k-datasoft/Multimodal-test-dataset-technicalindicators

KRX Investment Warning Prediction Dataset (OHLCV + Technical Indicators + Korean News)

Dataset Summary

This dataset is a test dataset for predicting Investment Warning (투자주의종목) designations in the Korean stock market (KRX). It contains raw daily OHLCV price data, 13 technical indicators, and Korean news text (title + body), designed for multimodal anomaly detection / binary classification.

Important: No normalization/scaling is applied. All values are raw.

• Date range: 2025-07-01 ~ 2025-09-30
• Prediction horizon: whether a stock will be designated as an investment warning within the next 1 trading day

Task

Binary classification:

• Label 0: Normal trading (no investment warning designation within the next 1 trading day)
• Label 1: Investment warning designation (within the next 1 trading day)

Label Alignment

For each (ticker, date=t), set label=1 if the stock is designated as an investment warning on t+1 (the next trading day).

Data Sources

Source	Description
Stock Prices	Daily OHLCV data for KRX listed stocks
Investment Warning	KRX investment warning designation history (labels)
News	Korean news articles per stock (title + body)

Dataset Format

This dataset is structured to be used directly with Hugging Face datasets, and consists of three columns:

• labels: Binary label (0 or 1)
• time_series: Price time-series information (OHLCV + Technical Indicators)
• texts: Korean news text mapped to the corresponding stock (title + body)

Example (Conceptual)

• labels: 0 or 1
• time_series: [[open, high, low, close, volume, rsi, macd, macd_signal, macd_hist, bb_upper, bb_middle, bb_lower, bb_width, sma_5, sma_20, ema_9, atr, obv], ...]
• texts: ["article1 ...", "article2 ..."]

Feature Details

Price & Indicators — time_series

Each sample has shape [10, 18] with the following 18 features:

Index	Feature	Description
0	open	Opening price (KRW)
1	high	High price (KRW)
2	low	Low price (KRW)
3	close	Closing price (KRW)
4	volume	Trading volume (shares)
5	rsi	Relative Strength Index (14-period)
6	macd	MACD line (12, 26)
7	macd_signal	MACD signal line (9-period)
8	macd_hist	MACD histogram
9	bb_upper	Bollinger Band upper (20, 2std)
10	bb_middle	Bollinger Band middle (20-SMA)
11	bb_lower	Bollinger Band lower (20, 2std)
12	bb_width	Bollinger Band width (normalized)
13	sma_5	Simple Moving Average (5-period)
14	sma_20	Simple Moving Average (20-period)
15	ema_9	Exponential Moving Average (9-period)
16	atr	Average True Range (14-period)
17	obv	On-Balance Volume

• No normalization/scaling is applied. All values are raw.
• Currency unit: KRW
• Volume: number of shares (not value)
• Technical indicators are computed with a lookback of 35 days to ensure stable values.

News — texts

• News is mapped to tickers via an exact ticker-code mapping.
• Deduplication has been applied.
• Each news item includes title + body (concatenated as a single string).

Dataset Statistics

• Total Samples: 10,605
• Label Distribution: {0: 10570, 1: 35}
• Sequence Length: 10
• Features per timestep: 18
• Undersampling: Majority class reduced to 10%

Recommended Metrics

Because investment warning events are likely to be rare (class imbalance), the following metrics are recommended:

• ROC-AUC, PR-AUC
• F1 (positive class), precision/recall
• Precision/recall at Top-k (useful for practical detection scenarios)
• (Optional) probability calibration

Usage

from datasets import load_dataset

dataset = load_dataset("k-datasoft/Multimodal-test-dataset-technicalindicators")

License

MIT License