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加密貨幣市場數據的質量對於學術研究和財務應用至關重要，但是，該行業面臨許多挑戰

The quality of cryptocurrency market data is critical for academic research and financial applications. Yet, the industry faces a number of challenges, including pervasive mislabeling, measurement errors and discrepancies in reported market metrics.

加密貨幣市場數據的質量對於學術研究和財務應用至關重要。然而，該行業面臨許多挑戰，包括廣泛的標籤，測量錯誤和報告的市場指標的差異。

A new academic research by Gustavo Schwenkler of the Santa Clara University, alongside Aakash Shah and Darren Yang of Indicia Labs, highlights these persistent issues, emphasizing the difficulty of relying on a single provider for all crypto market data needs.

聖塔克拉拉大學的古斯塔沃·施旺克勒（Gustavo Schwenkler）以及Indicia Labs的Aakash Shah和Darren Yang進行了一項新的學術研究，強調了這些持久問題，強調了難以依靠單個提供商來滿足所有加密市場數據需求的困難。

The researchers reviewed 20 of the most common crypto market data providers, examining their crypto data from January 2022 to October 2024. An in-depth analysis of a subset of eight providers – CoinCap, CoinGecko, CoinMarketCap, CoinPaprika, CryptoCompare, Live Coin Watch, Nomics, and Santiment – from November 2018 to October 2024 revealed concerning trends.

The analysis revealed a substantial level of inconsistency in how cryptocurrencies are identified. As much as 21% of all coins in a provider’s dataset could undergo ID changes within a 24-month period without any disclosure, rendering direct comparisons across time difficult.

該分析表明，如何識別加密貨幣有很大的不一致性。提供商數據集中所有硬幣中多達21％的硬幣可以在24個月內進行ID變化，而無需任何披露，從而在困難的情況下進行了直接比較。

Moreover, some providers kept the same ID for a coin even after a fork or a swap that rendered a new coin, effectively misrepresenting the asset’s identity. Additionally, certain providers used the same ID for distinct cryptocurrencies, a problem encountered in 16% of the sample from CoinGecko.

此外，即使在叉或交換後，一些提供商仍保留了硬幣的同一ID，從而有效地歪曲了資產的身份。此外，某些提供商將相同的ID用於不同的加密貨幣，這是Coingecko的16％樣本中遇到的問題。

This analysis also unveiled significant inconsistencies in the reported data across providers. For instance, the daily close price for a cryptocurrency can vary to a great extent from one provider to another. In extreme cases, the price discrepancies at the close of a particular day could be quite large.

該分析還揭示了跨提供商報告的數據的明顯不一致。例如，加密貨幣的每日近距離價格可能會在很大程度上從一個提供商到另一個提供商。在極端情況下，特定日期結束時的價格差異可能很大。

These inconsistencies arise because providers collect and aggregate data from various public platforms. Because providers have discretion over which prices they select at any given time and how they aggregate them, inconsistencies are to be expected.

這些矛盾之處之所以出現，是因為提供商從各種公共平台收集和匯總數據。由於提供者在任何給定時間選擇哪些價格以及他們如何匯總的價格都有酌處權，因此預期的是不一致。

Beyond price discrepancies, trading volume metrics also exhibit strong variations across providers. This is especially pervasive for reported volumes, where in almost 70% of the daily instances in the six-year sample, the daily aggregate volume for a coin reported by a provider deviated by more than 5% from the median volume reported across providers.

除了價格差異之外，交易量指標還表現出各個提供商的差異。這對於報導的量尤其普遍，在六年樣本中，幾乎70％的每日實例中，每日總量的每日總量是由提供商偏離5％以上的提供商中間量的代理商報告的每日總量。

This issue is particularly prominent for large coins that are listed on many exchanges and is exacerbated by the practice of wash trading, where exchanges artificially inflate their reported trading volumes to appear more liquid.

對於在許多交易所上列出的大型硬幣而言，此問題尤為突出，並且因Wash Trading的做法而加劇了，在這種做法中，交易所的交換人為地膨脹了其報告的交易量，以表現出更多的流動性。

In traditional capital markets, data is standardized across large vendors or by regulatory bodies. However, the crypto industry lacks such standardization, with providers differing in data definitions, delivery technologies, and reporting methodologies.

在傳統的資本市場中，數據在大型供應商或監管機構中進行標準化。但是，加密行業缺乏這樣的標準化，提供者的數據定義，交付技術和報告方法不同。

As digital assets gain traction among institutional investors, the demand for robust and reliable market data infrastructure has intensified as investors seek high-quality data for informed trading strategies and regulatory compliance.

隨著數字資產在機構投資者之間的吸引力，隨著投資者尋求高質量的數據以獲得知情的交易策略和監管合規性，對穩健和可靠的市場數據基礎設施的需求不斷加劇。

This has fueled the growth of crypto data providers like CCData, CoinDesk, Compadre, CryptoCompare, Digital Asset Analytics, Factor Factor, FTX, Kaiko, Lungo, and TRM offering cleansed and normalized data from multiple independent sources, despite the absence of agreed data quality standards or auditing. These sources include blockchains themselves, but also centralized exchanges, decentralized finance (DeFi), and derivatives markets.

這加劇了加密數據提供商的增長，例如CCDATA，COINDESK，COMPADRE，CRYPTOCARE，數字資產分析，因子因子，FTX，Kaiko，Lungo和TRM，儘管缺乏商定的數據質量標准或審計，但仍能從多個獨立源提供清潔和歸一化數據。這些來源包括區塊鏈本身，還包括集中式交流，分散的財務（DEFI）和衍生品市場。

Beyond consolidating and normalizing data, some vendors also deliver derived metrics, signals, and indicators, empowering clients with actionable information. For instance, DeFi analytics providers like DefiLlama and DeFiPulse provide insights into liquidity pool sizes, interest rates, and other crucial metrics within the decentralized finance ecosystem.

除了合併和標準化數據之外，一些供應商還提供了派生的指標，信號和指標，還可以通過可行的信息授權客戶。例如，諸如Defillama和Defipulse之類的Defi分析提供商提供了對分散融資生態系統中流動性池大小，利率和其他關鍵指標的見解。

Crypto data is a rapidly emerging sub-industry that’s playing a significant role in the broader digital asset ecosystem. Explored in a new report by Financial Technology (FT) Partners, a fintech-focused investment bank, this ecosystem encompasses five main verticals:

加密數據是一個快速新興的子行業，在更廣泛的數字資產生態系統中發揮了重要作用。該生態系統在金融技術（FT）合作夥伴的新報告（FT）合作夥伴的一份新報告中探討，該生態系統涵蓋了五個主要垂直領域：

This sector has seen a strong interest from investors, with major funding rounds such as Chainalysis’s $170 million Series F, Lukka’s $110 million Series E, and Kaiko’s $53 million Series B.

該行業對投資者產生了濃厚的興趣，包括Chainalysis的1.7億美元系列F，Lukka的1.1億美元系列E和Kaiko的5300萬美元系列B。

In terms of mergers and acquisitions (M&A) activity, prominent players like Chainalysis, Lukka and Amberdata have been acquiring smaller startups to integrate new technologies and expand their product offerings.

在合併和收購（併購）活動方面，諸如Chainalysis，Lukka和Amberdata之類的傑出參與者一直在收購較小的初創企業，以整合新技術並擴展其產品。

Chainalysis acquired Excygent, a specialist in cybercrime investigation; Transpose, a blockchain data and infrastructure company; and Alterya, an AI-powered fraud detection solution provider. Meanwhile, Lukka acquired Blox Finance, a crypto accounting and financial data management software business; Venato, a Web3 blockchain analytics startup; and Coinfirm, a top-tier European based blockchain analytics software company.

鏈分析獲得了網絡犯罪研究專家Excygent；轉置，區塊鏈數據和基礎架構公司；和Antya，AI-Power的欺詐檢測解決方案提供商。同時，盧卡（Lukka）收購了Blox Finance，Crypto會計和財務數據管理軟件業務； Venato，Web3區塊鏈分析初創公司；以及CoInfirm，這是一家歐洲頂級區塊鏈分析軟件公司。

On the other hand, Kaiko acquired Kesitys, a data analytics company; and Vinter, a leading European crypto index provider.

另一方面，Kaiko收購了數據分析公司Kesitys；和歐洲領先的加密指數提供商Vinter。