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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.
Por que isso importa
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.
- · Feito para Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals..
- · Monetização mais provável: SaaS subscription.
A Dor · Narrativa
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.
Detalhe da pontuação
Sinal de Mercado
Go-to-Market
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
Escopo do MVP · 1–2 semanas
- 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
Diferenciação
Por que isso pode falhar
Auto-refutação — o sinal de confiança mais importante
- 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.
Resumo das evidências
Como a IA sintetizou este insight — sem citações literais
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.
Plano de Ação
Valide esta oportunidade antes de escrever código
Próximo Passo Recomendado
Construir
Sinais de demanda fortes. Há dor real e disposição a pagar — comece a construir um MVP.
Kit de Textos para Landing Page
Textos prontos para colar, baseados na linguagem real da comunidade Reddit
Título Principal
Point-in-Time Earnings Data API
Subtítulo
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.
Para Quem É
Para Independent quants, small hedge funds, and systematic traders who backtest equity strategies using earnings or fundamentals.
Lista de Funcionalidades
✓ 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
Onde Validar
Compartilhe sua landing page no r/r/algotrading — é exatamente lá que esses pontos de dor foram descobertos.
Cadastre-se para desbloquear a análise profunda completa
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