps for ChatGPT-based risk monitoring
A conceptual understanding is useful, but applying ChatGPT to risk management requires a structured process. This workflow turns scattered data points into a clear, daily risk assessment.
Step 1: Data ingestion
The system’s accuracy depends on the quality, timeliness and integration of its inputs. Continuously collect and update three primary data streams:
Market structure data: Open interest, perpetual funding rates, futures basis and implied volatility (e.g., DVOL) from major derivatives exchanges.
Onchain data: Indicators such as net stablecoin flows onto/off of exchanges, large “whale” wallet transfers, wallet-concentration ratios and exchange reserve levels.
Textual (narrative) data: Macroeconomic headlines, regulatory announcements, exchange updates and high-engagement social media posts that shape sentiment and narrative.
Step 2: Data hygiene and pre-processing
Raw data is inherently noisy. To extract meaningful signals, it must be cleaned and structured. Tag each data set with metadata — including timestamp, source and topic — and apply a heuristic polarity score (positive, negative or neutral). Most importantly, filter out duplicate entries, promotional “shilling” and bot-generated spam to maintain data integrity and trustworthiness.
Step 3: ChatGPT synthesis
Feed the aggregated and cleaned data summaries into the model using a defined schema. Consistent, well-structured input formats and prompts are essential for generating reliable and useful outputs.
Example synthesis prompt:
“Act as a crypto market risk analyst. Using the provided data, produce a concise risk bulletin. Summarize current leverage conditions, volatility structure and dominant sentiment tone. Conclude by assigning a 1-5 risk rating (1=Low, 5=Critical) with a brief rationale.”
Step 4: Establish operational thresholds
The model’s output should feed into a predefined decision-making framework. A simple, color-coded risk ladder often works best.
The system should escalate automatically. For instance, if two or more categories — such as leverage and sentiment — independently trigger an “Alert,” the overall system rating should shift to “Alert” or “Critical.”
Step 5: Verification and grounding
All AI-generated insights should be treated as hypotheses, not facts, and must be verified against primary sources. If the model flags “high exchange inflows,” for example, confirm that data using a trusted onchain dashboard. Exchange APIs, regulatory filings and reputable financial data providers serve as anchors to ground the model’s conclusions in reality.
Step 6: The continuous feedback loop
After each major volatility event, whether a crash or a surge, conduct a post-mortem analysis. Evaluate which AI-flagged signals correlated most strongly with actual market outcomes and which ones proved to be noise. Use these insights to adjust input data weightings and refine prompts for future cycles.
Capabilities vs. limitations of ChatGPT
Recognizing what AI can and cannot do helps prevent its misuse as a “crystal ball.”
Capabilities:
Synthesis: Transforms fragmented, high-volume information, including thousands of posts, metrics and headlines, into a single, coherent summary.
Sentiment detection: Detects early shifts in crowd psychology and narrative direction before they appear in lagging price action.
Pattern recognition: Spots non-linear combinations of multiple stress signals (e.g., high leverage + negative sentiment + low liquidity) that often precede volatility spikes.
Structured output: Delivers clear, well-articulated narratives suitable for risk briefings and team updates.
Limitations:
Black-swan events: ChatGPT cannot reliably anticipate unprecedented, out-of-sample macroeconomic or political shocks.
Data dependency: It depends entirely on the freshness, accuracy and relevance of the input data. Outdated or low-quality inputs will distort outcomes — garbage in, garbage out.
Microstructure blindness: LLMs do not fully capture the complex mechanics of exchange-specific events (for example, auto-deleverage cascades or circuit-breaker activations).
Probabilistic, not deterministic: ChatGPT provides risk assessments and probability ranges (e.g., “25% chance of a drawdown”) rather than firm predictions (“the market will crash tomorrow”).
The October 2025 crash in practice
Had this six-step workflow been active before Oct. 10, 2025, it likely would not have predicted the exact day of the crash. However, it would have systematically increased its risk rating as stress signals accumulated. The system might have observed:
Derivatives buildup: Record-high open interest on Binance and OKX, combined with negative funding rates, indicates crowded long positioning.
Narrative fatigue: AI sentiment analysis could reveal declining mentions of the “Uptober rally,” replaced by growing discussions of “macro risk” and “tariff fears.”
Volatility divergence: The model would flag that crypto implied volatility was surging even as the traditional equity VIX remained flat, giving a clear crypto-specific warning.
Liquidity fragility: Onchain data could indicate shrinking stablecoin exchange balances, signaling fewer liquid buffers to meet margin calls.
Combining these elements, the model could have issued a “Level 4 (Alert)” classification. The rationale would note that the market structure was extremely fragile and vulnerable to an external shock. Once the tariff shock hit, the liquidation cascades unfolded in a way consistent with risk-clustering rather than precise timing.
The episode underscores the core point: ChatGPT or similar tools can detect accumulating vulnerability, but they cannot reliably predict the exact moment of rupture.
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
A sharp Bitcoin decline often triggers systemic contagion, driving altcoins lower through both liquidity and confidence channels.
During crises, the market tends to view crypto as a single risk asset rather than valuing individual utility, as seen in the high BTC-ETH and BTC-XRP correlations.
Correlation and beta analyses are essential for quantifying how deeply Ether and XRP depend on Bitcoin’s performance.
Monitoring correlation indicators, using derivatives and maintaining stable or yield-bearing assets can help hedge against Bitcoin-related shocks.
The dominance of Bitcoin (BTC) in the cryptocurrency market has long been the defining feature of crypto cycles. But what happens if Bitcoin’s dominance fades or its price plunges by 50%? In that scenario, two of the largest coins, Ether (ETH) and XRP (XRP), become critical test cases for how the market reshuffles.
This article explains how to evaluate ETH and XRP during a Bitcoin shock, measuring dependence, assessing risk and devising effective hedging strategies.
Why Bitcoin dominance matters
In traditional equity markets, when the biggest player in a sector stumbles, the ripple effects are immediate. Smaller firms often lose value as they depend on the leader’s ecosystem, investor confidence, supply-chain links and reputation. The same logic applies to crypto: Bitcoin serves as the “anchor asset.” When Bitcoin weakens, the entire market loses its sense of stability and direction.
Historically, Bitcoin has held a large share of the crypto market’s capitalization, known as the “dominance” metric. Most altcoins, including Ether and XRP, have shown a strong correlation with Bitcoin’s price movements.
For example, following the Oct. 10, 2025, tariff announcement, the crypto market experienced a broad liquidation event, with Bitcoin falling sharply. According to CoinMetrics, the BTC-ETH correlation rose from 0.69 to 0.73, while the BTC-XRP correlation increased from 0.75 to 0.77 over the next eight days.
This sharp convergence confirms that during a liquidity crisis driven by macroeconomic fear, altcoins don’t decouple based on their individual utility. Metrics such as Ether’s transaction volume or XRP’s institutional adoption offer little protection in such scenarios.
Instead, the high positive correlation serves as an empirical measure of shared systemic risk. It shows that the market views the entire crypto sector as a single asset class. This amplifies the downstream effects of a BTC-led collapse on ETH and XRP.
The implication is clear: If Bitcoin’s dominance drops or its price collapses, ETH and XRP are unlikely to move independently. They would likely suffer through two channels:
Liquidity/structural channel
Market structure, including derivatives, exchange flows and investor behavior tied to BTC, weakens. A major Bitcoin crash could trigger large-scale liquidations driven by margin calls and cascading sell-offs. This often leads to massive capital outflows that hit all crypto assets, regardless of their fundamentals. They fall simply because they share the same risk basket.
Sentiment channel
A breakdown of the original decentralized asset undermines the core thesis of the entire crypto industry. It erodes investor confidence in the long-term viability of cryptocurrencies. As fear takes hold, investors tend to move toward safer assets such as fiat or gold. The result is a prolonged bear market that weakens investment appetite for both Ether and XRP.
How to measure Bitcoin dependence and risk
Step 1: Define the shock scenario
The analysis begins by selecting a plausible, high-impact Bitcoin event. This could involve defining a specific price shock, such as a 50% BTC drop within 30 days, or a structural shift, for example, Bitcoin’s dominance falling from 60% to 40%.
Step 2: Quantify dependence
The next step is to calculate the current Pearson correlation coefficient between ETH, XRP and BTC. This statistical measure captures the linear relationship between the assets’ daily returns, providing a baseline for dependence. A value closer to +1 indicates that the altcoin is strongly tied to BTC’s performance.
Step 3: Estimate immediate price response
Using correlation data, apply regression analysis to calculate each altcoin’s beta (β) relative to BTC. The beta coefficient estimates the expected price movement of the altcoin for every one-unit change in Bitcoin. This is similar to calculating a stock’s beta relative to a benchmark index like the S&P 500 in traditional finance.
For example, if ETH’s β to BTC is 1.1 and the defined scenario assumes a 50% drop in BTC, the implied ETH move would be -55% (1.1 × -50%).
Step 4: Adjust for liquidity and structural risk
Adjustment requires going beyond the simple beta calculation by factoring in key market structure risks. Thin exchange order books should be analyzed to account for liquidity risk, while high derivatives open interest must be assessed for structural risk and potential cascading liquidations.
For instance, if the implied -55% move from Step 3 is compounded by shallow liquidity, the actual realized loss could increase by another 10%, resulting in a total -65% drop. Additionally, review open interest and margin positions, since high leverage can accelerate the decline through cascading liquidations.
What happens to Ether and XRP in a Bitcoin shock scenario?
In traditional finance, a sharp sell-off in the S&P 500 or the sudden collapse of a major broker often triggers a rapid, indiscriminate flight to safety — an effect known as “financial contagion.” The cryptocurrency market exhibits a similar dynamic, but in a faster and often more amplified form, typically sparked by a Bitcoin-centered shock.
Data from previous crises, including the FTX and Terra collapses, reveal a clear pattern: When Bitcoin falls, altcoins are typically dragged down with it. Bitcoin continues to serve as the market’s primary risk indicator.
In such a scenario, liquidity often rushes into stablecoins or exits the market entirely in search of protection from volatile assets. Although Ether benefits from robust layer-1 utility, it is not immune; during market stress, its correlation with Bitcoin often increases, as institutional capital treats both as risk assets. However, Ether’s staking lock-up and broad decentralized application ecosystem may provide a utility-driven floor, potentially helping it rebound more rapidly once the crisis subsides.
Assets such as XRP, on the other hand, which face higher regulatory and structural risks and lack Ether’s extensive, organic onchain yield mechanisms, could be hit disproportionately. Such shocks often trigger a vicious cycle in which collective loss of confidence outweighs fundamental token utility, driving a correlated market-wide decline.
Did you know? While Bitcoin is typically uncorrelated with the S&P 500, during periods of extreme financial stress — such as the COVID-19 pandemic — its correlation with the equity index tends to tighten significantly.
How to hedge your strategy if BTC loses dominance or its price falls
Hedging a crypto portfolio against a sharp Bitcoin decline requires more than basic diversification. Systemic shocks have shown that extreme correlations often erase the benefits of spreading risk.
Explore derivatives
During periods of extreme panic, the futures market can trade at a steep discount to the spot price. This creates opportunities for sophisticated traders to pursue relatively low-risk, non-directional arbitrage. In doing so, they exploit market inefficiencies as a hedge against volatility rather than taking directional price exposure.
Diversify your portfolio with risk buffers
Hold positions in tokenized gold, real-world assets (RWAs) or fiat-backed stablecoins to preserve portfolio value. These assets act as liquidity reserves when crypto markets spiral downward.
Monitor dominance and correlation ratios
Tracking the rolling short-term correlation of ETH and XRP to BTC can serve as a real-time warning signal that diversification benefits are disappearing. It confirms when immediate hedging action may be necessary.
Rebalance to yield-bearing positions
Shift part of your holdings into staking, lending or liquidity pools that generate yield regardless of market direction. The steady yield can help offset valuation losses and improve recovery potential.
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
Explore the best crypto wallets of November 2025, focusing on security, versatility, and ease of use as the cryptocurrency market continues to expand.
With the ever-growing popularity of cryptocurrencies, the demand for secure and reliable crypto wallets has intensified. These digital tools are essential for managing, sending, and receiving cryptocurrencies, making them indispensable in today’s financial landscape. According to CoinMarketCap, the list of the best crypto wallets for November 2025 has been curated with a focus on security, versatility, and user-friendliness.
Why Crypto Wallets Are Essential
As digital currencies gain traction, having a secure method to store them becomes critical. Crypto wallets offer a safe haven for digital assets, protecting them from online threats. They enable seamless transactions, whether it’s receiving a paycheck or transferring funds to another user. This assurance of security and convenience makes them a staple for both novice and experienced crypto users.
Features of Top Crypto Wallets
The top crypto wallets for November 2025 have been selected based on several criteria. Security remains the paramount concern, ensuring that users’ digital assets are safeguarded against breaches. Additionally, these wallets boast versatility, supporting a wide range of cryptocurrencies to cater to diverse investment portfolios. Ease of use is another crucial factor, with intuitive interfaces that simplify the management of digital currencies even for beginners.
The Leading Wallets of November 2025
Among the standout options is the Base App, which saw significant upgrades in July 2025 by Coinbase. This wallet has been praised for its robust security features and user-friendly design, making it a top choice for many cryptocurrency enthusiasts. Other wallets on the list similarly emphasize strong encryption methods and multi-currency support, ensuring that users can manage their assets with confidence.
For more details on the best crypto wallets of November 2025, visit CoinMarketCap’s official site.
Bitcoin spot market trading volume hits $300 billion in volatile October 2025.
Binance leads the pack with $174 billion traded, new research reveals.
Traders are exhibiting “highly constructive” behavior regarding future market stability.
Bitcoin (BTC) exchanges saw a giant $300 billion in spot trading volume during “Uptober” 2025.
New data from the onchain analytics platform CryptoQuant shows that despite BTC price lows, the market remains “healthy.”
Binance leads Bitcoin spot volume rebound
Bitcoin exchanges experienced no let-up in spot trading volume this month, despite the price dropping nearly 20% from its all-time high.
Gathering spot-market data from across global exchanges, CryptoQuant reveals that, so far in October, the total spot volume tally exceeds $300 billion.
“This October has seen a renewed surge of interest in the spot market, particularly on Binance,” contributor Darkfost wrote in one of its “Quicktake” blog posts.
“Major exchanges recorded more than $300B in Bitcoin spot volume this month, with $174B coming from Binance alone, making it the second-highest month of the year.”
Bitcoin spot trading volume. Source: CryptoQuant
The figures are important for Bitcoin bulls, as a spot-driven market tends to become more resistant to short-term volatility than one where derivatives account for the majority of volume.
“This trend highlights growing participation from both retail traders and institutional players, who appear increasingly active on the spot side,” Darkfost added.
Bitcoin futures open interest (screenshot). Source: CoinGlass
The event also liquidated a record $20 billion of long and short positions, with commentators suspecting that the actual total was far higher.
CryptoQuant now argues that traders have shifted back to spot markets as a result.
“This is a highly constructive signal,” the blog post concluded.
“A market driven more by spot trading rather than derivatives is generally healthier, more stable, as it less vulnerable to extreme volatility driven by excessive open interest expansion. It also reflects stronger organic demand and greater overall market resilience.”
Since the dip, leveraged traders have variously won and lost big as a result of market fluctuations.
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
Despite growing blockchain activity on Ethereum, gas fees on the world’s largest smart contract network remain near historic lows, signaling a more mature and scalable infrastructure ready for advanced real-world use cases.
Ethereum transaction fees remained at a historic low of just 0.16 gwei, or about $0.01 per transaction. Gas fees were slightly higher for token swaps at $0.15 and at $0.27 for non-fungible token (NFT) sales, according to blockchain data aggregator Milkroad.
The low costs stand in sharp contrast to previous periods of high network activity, when demand often sent fees soaring, which was one of Ethereum’s biggest criticisms in past cycles.
Ethereum gas fees, 1-month chart. Source: milkroad.com/ethereum
Daily transactions on the network rose to 1.6 million on Tuesday, marking a near one-month high, last seen at the beginning of October before the record $19 billion liquidation event.
Ethereum total transactions, 1-year chart. Source: app.Nansen.ai
Active addresses also rose to similar values, peaking at a monthly high of 695,872 on Saturday, according to crypto intelligence platform Nansen.
Ethereum’s historically low gas fees follow the Dencun and Pectra upgrades, both designed to lower transaction costs and expand throughput.
Deployed in May, the Pectra upgrade has doubled the blob capacity of layer-2 (L2) networks, cutting the transaction fees on L2s by around 50%. This upgrade also served to offload more transactions from the mainnet to further cut costs.
Ethereum’s previous major upgrade, Dencun, has also managed to cut L2 transaction fees and offload more transactions from the L1, making average Ethereum transaction fees cheaper by 95% a year after it was deployed on March 13, 2024, Cointelegraph reported.
Bitcoin ETFs saw $839 million in inflows while gold ETFs lost $4.1 billion.
Historical patterns suggest an 8.3% gold rebound ahead.
BTC is holding strong above a technical support, eyeing $150,000 by year’s end.
Gold’s shine is fading fast, just as its “digital” rival, Bitcoin (BTC), recovers lost ground.
Just a week after notching a record above $4,381, the precious metal has retreated by more than 10.60%, sinking to as low as $3,915 on Thursday, its steepest seven-day drop since April.
XAU/USD vs. BTC/USDT daily chart comparison. Source: TradingView
The correction in gold coincides with a nearly 6.70% jump in Bitcoin price, highlighting a sharp divergence as the US and China move closer to a trade agreement.
The shift followed Donald Trump’s remarks about an “amazing meeting” with Xi Jinping on Thursday, in which the two leaders agreed to reduce fentanyl tariffs from 20% to 10%, effective immediately.
With risk appetite improving and crypto markets heating up, could gold’s correction below $4,000 support be a sign that traders are rotating back into Bitcoin in the months ahead?
Bitcoin ETFs attract $839 million amid gold’s plunge
US-listed Bitcoin ETFs have absorbed $839 million in net inflows since gold hit its record high on Oct. 20, with holdings rising consecutively in the last four sessions, data from Farside Investors shows.
In contrast, gold-backed ETFs experienced total outflows of about 1.064 million ounces (nearly $4.1 billion) since Oct. 22, according to Bloomberg data.
This includes the largest one-day withdrawal in over six months on Monday, when investors withdrew 0.448 million ounces of gold exposure.
Gold-backed ETFs net daily inflows. Source: Bloomberg
BTC technicals now indicate a strong floor near $101,790.
BTC/USD weekly chart. Source: TradingView
That aligns with the 20-week exponential moving average (20-week EMA; the green wave) and 1.0 Fibonacci retracement level. Holding above the support confluence increases BTC’s odds of hitting $150,000 by year’s end.
Gold is still up around 50% year-to-date, buoyed by record central-bank purchases, persistent fiscal imbalances, and the ongoing “debasement trade,” where investors seek protection from ballooning government debt and weakening fiat currencies.
Metal trader David Bateman argues that gold’s bull run remains fundamentally intact despite the ongoing correction.
Source: X
Technicals further indicate that gold remains in a bull market correction, with the metal still holding firm above its 50-day exponential moving average (50-day EMA, represented by the red wave).
Gold has bounced from the 50-day EMA support every time in the past two years, resulting in rebounds of 4-33%, as shown below.
XAU/USD daily chart. Source: TradingView
Also, gold’s past 10% corrections over the last three decades have consistently led to sharp rebounds within days, signaling a likely short-term bottom rather than deeper downside.
The previous ten instances of such steep drops all produced positive two-month returns, averaging an 8.3% recovery, according to data highlighted by Sabu Trades.
Gold returns post 10% correction. Source: Sabu Trades
Gold could revisit the $4,200–$4,250 zone by December, effectively retesting its record highs and reaffirming the metal’s broader uptrend, if the pattern holds.
The metal can further hit HSBC’s $5,000 target in 2026 as long as it holds above the red wave.
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
x402 enables pay-per-use functionality on the internet.
The current momentum is infrastructure-led, driven by Coinbase and Cloudflare.
PING was a catalyst, but the real story is protocol adoption, not the token.
You can test it quickly by spinning up an endpoint and verifying the 402 → pay → grant flow.
X402 is a straightforward way to enable pay-per-use on the internet. When you access a paid application programming interface (API) or file, the server responds with the web’s built-in “402 Payment Required” message, specifying the price — often just a few cents in USDC (USDC) — and where to send the payment.
You send the onchain payment from your wallet, repeat the request, and the server delivers the result. There are no accounts, passwords, API keys or monthly plans — just a one-time payment linked to that specific request.
The “second wave” of x402
The idea isn’t new. The 402 status code has existed in HTTP for years, but it lacked a practical blueprint until 2025, when Coinbase packaged a clear protocol around it (“x402”). The company published documentation and code and offered a managed gateway for developers. Soon after, Cloudflare partnered with Coinbase to co-launch the x402 Foundation initiative, formalizing the standard and bringing support to mainstream developer tools.
You may have first heard about x402 when a token called PING drew attention to it. The token buzz faded, but the protocol endured because it solves a common problem: charging per API call, per AI inference or per download without requiring users to create accounts.
That utility, combined with new tooling for AI agents that can pay automatically, is driving a second wave focused on real usage rather than price charts.
Did you know? X402 is becoming the default way for AI agents to pay for things on their own. Cloudflare is adding native x402 support to its Agents SDK and MCP servers. Coinbase’s new Payments MCP allows popular large language models to hold a wallet and complete requests without API keys.
What is PING, who’s behind it, and how does it relate to x402?
PING is a memecoin on Base (Coinbase’s layer 2). It was the first public token mint executed through an x402 flow, which is why it grabbed headlines. Early buyers didn’t sign up on a website; they accessed a uniform resource locator (URL), received a “402 Payment Required” message, paid a small amount in USDC onchain, retried the request and received PING. Think of it as a live demo of x402’s pay-per-request model applied to minting.
The token was launched by the X account Ping.observer. Public coverage and listings consistently attribute PING to this account. There is no official team page or white paper beyond that and no credible disclosures of VC backing specific to the PING token itself.
X402 provided the infrastructure, while PING served as its first large-scale test case. The token’s pay-to-mint mechanic stress-tested the protocol and spotlighted x402’s core principle: charging a tiny onchain fee per request. That includes API calls, AI inferences, file downloads or, in this case, a mint, all without requiring accounts or API keys.
After the initial spike and retrace, the lasting impact was not the token price but the influx of developers and endpoints experimenting with x402.
Did you know? PING reached an all-time high of around $0.0776 on Oct. 25, 2025, before pulling back in the days that followed.
How to try x402 (developer quick start)
1) Get the gist
X402 is a simple handshake. You call a paid URL and the server replies with “402 Payment Required” and the price in USDC. You send the onchain payment, then call the URL again with the payment proof to get the result. That’s it.
2) Choose your setup
Managed: Use Coinbase’s hosted x402 gateway with dashboards and built-in Know Your Transaction (KYT) checks. It’s ideal for a quick proof of concept.
Do it yourself (DIY)/spec: Clone the open-source x402 reference implementation and run a minimal seller and buyer locally if you want full control.
3) Expose one paid endpoint
Pick any route (for example, “/inference”). When someone accesses it without paying, return a “402” response along with the payment details, including the amount, asset (USDC), destination address and expiry. If you can trigger that response using “curl,” you’re speaking x402 correctly.
4) Complete one paid request
Use the sample client or the managed gateway to detect the “402,” make the onchain payment, and then retry the request. Access should update automatically once the payment is confirmed, with no accounts, API keys or OAuth required.
5) Optional: Test with an AI agent
If you work with agents, spin up the model context protocol (MCP) example. The interceptor will detect the “402,” make the payment from the agent’s wallet and reissue the request automatically. It’s a quick way to confirm agent-to-endpoint flows.
Top tip: Start on a testnet as outlined in the quickstart. Once the 402 → pay → grant loop is stable, switch the configuration to mainnet.
Risks, timelines and what to watch next
What can still go wrong
X402 is still relatively new. The specification and reference code may continue to evolve, and most live setups currently use USDC. Over-reliance on a single managed gateway or a single asset introduces both vendor and asset concentration risk. It’s also important to keep token narratives separate from protocol progress.
Governance to track
Watch for the formal launch details of the x402 Foundation, including its charter, member list and roadmap. That event will mark the protocol’s shift from a product to a standard. Also, keep an eye on Cloudflare’s developer ecosystem (Agents SDK and MCP) since mainstream tooling often comes before widespread adoption.
Adoption signals
You’re looking for real endpoints that return “402” responses with payment parameters, then unlock access after an onchain payment, with no accounts or API keys required in between. More quickstarts, documentation and GitHub activity are positive indicators on the supply side.
Broader distribution across cloud services, Content Delivery Networks (CDNs) and agent frameworks beyond the early partners, along with support for additional assets and networks, will make x402 increasingly difficult to ignore. Continued progress in “agentic commerce” integrations is also likely to attract developers who don’t typically work with crypto.
How to stay current
Follow the primary sources: Coinbase’s product pages, documentation and GitHub for protocol updates, along with Cloudflare’s blog and press releases for foundation news and SDK support. Treat anything outside those channels, especially token chatter, as background noise.
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
Bitcoin’s failure to rise above $118,000 may have attracted profit-booking by short-term traders, resulting in a drop toward $107,000.
Several major altcoins turned down from their overhead resistance levels, signaling that the bears remain sellers on rallies.
Bitcoin (BTC) bulls are attempting to sustain the price above $111,000, but the bears have continued to exert selling pressure. Glassnode wrote in its latest Weekly Market Impulse report that BTC’s recent recovery was not supported by increased participation, signaling a “potential consolidation phase.”
A slightly cautious view came from crypto market intelligence company 10x Research, which said that BTC’s current bull market cycle may not get extended beyond the traditional four-year cycle, as BTC has become too expensive for sustained retail purchases. The company projected a cycle top of $125,000 based on their research methodology.
BTC remains stuck inside the large range, but a minor positive in favor of the bulls is that investors continue to buy spot BTC exchange-traded funds. According to Farside Investors’ data, the BTC ETFs have recorded net inflows of $462.6 million over the past four days.
What are the critical support and resistance levels to watch for in BTC and the major altcoins? Let’s analyze the charts of the top 10 cryptocurrencies to find out.
Bitcoin price prediction
BTC’s failure to stay above the 50-day simple moving average ($114,278) attracted sellers, pulling the price below the 20-day exponential moving average ($112,347).
If the price closes below the 20-day EMA, the bears will try to yank the BTC/USDT pair to the critical support at $107,000. Buyers are expected to defend the $107,000 level with all their might, as a break below it will complete a double-top pattern. The Bitcoin price may then slump to $100,000.
The $118,000 level is a key resistance to watch on the upside. A break and close above it could propel the pair to the all-time high of $126,199.
Ether price prediction
Ether (ETH) turned down from the 50-day SMA ($4,220) on Monday, indicating that the bears are active at higher levels.
Sellers are attempting to pull the price to the support line of the descending triangle pattern, which is a critical level to watch out for. A break and close below the support line could sink the Ether price to $3,350.
The bulls will have to push the price above the 50-day SMA to signal strength. The ETH/USDT pair could then climb to the resistance line, where the sellers are likely to pose a strong challenge. Buyers will have to overcome the barrier at the resistance line to signal the start of the next leg of the up move.
BNB price prediction
BNB (BNB) turned down from the 38.2% Fibonacci retracement level of $1,156 on Monday, but a minor positive is that the bulls defended the 50-day SMA ($1,076) on Tuesday.
The flattish 20-day EMA ($1,119) and the RSI near the midpoint do not give a clear advantage either to the bulls or the bears. If the price turns down and breaks below the 50-day SMA, it signals the start of a deeper correction to $1,021 and later to $932. Such a move suggests that the BNB/USDT pair may have topped out in the near term.
Conversely, a break and close above $1,156 indicates strong buying at lower levels. The BNB price may then surge to the 61.8% retracement level of $1,239.
XRP price prediction
XRP (XRP) has been trading between the breakdown level of $2.69 and the 20-day EMA ($2.56) for the past few days.
The tight range trading is likely to be followed by a range expansion. If the price turns down and breaks below the 20-day EMA, it suggests that the bears have overpowered the bulls. The XRP price could then drop to $2.20.
On the contrary, a break and close above $2.69 could propel the XRP/USDT pair to the downtrend line. Sellers are expected to vigorously defend the downtrend line, as a break above it opens the gates for a rally to $3.20 and then $3.38.
Solana price prediction
Buyers pushed Solana (SOL) above the 20-day EMA ($196) on Sunday but are struggling to sustain the higher levels.
The flattish 20-day EMA and the RSI near the midpoint signal a balance between supply and demand. If the price closes above the 20-day EMA, the SOL/USDT pair could rise to the resistance line. Buyers will have to push the price above the resistance line to gain strength.
Alternatively, if the price turns down and breaks below $190, it suggests that the bears are in control. The pair could then descend to $177 and eventually to the support line of the channel.
Dogecoin price prediction
Dogecoin (DOGE) turned down from the $0.21 overhead resistance on Monday, signaling that the bears are aggressively defending the level.
The bears will try to build upon their advantage by pulling the Dogecoin price below the $0.17 level. If they manage to do that, the DOGE/USDT pair could decline to the critical support at $0.14. Buyers are expected to defend the $0.14 level with all their might, as a break below it would clear the path for a retest of the $0.10 level.
The first sign of strength will be a close above $0.21. If that happens, the pair could rise to the 50-day SMA ($0.23) and later to $0.27.
Cardano price prediction
Cardano (ADA) turned down from the 20-day EMA ($0.68) on Monday, indicating that the sentiment remains negative.
The bears will attempt to sink the Cardano price below the $0.59 support. If they can pull it off, the ADA/USDT pair could plunge toward the vital support at $0.50. Buyers are expected to fiercely defend the $0.50 level.
On the upside, a break and close above the 20-day EMA signals that the bulls are attempting a comeback. The pair could then rally to the breakdown level of $0.75 and subsequently to the downtrend line.
Buyers will attempt to strengthen their position by pushing the Hyperliquid price above the $51.50 overhead resistance. If they manage to do that, the HYPE/USDT pair could retest the all-time high at $59.41.
Sellers are likely to have other plans. They will try to defend the $51.50 level and pull the price below the 20-day EMA ($42.64). If they succeed, the pair could plummet toward the crucial support at $35.50.
Chainlink price prediction
Chainlink (LINK) turned down from the 20-day EMA ($18.52), indicating that the bears are selling on rallies.
The bears will attempt to pull the Chainlink price to $16.71 and then to the strong support at $15.43, where the buyers are expected to step in.
Contrarily, if the price turns up from the current level and breaks above the 20-day EMA, it suggests that the selling pressure is reducing. The LINK/USDT pair could then rally to the resistance line. Buyers will have to push and maintain the price above the resistance line to signal that the correction may be over.
Bitcoin Cash price prediction
Bitcoin Cash (BCH) has reached the resistance line of the falling wedge pattern, where the bears are posing a strong challenge.
The upsloping 20-day EMA ($527) and the RSI in the positive territory indicate the path of least resistance is to the upside. A close above the resistance line opens the doors for a rally to $615 and then $651.
Sellers will have to swiftly pull the Bitcoin Cash price back below the 20-day EMA to regain control. The BCH/USDT pair could then fall toward the strong support at $450.
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
