README.md

# 📈 Kraken Trading Data Collection

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## Overview

**High-frequency cryptocurrency market data from Kraken exchange** - perfect for algorithmic trading, time-series forecasting, and market microstructure analysis.

This dataset includes **real-time price, volume, and order book data** for 9 major cryptocurrency trading pairs, collected via WebSocket streaming and REST API polling.

---

## 📊 Included Trading Pairs

| Pair | Asset | Base Currency | Typical Daily Volume |
|------|-------|---------------|---------------------|
| `XXBTZUSD` | Bitcoin | USD | $500M - $2B |
| `XETHZUSD` | Ethereum | USD | $200M - $800M |
| `XXRPZUSD` | Ripple (XRP) | USD | $50M - $200M |
| `ADAUSD` | Cardano | USD | $30M - $150M |
| `DOGEUSD` | Dogecoin | USD | $40M - $300M |
| `BNBUSD` | Binance Coin | USD | $20M - $100M |
| `SOLUSD` | Solana | USD | $100M - $400M |
| `DOTUSD` | Polkadot | USD | $15M - $80M |
| `MATICUSD` | Polygon | USD | $20M - $100M |
| `LTCUSD` | Litecoin | USD | $30M - $150M |

---

## 🎯 Use Cases

### 1. **Algorithmic Trading**
- **Backtesting strategies** - Test mean reversion, momentum, arbitrage
- **Order book analysis** - Study bid-ask spreads, depth, liquidity
- **Market making** - Identify optimal quote placement
- **High-frequency trading** - Sub-second price movements

### 2. **Time-Series Forecasting**
- **Price prediction** - LSTM, Transformer, ARIMA models
- **Volatility modeling** - GARCH, stochastic volatility
- **Regime detection** - Bull/bear market classification
- **Anomaly detection** - Flash crashes, wash trading, manipulation

### 3. **Market Microstructure Research**
- **Bid-ask spread dynamics** - How does liquidity evolve?
- **Order flow imbalance** - Predict short-term price movements
- **Trade execution analysis** - Optimal execution strategies (TWAP, VWAP)
- **Cross-exchange arbitrage** - Price discrepancies with Binance, Coinbase

### 4. **Risk Management**
- **Value at Risk (VaR)** - Estimate portfolio risk
- **Correlation analysis** - How do crypto assets move together?
- **Drawdown modeling** - Maximum loss scenarios
- **Liquidity risk** - Can you exit positions without slippage?

---

## 📁 Dataset Structure

```
kraken-trading-data/
├── trades/                  # Individual trade executions
│   ├── XXBTZUSD_trades_2024-01.csv
│   ├── XETHZUSD_trades_2024-01.csv
│   └── ...
├── ohlcv/                   # OHLCV candlestick data (1min, 5min, 1h, 1d)
│   ├── XXBTZUSD_1min.csv
│   ├── XXBTZUSD_5min.csv
│   └── ...
├── orderbook/               # Limit order book snapshots
│   ├── XXBTZUSD_orderbook_2024-01.csv
│   └── ...
├── metadata.json            # Collection timestamps, pair info
└── README.md
```

### Data Fields

#### Trades (`trades/`)
| Field | Type | Description |
|-------|------|-------------|
| `timestamp` | int64 | Unix timestamp (milliseconds) |
| `price` | float64 | Execution price (USD) |
| `volume` | float64 | Trade volume (asset units) |
| `side` | str | `buy` or `sell` (taker side) |
| `trade_id` | str | Unique trade identifier |

#### OHLCV (`ohlcv/`)
| Field | Type | Description |
|-------|------|-------------|
| `timestamp` | int64 | Candle open time (Unix ms) |
| `open` | float64 | Opening price |
| `high` | float64 | Highest price in period |
| `low` | float64 | Lowest price in period |
| `close` | float64 | Closing price |
| `volume` | float64 | Total volume traded |
| `trade_count` | int32 | Number of trades |

#### Order Book (`orderbook/`)
| Field | Type | Description |
|-------|------|-------------|
| `timestamp` | int64 | Snapshot time (Unix ms) |
| `side` | str | `bid` or `ask` |
| `price` | float64 | Limit order price |
| `volume` | float64 | Volume available at price level |
| `cumulative_volume` | float64 | Cumulative depth |

---

## 🚀 Quick Start

### Load the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("GotThatData/kraken-trading-data")
```

### Example: Load Bitcoin Trades

```python
import pandas as pd

# Load Bitcoin trades for January 2024
df = pd.read_csv("hf://datasets/GotThatData/kraken-trading-data/trades/XXBTZUSD_trades_2024-01.csv")

# Convert timestamp to datetime
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

# Set as index
df = df.set_index('timestamp')

# Display first rows
print(df.head())
```

**Output:**
```
                             price      volume  side         trade_id
timestamp                                                            
2024-01-01 00:00:01.234  42150.5    0.025000   buy   1704067201234-1
2024-01-01 00:00:01.567  42149.8    0.100000   sell  1704067201567-2
...
```

### Example: Resample to 1-Hour OHLCV

```python
# Resample trades to hourly candles
ohlcv = df.resample('1H').agg({
    'price': ['first', 'max', 'min', 'last'],
    'volume': 'sum',
    'trade_id': 'count'
})

# Flatten column names
ohlcv.columns = ['open', 'high', 'low', 'close', 'volume', 'trade_count']
print(ohlcv.head())
```

### Example: Calculate Moving Average Crossover

```python
import matplotlib.pyplot as plt

# Load 1-minute OHLCV
df = pd.read_csv("hf://datasets/GotThatData/kraken-trading-data/ohlcv/XXBTZUSD_1min.csv")
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
df = df.set_index('timestamp')

# Calculate moving averages
df['MA_20'] = df['close'].rolling(window=20).mean()
df['MA_50'] = df['close'].rolling(window=50).mean()

# Plot
plt.figure(figsize=(14, 7))
plt.plot(df['close'], label='BTC Price', alpha=0.5)
plt.plot(df['MA_20'], label='20-period MA')
plt.plot(df['MA_50'], label='50-period MA')
plt.legend()
plt.title('Bitcoin Price with Moving Averages')
plt.show()
```

### Example: Order Book Visualization

```python
import seaborn as sns

# Load order book snapshot
ob = pd.read_csv("hf://datasets/GotThatData/kraken-trading-data/orderbook/XXBTZUSD_orderbook_2024-01.csv")

# Filter for a specific timestamp
snapshot = ob[ob['timestamp'] == ob['timestamp'].iloc[0]]

# Separate bids and asks
bids = snapshot[snapshot['side'] == 'bid'].sort_values('price', ascending=False)
asks = snapshot[snapshot['side'] == 'ask'].sort_values('price')

# Plot depth chart
plt.figure(figsize=(12, 6))
plt.step(bids['price'], bids['cumulative_volume'], where='pre', label='Bids', color='green')
plt.step(asks['price'], asks['cumulative_volume'], where='post', label='Asks', color='red')
plt.xlabel('Price (USD)')
plt.ylabel('Cumulative Volume (BTC)')
plt.title('BTC/USD Order Book Depth')
plt.legend()
plt.show()
```

---

## 📊 Sample Analysis: Predict Next-Hour Price Movement

```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Load hourly OHLCV
df = pd.read_csv("hf://datasets/GotThatData/kraken-trading-data/ohlcv/XXBTZUSD_1h.csv")
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
df = df.set_index('timestamp')

# Feature engineering
df['returns'] = df['close'].pct_change()
df['volatility'] = df['returns'].rolling(window=24).std()
df['volume_ma'] = df['volume'].rolling(window=24).mean()

# Target: 1 if price goes up next hour, 0 otherwise
df['target'] = (df['close'].shift(-1) > df['close']).astype(int)

# Drop NaNs
df = df.dropna()

# Features and target
features = ['open', 'high', 'low', 'close', 'volume', 'returns', 'volatility', 'volume_ma']
X = df[features]
y = df['target']

# Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.2%}")
```

---

## 🔬 Research Applications

### Published Papers Using This Dataset

*(Open a PR to add your work!)*

### Potential Research Questions

1. **Can transformers outperform LSTMs for crypto price prediction?**
2. **How does order book imbalance predict short-term price movements?**
3. **What's the optimal rebalancing frequency for a crypto portfolio?**
4. **Do Twitter sentiment signals correlate with price volatility?**
5. **Can you detect wash trading or market manipulation in the data?**

---

## 📝 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{daugherty2024kraken,
  author = {Bryan Daugherty},
  title = {Kraken Trading Data Collection},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/GotThatData/kraken-trading-data}
}
```

---

## 🔗 Related Resources

- **Kraken API Docs:** [https://docs.kraken.com/rest/](https://docs.kraken.com/rest/)
- **ccxt Library:** [https://github.com/ccxt/ccxt](https://github.com/ccxt/ccxt) - Unified crypto exchange API
- **TA-Lib:** [https://ta-lib.org/](https://ta-lib.org/) - Technical analysis library

---

## ⚙️ Data Collection Methodology

Data was collected using:
1. **Kraken WebSocket API** - Real-time trade stream (sub-second latency)
2. **Kraken REST API** - Order book snapshots (every 10 seconds)
3. **ccxt Library** - Normalized OHLCV data (1min, 5min, 1h, 1d intervals)

**Collection Period:** January 2024 - December 2024 (ongoing updates)  
**Update Frequency:** Daily (new data added at 00:00 UTC)

---

## 📜 License

MIT License - free for academic and commercial use.

**Disclaimer:** This data is for research and educational purposes only. Past performance does not guarantee future results. Cryptocurrency trading involves significant risk.

---

## 🙏 Acknowledgments

- **Kraken Exchange** - For providing free, high-quality market data APIs
- **ccxt Community** - For building robust exchange integration tools
- **Hugging Face** - For hosting and serving this dataset

---

## 🐛 Known Issues & Future Work

- **Missing Data:** Some gaps during exchange maintenance windows (< 0.1% of total data)
- **Timezone:** All timestamps are UTC
- **Delisting:** BNBUSD was delisted in June 2024 (data ends June 30)

**Roadmap:**
- [ ] Add L2 order book data (full depth, not just top 20 levels)
- [ ] Include funding rate data for perpetual futures
- [ ] Add cross-exchange arbitrage opportunities dataset
- [ ] Create Gradio Space for interactive data exploration

---

## 💬 Feedback & Contributions

Found a bug? Want to request additional pairs or features?

- **Discussions:** [Open a discussion](https://huggingface.co/datasets/GotThatData/kraken-trading-data/discussions)
- **Contact:** YourFriends@smartledger.solutions

---

*"In trading, the only constant is change. Adapt or fade away."* — Paul Tudor Jones