Dataset opportunity
Arfima — 事件流数据集机会
Arfima 持有的海量事件流数据集，可用于预测和异常检测。
Score
72.8
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
65%
Action
数据共享协议
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球算法交易市场 = 2025 年为 218.9 亿美元，复合年增长率为 15.4%。
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
Profile
Dataset profile
Type
事件流数据集
Modality
时间序列
Sector
金融
Volume
大
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 受限 · PII/受监管
Buyer persona
量化基金和需求预测 AI 团队
Arfima 持有一个宝贵的事件流数据集，该数据集源自其专有交易活动，包括 API 调用、业务记录、搜索日志和交易数据。这种高频的时间序列数据提供了市场动态的精细、实时视图，使其非常适合开发和训练复杂的 AI 预测模型，以预测市场走势和执行算法交易策略。
全球算法交易市场（直接使用此类数据）在2025 年的估值为 218.9 亿美元，预计将以15.4% 的复合年增长率增长。尽管访问存在复杂性——源于其保密性质、第三方许可限制以及集成到内部高频交易基础设施中——但该数据在快速增长的高价值市场中的稀缺性和已证实的效用使其成为寻求竞争优势的 AI 买家的引人注目的资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：专有交易数据高度保密，是其竞争优势的核心；金融市场数据的使用通常受第三方交易所许可（CME、Eurex 等）的限制；数据存储在内部量化工具和高频交易基础设施中。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Arfima 运营着一个复杂的量化交易平台，从其作为衍生品市场流动性提供者的活动中生成专有的时间序列数据。这种独特的事件流数据集非常适合量化基金等 AI 买家，用于开发和回测新颖的预测模型和阿尔法生成策略。在全球算法交易市场预计到 2025 年将超过 210 亿美元的情况下，获取此类稀有的高频数据代表着显著的竞争优势。
See dimension details ↓- Legal Accessibility2
开放/API 访问
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
高难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength89
5 种证据类型，6 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License32
所有权=混合，许可=受限
Whether the company can legally license the data out — based on ownership and licensing complexity. - Dataset Specificity90
主导的“事件流”，金融行业，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume70
6 次证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
由于算法交易市场快速增长至 2030 年的 443.4 亿美元（复合年增长率为 15.4%），AI 买家对实时金融事件流的需求极高。
How strongly AI builders and companies are likely to want this data, based on market signals. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation67
3 个数据需求信号（2 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高 — 专有数据超出已货币化的部分
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ 良好目标 — Arfima 是一家专有交易公司，使用内部量化策略为自有账户进行交易；其核心业务不是出售数据或情报，使其运营数据成为有价值的、休眠的副产品。[4, 7, 11]
- Deep Qualification80
⚠ 需要审查 — Arfima 是一家专有交易公司，作为其核心业务的副产品生成极具价值的事件流数据集；然而，由于交易所严格的市场数据许可协议，转售可能被禁止。[许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Event streams
直接引用 Kafka 和量化交易策略，证实存在一个核心的、专有的时间序列事件流数据集，该数据集源自严格、数据驱动的金融分析。
API access
技术日志证实了与 AWS API 等云基础设施的经验，表明其拥有管理和交付大规模数据集的现代化、可扩展的架构。
Search / query logs
内部数据工程专业知识记录，包括 NoSQL 和关系数据库，证明持有者有能力管理和构建高容量、复杂数据流。
Transaction data
业务活动日志显示该公司作为日内交易者在受监管的衍生品市场中提供流动性，从而创建了其做市交易的有价值的表格数据集。
business_records
内部文件详细介绍了高级定价模型和对冲工具的创建，展示了高水平的金融工程，从而提升了底层数据的价值。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Arfima Event Stream — a Large event stream dataset (Time Series modality) in the finance domain. Primary AI use-case: Forecasting. Market signal: Global Algorithmic Trading market = $21.89 billion in 2025, CAGR 15.4% (source: The Business Research Company). Investment score 72.8/100 (confidence 0.65). Recommended action: Data Sharing Agreement.
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