Litecoin Drops 87% Trading Volume In Q1 2022

newsbtcОпубліковано о 2022-04-29Востаннє оновлено о 2022-04-29

Анотація

The interest in cryptocurrencies continues to wane in 2022, as does Litecoin's popularity. The trading volume of Litecoin was eight times lower in the first quarter of 2022 than it...

The interest in cryptocurrencies continues to wane in 2022, as does Litecoin’s popularity. The trading volume of Litecoin was eight times lower in the first quarter of 2022 than it was in the first quarter of 2021.
For example, the trading volume from January to March 2022 was around $82 billion, 87% less than in Q1 2021. In the first quarter of 2021, the trading volume was $674.9 billion. Litecoin (LTC) trading volume crashed by more than $590 billion in quarterly comparison.
The sharp decline in Litecoin’s value results from the negative crypto market sentiment. The price dropped 70% from its highest point, $412, attained back in May 2021.

LTC started the day in green with a 1.72 increase, currently trading at $102.37 | Source: LTC/USD price chart from Tradingview.com The most popular stablecoin pair for Litecoin’s native LTC was the United State Dollar Tether (USDT) in the first quarter of 2022.
Litecoin Month Wise Comparison Of Trading Volume
January 2021 was the month of Litecoin, with a trading volume of $284.52 billion. LTC hit a single-day high of $17.99 billion. But in January 2022, LTC trading volume dropped by 89%. The coin’s approximately trading volume was $31.48 billion, with a single-day high of $2.09 billion.
Similarly, February 2021 also performed well. The trading volume of Litecoin reached around $257.49 billion, with a single-day high of $16.57 billion. However, in February 2022, the coin performance dropped by 90% compared to Feb 2021. As a result, the single-day high of Feb 2022 was $1.68 billion.
Litecoin saw a decline in trading volume in March 2021 compared to January and February 2021. The total trading volume for March was $132.91 billion, with a single-day high of $8.08 billion. LTC trading volume was $24.98 for March 2022 with a single-day high of $1.35 billion, 81% less than March 2021.
On January 1, 2022, Litecoin opened at $146.54. On January 2, the coin reached its quarterly high of $152.94. The closing of the first quarter was $123.72. Overall a 15% decrease in Litecoin’s opening and closing price in Q1 2022.
For comparison purposes, on January 1, 2021, Litecoin opened at $124.67 per coin. On Feb 20, LTC reached its quarterly high of $245.96. The closing of the first quarter of 2021 was at $197.5. Litecoin performed well during the first quarter with a 58% spike.
LTC $100 Support Is Under Attack
Litecoin has been trading down for most of the past year. In November, LTC was below $300 after it couldn’t stay above that level. The coin has been testing the $100 level since January. However, the overall sentiment in the crypto market is still bearish. This cryptocurrency has been making lower highs, which is a bad sign, but the $100 support zone is still holding.
There was a spike in activity in wallets holding more than $100k a week ago. That didn’t cause the price of Litecoin to go up, though. Instead, it stayed bearish and kept going down. Yesterday, the sellers failed to break below the $100 support level. That was a good sign. But today, the pressure is still bearish, so the digital asset might see a breakout to the downside.

Пов'язані матеріали

Bitcoin Liquidation Shakeout Leaves Traders Watching $66K Resistance And $61K Support

Bitcoin's recent volatility triggered a major liquidation event, wiping out roughly $980 million in leveraged positions within 24 hours as price swung from ~$64,100 down to $60,700 and back above $63,000. Traders are now watching key technical levels. Major pivot resistance is identified at $66,247, with reclaimed highs near $64,234 and next resistance at $65,890. Critical support zones lie between $63,127-$63,354, then $62,459, with broader downside targets around $59,150-$60,171. Liquidation heatmaps show significant liquidity clusters above ($63.5K-$66K) and below ($58.5K-$61K) spot price, creating a volatile corridor. The market is in a corrective phase; a clean break above resistance is needed for a bullish shift, while failure to hold low-$63K support risks a move toward lower liquidity zones.

bitcoinist9 хв тому

Bitcoin Liquidation Shakeout Leaves Traders Watching $66K Resistance And $61K Support

bitcoinist9 хв тому

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

链捕手10 хв тому

U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

链捕手10 хв тому

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News1 год тому

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News1 год тому

Solayer Launches Margin Trade to Consolidate Perpetual Multi-Asset Trading on Mainnet

Solayer, a high-performance SVM-compatible Layer 1 blockchain, has launched its Margin Trade platform on mainnet. This unified cross-asset perpetual trading platform merges traditional and digital finance, developed with input from institutional and crypto trading experts. It offers trading for a diverse range of assets, including top cryptocurrencies, commodities like gold and oil, and equities such as the synthetic MT500 index. Key features include cross-margin trading with a unified collateral pool for improved capital efficiency, real-time execution, and full on-chain transparency with non-custodial asset ownership. The launch includes the first perpetual market for Pearl Research ($PRL) with up to 3x leverage. Future plans involve expanding asset listings and adding isolated margin functionality. Built on the high-throughput Solayer network capable of 330,000 TPS, the platform aims to provide the speed required for serious trading. Following successful testnet phases, Margin Trade's mainnet debut marks a significant step in Solayer's goal to unify traditional and crypto markets on-chain with institutional-grade decentralized finance infrastructure.

TheNewsCrypto2 год тому

Solayer Launches Margin Trade to Consolidate Perpetual Multi-Asset Trading on Mainnet

TheNewsCrypto2 год тому

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit4 год тому

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit4 год тому

Торгівля

Спот

Ф'ючерси

Обговорення

Ласкаво просимо до спільноти HTX. Тут ви можете бути в курсі останніх подій розвитку платформи та отримати доступ до професійної ринкової інформації. Нижче представлені думки користувачів щодо ціни LTC (LTC).