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README.md

@@ -1,78 +1,134 @@
 ---
-license: mit
-task_categories:
-- time-series-forecasting
-- text-classification
 language:
-- ko
-size_categories:
-- 10K<n<100K
-dataset_info:
-  features:
-  - name: labels
-    dtype: int64
-  - name: time_series
-    list:
-      list: float64
-  - name: texts
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 22975241
-    num_examples: 10605
-  download_size: 14726776
-  dataset_size: 22975241
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  - 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
 ---
 
-# TIPS Multimodal Test Dataset
+# KRX Investment Warning Prediction Dataset (OHLCV + Technical Indicators + Korean News)
 
-## Dataset Description
+## Dataset Summary
 
-This dataset contains test data for multimodal stock prediction using time series and text data.
+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**.
 
-### Dataset Structure
+**Important:** No normalization/scaling is applied. All values are raw.
 
-- **labels**: Binary labels for stock price prediction (0: down/neutral, 1: up)
-- **time_series**: Time series features for stock data
-- **texts**: Korean news text data related to stocks
+- **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**
 
-### Data Splits
+## Task
 
-This is the test split of the TIPS multimodal dataset.
+Binary classification:
 
-### Usage
+- **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)
 
-```python
-from datasets import load_dataset
+### Label Alignment
 
-dataset = load_dataset("k-datasoft/Multimodal-test-dataset-technicalindicators")
-```
+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 Fields
+## Data Sources
 
-- `labels`: int - Binary classification label
-- `time_series`: array - Time series features
-- `texts`: string - Korean news text
+| 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) |
 
-### Citation
+## Dataset Format
 
-If you use this dataset, please cite:
+This dataset is structured to be used directly with Hugging Face `datasets`, and consists of **three columns**:
 
-```
-@dataset{tips_multimodal_test,
-  title={TIPS Multimodal Test Dataset},
-  author={Your Name},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/datasets/k-datasoft/Multimodal-test-dataset-technicalindicators}
-}
+- **`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
+## License
 
 MIT License