Datasets:
language:
- ko
pretty_name: >-
KRX Investment Warning Prediction Dataset (OHLCV + Technical Indicators +
Korean News)
tags:
- finance
- krx
- korea
- time-series
- ohlcv
- technical-indicators
- news
- multimodal
- anomaly-detection
- binary-classification
task_categories:
- text-classification
- time-series-forecasting
task_ids:
- binary-classification
license: mit
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 (0or1)time_series: Price time-series information (OHLCV + Technical Indicators)texts: Korean news text mapped to the corresponding stock (title + body)
Example (Conceptual)
labels:0or1time_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