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Point-in-Time Earnings Data API
Build a developer-focused API and dataset that delivers earnings calendars, reported metrics, amendment history, and exact publication timestamps in a backtest-safe format. The strongest need is not raw data alone, but confidence that users are not training on information that was unavailable at the time.
为什么这很重要
You are trying to test whether earnings events help or hurt your strategy, but the harder problem is knowing whether your historical data matches what the market actually knew at the time. If a company revised a filing later, or if the event timestamp is wrong, your model can quietly learn from future information. Existing data sources may be cheap or accessible, but they rarely make amendment history and event timing easy to trust. As a result, you spend time stitching together feeds, checking edge cases, and still worry that your backtest is contaminated by leakage.
- · 专为 Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals. 打造。
- · 最可能的变现方式：SaaS subscription。
痛点叙事
You are trying to test whether earnings events help or hurt your strategy, but the harder problem is knowing whether your historical data matches what the market actually knew at the time. If a company revised a filing later, or if the event timestamp is wrong, your model can quietly learn from future information. Existing data sources may be cheap or accessible, but they rarely make amendment history and event timing easy to trust. As a result, you spend time stitching together feeds, checking edge cases, and still worry that your backtest is contaminated by leakage.
得分构成
市场信号
Go-to-Market 启动方案
Solo and small-team quants running equity factor or ML backtests that incorporate earnings-related features.
~20K-50K active globally, with 1K-3K high-intent paying prospects
SEO long-tail
$99/month
10 paying users who upload or test at least one backtest pipeline within 30 days
MVP 方案 · 1-2 周
- Define a minimal schema for earnings events, original values, amendments, and publication timestamps
- Ingest one vendor's earnings calendar and one fundamentals source into normalized tables
- Build a simple FastAPI endpoint for symbol-plus-date queries
- Create a validation notebook showing point-in-time retrieval for 20 symbols
- Publish a landing page with sample data and waitlist capture
- Add bulk Parquet export by date range and universe
- Implement amendment history retrieval and flagging
- Ship a Python client with a DuckDB integration example
- Add metadata pages for coverage, missingness, and update lag
- Run outreach to quant newsletters and collect 10 design-partner calls
差异化
为什么这件事可能失败
自我反驳——最重要的信任度信号
- 1The economics may break if upstream data licensing is expensive or restrictive enough to kill margins.
- 2Advanced quants may prefer to buy directly from established vendors and build their own point-in-time pipeline.
- 3If validation is not rigorous and public, users will not trust the core claim of backtest safety.
证据综述
AI 如何合成此洞察——无原话引用
Multiple commenters focused on data quality rather than model architecture. Roughly four mentioned timing, amendments, survivorship bias, or publication-date correctness, while several others raised plain access and coverage concerns. The combination suggests a strong commercial opening for a trust-centric research data product rather than just another generic market data feed.
行动计划
在写代码之前，先验证这个商机
推荐下一步
直接做
需求信号强烈。痛点真实、付费意愿明确——启动 MVP 开发。
落地页文案包
基于真实 Reddit 评论整理的即用文案，可直接粘贴到落地页
主标题
Point-in-Time Earnings Data API
副标题
Build a developer-focused API and dataset that delivers earnings calendars, reported metrics, amendment history, and exact publication timestamps in a backtest-safe format. The strongest need is not raw data alone, but confidence that users are not training on information that was unavailable at the time.
目标用户
适合：Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals.
功能列表
✓ Point-in-time earnings and filing timestamps ✓ Original versus amended metric history ✓ Backtest-safe API and bulk Parquet export ✓ Coverage and survivorship-bias documentation ✓ Python and DuckDB client libraries
去哪里验证
把落地页链接发布到 r/r/algotrading——这里就是这些痛点被发现的地方。
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