82 lines
1.9 KiB
Python
82 lines
1.9 KiB
Python
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"""Sample data generators for testing."""
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from faker import Faker
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fake = Faker()
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def generate_ohlcv_data(
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symbol: str = "BTC/USD",
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periods: int = 100,
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start_price: float = 50000.0,
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timeframe: str = "1h"
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) -> pd.DataFrame:
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"""Generate sample OHLCV data.
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Args:
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symbol: Trading pair
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periods: Number of periods
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start_price: Starting price
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timeframe: Data timeframe
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Returns:
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DataFrame with OHLCV data
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"""
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dates = pd.date_range(
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start=datetime.now() - timedelta(hours=periods),
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periods=periods,
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freq=timeframe
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)
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# Generate realistic price movement
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prices = [start_price]
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for i in range(1, periods):
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change = np.random.randn() * 0.02 # 2% volatility
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prices.append(prices[-1] * (1 + change))
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return pd.DataFrame({
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'timestamp': dates,
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'open': prices,
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'high': [p * 1.01 for p in prices],
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'low': [p * 0.99 for p in prices],
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'close': prices,
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'volume': [1000.0 + np.random.randn() * 100 for _ in range(periods)]
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})
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def generate_trade_data(
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symbol: str = "BTC/USD",
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count: int = 10
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) -> list:
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"""Generate sample trade data.
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Args:
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symbol: Trading pair
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count: Number of trades
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Returns:
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List of trade dictionaries
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"""
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trades = []
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base_price = 50000.0
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for i in range(count):
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trades.append({
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'order_id': f'trade_{i}',
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'symbol': symbol,
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'side': 'buy' if i % 2 == 0 else 'sell',
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'type': 'market',
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'price': base_price + np.random.randn() * 100,
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'amount': 0.01,
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'cost': 500.0,
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'fee': 0.5,
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'status': 'filled',
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'timestamp': datetime.now() - timedelta(hours=count-i)
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})
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return trades
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