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Smart OHLC Data API (OHLC + Sequencing)
A data API that provides OHLC bars augmented with intrabar sequencing (e.g., Open -> High -> Low -> Close). This solves the stop-loss/take-profit ambiguity without the heavy compute and storage costs of full tick data.
Pourquoi c'est important
When you build algorithmic trading strategies on higher timeframes, you inevitably face the problem of intrabar ambiguity. If a single fifteen-minute candle hits both your stop loss and your take profit, standard data cannot tell you which happened first. You are forced to either buy expensive, massive high-resolution datasets that slow down your backtesting, or make blind assumptions that ruin your strategy's realistic performance metrics. You need a way to know the sequence of price movements without downloading gigabytes of noise.
- · Conçu pour Retail algorithmic traders and quantitative hobbyists who backtest swing and intraday strategies..
- · Monétisation la plus probable : SaaS subscription.
La douleur · Récit
When you build algorithmic trading strategies on higher timeframes, you inevitably face the problem of intrabar ambiguity. If a single fifteen-minute candle hits both your stop loss and your take profit, standard data cannot tell you which happened first. You are forced to either buy expensive, massive high-resolution datasets that slow down your backtesting, or make blind assumptions that ruin your strategy's realistic performance metrics. You need a way to know the sequence of price movements without downloading gigabytes of noise.
Détail du score
Signal du marché
Mise sur le marché
Independent quantitative developers and algorithmic traders building custom backtesting pipelines in Python.
~50,000 active globally across trading communities and GitHub.
Hacker News launch and algorithmic trading subreddits.
$29/month for API access to 5 years of historical smart-OHLC data.
15 paying users from initial community launches and direct outreach.
Périmètre MVP · 1–2 semaines
- Define the JSON/Parquet schema for OHLC-Path data
- Source 1 year of raw tick data for a single popular asset (e.g., SPY or BTC)
- Write a Python script to aggregate the raw ticks into the OHLC-Path format
- Validate the accuracy of the sequencing against the raw data
- Set up a basic FastAPI endpoint to serve the processed data
- Deploy the API to a scalable cloud provider (e.g., AWS or Render)
- Create documentation with Python code examples for backtesting integration
- Build a simple backtest script demonstrating the accuracy difference vs standard OHLC
- Integrate Stripe for subscription management and API key generation
- Draft and publish launch posts on developer and trading forums
Différenciation
Pourquoi cela pourrait échouer
Auto-contre-argument — le signal de confiance le plus important
- 1Traders might find that simply using 1-minute data resolves enough ambiguity for their specific needs without paying for a new service.
- 2The cost of acquiring commercial licenses to redistribute derived market data might exceed early revenue.
- 3Institutional players already have internal tools for this, limiting the market strictly to price-sensitive retail users.
Résumé des preuves
Comment l'IA a synthétisé cet aperçu — pas de citations textuelles
Multiple algorithmic traders highlighted that while standard aggregated data is fine for general trends, it fails completely when conditional orders like stop-losses are triggered within a single bar. Users explicitly mentioned the need to know intrabar sequencing to avoid making false optimistic or pessimistic assumptions, while also noting the prohibitive storage and compute costs of using raw high-resolution data.
Plan d'Action
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Prochaine Étape Recommandée
Valider
Signaux prometteurs. Créez une landing page, collectez des emails, puis décidez si vous construisez.
Kit de Textes pour Landing Page
Textes prêts à coller, basés sur le langage réel de la communauté Reddit
Titre Principal
Smart OHLC Data API (OHLC + Sequencing)
Sous-titre
A data API that provides OHLC bars augmented with intrabar sequencing (e.g., Open -> High -> Low -> Close). This solves the stop-loss/take-profit ambiguity without the heavy compute and storage costs of full tick data.
Pour Qui
Pour Retail algorithmic traders and quantitative hobbyists who backtest swing and intraday strategies.
Liste des Fonctionnalités
✓ Historical data API delivering OHLC + Path (sequencing) data ✓ Pre-processed datasets for major equities and crypto pairs ✓ Python SDK for easy integration into Pandas/Polars workflows
Où Valider
Partagez votre landing page sur r/r/algotrading — c'est exactement là que ces points de douleur ont été découverts.
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