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Point-in-Time Equity Universe API
Build a developer-first API that returns historical index membership, delisted securities, and point-in-time tradable universes for equities. The product solves the most common early-stage quant mistake: using today's constituents and incomplete free data to test historical stock-picking strategies.
이것이 중요한 이유
You start with an ordinary stock-selection idea, pull prices from a free source, and only later discover your test universe quietly excluded many names that disappeared, merged, or left the index. The result looks cleaner than reality because your historical screen is built from survivors and incomplete constituent lists. Paid institutional datasets exist, but they feel expensive and operationally heavy when you are still validating ideas. What you really need is a simple way to ask, for any date, which names were actually eligible, which later delisted, and how exits should be represented so your research is not invalid from the first line of code.
- · Independent quant traders, small systematic funds, and research engineers building stock-selection backtests in Python who need valid historical universes without institutional data budgets.을(를) 위해 제작되었습니다.
- · 가장 유력한 수익화 모델: SaaS subscription.
고충 · 내러티브
You start with an ordinary stock-selection idea, pull prices from a free source, and only later discover your test universe quietly excluded many names that disappeared, merged, or left the index. The result looks cleaner than reality because your historical screen is built from survivors and incomplete constituent lists. Paid institutional datasets exist, but they feel expensive and operationally heavy when you are still validating ideas. What you really need is a simple way to ask, for any date, which names were actually eligible, which later delisted, and how exits should be represented so your research is not invalid from the first line of code.
점수 세부
시장 신호
시장 진출 전략
Solo and small-team quant researchers running equity backtests in Python who currently rely on yfinance or ad hoc CSV universes.
~20K-60K active globally
SEO long-tail
$49/month
20 paying users who connect the API to a live notebook or backtest within 30 days
MVP 범위 · 1~2주
- Define MVP scope as one index family plus US equities only
- Ingest public index change logs into dated membership tables
- Create a simple schema for security, listing, active date range, and status
- Build a basic API endpoint that returns members for a given index and date
- Prepare a notebook showing the difference between current and historical constituents
- Add delisted symbol handling with terminal event types such as acquisition or delisting
- Ship a Python SDK wrapper for date-based universe queries
- Add CSV and Parquet export for downloaded universes
- Implement a basic bias checker that flags use of current constituents in historical periods
- Launch a landing page with one sample backtest case study and Stripe checkout
차별화
실패 가능 요인
자가 반박 — 가장 중요한 신뢰 신호
- 1Users may prefer established data vendors once they realize data quality matters, making it hard to win on trust alone.
- 2Historical universe accuracy may require data sources that are too costly to support attractive pricing.
- 3Some hobbyists only need a one-time download and will not sustain recurring subscription revenue.
근거 요약
AI가 이 인사이트를 합성한 방법 — 직접 인용 없음
The discussion repeatedly returns to one issue: valid stock-selection backtests require date-correct index membership and delisted names, while common free workflows do not provide either. Roughly a dozen comments reinforced that historical constituent data is essential and that many users eventually pay for it. Several also pointed out that the problem affects even short backtests, making this a recurring need rather than a niche archival feature.
액션 플랜
코드를 작성하기 전에 이 기회를 검증하세요
권장 다음 단계
개발 시작
강한 수요 신호 감지. 실제 고통과 지불 의지 확인 — MVP 개발을 시작하세요.
랜딩 페이지 카피 키트
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헤드라인
Point-in-Time Equity Universe API
서브 헤드라인
Build a developer-first API that returns historical index membership, delisted securities, and point-in-time tradable universes for equities. The product solves the most common early-stage quant mistake: using today's constituents and incomplete free data to test historical stock-picking strategies.
대상 사용자
대상: Independent quant traders, small systematic funds, and research engineers building stock-selection backtests in Python who need valid historical universes without institutional data budgets.
기능 목록
✓ API for historical index constituents by date ✓ Delisted and acquired security coverage with exit return handling ✓ Point-in-time eligibility filters such as listing age and liquidity thresholds ✓ CSV/Parquet export plus Python SDK ✓ Bias warnings when users request impossible historical universes
어디서 검증할까요
r/r/algotrading에 랜딩 페이지 링크를 공유하세요 — 바로 이 고통이 발견된 곳입니다.
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