README.md

metadata

license: cc-by-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - finance
  - stock-market
  - candlestick-chart
  - technical-analysis
  - sp500
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': down_3plus
            '1': down_2_3
            '2': down_1_2
            '3': up_1_2
            '4': up_2_3
            '5': up_3plus
    - name: ticker
      dtype: string
    - name: end_date
      dtype: string
    - name: pct_return
      dtype: float32
  splits:
    - name: train
      num_bytes: 21467124746
      num_examples: 1070721
    - name: test
      num_bytes: 5947059941
      num_examples: 301890
  download_size: 27126018753
  dataset_size: 27414184687

SP500-Chart-Dataset

A large-scale candlestick chart image dataset for financial image classification research, covering 501 S&P 500 stocks.

Overview

Item	Value
Stocks	501 S&P 500 constituents
Total Images	1,374,694
Period	2010-01 – 2025-03
Image Size	~480×480 px (4×4 inch, 120 dpi)
Chart Type	Candlestick (OHLCV) with technical indicators
Labels	6-class forward return (±1%/±2%/±3%)

Download

Download sp500_images.zip (26 GB) from the Files tab and extract:

unzip sp500_images.zip

Directory structure after extraction:

cross_sectional_data/images/{TICKER}/{CLASS}/{TICKER}_{INDEX}_{DATE}.png

Metadata JSON files are in the metadata/ folder.

Chart Specification

Each chart is a 20-trading-day candlestick chart rendered with mplfinance:

• OHLCV candlestick bars (Charles style)
• Volume bars (bottom panel)
• MA5 (blue dotted) — 5-day moving average
• MA60 (red dashed) — 60-day moving average
• MA120 (green solid) — 120-day moving average
• Bollinger Bands (grey shaded area, α=0.15) — 20-day ± 2σ

All technical indicators are guaranteed to be visible in every image. The y-axis is automatically scaled to include all indicator values. Prices are percentage-normalized relative to the first closing price in each window (0% baseline), making visual patterns scale-invariant across stocks.

Label Definition

Labels are based on the 5-day forward return:

Label	Return Range
down_3plus	r < −3%
down_2_3	−3% ≤ r < −2%
down_1_2	−2% ≤ r < −1%
up_1_2	1% < r ≤ 2%
up_2_3	2% < r ≤ 3%
up_3plus	r > 3%

Returns in [−1%, +1%] are excluded as ambiguous.

Temporal Split

• Train: end_date < 2022-12-21
• Test: end_date >= 2023-01-01
• Embargo: 10 calendar days

Usage

import json
from pathlib import Path
from PIL import Image

with open('metadata/samples_AAPL.json') as f:
    meta = json.load(f)

sample = meta['samples'][0]
img_path = Path('cross_sectional_data/images') / sample['ticker'] / sample['label'] / \
    f"{sample['ticker']}_{sample['index']}_{sample['end_date'].replace('-','')}.png"
img = Image.open(img_path)

Citation

@misc{sp500chart2025,
  title={SP500-Chart-Dataset},
  author={Ahn, Jaehyun},
  year={2025},
  howpublished={\url{https://github.com/JaehyunAhn/SP500-Chart-Dataset}},
  note={Yonsei University}
}

License

CC BY 4.0