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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.
為什麼這很重要
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.
- · 專為 Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals. 打造。
- · 最可能的變現方式：SaaS subscription。
痛點敘事
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.
得分構成
市場信號
Go-to-Market 啟動方案
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 週
- 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
差異化
為什麼這件事可能失敗
自我反駁——最重要的信任度信號
- 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.
證據綜述
AI 如何合成此洞察——無原話引用
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.
行動計畫
在寫程式之前，先驗證這個商機
建議下一步
直接做
需求訊號強烈。痛點真實、付費意願明確——啟動 MVP 開發。
落地頁文案包
基於真實 Reddit 評論整理的即用文案，可直接貼到落地頁
主標題
Point-in-Time Earnings Data API
副標題
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.
目標使用者
適合：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
去哪裡驗證
把落地頁連結發布到 r/r/algotrading——這裡就是這些痛點被發現的地方。
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