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