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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.
이것이 중요한 이유
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.
- · Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals.을(를) 위해 제작되었습니다.
- · 가장 유력한 수익화 모델: SaaS subscription.
고충 · 내러티브
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.
점수 세부
시장 신호
시장 진출 전략
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 범위 · 1~2주
- 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
차별화
실패 가능 요인
자가 반박 — 가장 중요한 신뢰 신호
- 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.
근거 요약
AI가 이 인사이트를 합성한 방법 — 직접 인용 없음
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.
액션 플랜
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권장 다음 단계
개발 시작
강한 수요 신호 감지. 실제 고통과 지불 의지 확인 — MVP 개발을 시작하세요.
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헤드라인
Point-in-Time Earnings Data API
서브 헤드라인
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.
대상 사용자
대상: Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals.
기능 목록
✓ 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
어디서 검증할까요
r/r/algotrading에 랜딩 페이지 링크를 공유하세요 — 바로 이 고통이 발견된 곳입니다.
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