3 key Solana metrics explain exactly why SOL price is down

CointelegraphPublicado em 2022-10-20Última atualização em 2022-10-20

Resumo

The past eighty days have been moderately bearish for cryptocurrencies as the altcoin market capitalization declined by 16%.

The past eighty days have been moderately bearish for cryptocurrencies as the altcoin market capitalization declined by 16%. The downside movement can be partially explained by the U.S. Federal Reserve’s quantitative tightening, rising interest rates and halting of asset purchases. Although they are aimed at curbing inflationary pressure, the policy also increases the borrowing costs for consumers and businesses.
Solana's (SOL) downfall has been even more brutal, with the altcoin facing a 29% correction since August. The smart contract network focuses on low fees and speed, but the frequent outages highlight a centralization issue.

Solana/USD price (blue) vs. altcoin capitalization (orange). Source: TradingViewThe latest setback occurred on Sept. 30 after a misconfigured validator halted the blockchain transactions. A duplicate node instance caused the network to fork, as the remaining nodes could not agree on the correct chain version.
Recently, Solana's co-founder Anatoly Yakovenko placed his bets on Firedancer, a scaling solution developed by Jump Crypto in partnership with the Solana Foundation. Dubbed the long-term fix to the network outage problem, the mechanism should be ready for testing in the coming months.
On Oct. 11, Solana-based decentralized finance exchange Mango Markets was hit with an exploit of over $115 million. The attacker successfully manipulated the value of MNGO native token collateral, taking out "massive loans" from Mango's treasury.
Solana’s TVL and the number of active addresses dropped
Solana's primary decentralized application metric started to display weakness earlier in November. The network's total value locked (TVL), which measures the amount deposited in its smart contracts, broke to its lowest level since Sept. 2021 at 30.4 million SOL.

Solana network Total Value Locked, SOL. Source: Defi LlamaThere are other factors which influence Solana’s decrease in value and TVL. To confirm whether DApp use has effectively decreased, investors should also analyze the number of active addresses within the ecosystem.

Solana dApps 30-day on-chain data. Source: DappRadarOct. 19 data from DappRadar shows that the number of Solana network addresses interacting with decentralized applications declined in 13 of the top 20 DApps. The reduced interest was also reflected in SOL's futures markets.
Fixed-month contracts usually trade at a slight premium to spot markets because investors demand more money to withhold the settlement. Whenever this indicator fades or turns negative, this is an alarming, bearish red flag signaling a situation known as backwardation.

Solana 3-month futures annualized basis. Source: Laevitas.chThe above chart shows how Solana futures have been trading at a 7% discount versus the current spot price. This data is concerning since it signals a lack of interest from leverage buyers.
SOL will continue to underperform until it flips these metrics
It's difficult to pinpoint the exact reason for Solana's price drop, but it is clear that centralization issues, a decrease in the network's DApp use and fading interest from derivatives traders certainly played a role.
Should the sentiment flip, there should be an inflow of deposits, increasing Solana's TVL and the number of active addresses. Consequently, the above data suggest that Solana holders should not expect a price bounce anytime soon because the network health metrics remain under pressure.

Leituras Relacionadas

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

The article "Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?" argues that Ethereum, like Linux before it, will triumph over closed, proprietary systems in finance due to its open, permissionless, and credibly neutral nature. It draws a historical parallel: just as the open internet defeated corporate private networks and Linux outcompeted proprietary Unix systems, open financial infrastructure like Ethereum will surpass private blockchains. The core advantage lies in the "bazaar" development model (as described in Eric Raymond's "The Cathedral and the Bazaar"), where decentralized, permissionless innovation by a global community of developers outpaces the controlled "cathedral" approach of centralized entities. This model fosters rapid innovation, as seen with Ethereum standards like ERC-20 and applications like Uniswap, which were built without needing permission. Ethereum's key, irreplicable strength is its credible neutrality: transparent, equally applicable, immutable rules that allow anyone to participate. This ensures sovereign independence, meaning no single entity (company, government) can control or change its core rules—a critical feature for global financial infrastructure. In contrast, private blockchains and consortium chains (like SWIFT or various bank-led projects) suffer from platform risk, central control, and an inability to attract broad developer ecosystems, leading to frequent failures. The article notes that major institutions (e.g., BlackRock, JPMorgan, Coinbase, Robinhood) are already building on Ethereum or its Layer 2 networks, recognizing its security, developer ecosystem, and network effects. While critics argue finance requires accountable, controlled systems, the response is that compliance (KYC, regulations) can be built at the application layer on top of a neutral settlement layer like Ethereum, just as secure commerce was built on the open internet via HTTPS. Ultimately, the thesis is that attempting to build walled-garden, proprietary financial networks is a flawed strategy that stifles innovation. The winning approach is to build applications on top of open, credibly neutral infrastructure like Ethereum, which is poised to become the foundational settlement layer for global finance.

Foresight NewsHá 4m

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

Foresight NewsHá 4m

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbitHá 11m

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbitHá 11m

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbitHá 15m

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbitHá 15m

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbitHá 16m

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

marsbitHá 16m

Trading

Spot
Futuros
活动图片