python_market_data_service/xysz/amazing_data_examples.py

"""
AmazingData 数据源适配器 - 使用示例

本文件展示了如何使用 AmazingDataAdapter 获取各类金融数据
"""

from amazing_data_adapter import (
    AmazingDataAdapter, 
    DataSourceConfig, 
    SecurityType, 
    Market, 
    Period,
    create_adapter
)
import pandas as pd


def demo_basic_usage():
    """基础使用示例"""
    
    # 方式1: 使用配置对象创建
    config = DataSourceConfig(
        username='your_username',
        password='your_password',
        host='your_host',
        port=8080,
        local_path='./amazing_data_cache/',
        use_local_cache=True
    )
    adapter = AmazingDataAdapter(config)
    
    # 方式2: 使用便捷函数创建
    # adapter = create_adapter(
    #     username='your_username',
    #     password='your_password',
    #     host='your_host',
    #     port=8080
    # )
    
    # 连接数据源
    if not adapter.connect():
        print("连接失败")
        return
    
    try:
        # ============== 基础数据 ==============
        print("\n=== 基础数据 ===")
        
        # 获取沪深A股代码列表
        stock_codes = adapter.get_code_list(SecurityType.STOCK_A)
        print(f"沪深A股数量: {len(stock_codes)}")
        
        # 获取ETF代码列表
        etf_codes = adapter.get_code_list(SecurityType.ETF)
        print(f"ETF数量: {len(etf_codes)}")
        
        # 获取期货代码列表
        future_codes = adapter.get_code_list(SecurityType.FUTURE)
        print(f"期货数量: {len(future_codes)}")
        
        # 获取证券基本信息
        stock_info = adapter.get_code_info(SecurityType.STOCK_A)
        print(f"\n证券信息列: {stock_info.columns.tolist()}")
        print(stock_info.head())
        
        # 获取交易日历
        calendar = adapter.get_trading_calendar(Market.SH)
        print(f"\n交易日数量: {len(calendar)}")
        print(f"最近交易日: {calendar[-5:]}")
        
        # ============== 历史行情数据 ==============
        print("\n=== 历史行情数据 ===")
        
        # 获取日K线数据
        sample_codes = stock_codes[:5]  # 取前5只股票作为示例
        kline_data = adapter.get_kline(
            codes=sample_codes,
            start_date='20240101',
            end_date='20241231',
            period=Period.DAILY
        )
        
        for code, df in list(kline_data.items())[:2]:
            print(f"\n{code} K线数据:")
            print(df.head())
        
        # 获取分钟K线
        min_kline = adapter.get_kline(
            codes=['000001.SZ'],
            start_date='20241201',
            end_date='20241231',
            period=Period.MIN60
        )
        
        # ============== 复权因子 ==============
        print("\n=== 复权因子 ===")
        
        adj_factor = adapter.get_adj_factor(
            codes=['000001.SZ', '600000.SH'],
            is_local=False  # 强制从服务器获取最新数据
        )
        print("单次复权因子:")
        print(adj_factor.head())
        
        backward_factor = adapter.get_backward_factor(
            codes=['000001.SZ', '600000.SH']
        )
        print("\n后复权因子:")
        print(backward_factor.head())
        
        # ============== 财务数据 ==============
        print("\n=== 财务数据 ===")
        
        # 获取资产负债表
        balance_sheet = adapter.get_balance_sheet(
            codes=['000001.SZ', '600000.SH'],
            start_date=20240101,
            end_date=20241231
        )
        
        for code, df in balance_sheet.items():
            print(f"\n{code} 资产负债表字段:")
            print(df.columns.tolist()[:10])  # 显示前10个字段
        
        # 获取现金流量表
        cash_flow = adapter.get_cash_flow(
            codes=['000001.SZ'],
            start_date=20240101,
            end_date=20241231
        )
        
        # 获取利润表
        income = adapter.get_income_statement(
            codes=['000001.SZ'],
            start_date=20240101,
            end_date=20241231
        )
        
        # 获取业绩快报
        profit_express = adapter.get_profit_express(
            codes=['000001.SZ', '600000.SH']
        )
        print(f"\n业绩快报:\n{profit_express.head()}")
        
        # ============== 股东股本数据 ==============
        print("\n=== 股东股本数据 ===")
        
        # 获取十大股东
        top10_holders = adapter.get_top10_shareholders(
            codes=['000001.SZ'],
            start_date=20240101,
            end_date=20241231
        )
        print(f"十大股东:\n{top10_holders.head()}")
        
        # 获取股东户数
        holder_count = adapter.get_shareholder_count(
            codes=['000001.SZ'],
            start_date=20240101,
            end_date=20241231
        )
        print(f"\n股东户数:\n{holder_count.head()}")
        
        # 获取股本结构
        equity_structure = adapter.get_equity_structure(
            codes=['000001.SZ']
        )
        print(f"\n股本结构:\n{equity_structure.head()}")
        
        # ============== 融资融券数据 ==============
        print("\n=== 融资融券数据 ===")
        
        # 获取融资融券汇总
        margin_summary = adapter.get_margin_summary(
            start_date=20240101,
            end_date=20241231
        )
        print(f"融资融券汇总:\n{margin_summary.head()}")
        
        # 获取个股融资融券明细
        margin_detail = adapter.get_margin_detail(
            codes=['000001.SZ'],
            start_date=20241201,
            end_date=20241231
        )
        
        # ============== 交易异动数据 ==============
        print("\n=== 交易异动数据 ===")
        
        # 获取龙虎榜
        longhu = adapter.get_longhu_bang(
            codes=['000001.SZ'],
            start_date=20241201,
            end_date=20241231
        )
        print(f"龙虎榜数据:\n{longhu.head()}")
        
        # 获取大宗交易
        block_trade = adapter.get_block_trading(
            codes=['000001.SZ'],
            start_date=20241201,
            end_date=20241231
        )
        print(f"\n大宗交易:\n{block_trade.head()}")
        
        # ============== 指数数据 ==============
        print("\n=== 指数数据 ===")
        
        # 获取指数成分股 (沪深300)
        index_constituents = adapter.get_index_constituents(['000300.SH'])
        for code, df in index_constituents.items():
            print(f"\n{code} 成分股数量: {len(df)}")
            print(df.head())
        
        # 获取指数成分股权重
        index_weights = adapter.get_index_weights(
            codes=['000300.SH', '000905.SH'],  # 沪深300和中证500
            start_date=20241201,
            end_date=20241231
        )
        
        # ============== ETF数据 ==============
        print("\n=== ETF数据 ===")
        
        # 获取ETF申赎数据
        etf_info, etf_constituents = adapter.get_etf_pcf(['510050.SH'])
        print(f"ETF基本信息:\n{etf_info.head()}")
        
        # 获取基金份额
        fund_share = adapter.get_fund_share(
            codes=['510050.SH'],
            start_date=20240101,
            end_date=20241231
        )
        
        # ============== 可转债数据 ==============
        print("\n=== 可转债数据 ===")
        
        # 获取可转债发行数据
        kzz_codes = adapter.get_code_list(SecurityType.KZZ)
        print(f"可转债数量: {len(kzz_codes)}")
        
        if kzz_codes:
            kzz_issuance = adapter.get_kzz_issuance(kzz_codes[:3])
            for code, df in list(kzz_issuance.items())[:1]:
                print(f"\n{code} 发行信息:\n{df.head()}")
        
    finally:
        # 断开连接
        adapter.disconnect()


def demo_data_processing():
    """数据处理示例 - 展示如何加工获取的数据"""
    
    adapter = create_adapter(
        username='your_username',
        password='your_password',
        host='your_host',
        port=8080
    )
    
    if not adapter.connect():
        return
    
    try:
        # 获取平安银行(000001.SZ)的日K线数据
        kline_data = adapter.get_kline(
            codes=['000001.SZ'],
            start_date='20240101',
            end_date='20241231',
            period=Period.DAILY
        )
        
        df = kline_data['000001.SZ']
        
        # 计算技术指标
        # 1. 移动平均线
        df['MA5'] = df['close'].rolling(window=5).mean()
        df['MA10'] = df['close'].rolling(window=10).mean()
        df['MA20'] = df['close'].rolling(window=20).mean()
        
        # 2. 涨跌幅
        df['return'] = df['close'].pct_change()
        
        # 3. 波动率 (20日)
        df['volatility'] = df['return'].rolling(window=20).std() * (252 ** 0.5)
        
        # 4. 成交量移动平均
        df['volume_MA5'] = df['volume'].rolling(window=5).mean()
        
        print("加工后的数据:")
        print(df[['close', 'MA5', 'MA10', 'MA20', 'return', 'volatility']].tail(10))
        
        # 获取复权因子并计算复权价格
        adj_factor = adapter.get_backward_factor(['000001.SZ'])
        
        # 合并K线和复权因子
        df['trade_date'] = df.index.strftime('%Y%m%d').astype(int)
        df = df.merge(
            adj_factor[['000001.SZ']].reset_index(),
            left_on='trade_date',
            right_on='index',
            how='left'
        )
        df = df.rename(columns={'000001.SZ': 'adj_factor'})
        
        # 计算后复权价格
        df['adj_close'] = df['close'] * df['adj_factor']
        
        print("\n复权后的价格:")
        print(df[['close', 'adj_factor', 'adj_close']].tail(10))
        
    finally:
        adapter.disconnect()


def demo_batch_processing():
    """批量处理示例"""
    
    adapter = create_adapter(
        username='your_username',
        password='your_password',
        host='your_host',
        port=8080
    )
    
    if not adapter.connect():
        return
    
    try:
        # 获取沪深300成分股
        index_constituents = adapter.get_index_constituents(['000300.SH'])
        hs300_codes = index_constituents['000300.SH']['CON_CODE'].tolist()
        
        print(f"沪深300成分股数量: {len(hs300_codes)}")
        
        # 批量获取财务数据 (分批处理避免超时)
        batch_size = 50
        all_balance_sheets = {}
        
        for i in range(0, len(hs300_codes), batch_size):
            batch_codes = hs300_codes[i:i+batch_size]
            print(f"处理第 {i//batch_size + 1} 批，共 {len(batch_codes)} 只股票")
            
            batch_data = adapter.get_balance_sheet(
                codes=batch_codes,
                start_date=20240930,
                end_date=20240930
            )
            all_balance_sheets.update(batch_data)
        
        # 合并所有数据
        combined_data = []
        for code, df in all_balance_sheets.items():
            if not df.empty:
                df['code'] = code
                combined_data.append(df)
        
        if combined_data:
            result_df = pd.concat(combined_data, ignore_index=True)
            print(f"\n合并后的数据形状: {result_df.shape}")
            print(result_df[['code', 'REPORTING_PERIOD', 'TOTAL_ASSETS', 'TOTAL_CUR_ASSETS']].head())
        
    finally:
        adapter.disconnect()


if __name__ == "__main__":
    print("=" * 60)
    print("AmazingData 数据源适配器 - 使用示例")
    print("=" * 60)
    
    # 注：实际运行前请替换用户名、密码和服务器地址
    print("\n提示: 请先在代码中替换 username, password, host, port 为实际值")
    
    # demo_basic_usage()
    # demo_data_processing()
    # demo_batch_processing()