Fast Options Tick Data Database

Problem Context

Quant researchers and algorithmic traders need to store and analyze high-frequency options tick data (per-second granularity) for backtesting. They receive daily CSV files but struggle with slow database ingestion, inefficient schema design, and backtesting queries that degrade as historical data grows. Their goal is to build a system that handles fast daily updates while keeping backtesting fast, even with years of data.

Pain Points

Current solutions either require custom database setup (slow and error-prone) or use general-purpose databases that become unusably slow as data grows. Users waste time manually designing schemas, optimizing queries, and troubleshooting performance issues. Existing tools either lack the speed needed for backtesting or are too complex for individual researchers to manage.

Impact

Slow backtesting means missed trading opportunities, wasted research time, and lost revenue. Quant researchers spend hours optimizing databases instead of analyzing data. As historical data grows, queries slow down to the point where backtesting becomes impractical, forcing users to discard old data or accept poor performance.

Urgency

This problem cannot be ignored because slow backtesting directly impacts trading decisions and revenue. As data grows daily, the issue worsens over time, making it critical to solve now rather than later. Quant researchers cannot afford to lose time or trading signals due to technical limitations in their database setup.

Target Audience

Quantitative researchers, algorithmic traders, hedge funds, proprietary trading firms, and retail traders who rely on backtesting for options strategies. This includes individuals and small teams who lack the resources for custom database solutions but need high performance. The problem is global, affecting traders in any market with index options (e.g., NIFTY, S&P 500, FTSE).

Solution Approach

A cloud-hosted, pre-optimized database service specifically designed for options tick data. It handles fast daily ingestion of CSV files, stores data efficiently in a hybrid time-series/relational schema, and accelerates backtesting queries with pre-built analytics. Users upload their data, and the system automatically ingests, processes, and makes it query-ready for backtesting without manual setup.

Key Features

1. Optimized Schema: A hybrid time-series/relational design ensures fast writes for new data and fast reads for historical backtesting.
2. Pre-Built Queries: Includes ready-to-use SQL queries for calculating Greeks, building option chains, and running backtests, saving users months of development time.
3. Scalable Storage: Handles years of tick data while keeping query performance fast, even as data grows.

User Experience

Users upload their daily CSV files via a simple dashboard or API. The system automatically processes the data, calculates Greeks, and builds option chains in the background. They can then run backtests with pre-optimized queries that return results in seconds, not hours. The dashboard provides visualizations of query results, and users can export data for further analysis. No database setup or query optimization is required.

Differentiation

Unlike general-purpose databases (e.g., PostgreSQL, MySQL) or enterprise tools (e.g., Bloomberg, QuantConnect), this solution is pre-optimized for options tick data, with a schema and queries tailored to quant research needs. It eliminates the need for custom development, offers faster ingestion and backtesting than alternatives, and scales automatically with data growth. Competitors either lack speed or require expensive enterprise licenses.

Scalability

The service scales horizontally in the cloud, so users can add more storage or compute resources as their data grows. Pricing is tiered based on data volume and query needs, allowing users to start small and expand as their research requirements increase. Additional markets (e.g., S&P 500, FTSE) can be added via add-ons, and the system supports API access for integration with other tools.

Expected Impact

Users save *hours per week- on database setup and query optimization, freeing up time for actual research. Backtesting becomes faster and more reliable, leading to better trading strategies and fewer missed opportunities. The solution grows with their data, ensuring performance doesn’t degrade over time. For teams, it reduces the need for custom dev work, lowering operational costs.