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candle-annotator/openspec/changes/candle-annotator-app/specs/data-ingestion/spec.md
Marko Djordjevic d04b673cfa feat: initialize Next.js project with database schema 
- Set up Next.js with App Router, TypeScript, Tailwind CSS
- Configure shadcn/ui with dark theme
- Install dependencies: lightweight-charts, papaparse, lucide-react
- Set up Drizzle ORM with better-sqlite3
- Create database schema for candles and annotations tables
- Generate migration SQL
2026-02-12 10:23:02 +01:00

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ADDED Requirements

Requirement: CSV file upload

The system SHALL provide a file upload component that accepts CSV files containing OHLC candle data. The CSV format MUST have columns: time, open, high, low, close. The time column SHALL accept both YYYY-MM-DD date strings and Unix timestamps (integer seconds).

Scenario: Valid CSV upload

  • WHEN user uploads a CSV file with valid headers (time, open, high, low, close) and valid data rows
  • THEN system parses all rows and stores them in the candles database table

Scenario: CSV with Unix timestamps

  • WHEN user uploads a CSV where the time column contains Unix timestamps (e.g., 1700000000)
  • THEN system stores the timestamps as integers in the database and renders candles correctly on the chart

Scenario: CSV with date strings

  • WHEN user uploads a CSV where the time column contains date strings (e.g., "2024-01-15")
  • THEN system converts dates to Unix timestamps and stores them in the database

Scenario: Invalid CSV format

  • WHEN user uploads a CSV missing required headers or containing malformed data
  • THEN system displays an error message describing the issue and does not store any partial data

Scenario: Duplicate upload

  • WHEN user uploads a CSV containing candle times that already exist in the database
  • THEN system replaces existing candle records with the new data (upsert behavior)

Requirement: CSV parsing with papaparse

The system SHALL use the papaparse library for CSV parsing. Parsing SHALL handle large files by using streaming mode for files exceeding 10,000 rows. Parsed records SHALL be inserted into SQLite within a single database transaction for atomicity.

Scenario: Large file parsing

  • WHEN user uploads a CSV with more than 10,000 rows
  • THEN system uses streaming parse and batch inserts within a transaction, completing without memory issues

Scenario: Transaction atomicity

  • WHEN a parse error occurs midway through a CSV file
  • THEN system rolls back the entire transaction and no partial data is stored

Requirement: Candles database table

The system SHALL store candle data in a candles table with columns: id (integer primary key, auto-increment), time (integer, Unix timestamp, unique), open (real), high (real), low (real), close (real). The time column MUST have a unique constraint.

Scenario: Schema structure

  • WHEN the database is initialized
  • THEN the candles table exists with all required columns and constraints