AmazingData 数据源适配器 - 完整文档
目录
- 适配器代码
- 接口详细说明
- 使用示例
- 数据结构说明
一、适配器代码
1. 主适配器代码 (amazing_data_adapter.py)
"""
AmazingData 数据源适配器
基于银河证券星耀数智量化平台 SDK 的封装
提供统一、简洁的金融数据获取接口
"""
import pandas as pd
from typing import List, Dict, Optional, Union, Tuple
from datetime import datetime, date
from dataclasses import dataclass
from enum import Enum
import logging
# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class SecurityType(Enum):
"""证券类型枚举"""
STOCK_A = "EXTRA_STOCK_A" # 沪深A股
STOCK_A_SH_SZ = "EXTRA_STOCK_A_SH_SZ" # 沪深A股（沪深）
INDEX_A = "EXTRA_INDEX_A" # 沪深指数
ETF = "EXTRA_ETF" # ETF
FUTURE = "EXTRA_FUTURE" # 期货
KZZ = "EXTRA_KZZ" # 可转债
GLRA = "EXTRA_GLRA" # 逆回购
HKT = "EXTRA_HKT" # 港股通
ETF_OP = "EXTRA_ETF_OP" # ETF期权
class Market(Enum):
"""市场枚举"""
SH = "SH" # 上海
SZ = "SZ" # 深圳
BJ = "BJ" # 北京
class Period(Enum):
"""周期枚举"""
MIN1 = "min1"
MIN5 = "min5"
MIN15 = "min15"
MIN30 = "min30"
MIN60 = "min60"
DAILY = "daily"
WEEKLY = "weekly"
MONTHLY = "monthly"
@dataclass
class DataSourceConfig:
"""数据源配置"""
username: str
password: str
host: str
port: int
local_path: str = "./amazing_data_cache/"
use_local_cache: bool = True
class AmazingDataAdapter:
"""
AmazingData 数据源适配器
封装银河证券星耀数智 SDK，提供统一的数据获取接口
"""
def __init__(self, config: DataSourceConfig):
self.config = config
self._ad = None
self._base_data = None
self._market_data = None
self._info_data = None
self._calendar = None
self._is_logged_in = False
def connect(self) -> bool:
"""连接到数据源"""
try:
import AmazingData as ad
self._ad = ad
ad.login(
username=self.config.username,
password=self.config.password,
host=self.config.host,
port=self.config.port
)
self._base_data = ad.BaseData()
self._info_data = ad.InfoData()
self._calendar = self._base_data.get_calendar()
self._market_data = ad.MarketData(self._calendar)
self._is_logged_in = True
logger.info("成功连接到 AmazingData 数据源")
return True
except Exception as e:
logger.error(f"连接失败: {e}")
return False
def disconnect(self):
"""断开连接"""
if self._is_logged_in and self._ad:
try:
self._ad.logout(self.config.username)
logger.info("已断开与 AmazingData 的连接")
except Exception as e:
logger.warning(f"断开连接时出错: {e}")
self._is_logged_in = False
# ==================== 基础数据接口 ====================
def get_code_list(self, security_type: SecurityType = SecurityType.STOCK_A) -> List[str]:
"""
获取代码列表
Args:
security_type: 证券类型，可选 STOCK_A, ETF, FUTURE, KZZ, INDEX_A 等
Returns:
证券代码列表，如 ['000001.SZ', '600000.SH', ...]
"""
self._check_login()
if security_type == SecurityType.FUTURE:
return self._base_data.get_future_code_list(security_type=security_type.value)
elif security_type == SecurityType.ETF_OP:
return self._base_data.get_option_code_list(security_type=security_type.value)
else:
return self._base_data.get_code_list(security_type=security_type.value)
def get_code_info(self, security_type: SecurityType = SecurityType.STOCK_A) -> pd.DataFrame:
"""
获取证券信息
Returns:
DataFrame 包含字段：
- symbol: 证券简称
- security_status: 产品状态标志
- pre_close: 昨收价
- high_limited: 涨停价
- low_limited: 跌停价
- price_tick: 最小价格变动单位
"""
self._check_login()
return self._base_data.get_code_info(security_type=security_type.value)
def get_trading_calendar(self, market: Market = Market.SH) -> List[int]:
"""
获取交易日历
Returns:
交易日列表，格式为 [20240102, 20240103, ...]
"""
self._check_login()
return self._base_data.get_calendar(market=market.value)
def get_adj_factor(self, codes: List[str], is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取单次复权因子
Args:
codes: 股票代码列表
is_local: 是否使用本地缓存
Returns:
DataFrame (index: 日期, columns: 股票代码)
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._base_data.get_adj_factor(
code_list=codes, local_path=self.config.local_path, is_local=is_local
)
def get_backward_factor(self, codes: List[str], is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取后复权因子
Returns:
DataFrame (index: 日期, columns: 股票代码)
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._base_data.get_backward_factor(
code_list=codes, local_path=self.config.local_path, is_local=is_local
)
# ==================== 历史行情数据接口 ====================
def get_kline(self, codes: Union[str, List[str]],
start_date: Union[str, int, date],
end_date: Union[str, int, date],
period: Period = Period.DAILY) -> Dict[str, pd.DataFrame]:
"""
获取历史K线数据
Args:
codes: 证券代码或列表，如 '000001.SZ' 或 ['000001.SZ', '600000.SH']
start_date: 开始日期，支持 '20240101' 或 20240101 或 date对象
end_date: 结束日期
period: K线周期，可选 MIN1/MIN5/MIN15/MIN30/MIN60/DAILY/WEEKLY/MONTHLY
Returns:
Dict[代码, DataFrame]，DataFrame包含字段：
- open: 开盘价
- high: 最高价
- low: 最低价
- close: 收盘价
- volume: 成交量
- amount: 成交金额
"""
self._check_login()
if isinstance(codes, str):
codes = [codes]
start_date = self._format_date(start_date)
end_date = self._format_date(end_date)
return self._market_data.query_kline(
code_list=codes, begin_date=start_date, end_date=end_date,
period=getattr(self._ad.constant.Period, period.value).value
)
def get_snapshot(self, codes: Union[str, List[str]],
start_date: Union[str, int, date],
end_date: Union[str, int, date]) -> Dict[str, pd.DataFrame]:
"""
获取历史快照数据
Returns:
Dict[代码, DataFrame]，包含Level-1行情快照
"""
self._check_login()
if isinstance(codes, str):
codes = [codes]
start_date = self._format_date(start_date)
end_date = self._format_date(end_date)
return self._market_data.query_snapshot(
code_list=codes, begin_date=start_date, end_date=end_date
)
# ==================== 财务数据接口 ====================
def get_balance_sheet(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取资产负债表
Args:
codes: 股票代码列表
start_date: 开始报告期 (如 20240930 表示2024年三季报)
end_date: 结束报告期
Returns:
Dict[代码, DataFrame]，主要字段包括：
- TOTAL_ASSETS: 资产总计
- TOTAL_CUR_ASSETS: 流动资产合计
- TOTAL_NONCUR_ASSETS: 非流动资产合计
- TOTAL_LIAB: 负债合计
- TOTAL_CUR_LIAB: 流动负债合计
- TOT_SHARE_EQUITY_INCL_MIN_INT: 股东权益合计
- CURRENCY_CAP: 货币资金
- NOTES_RECEIVABLE: 应收票据
- ACCT_RECEIVABLE: 应收账款
- INV: 存货
- FIX_ASSETS: 固定资产
- NOTES_PAYABLE: 应付票据
- ACCT_PAYABLE: 应付账款
- ST_BORROWING: 短期借款
- LT_LOAN: 长期借款
"""
return self._get_financial_data('get_balance_sheet', codes, start_date, end_date, is_local)
def get_cash_flow(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取现金流量表
主要字段：
- NET_CASH_FLOWS_OPERA_ACT: 经营活动现金流净额
- NET_CASH_FLOWS_INV_ACT: 投资活动现金流净额
- NET_CASH_FLOWS_FIN_ACT: 筹资活动现金流净额
- NET_INCR_CASH_AND_CASH_EQU: 现金及现金等价物净增加额
- CASH_RECP_SG_AND_RS: 销售商品提供劳务收到的现金
- CASH_PAY_GOODS_SERVICES: 购买商品接受劳务支付的现金
"""
return self._get_financial_data('get_cash_flow', codes, start_date, end_date, is_local)
def get_income_statement(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取利润表
主要字段：
- TOT_OPERA_REV: 营业总收入
- OPERA_REV: 营业收入
- TOT_OPERA_COST: 营业总成本
- OPERA_PROFIT: 营业利润
- TOTAL_PROFIT: 利润总额
- NET_PRO_INCL_MIN_INT_INC: 净利润
- BASIC_EPS: 基本每股收益
- DILUTED_EPS: 稀释每股收益
- RD_EXP: 研发费用
- LESS_SELLING_EXP: 销售费用
- LESS_ADMIN_EXP: 管理费用
- LESS_FIN_EXP: 财务费用
"""
return self._get_financial_data('get_income', codes, start_date, end_date, is_local)
def get_profit_express(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取业绩快报
主要字段：
- TOTAL_ASSETS: 总资产
- NET_PRO_EXCL_MIN_INT_INC: 净利润
- TOT_OPERA_REV: 营业总收入
- TOTAL_PROFIT: 利润总额
- OPERA_PROFIT: 营业利润
- EPS_BASIC: 基本每股收益
- ROE_WEIGHTED: 净资产收益率-加权
- YOY_GR_NET_PROFIT_PARENT: 同比增长率:归属母公司股东的净利润
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_profit_express(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
def get_profit_notice(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取业绩预告
主要字段：
- P_TYPECODE: 业绩预告类型代码
- P_CHANGE_MAX: 预告净利润变动幅度上限
- P_CHANGE_MIN: 预告净利润变动幅度下限
- NET_PROFIT_MAX: 预告净利润上限(万元)
- NET_PROFIT_MIN: 预告净利润下限(万元)
- P_REASON: 业绩变动原因
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_profit_notice(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
# ==================== 股东股本数据接口 ====================
def get_top10_shareholders(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取十大股东数据
主要字段：
- HOLDER_NAME: 股东名称
- HOLDER_QUANTITY: 持股数
- HOLDER_PCT: 持股比例(%)
- HOLDER_HOLDER_CATEGORY: 股东性质(1:个人, 2:公司)
- FLOAT_QTY: 流通股数量
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_share_holder(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
def get_shareholder_count(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取股东户数数据
主要字段：
- HOLDER_TOTAL_NUM: A股、B股、H股、境外股的总户数
- HOLDER_NUM: A股股东户数
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_holder_num(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
def get_equity_structure(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取股本结构数据
主要字段：
- TOT_SHARE: 总股本(万股)
- FLOAT_SHARE: 流通股(万股)
- FLOAT_A_SHARE: 流通A股(万股)
- RESTRICTED_A_SHARE: 限售A股(万股)
- TOT_RESTRICTED_SHARE: 限售股合计
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_equity_structure(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
# ==================== 融资融券数据接口 ====================
def get_margin_summary(self,
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取融资融券成交汇总
主要字段：
- TRADE_DATE: 交易日期
- SUM_BORROW_MONEY_BALANCE: 融资余额(元)
- SUM_PURCH_WITH_BORROW_MONEY: 融资买入额(元)
- SUM_REPAYMENT_OF_BORROW_MONEY: 融资偿还额(元)
- SUM_SEC_LENDING_BALANCE: 融券余额(元)
- SUM_SALES_OF_BORROWED_SEC: 融券卖出量
- SUM_MARGIN_TRADE_BALANCE: 融资融券余额(元)
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_margin_summary(
local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
def get_margin_detail(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取融资融券交易明细
主要字段：
- BORROW_MONEY_BALANCE: 融资余额
- PURCH_WITH_BORROW_MONEY: 融资买入额
- REPAYMENT_OF_BORROW_MONEY: 融资偿还额
- SEC_LENDING_BALANCE: 融券余额
- SALES_OF_BORROWED_SEC: 融券卖出量
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_margin_detail(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
# ==================== 交易异动数据接口 ====================
def get_longhu_bang(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取龙虎榜数据
主要字段：
- TRADE_DATE: 交易日期
- REASON_TYPE_NAME: 上榜原因
- CHANGE_RANGE: 涨跌幅(%)
- TRADER_NAME: 营业部名称
- BUY_AMOUNT: 买入金额(元)
- SELL_AMOUNT: 卖出金额(元)
- FLOW_MARK: 买卖表示(1买入, 2卖出)
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_long_hu_bang(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
def get_block_trading(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> pd.DataFrame:
"""
获取大宗交易数据
主要字段：
- TRADE_DATE: 交易日期
- B_SHARE_PRICE: 成交价(元)
- B_SHARE_VOLUME: 成交量(万股)
- B_SHARE_AMOUNT: 成交金额(万元)
- B_BUYER_NAME: 买方营业部名称
- B_SELLER_NAME: 卖方营业部名称
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_block_trading(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
# ==================== 指数数据接口 ====================
def get_index_constituents(self, codes: List[str],
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取指数成分股
Args:
codes: 指数代码列表，如 ['000300.SH', '000905.SH']
Returns:
Dict[指数代码, DataFrame]
DataFrame字段：
- INDEX_CODE: 指数代码
- CON_CODE: 成分股代码
- INDATE: 纳入日期
- OUTDATE: 剔除日期
- INDEX_NAME: 指数名称
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_index_constituent(
code_list=codes, local_path=self.config.local_path, is_local=is_local
)
def get_index_weights(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取指数成分股权重
支持指数：
- 000016.SH: 上证50
- 000300.SH: 沪深300
- 000905.SH: 中证500
- 000906.SH: 中证800
- 000852.SH: 中证1000
DataFrame字段：
- INDEX_CODE: 指数代码
- CON_CODE: 标的代码
- TRADE_DATE: 生效日期
- WEIGHT: 权重(%)
- CLOSE: 收盘价
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_index_weight(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
# ==================== ETF数据接口 ====================
def get_etf_pcf(self, codes: List[str]) -> Tuple[pd.DataFrame, Dict[str, pd.DataFrame]]:
"""
获取ETF申赎数据
Returns:
(etf_info, etf_constituents)
etf_info字段：
- creation_redemption_unit: 每个篮子对应的ETF份数
- max_cash_ratio: 最大现金替代比例
- creation: 是否允许申购
- redemption: 是否允许赎回
etf_constituents字段：
- underlying_symbol: 成份证券简称
- component_share: 成份证券数量
- substitute_flag: 现金替代标志
"""
self._check_login()
return self._base_data.get_etf_pcf(code_list=codes)
def get_fund_share(self, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取基金份额数据
主要字段：
- FUND_SHARE: 基金份额(万份)
- TOTAL_SHARE: 基金总份额(万份)
- FLOAT_SHARE: 流通份额(万份)
- CHANGE_REASON: 份额变动原因
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_fund_share(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
# ==================== 可转债数据接口 ====================
def get_kzz_issuance(self, codes: List[str],
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
"""
获取可转债发行数据
主要字段：
- STOCK_CODE: 正股代码
- LISTED_DATE: 上市日期
- PLAN_SCHEDULE: 方案进度
- CLAUSE_INI_CONV_PRICE: 初始转换价格
- LIST_ISSUE_SIZE: 发行规模(万元)
- LIST_ISSUE_QUANTITY: 发行数量(万张)
- TERM_YEAR: 借款期限(年)
- COUPON_RATE: 利率(%)
"""
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
return self._info_data.get_kzz_issuance(
code_list=codes, local_path=self.config.local_path, is_local=is_local
)
# ==================== 辅助方法 ====================
def _check_login(self):
if not self._is_logged_in:
raise RuntimeError("未连接到数据源，请先调用 connect()")
def _format_date(self, d: Union[str, int, date]) -> int:
if isinstance(d, int):
return d
elif isinstance(d, str):
return int(d.replace("-", "").replace("/", ""))
elif isinstance(d, date):
return int(d.strftime("%Y%m%d"))
else:
raise ValueError(f"不支持的日期格式: {d}")
def _get_financial_data(self, method: str, codes: List[str],
start_date: Optional[Union[str, int, date]] = None,
end_date: Optional[Union[str, int, date]] = None,
is_local: Optional[bool] = None) -> Dict[str, pd.DataFrame]:
self._check_login()
is_local = is_local if is_local is not None else self.config.use_local_cache
method = getattr(self._info_data, method)
return method(
code_list=codes, local_path=self.config.local_path, is_local=is_local,
begin_date=self._format_date(start_date) if start_date else None,
end_date=self._format_date(end_date) if end_date else None
)
def create_adapter(username: str, password: str, host: str, port: int,
local_path: str = "./amazing_data_cache/",
use_local_cache: bool = True) -> AmazingDataAdapter:
"""快速创建适配器实例"""
config = DataSourceConfig(
username=username, password=password, host=host, port=port,
local_path=local_path, use_local_cache=use_local_cache
)
return AmazingDataAdapter(config)
二、接口详细说明
1. 基础数据接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_code_list |
获取代码列表 |
List[str] |
代码列表 |
get_code_info |
获取证券信息 |
DataFrame |
symbol, pre_close, high_limited, low_limited |
get_trading_calendar |
获取交易日历 |
List[int] |
交易日列表 |
get_adj_factor |
单次复权因子 |
DataFrame |
index=日期, columns=代码 |
get_backward_factor |
后复权因子 |
DataFrame |
index=日期, columns=代码 |
2. 历史行情接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_kline |
K线数据 |
Dict[str, DataFrame] |
open, high, low, close, volume, amount |
get_snapshot |
历史快照 |
Dict[str, DataFrame] |
Level-1行情数据 |
Period 周期选项：
Period.MIN1 / Period.MIN5 / Period.MIN15 / Period.MIN30 / Period.MIN60Period.DAILY / Period.WEEKLY / Period.MONTHLY
3. 财务数据接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_balance_sheet |
资产负债表 |
Dict[str, DataFrame] |
TOTAL_ASSETS, TOTAL_LIAB, TOT_SHARE_EQUITY |
get_cash_flow |
现金流量表 |
Dict[str, DataFrame] |
NET_CASH_FLOWS_OPERA_ACT, NET_CASH_FLOWS_INV_ACT |
get_income_statement |
利润表 |
Dict[str, DataFrame] |
TOT_OPERA_REV, NET_PRO_INCL_MIN_INT_INC, BASIC_EPS |
get_profit_express |
业绩快报 |
DataFrame |
ROE_WEIGHTED, YOY_GR_NET_PROFIT_PARENT |
get_profit_notice |
业绩预告 |
DataFrame |
P_CHANGE_MAX, P_CHANGE_MIN, NET_PROFIT_MAX |
4. 股东股本接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_top10_shareholders |
十大股东 |
DataFrame |
HOLDER_NAME, HOLDER_QUANTITY, HOLDER_PCT |
get_shareholder_count |
股东户数 |
DataFrame |
HOLDER_TOTAL_NUM, HOLDER_NUM |
get_equity_structure |
股本结构 |
DataFrame |
TOT_SHARE, FLOAT_SHARE, RESTRICTED_A_SHARE |
5. 融资融券接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_margin_summary |
融资融券汇总 |
DataFrame |
SUM_BORROW_MONEY_BALANCE, SUM_MARGIN_TRADE_BALANCE |
get_margin_detail |
个股融资融券 |
Dict[str, DataFrame] |
BORROW_MONEY_BALANCE, SEC_LENDING_BALANCE |
6. 交易异动接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_longhu_bang |
龙虎榜 |
DataFrame |
TRADER_NAME, BUY_AMOUNT, SELL_AMOUNT, FLOW_MARK |
get_block_trading |
大宗交易 |
DataFrame |
B_SHARE_PRICE, B_SHARE_VOLUME, B_BUYER_NAME |
7. 指数数据接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_index_constituents |
指数成分股 |
Dict[str, DataFrame] |
INDEX_CODE, CON_CODE, INDATE, OUTDATE |
get_index_weights |
成分股权重 |
Dict[str, DataFrame] |
INDEX_CODE, CON_CODE, WEIGHT |
支持的指数代码：
000016.SH - 上证50000300.SH - 沪深300000905.SH - 中证500000906.SH - 中证800000852.SH - 中证1000
8. ETF数据接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_etf_pcf |
ETF申赎数据 |
Tuple[DataFrame, Dict] |
creation_redemption_unit, component_share |
get_fund_share |
基金份额 |
Dict[str, DataFrame] |
FUND_SHARE, TOTAL_SHARE |
9. 可转债数据接口
| 接口名 |
功能 |
返回类型 |
主要字段 |
get_kzz_issuance |
可转债发行 |
Dict[str, DataFrame] |
CLAUSE_INI_CONV_PRICE, LIST_ISSUE_SIZE, COUPON_RATE |
三、使用示例
基础使用
from amazing_data_adapter import create_adapter, SecurityType, Period
# 创建并连接
adapter = create_adapter(
username='your_username',
password='your_password',
host='your_host',
port=8080
)
if adapter.connect():
# 获取A股列表
codes = adapter.get_code_list(SecurityType.STOCK_A)
print(f"A股数量: {len(codes)}")
# 获取平安银行K线
kline = adapter.get_kline(
codes=['000001.SZ'],
start_date='20240101',
end_date='20241231',
period=Period.DAILY
)
print(kline['000001.SZ'].head())
adapter.disconnect()
获取财务数据
# 获取资产负债表
balance = adapter.get_balance_sheet(
codes=['000001.SZ', '600000.SH'],
start_date=20240930,
end_date=20240930
)
for code, df in balance.items():
print(f"{code} 总资产: {df['TOTAL_ASSETS'].values[0]}")
获取指数成分股
# 沪深300成分股
constituents = adapter.get_index_constituents(['000300.SH'])
df = constituents['000300.SH']
print(f"成分股数量: {len(df)}")
print(df[['CON_CODE', 'INDATE']].head())
批量处理
# 分批获取数据避免超时
all_codes = adapter.get_code_list(SecurityType.STOCK_A)
batch_size = 50
for i in range(0, 100, batch_size): # 只取前100只演示
batch = all_codes[i:i+batch_size]
data = adapter.get_balance_sheet(batch)
print(f"已处理 {i+len(batch)} 只股票")
四、数据结构说明
K线数据字段
| 字段名 |
类型 |
说明 |
| open |
float |
开盘价 |
| high |
float |
最高价 |
| low |
float |
最低价 |
| close |
float |
收盘价 |
| volume |
int |
成交量(股) |
| amount |
float |
成交金额(元) |
资产负债表关键字段
| 字段名 |
说明 |
| TOTAL_ASSETS |
资产总计 |
| TOTAL_CUR_ASSETS |
流动资产合计 |
| TOTAL_NONCUR_ASSETS |
非流动资产合计 |
| TOTAL_LIAB |
负债合计 |
| TOT_SHARE_EQUITY_INCL_MIN_INT |
股东权益合计(含少数股东) |
| CURRENCY_CAP |
货币资金 |
| NOTES_RECEIVABLE |
应收票据 |
| ACCT_RECEIVABLE |
应收账款 |
| INV |
存货 |
利润表关键字段
| 字段名 |
说明 |
| TOT_OPERA_REV |
营业总收入 |
| OPERA_PROFIT |
营业利润 |
| TOTAL_PROFIT |
利润总额 |
| NET_PRO_INCL_MIN_INT_INC |
净利润(含少数股东) |
| BASIC_EPS |
基本每股收益 |
| DILUTED_EPS |
稀释每股收益 |
现金流量表关键字段
| 字段名 |
说明 |
| NET_CASH_FLOWS_OPERA_ACT |
经营活动现金流净额 |
| NET_CASH_FLOWS_INV_ACT |
投资活动现金流净额 |
| NET_CASH_FLOWS_FIN_ACT |
筹资活动现金流净额 |
| NET_INCR_CASH_AND_CASH_EQU |
现金及现金等价物净增加额 |
五、注意事项
- 连接管理: 使用前先调用
connect()，使用后调用 disconnect()
- 日期格式: 支持
20240101、"2024-01-01" 或 date 对象
- 本地缓存: 默认启用，可设置
is_local=False 强制从服务器获取
- 批量处理: 大量数据建议分批获取，每批 50-100 个代码
- 错误处理: 连接断开会抛出
RuntimeError，需做好异常处理
文件保存位置: /root/.openclaw/workspace/amazing_data_adapter.py
