Dataset opportunity
Genevatrading — 事件流数据集机会
Genevatrading 持有的中等事件流数据集，可用于预测和异常检测。
Score
42.5
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
58%
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 189 亿美元，复合年增长率为 7.85%。
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.
Profile
Dataset profile
Type
事件流数据集
Modality
时间序列
Sector
金融
Volume
中等
Freshness
实时
Rarity
中等
Accessibility
受限
Legal
公司所有 — 受限 · 个人身份信息/受监管
Buyer persona
量化基金和需求预测人工智能团队
Genevatrading 持有源自其核心业务的专有事件流数据集，包括高保真交易数据、业务记录和公开市场数据源。此时间序列数据提供了微秒级别精度的市场执行和订单流视图，对于开发和回测预测人工智能用例的预测模型（尤其是在算法交易策略中）具有极高的价值。
商业价值锚定在庞大且不断增长的算法交易市场，该市场在 2025 年的估值为 189 亿美元，预计将以 7.85% 的复合年增长率增长。[7] 尽管访问复杂——需要就严格的交易所协议进行谈判并澄清派生数据所有权的法律问题——但这种非公开执行数据的固有稀缺性和产生超额收益的潜力提供了显著的竞争优势，证明了对复杂人工智能买家进行尽职调查的合理性。⚠ 尽职调查（有价值的数据，可协商的访问权限）：专有交易公司对其执行数据和超额收益信号高度保密；数据使用可能受严格的第三方交易所协议（CME、ICE 等）管辖；由于交易所条款，派生高保真数据集的所有权需要法律澄清 · 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 Genevatrading 拥有一个专有的、高保真的金融事件流，记录时间长达 25 年，时间戳精确到纳秒。这种时间序列数据是量化基金和人工智能团队构建算法交易预测模型的基石资产。在一个预计到 2025 年将近 190 亿美元的市场中，该数据集为发现和回测跨不同资产类别的新超额收益策略提供了一个稀有的真相来源。
See dimension details ↓- Dataset Specificity78
主导的“事件流”，金融行业，2 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 Rarity46
专有领域数据（公开降低稀缺性）
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 个证据命中
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 Value74
适合预测
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
人工智能买家需求极高，这得益于竞争激烈的算法交易市场对专有超额收益信号的需求，该市场正以 7.85% 的复合年增长率扩张。[7]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility18
公开/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 Feasibility32
高难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength77
4 种证据类型，5 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License66
所有权=公司所有，许可=受限
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 数据胃口信号（0 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 Audit42
⚠ 审查 — Geneva Trading 是一家专有交易公司，其核心业务是交易和利用高保真数据和分析来产生自身超额收益，而不是将数据作为产品出售，但它属于出售情报/人工智能作为其核心功能的排除类别。问题：该公司的核心业务是专有交易，这在很大程度上依赖于使用数据和分析作为核心能力来产生利润。[3, 8]；该公司为其内部交易员提供“数据分析服务”和“高保真数据”，以增强决策能力并提高交易成功率。[6, 11]；这种模式相当于将情报/人工智能作为产品出售，这是一个明确的排除标准；该公司已经是数据/分析市场中为自身利益而进行的复杂参与者，因此不适合闲置数据市场。[2, 11]
- Deep Qualification60
✓ 通过 — Geneva Trading 是一家专有交易公司，这使其成为所述高价值交易数据集的潜在持有者。然而，由于管理市场数据的限制性交易所协议，许可此数据的能力高度不确定。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Event streams
这是专有的、高保真的事件流的直接证据，具有纳秒级时间戳，对于训练精确的高频交易和市场预测模型至关重要。
Public datasets
这描述了公司内部方法，使用精细数据进行回测和策略优化，这表明其拥有高质量资产的复杂数据文化。
Transaction data
这表明存在深度、高分辨率的历史数据，这是人工智能团队在部署新交易策略之前进行回测和验证其性能的关键组成部分。
business_records
这证实了持有者作为流动性提供商的25 年历史，表明该数据集的来源以及从能源到数字资产的多个交易所和资产类别的广泛覆盖。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Genevatrading Event Stream — a Moderate event stream dataset (Time Series modality) in the finance domain. Primary AI use-case: Forecasting. Market signal: Global Algorithmic Trading Market = $18.9B in 2025, CAGR 7.85% (source: Straits Research). Investment score 42.5/100 (confidence 0.58). Recommended action: Data Sharing Agreement.
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