---
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.