| --- |
| 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 (`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 |
| |
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("k-datasoft/Multimodal-test-dataset-technicalindicators") |
| ``` |
| |
| ## License |
| |
| MIT License |
| |
