aszerW
1fca536c95
归档内容： - core/ (数据源、因子计算、通用工具) → archive/legacy_core/ - strategies/rotation/engine.py, portfolio.py, report.py → archive/legacy_core/ - scripts/ (run_rotation, daily_scheduler) → archive/legacy_scripts/ - examples/ → archive/legacy_examples/ - tests/ (实验、对比测试) → archive/legacy_tests/ - 单独文件 (fetch_*.py, 动量.py, 全球市场.py等) → archive/single_files/ 保留新结构： - framework/ (抽象接口) - strategies/shared/ (定制组件) - strategies/rotation/strategy.py (新策略) - 外层配置: .env, .dockerignore, build-and-push.sh, hk_ecs.pem, README.md, requirements.txt - Docker相关: Dockerfile, Dockerfile_base, docker-compose.yml 更新README反映新框架架构
120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
"""
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使用 Tushare 获取 AU9999 黄金数据
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"""
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import os
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import pandas as pd
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import tushare as ts
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from datetime import datetime, timedelta
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from typing import Optional
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from dotenv import load_dotenv
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def get_tushare_token() -> str:
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"""从环境变量获取 Tushare token"""
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load_dotenv()
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token = os.environ.get('TUSHARE_TOKEN')
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if not token:
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raise ValueError("未设置 TUSHARE_TOKEN 环境变量")
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return token
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def fetch_au9999(start_date: str, end_date: str) -> Optional[pd.DataFrame]:
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"""
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获取 AU9999 黄金现货数据（使用上期所黄金主力合约 AU.SHF）
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Args:
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start_date: 开始日期 (YYYY-MM-DD)
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end_date: 结束日期 (YYYY-MM-DD)
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Returns:
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DataFrame with columns: date, open, high, low, close, vol
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"""
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try:
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# 初始化 Tushare
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pro = ts.pro_api(get_tushare_token())
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# 转换日期格式
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start_str = start_date.replace('-', '')
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end_str = end_date.replace('-', '')
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print(f"从 Tushare 获取 AU9999 数据...")
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print(f"时间范围: {start_date} ~ {end_date}")
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# 获取黄金期货主力合约数据
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# ts_code: AU.SHF 表示上海期货交易所黄金主力合约
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df = pro.fut_daily(ts_code='AU.SHF', start_date=start_str, end_date=end_str)
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if df is None or df.empty:
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print("未获取到数据")
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return None
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# 标准化列名
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df = df.rename(columns={
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'trade_date': 'date',
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'open': 'open',
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'high': 'high',
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'low': 'low',
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'close': 'close',
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'vol': 'volume',
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})
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# 转换日期格式
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date')
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df = df.sort_index()
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# 选择需要的列
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df = df[['open', 'high', 'low', 'close', 'volume']]
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print(f"✓ 获取成功: {len(df)} 条数据")
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print(f"时间范围: {df.index[0].strftime('%Y-%m-%d')} ~ {df.index[-1].strftime('%Y-%m-%d')}")
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return df
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except Exception as e:
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print(f"获取数据失败: {e}")
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return None
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def print_au9999_data(df: pd.DataFrame):
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"""打印 AU9999 数据"""
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print("\n" + "="*80)
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print("AU9999 黄金数据 (上期所主力合约 AU.SHF)")
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print("="*80)
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print(f"{'日期':<15} {'开盘价':>12} {'最高价':>12} {'最低价':>12} {'收盘价':>12} {'成交量':>12}")
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print("-"*80)
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for date, row in df.iterrows():
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date_str = date.strftime('%Y-%m-%d')
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print(f"{date_str:<15} {row['open']:>12.2f} {row['high']:>12.2f} {row['low']:>12.2f} {row['close']:>12.2f} {row['volume']:>12.2f}")
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print("="*80)
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def main():
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"""主函数"""
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# 计算最近30天的日期范围
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end_date = datetime.now()
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start_date = end_date - timedelta(days=30)
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start_str = start_date.strftime('%Y-%m-%d')
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end_str = end_date.strftime('%Y-%m-%d')
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# 获取数据
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df = fetch_au9999(start_str, end_str)
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if df is not None:
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print_au9999_data(df)
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# 保存到CSV
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output_file = "au9999_data.csv"
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df.to_csv(output_file)
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print(f"\n数据已保存: {output_file}")
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else:
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print("获取数据失败")
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if __name__ == "__main__":
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main()
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