Tron Stablecoin Volume Exceeds XRP Activity By More Than 10 Times: Data

bitcoinistPublished on 2025-12-23Last updated on 2025-12-23

Abstract

Tron's USDT and USDC stablecoin transfer volume has surged, now exceeding the entire XRP network's activity by more than ten times, according to Glassnode data. The 90-day average for Tron's stablecoin volume stands at $24.2 billion daily, compared to XRP's $2.2 billion. This highlights Tron's significant role as a core settlement layer for stablecoins. Furthermore, stablecoins dominate overall crypto transaction volume. Combined, USDC and USDT process $192 billion daily, nearly double the volume of the top five non-stablecoin assets. USDC leads all assets at $124 billion, followed by Bitcoin ($81B) and USDT ($68B). XRP ranks sixth with its $2.2 billion volume, behind Solana and Ethereum. Meanwhile, XRP's price is trading around $1.93.

Data shows the transaction volume of USDT and USDC on Tron is now more than 10 times the transfer volume of the entire XRP network.

Tron Stablecoin Volume Is Significantly Higher Than XRP Activity

In a new post on X, Glassnode lead research analyst CryptoVizArt.₿ has discussed how stablecoin settlement on the Tron network compares against the transaction activity of XRP. Stablecoins are digital assets that have their value pegged to a fiat currency. The vast majority of this space is currently dominated by two tokens tied to the US dollar: USDT and USDC.

These cryptocurrencies are available on several blockchains, with a major one being Tron. Below is the chart shared by CryptoVizArt.₿ that shows the trend in the 90-day simple moving average (SMA) of the combined transfer volume of USDT and USDC on the network over the last few years.

Looks like the metric has been following an upward trajectory | Source: @CryptoVizArt on X

As displayed in the graph, USDT and USDC have seen their Tron volume follow a rapid uptrend during the last year, suggesting that users have increasingly been using the network for stablecoin settlements.

The 90-day SMA value of the metric is currently sitting at $24.2 billion. In the same chart, the analyst has also attached the data for the transfer volume of the XRP blockchain and from its graph, it’s apparent that the network’s transaction activity pales in comparison to the stablecoin settlement that occurs on Tron.

More specifically, XRP observes just $2.2 billion in transfers every day, a tenth of the Tron stablecoin transactions. “This reinforces Tron’s role as a core settlement layer for stablecoin liquidity,” noted CryptoVizArt.₿.

Glassnode’s official X handle has also made a post about how stablecoins compare against the major cryptocurrencies in terms of the metric.

The trend in the transfer volume of the various top cryptocurrencies | Source: Glassnode on X

As is apparent in the above chart, USDC is currently the most dominant asset in transaction activity out of the major assets with a volume of $124 billion. Bitcoin is second at $81 billion, while USDT is third at $68 billion.

Among the rest, Solana and Ethereum both beat XRP to the fourth and fifth spots with transaction volumes of $9.6 billion and $7.9 billion, respectively. BNB is just behind XRP at $1.6 billion.

The top two stablecoins combined are pulling $192 billion in transaction activity every day, which is almost twice the transfer volume that the top five non-stablecoin cryptocurrencies are witnessing. “Stablecoins have become the primary liquidity rails, while native asset transfers remain comparatively subdued,” said Glassnode.

XRP Price

At the time of writing, XRP is trading around $1.93, down nearly 2% over the last week.

The price of the coin seems to have been moving sideways over the last few days | Source: XRPUSDT on TradingView

Trending Cryptos

Related Questions

QWhat is the current 90-day SMA of the combined transfer volume of USDT and USDC on the Tron network?

AThe 90-day simple moving average (SMA) of the combined transfer volume of USDT and USDC on the Tron network is currently $24.2 billion.

QHow does the daily transfer volume of XRP compare to the Tron stablecoin volume?

AThe daily transfer volume of XRP is $2.2 billion, which is about one-tenth (or 10 times less) than the Tron stablecoin volume.

QAccording to the data, which two assets have the highest daily transfer volume among major cryptocurrencies?

AUSDC has the highest daily transfer volume at $124 billion, and Bitcoin is second with $81 billion.

QWhat role does the data suggest Tron plays in the stablecoin ecosystem?

AThe data reinforces Tron's role as a core settlement layer for stablecoin liquidity.

QWhat was the price of XRP and its weekly performance at the time of writing?

AAt the time of writing, XRP was trading around $1.93, down nearly 2% over the previous week.

Related Reads

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

The article titled "Gate Research Institute: Are Crypto Financial Products Sparking a 'Wall Street' Wave—Competition or Convergence?" explores the evolving relationship between the crypto ecosystem and traditional finance (TradFi). The piece begins by reflecting on Bitcoin's original 2009 vision of decentralization, disintermediation, and moving away from banks. It then contrasts this with the 2024 landscape, where key crypto assets like Bitcoin are increasingly held through Wall Street products like ETFs issued by giants like BlackRock. The article questions whether this signifies that TradFi is systematically taking over the rights to issue, price, custody, and distribute crypto financial assets. The core argument is that this is not a zero-sum takeover but rather a bidirectional convergence where each side addresses the other's weaknesses. Crypto offers 24/7 global markets, programmable settlement, and open access but lacks compliant channels, institutional-grade custody, deep fiat liquidity, and mainstream distribution. TradFi possesses these but is constrained by legacy systems, limited operating hours, and slow settlement. Two primary convergence paths are highlighted: * **Path A (CEX to TradFi):** Exemplified by Gate, which has progressed from offering tokenized stocks and CFDs to providing direct, real stock trading (US, Hong Kong, South Korea) within its platform, using USDT. * **Path B (TradFi to Crypto):** Exemplified by Robinhood, which has integrated crypto trading, acquired exchanges like Bitstamp, and is moving traditional assets like stocks onto the blockchain via tokenization and its own Layer 2. Both paths are ultimately competing to become the next-generation, unified financial account—a "super account" where users can seamlessly trade cryptocurrencies, stocks, ETFs, RWA (Real World Assets), and tokenized treasury products in one interface. The growth of RWA and tokenized treasuries (e.g., BlackRock's BUIDL) is presented as the asset-layer fusion, providing stable, yield-bearing assets on-chain and acting as a bridge between the two worlds. In conclusion, the "Wall Street-ization" of crypto is framed as a mutual transformation. Decentralized ideals persist in the protocol layer, while at the application layer, a more efficient, global, and accessible unified capital market is emerging from this convergence. The future competition lies not between crypto exchanges and stockbrokers, but between platforms vying to offer the most comprehensive asset coverage, liquidity, and user experience within a single account.

marsbit4m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

marsbit4m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit8m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit8m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit10m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit10m ago

Trading

Spot

Hot Articles

How to Buy TRX

Welcome to HTX.com! We've made purchasing TRON (TRX) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy TRON (TRX) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your TRON (TRX)After purchasing your TRON (TRX), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade TRON (TRX)Easily trade TRON (TRX) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

26.7k Total ViewsPublished 2024.03.29Updated 2026.06.02

How to Buy TRX

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of TRX (TRX) are presented below.

活动图片