"""
统一数据获取接口使用示例
========================
展示如何使用 UniversalDataFetcher 获取各种资产的K线数据
"""

import sys
from pathlib import Path

# 添加项目根目录到路径
sys.path.insert(0, str(Path(__file__).parent.parent))

from dotenv import load_dotenv
load_dotenv()

from core.datasource.universal_fetcher import (
    UniversalDataFetcher,
    detect_asset_type,
    fetch_kline
)
import pandas as pd


# ============================================================
# 示例1: 快速上手 - 获取单只标的
# ============================================================
def example_basic():
    """基础用法：获取单只标的的K线数据"""
    print("\n" + "="*60)
    print("示例1: 基础用法")
    print("="*60)
    
    # 获取A股指数
    df = fetch_kline("000300.SH", "2024-01-01", "2024-03-31")
    
    if df is not None:
        print(f"\n沪深300指数数据:")
        print(f"  数据量: {len(df)} 条")
        print(f"  日期范围: {df.index.min()} ~ {df.index.max()}")
        print(f"  列: {list(df.columns)}")
        print(f"\n最新5条数据:")
        print(df.tail())


# ============================================================
# 示例2: 检测资产类型
# ============================================================
def example_detect_type():
    """自动检测资产类型"""
    print("\n" + "="*60)
    print("示例2: 资产类型检测")
    print("="*60)
    
    codes = [
        "000300.SH",   # A股指数
        "510300.SH",   # A股ETF
        "600000.SH",   # A股股票
        "NDX",         # 美股指数
        "AAPL",        # 美股股票
        "HSI",         # 港股指数
        "AU.SHF",      # 期货
        "BTC",         # 加密货币
    ]
    
    print("\n资产类型检测结果:")
    for code in codes:
        asset_type = detect_asset_type(code)
        print(f"  {code:15s} -> {asset_type}")


# ============================================================
# 示例3: 批量获取多只标的
# ============================================================
def example_batch_fetch():
    """批量获取多只标的"""
    print("\n" + "="*60)
    print("示例3: 批量获取")
    print("="*60)
    
    # 定义要获取的标的列表
    codes = [
        "000300.SH",   # 沪深300
        "399006.SZ",   # 创业板指
        "510300.SH",   # 沪深300ETF
        "NDX",         # 纳斯达克100
        "HSTECH.HK",   # 恒生科技
    ]
    
    # 使用上下文管理器（推荐）
    fetcher = UniversalDataFetcher()
    with fetcher:
        results = fetcher.fetch_multiple(codes, "2024-01-01", "2024-03-31")
    
    # 处理结果
    print("\n获取结果汇总:")
    for code, df in results.items():
        if df is not None:
            print(f"  ✓ {code:15s} {len(df):4d} 条, "
                  f"收盘价: {df['close'].iloc[-1]:.3f}")
        else:
            print(f"  ✗ {code:15s} 无数据")


# ============================================================
# 示例4: 跨市场组合分析
# ============================================================
def example_cross_market():
    """跨市场数据分析示例"""
    print("\n" + "="*60)
    print("示例4: 跨市场组合分析")
    print("="*60)
    
    # 定义全球资产组合
    portfolio = {
        "000300.SH": "沪深300",
        "NDX": "纳斯达克100",
        "HSI": "恒生指数",
        "N225": "日经225",
        "GDAXI": "德国DAX",
    }
    
    # 获取数据
    fetcher = UniversalDataFetcher()
    with fetcher:
        results = fetcher.fetch_multiple(
            list(portfolio.keys()),
            "2024-01-01",
            "2024-12-31"
        )
    
    # 合并收盘价
    close_prices = pd.DataFrame()
    for code, name in portfolio.items():
        if results[code] is not None:
            close_prices[name] = results[code]['close']
    
    # 计算收益率
    if len(close_prices) > 0:
        returns = close_prices.pct_change().dropna()
        
        print("\n各市场收益率统计:")
        print((returns.mean() * 252 * 100).round(2).to_string())
        
        print("\n相关系数矩阵:")
        print(returns.corr().round(3).to_string())


# ============================================================
# 示例5: 结合技术指标计算
# ============================================================
def example_with_indicators():
    """结合技术指标计算"""
    print("\n" + "="*60)
    print("示例5: 技术指标计算")
    print("="*60)
    
    # 获取数据
    df = fetch_kline("000300.SH", "2024-01-01", "2024-06-30")
    
    if df is None:
        print("数据获取失败")
        return
    
    # 计算移动平均线
    df['MA5'] = df['close'].rolling(window=5).mean()
    df['MA20'] = df['close'].rolling(window=20).mean()
    df['MA60'] = df['close'].rolling(window=60).mean()
    
    # 计算RSI
    delta = df['close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))
    
    print("\n技术指标计算结果（最新10条）:")
    print(df[['close', 'MA5', 'MA20', 'MA60', 'RSI']].tail(10).round(2))


# ============================================================
# 示例6: 错误处理与重试
# ============================================================
def example_error_handling():
    """错误处理示例"""
    print("\n" + "="*60)
    print("示例6: 错误处理")
    print("="*60)
    
    # 测试无效代码
    invalid_codes = ["INVALID", "999999.SH", ""]
    
    for code in invalid_codes:
        print(f"\n尝试获取: {code}")
        df = fetch_kline(code, "2024-01-01", "2024-01-31")
        if df is None:
            print(f"  ✓ 正确返回 None")
        else:
            print(f"  ✗ 意外获取到数据")
    
    # 测试重试机制
    print("\n测试重试机制（网络错误）:")
    fetcher = UniversalDataFetcher()
    df = fetcher.fetch("NDX", "2024-01-01", "2024-01-31", retry=3)
    if df is not None:
        print(f"  ✓ 重试成功: {len(df)} 条")
    else:
        print(f"  ✗ 重试后仍失败")


# ============================================================
# 示例7: 与现有轮动策略集成
# ============================================================
def example_integration_with_rotation():
    """与现有轮动策略集成示例"""
    print("\n" + "="*60)
    print("示例7: 与轮动策略集成")
    print("="*60)
    
    # 模拟轮动策略的代码配置
    code_config = {
        "399006.SZ": {"name": "创业板指", "etf": "159915.SZ", "market": "A"},
        "NDX": {"name": "纳指100", "etf": "513100.SH", "market": "US"},
        "HSI": {"name": "恒生指数", "etf": "159920.SZ", "market": "HK"},
    }
    
    # 使用 UniversalDataFetcher 获取数据
    all_codes = list(code_config.keys())
    etf_codes = [cfg['etf'] for cfg in code_config.values() if cfg.get('etf')]
    
    fetcher = UniversalDataFetcher()
    with fetcher:
        # 获取指数数据（用于因子计算）
        index_data = fetcher.fetch_multiple(
            all_codes, "2024-01-01", "2024-03-31"
        )
        
        # 获取ETF数据（用于收益计算）
        etf_data = fetcher.fetch_multiple(
            etf_codes, "2024-01-01", "2024-03-31"
        )
    
    # 验证数据完整性
    print("\n数据完整性检查:")
    for code in all_codes:
        idx_df = index_data.get(code)
        etf_code = code_config[code].get('etf')
        etf_df = etf_data.get(etf_code) if etf_code else None
        
        print(f"  {code}:")
        print(f"    指数数据: {'✓' if idx_df is not None else '✗'} "
              f"({len(idx_df) if idx_df is not None else 0} 条)")
        if etf_code:
            print(f"    ETF数据 ({etf_code}): {'✓' if etf_df is not None else '✗'} "
                  f"({len(etf_df) if etf_df is not None else 0} 条)")


# ============================================================
# 运行所有示例
# ============================================================
if __name__ == "__main__":
    print("\n" + "="*60)
    print("统一数据获取接口 - 使用示例")
    print("="*60)
    
    # 选择要运行的示例
    examples = [
        ("基础用法", example_basic),
        ("资产类型检测", example_detect_type),
        ("批量获取", example_batch_fetch),
        ("跨市场组合分析", example_cross_market),
        ("技术指标计算", example_with_indicators),
        ("错误处理", example_error_handling),
        ("与轮动策略集成", example_integration_with_rotation),
    ]
    
    print("\n可用示例:")
    for i, (name, _) in enumerate(examples, 1):
        print(f"  {i}. {name}")
    
    # 运行特定示例或全部
    run_all = True  # 改为 False 可以选择性运行
    
    if run_all:
        for name, func in examples:
            try:
                func()
            except Exception as e:
                print(f"\n示例 '{name}' 运行失败: {e}")
                import traceback
                traceback.print_exc()
    else:
        # 运行单个示例
        example_index = 0  # 修改为 1-7 运行特定示例
        if 0 <= example_index < len(examples):
            examples[example_index][1]()
    
    print("\n" + "="*60)
    print("示例运行完成")
    print("="*60)