"""生成测试数据以便在无法连接Binance时使用"""

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from pathlib import Path


def generate_sample_data(
    symbol: str = 'BTC',
    timeframe: str = '1h',
    num_days: int = 365
):
    """生成模拟的OHLCV数据用于测试

    Args:
        symbol: 币种符号
        timeframe: 时间框架
        num_days: 生成数据的天数
    """
    # 计算数据点数量
    if timeframe == '1h':
        periods = num_days * 24
    elif timeframe == '15m':
        periods = num_days * 24 * 4
    else:
        periods = num_days * 24

    # 生成时间戳
    end_time = datetime.now()
    start_time = end_time - timedelta(days=num_days)

    if timeframe == '1h':
        timestamps = pd.date_range(start=start_time, end=end_time, freq='h')
    else:  # 15m
        timestamps = pd.date_range(start=start_time, end=end_time, freq='15min')

    # 生成价格数据（随机游走）
    base_price = {
        'BTC': 45000,
        'ETH': 2500,
        'SOL': 100
    }.get(symbol, 10000)

    np.random.seed(42)  # 固定种子以便复现

    # 生成收益率
    returns = np.random.normal(0.0001, 0.02, len(timestamps))
    prices = base_price * np.exp(np.cumsum(returns))

    # 生成OHLCV数据
    data = []
    for i, (ts, close) in enumerate(zip(timestamps, prices)):
        # 生成高低价
        volatility = close * 0.01
        high = close + np.random.uniform(0, volatility)
        low = close - np.random.uniform(0, volatility)

        # 开盘价（前一个收盘价）
        if i == 0:
            open_price = close
        else:
            open_price = prices[i-1]

        # 成交量
        volume = np.random.uniform(1000, 10000)

        data.append({
            'timestamp': ts,
            'open': open_price,
            'high': high,
            'low': low,
            'close': close,
            'volume': volume
        })

    df = pd.DataFrame(data)

    # 保存到缓存
    cache_dir = Path('./data/cache')
    cache_dir.mkdir(parents=True, exist_ok=True)

    cache_file = cache_dir / f"{symbol}_{timeframe}_1y_now.csv"
    df.to_csv(cache_file, index=False)

    print(f"✓ 已生成 {len(df)} 行模拟数据并保存到：{cache_file}")
    return df


if __name__ == "__main__":
    # 生成BTC、ETH、SOL的测试数据
    for symbol in ['BTC', 'ETH', 'SOL']:
        for timeframe in ['1h', '15m']:
            print(f"\n生成 {symbol} {timeframe} 数据...")
            generate_sample_data(symbol, timeframe)

    print("\n✓ 所有测试数据已生成！")