refactor: 归档旧代码，保留新框架结构
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
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archive/legacy_examples/examples/universal_fetcher_examples.py
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299
archive/legacy_examples/examples/universal_fetcher_examples.py
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"""
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统一数据获取接口使用示例
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========================
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展示如何使用 UniversalDataFetcher 获取各种资产的K线数据
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"""
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import sys
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from pathlib import Path
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# 添加项目根目录到路径
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from dotenv import load_dotenv
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load_dotenv()
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from core.datasource.universal_fetcher import (
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UniversalDataFetcher,
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detect_asset_type,
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fetch_kline
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)
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import pandas as pd
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# ============================================================
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# 示例1: 快速上手 - 获取单只标的
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# ============================================================
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def example_basic():
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"""基础用法：获取单只标的的K线数据"""
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print("\n" + "="*60)
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print("示例1: 基础用法")
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print("="*60)
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# 获取A股指数
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df = fetch_kline("000300.SH", "2024-01-01", "2024-03-31")
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if df is not None:
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print(f"\n沪深300指数数据:")
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print(f" 数据量: {len(df)} 条")
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print(f" 日期范围: {df.index.min()} ~ {df.index.max()}")
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print(f" 列: {list(df.columns)}")
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print(f"\n最新5条数据:")
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print(df.tail())
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# ============================================================
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# 示例2: 检测资产类型
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# ============================================================
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def example_detect_type():
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"""自动检测资产类型"""
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print("\n" + "="*60)
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print("示例2: 资产类型检测")
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print("="*60)
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codes = [
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"000300.SH", # A股指数
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"510300.SH", # A股ETF
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"600000.SH", # A股股票
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"NDX", # 美股指数
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"AAPL", # 美股股票
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"HSI", # 港股指数
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"AU.SHF", # 期货
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"BTC", # 加密货币
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]
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print("\n资产类型检测结果:")
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for code in codes:
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asset_type = detect_asset_type(code)
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print(f" {code:15s} -> {asset_type}")
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# ============================================================
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# 示例3: 批量获取多只标的
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# ============================================================
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def example_batch_fetch():
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"""批量获取多只标的"""
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print("\n" + "="*60)
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print("示例3: 批量获取")
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print("="*60)
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# 定义要获取的标的列表
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codes = [
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"000300.SH", # 沪深300
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"399006.SZ", # 创业板指
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"510300.SH", # 沪深300ETF
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"NDX", # 纳斯达克100
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"HSTECH.HK", # 恒生科技
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]
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# 使用上下文管理器（推荐）
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fetcher = UniversalDataFetcher()
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with fetcher:
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results = fetcher.fetch_multiple(codes, "2024-01-01", "2024-03-31")
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# 处理结果
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print("\n获取结果汇总:")
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for code, df in results.items():
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if df is not None:
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print(f" ✓ {code:15s} {len(df):4d} 条, "
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f"收盘价: {df['close'].iloc[-1]:.3f}")
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else:
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print(f" ✗ {code:15s} 无数据")
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# ============================================================
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# 示例4: 跨市场组合分析
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# ============================================================
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def example_cross_market():
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"""跨市场数据分析示例"""
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print("\n" + "="*60)
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print("示例4: 跨市场组合分析")
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print("="*60)
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# 定义全球资产组合
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portfolio = {
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"000300.SH": "沪深300",
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"NDX": "纳斯达克100",
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"HSI": "恒生指数",
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"N225": "日经225",
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"GDAXI": "德国DAX",
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}
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# 获取数据
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fetcher = UniversalDataFetcher()
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with fetcher:
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results = fetcher.fetch_multiple(
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list(portfolio.keys()),
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"2024-01-01",
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"2024-12-31"
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)
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# 合并收盘价
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close_prices = pd.DataFrame()
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for code, name in portfolio.items():
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if results[code] is not None:
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close_prices[name] = results[code]['close']
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# 计算收益率
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if len(close_prices) > 0:
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returns = close_prices.pct_change().dropna()
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print("\n各市场收益率统计:")
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print((returns.mean() * 252 * 100).round(2).to_string())
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print("\n相关系数矩阵:")
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print(returns.corr().round(3).to_string())
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# ============================================================
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# 示例5: 结合技术指标计算
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# ============================================================
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def example_with_indicators():
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"""结合技术指标计算"""
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print("\n" + "="*60)
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print("示例5: 技术指标计算")
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print("="*60)
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# 获取数据
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df = fetch_kline("000300.SH", "2024-01-01", "2024-06-30")
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if df is None:
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print("数据获取失败")
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return
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# 计算移动平均线
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df['MA5'] = df['close'].rolling(window=5).mean()
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df['MA20'] = df['close'].rolling(window=20).mean()
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df['MA60'] = df['close'].rolling(window=60).mean()
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# 计算RSI
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delta = df['close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
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rs = gain / loss
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df['RSI'] = 100 - (100 / (1 + rs))
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print("\n技术指标计算结果（最新10条）:")
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print(df[['close', 'MA5', 'MA20', 'MA60', 'RSI']].tail(10).round(2))
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# ============================================================
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# 示例6: 错误处理与重试
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# ============================================================
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def example_error_handling():
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"""错误处理示例"""
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print("\n" + "="*60)
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print("示例6: 错误处理")
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print("="*60)
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# 测试无效代码
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invalid_codes = ["INVALID", "999999.SH", ""]
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for code in invalid_codes:
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print(f"\n尝试获取: {code}")
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df = fetch_kline(code, "2024-01-01", "2024-01-31")
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if df is None:
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print(f" ✓ 正确返回 None")
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else:
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print(f" ✗ 意外获取到数据")
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# 测试重试机制
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print("\n测试重试机制（网络错误）:")
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fetcher = UniversalDataFetcher()
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df = fetcher.fetch("NDX", "2024-01-01", "2024-01-31", retry=3)
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if df is not None:
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print(f" ✓ 重试成功: {len(df)} 条")
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else:
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print(f" ✗ 重试后仍失败")
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# ============================================================
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# 示例7: 与现有轮动策略集成
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# ============================================================
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def example_integration_with_rotation():
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"""与现有轮动策略集成示例"""
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print("\n" + "="*60)
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print("示例7: 与轮动策略集成")
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print("="*60)
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# 模拟轮动策略的代码配置
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code_config = {
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"399006.SZ": {"name": "创业板指", "etf": "159915.SZ", "market": "A"},
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"NDX": {"name": "纳指100", "etf": "513100.SH", "market": "US"},
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"HSI": {"name": "恒生指数", "etf": "159920.SZ", "market": "HK"},
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}
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# 使用 UniversalDataFetcher 获取数据
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all_codes = list(code_config.keys())
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etf_codes = [cfg['etf'] for cfg in code_config.values() if cfg.get('etf')]
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fetcher = UniversalDataFetcher()
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with fetcher:
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# 获取指数数据（用于因子计算）
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index_data = fetcher.fetch_multiple(
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all_codes, "2024-01-01", "2024-03-31"
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)
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# 获取ETF数据（用于收益计算）
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etf_data = fetcher.fetch_multiple(
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etf_codes, "2024-01-01", "2024-03-31"
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)
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# 验证数据完整性
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print("\n数据完整性检查:")
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for code in all_codes:
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idx_df = index_data.get(code)
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etf_code = code_config[code].get('etf')
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etf_df = etf_data.get(etf_code) if etf_code else None
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print(f" {code}:")
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print(f" 指数数据: {'✓' if idx_df is not None else '✗'} "
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f"({len(idx_df) if idx_df is not None else 0} 条)")
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if etf_code:
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print(f" ETF数据 ({etf_code}): {'✓' if etf_df is not None else '✗'} "
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f"({len(etf_df) if etf_df is not None else 0} 条)")
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# ============================================================
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# 运行所有示例
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# ============================================================
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if __name__ == "__main__":
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print("\n" + "="*60)
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print("统一数据获取接口 - 使用示例")
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print("="*60)
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# 选择要运行的示例
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examples = [
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("基础用法", example_basic),
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("资产类型检测", example_detect_type),
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("批量获取", example_batch_fetch),
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("跨市场组合分析", example_cross_market),
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("技术指标计算", example_with_indicators),
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("错误处理", example_error_handling),
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("与轮动策略集成", example_integration_with_rotation),
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]
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print("\n可用示例:")
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for i, (name, _) in enumerate(examples, 1):
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print(f" {i}. {name}")
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# 运行特定示例或全部
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run_all = True # 改为 False 可以选择性运行
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if run_all:
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for name, func in examples:
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try:
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func()
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except Exception as e:
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print(f"\n示例 '{name}' 运行失败: {e}")
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import traceback
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traceback.print_exc()
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else:
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# 运行单个示例
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example_index = 0 # 修改为 1-7 运行特定示例
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if 0 <= example_index < len(examples):
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examples[example_index][1]()
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print("\n" + "="*60)
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print("示例运行完成")
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print("="*60)
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