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---
license: mit
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- finance
- trading
- bitcoin
- cryptocurrency
- technical-analysis
language:
- en
size_categories:
- 1K<n<10K
---
# 📊 Multi-Model Trading Data
Bitcoin (BTC-USD) historical price data with technical indicators for ML/DL trading models.
## 📁 Dataset Files
| File | Description | Rows | Columns |
| :--- | :--- | :--- | :--- |
| `btc_usd_historical.csv` | Raw OHLCV data | ~3,653 | 5 |
| `btc_usd_features.csv` | Processed with indicators | ~3,603 | 17 |
## 📅 Date Range
- **Start:** 2015-01-01
- **End:** 2025-01-01
- **Frequency:** Daily
## 📈 Features in `btc_usd_features.csv`
### Raw OHLCV
- `open`, `high`, `low`, `close`, `volume`
### Technical Indicators
| Feature | Description |
| :--- | :--- |
| `rsi` | Relative Strength Index (14-period) |
| `macd` | MACD Line |
| `macd_signal` | MACD Signal Line |
| `bb_width` | Bollinger Band Width |
| `atr` | Average True Range |
| `dist_sma50` | Distance from 50-day SMA |
| `obv_pct` | On-Balance Volume % Change |
| `adx` | Average Directional Index |
| `stoch_rsi_k` | Stochastic RSI %K |
| `stoch_rsi_d` | Stochastic RSI %D |
### Target Variables
- `return` — Next-day return (%)
- `target` — Binary label (1 = price up, 0 = price down)
## 🚀 Usage
```python
from datasets import load_dataset
# Load dataset
dataset = load_dataset("AdityaaXD/Multi-Model-Trading-Data")
# Or with pandas
import pandas as pd
df = pd.read_csv("hf://datasets/AdityaaXD/Multi-Model-Trading-Data/btc_usd_features.csv")
```
## 🔗 Related
- **Models:** [AdityaaXD/Multi-Model-AI-Trading-Bot](https://huggingface.co/AdityaaXD/Multi-Model-AI-Trading-Bot)
## ⚠️ Disclaimer
This dataset is for **educational and research purposes only**. Not financial advice.
## 📝 Source
Data fetched from Yahoo Finance using `yfinance` library.
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