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

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:

• —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