starting planning

.gitignore

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+.claude

CLAUDE.md

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+say "hi marko" at the begining
+
+Keep important deployment steps in DEPLOYMENT.md
+keep project overview updates and important techical changes in README.md
+
+for next.js, tailwind csss, shadcn-ui, next-auth, fastapi
+use context7, lightweight charts
+
+Always use Context7 MCP when I need library/API documentation, code generation, setup or configuration steps without me having to explicitly ask.
+
+commit after every task.
+pause after every section.
+
+
+CLAUDE_DESCRIPTION.md is used for LLMs to understand the project faster. Keep it up to date with the latest changes and updates.

PLAN.md

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+This is a great project. Combining financial charting with manual labeling is the first step toward building a custom machine-learning model for trading.
+
+To give you the best "TradingView" feel, we will use **Next.js** for the framework and **Lightweight Charts** (by TradingView) for the engine. For the drawing and labeling layer, we’ll implement a custom "Overlay" system to handle clicks and coordinate mapping.
+
+---
+
+## 🛠 Technical Architecture
+
+### 1. The Stack
+
+* **Framework:** Next.js (App Router) for a full-stack React experience.
+* **Charting Engine:** `lightweight-charts` (the library behind TradingView's lightweight version).
+* **Database:** SQLite using **Drizzle ORM** (lightweight, fast, and type-safe).
+* **State Management:** React `useState` and `useRef` to track the "Active Labeling Mode."
+* **Data Ingestion:** `papaparse` for fast CSV parsing.
+
+### 2. Data Schema
+
+We need two main tables in SQLite:
+
+* **`candles`**: Stores the OHLC data (Open, High, Low, Close, Time).
+* **`annotations`**: Stores the labels.
+* `id`: Primary key.
+* `timestamp`: The exact candle time the label belongs to.
+* `label_type`: "break_up", "trend_down", etc.
+* `geometry`: JSON string (to store line coordinates if drawing lines).
+
+
+
+### 3. The "Annotation Overlay" Logic
+
+Lightweight Charts is optimized for performance, not "drawing" by default. To make it feel interactive:
+
+1. **Coordinate Mapping:** We use the `chart.timeScale().coordinateToTime()` and `series.priceToCoordinate()` methods to convert a user's mouse click on the screen into a specific price and time in the database.
+2. **Visual Markers:** We use the built-in `setMarkers` API to show icons (up/down arrows) for specific patterns like "Break Up."
+3. **Drawing Lines:** For lines, we will use "Price Lines" or a transparent **SVG Overlay** that sits on top of the chart and scales as the user zooms.
+
+---
+
+## 📝 Coding Instructions for LLM
+
+**Copy and paste the following prompt into your coding LLM (e.g., Claude 3.5 Sonnet or GPT-4o):**
+
+---
+
+### **Prompt for AI Developer**
+
+**Objective:** Build a Next.js web application for EUR/USD candle annotation.
+**Tech Stack:** Next.js (App Router), TypeScript, Tailwind CSS, `lightweight-charts`, `lucide-react`, `papaparse`, and SQLite (using Drizzle ORM).
+
+**Core Features to Implement:**
+
+**1. Data Ingestion:**
+
+* Create a file upload component that accepts a CSV.
+* CSV Format: `time, open, high, low, close`.
+* Parse the CSV and store the records in a local SQLite database.
+
+**2. The Chart Canvas:**
+
+* Use `lightweight-charts` to render a candlestick chart.
+* Implement a "Toolbox" sidebar with buttons: "Label: Break Up", "Label: Break Down", "Draw Line", "Delete".
+* When a user clicks "Label: Break Up" and then clicks a candle on the chart, save that timestamp and label to the `annotations` table.
+
+**3. Interaction Logic:**
+
+* **Markers:** If an annotation exists at a timestamp, display it on the chart using the `series.setMarkers()` API (e.g., a green 'aboveBar' arrow for Break Up).
+* **Line Drawing:** Implement a basic "two-click" line tool. Click 1 sets start point (Price/Time); Click 2 sets end point. Save coordinates as JSON in SQLite.
+
+**4. Backend API:**
+
+* `POST /api/upload`: Parse CSV and populate DB.
+* `GET /api/annotations`: Fetch all saved labels for the current chart.
+* `POST /api/annotations`: Save a new label or drawing.
+
+**5. UI Requirements:**
+
+* Dark mode theme (Slate-900).
+* Sidebar for tool selection.
+* Main area for the chart (responsive height).
+* Simple "Export" button to download the `annotations` table as a new CSV (Timestamp, Label, Price).
+
+**Please provide the code in a modular structure: `/components`, `/lib/db`, and `/app/api`.**
+
+---
+
+## 🚀 Next Steps
+
+1. **Prepare your CSV:** Ensure your EUR/USD data has headers named `time` (in 'YYYY-MM-DD' or Unix format), `open`, `high`, `low`, and `close`.
+2. **Initialize the project:** If you are running this locally, you will need to run `npx create-next-app@latest` first.
+
+**Would you like me to generate the database schema file (Drizzle/SQLite) for you to get started?**

openspec/changes/candle-annotator-app/.openspec.yaml

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+schema: spec-driven
+created: 2026-02-12

openspec/changes/candle-annotator-app/proposal.md

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+## Why
+
+Building a custom machine-learning model for trading requires labeled training data. Currently there is no tool to manually annotate EUR/USD candlestick charts with pattern labels (break up, break down, trend lines) and export those annotations as structured data. This app provides a TradingView-like charting interface with an interactive labeling layer, enabling a trader to visually mark patterns on historical candle data and export the results for ML pipelines.
+
+## What Changes
+
+- New full-stack Next.js application (App Router, TypeScript, Tailwind CSS)
+- CSV upload for OHLC candle data (time, open, high, low, close) with parsing and SQLite persistence
+- Interactive candlestick chart powered by `lightweight-charts`
+- Annotation toolbox: point labels (Break Up, Break Down) and two-click line drawing
+- Visual markers on chart for existing annotations (arrows, lines)
+- Backend API for data ingestion, annotation CRUD, and CSV export
+- Dark mode UI with sidebar toolbox and responsive chart area
+
+## Capabilities
+
+### New Capabilities
+
+- `data-ingestion`: CSV file upload, parsing with papaparse, and storage of OHLC candle records in SQLite via Drizzle ORM
+- `chart-canvas`: Candlestick chart rendering using lightweight-charts with responsive layout and dark theme
+- `annotation-tools`: Interactive labeling (Break Up, Break Down markers) and two-click line drawing with coordinate mapping between screen and price/time
+- `backend-api`: REST endpoints for CSV upload (POST /api/upload), annotation read/write (GET/POST /api/annotations), and annotation export as CSV
+- `ui-shell`: Dark mode layout with sidebar toolbox, main chart area, and export button
+
+### Modified Capabilities
+
+(none — greenfield project)
+
+## Impact
+
+- **New dependencies**: next, react, typescript, tailwindcss, lightweight-charts, lucide-react, papaparse, drizzle-orm, better-sqlite3, shadcn-ui
+- **New database**: Local SQLite file with `candles` and `annotations` tables
+- **New API surface**: Three REST endpoints under /api/
+- **File structure**: /components, /lib/db, /app/api modular layout

openspec/config.yaml

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+schema: spec-driven
+
+# Project context (optional)
+# This is shown to AI when creating artifacts.
+# Add your tech stack, conventions, style guides, domain knowledge, etc.
+# Example:
+#   context: |
+#     Tech stack: TypeScript, React, Node.js
+#     We use conventional commits
+#     Domain: e-commerce platform
+
+# Per-artifact rules (optional)
+# Add custom rules for specific artifacts.
+# Example:
+#   rules:
+#     proposal:
+#       - Keep proposals under 500 words
+#       - Always include a "Non-goals" section
+#     tasks:
+#       - Break tasks into chunks of max 2 hours