commit 25a5e7447e37733aed0b074d44e205ccbb04f296 Author: Lxy Date: Tue Mar 17 00:22:07 2026 +0800 feat: 增加初始化代码 diff --git a/AmazingData/AmazingData-1.0.30-cp310-none-any.whl b/AmazingData/AmazingData-1.0.30-cp310-none-any.whl new file mode 100644 index 0000000..3e25648 Binary files /dev/null and b/AmazingData/AmazingData-1.0.30-cp310-none-any.whl differ diff --git a/AmazingData/AmazingData-1.0.30-cp311-none-any.whl b/AmazingData/AmazingData-1.0.30-cp311-none-any.whl new file mode 100644 index 0000000..f6c8adc Binary files /dev/null and b/AmazingData/AmazingData-1.0.30-cp311-none-any.whl differ diff --git a/AmazingData/AmazingData-1.0.30-cp312-none-any.whl b/AmazingData/AmazingData-1.0.30-cp312-none-any.whl new file mode 100644 index 0000000..bd45391 Binary files /dev/null and b/AmazingData/AmazingData-1.0.30-cp312-none-any.whl differ diff --git a/AmazingData/AmazingData-1.0.30-cp313-none-any.whl b/AmazingData/AmazingData-1.0.30-cp313-none-any.whl new file mode 100644 index 0000000..3c641fc Binary files /dev/null and b/AmazingData/AmazingData-1.0.30-cp313-none-any.whl differ diff --git a/AmazingData/AmazingData-1.0.30-cp38-none-any.whl b/AmazingData/AmazingData-1.0.30-cp38-none-any.whl new file mode 100644 index 0000000..ec2c7e8 Binary files /dev/null and b/AmazingData/AmazingData-1.0.30-cp38-none-any.whl differ diff --git a/AmazingData/AmazingData-1.0.30-cp39-none-any.whl b/AmazingData/AmazingData-1.0.30-cp39-none-any.whl new file mode 100644 index 0000000..25523fb Binary files /dev/null and b/AmazingData/AmazingData-1.0.30-cp39-none-any.whl differ diff --git a/AmazingData_完整文档.md b/AmazingData_完整文档.md new file mode 100644 index 0000000..27dff21 --- /dev/null +++ b/AmazingData_完整文档.md @@ -0,0 +1,922 @@ +# AmazingData 数据源适配器 - 完整文档 + +## 目录 +1. [适配器代码](#一适配器代码) +2. [接口详细说明](#二接口详细说明) +3. [使用示例](#三使用示例) +4. [数据结构说明](#四数据结构说明) + +--- + +## 一、适配器代码 + +### 1. 主适配器代码 (amazing_data_adapter.py) + +```python +""" +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.MIN60` +- `Period.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50 +- `000300.SH` - 沪深300 +- `000905.SH` - 中证500 +- `000906.SH` - 中证800 +- `000852.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 + +```python +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 + +```python +# 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 + +```python +# 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 + +```python +# 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 + +1. **连接管理**: 使用前先调用 `connect()`，使用后调用 `disconnect()` +2. **日期格式**: 支持 `20240101`、`"2024-01-01"` 或 `date` 对象 +3. **本地缓存**: 默认启用，可设置 `is_local=False` 强制从服务器获取 +4. **批量处理**: 大量数据建议分批获取，每批 50-100 个代码 +5. **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` diff --git a/AmazingData开发手册.pdf b/AmazingData开发手册.pdf new file mode 100644 index 0000000..c2f94fc Binary files /dev/null and b/AmazingData开发手册.pdf differ diff --git a/README_AmazingDataAdapter.md b/README_AmazingDataAdapter.md new file mode 100644 index 0000000..b1bedfe --- /dev/null +++ b/README_AmazingDataAdapter.md @@ -0,0 +1,268 @@ +# AmazingData 数据源适配器 + +基于中国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。 + +## 功能特性 + +- **简洁的API设计**: 封装复杂的SDK接口，提供直观的数据获取方法 +- **类型安全**: 使用Python类型注解，IDE友好的代码提示 +- **灵活的配置**: 支持本地缓存、参数自定义等配置选项 +- **全面的数据覆盖**: 支持行情、财务、股本、融资融券等多类金融数据 + +## 安装依赖 + +```bash +# 安装 AmazingData SDK (需从银河证券获取) +pip install tgw-1.*.*-py3-none-any.whl +pip install AmazingData-1.*.*-cp3x-none-any.whl + +# 安装其他依赖 +pip install pandas +``` + +## 快速开始 + +```python +from amazing_data_adapter import create_adapter, SecurityType, Period + +# 1. 创建适配器 +adapter = create_adapter( + username='your_username', + password='your_password', + host='your_host', + port=8080, + local_path='./data_cache/', # 本地缓存路径 + use_local_cache=True # 是否使用本地缓存 +) + +# 2. 连接数据源 +if adapter.connect(): + # 3. 获取数据 + codes = adapter.get_code_list(SecurityType.STOCK_A) + kline = adapter.get_kline( + codes=['000001.SZ'], + start_date='20240101', + end_date='20241231', + period=Period.DAILY + ) + + # 4. 断开连接 + adapter.disconnect() +``` + +## 功能模块 + +### 1. 基础数据 + +| 方法 | 说明 | +|------|------| +| `get_code_list(security_type)` | 获取代码列表 | +| `get_code_info(security_type)` | 获取证券基本信息 | +| `get_trading_calendar(market)` | 获取交易日历 | +| `get_adj_factor(codes)` | 获取单次复权因子 | +| `get_backward_factor(codes)` | 获取后复权因子 | + +### 2. 历史行情数据 + +| 方法 | 说明 | +|------|------| +| `get_kline(codes, start_date, end_date, period)` | 获取K线数据 | +| `get_snapshot(codes, start_date, end_date)` | 获取历史快照 | + +**支持的周期 (Period)**: +- `Period.MIN1` - 1分钟 +- `Period.MIN5` - 5分钟 +- `Period.MIN15` - 15分钟 +- `Period.MIN30` - 30分钟 +- `Period.MIN60` - 60分钟 +- `Period.DAILY` - 日线 +- `Period.WEEKLY` - 周线 +- `Period.MONTHLY` - 月线 + +### 3. 财务数据 + +| 方法 | 说明 | +|------|------| +| `get_balance_sheet(codes, start_date, end_date)` | 资产负债表 | +| `get_cash_flow(codes, start_date, end_date)` | 现金流量表 | +| `get_income_statement(codes, start_date, end_date)` | 利润表 | +| `get_profit_express(codes, start_date, end_date)` | 业绩快报 | +| `get_profit_notice(codes, start_date, end_date)` | 业绩预告 | + +### 4. 股东股本数据 + +| 方法 | 说明 | +|------|------| +| `get_top10_shareholders(codes, start_date, end_date)` | 十大股东 | +| `get_shareholder_count(codes, start_date, end_date)` | 股东户数 | +| `get_equity_structure(codes, start_date, end_date)` | 股本结构 | + +### 5. 融资融券数据 + +| 方法 | 说明 | +|------|------| +| `get_margin_summary(start_date, end_date)` | 融资融券汇总 | +| `get_margin_detail(codes, start_date, end_date)` | 个股融资融券明细 | + +### 6. 交易异动数据 + +| 方法 | 说明 | +|------|------| +| `get_longhu_bang(codes, start_date, end_date)` | 龙虎榜数据 | +| `get_block_trading(codes, start_date, end_date)` | 大宗交易 | + +### 7. 指数数据 + +| 方法 | 说明 | +|------|------| +| `get_index_constituents(codes)` | 指数成分股 | +| `get_index_weights(codes, start_date, end_date)` | 成分股权重 | + +**支持的指数**: +- `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 + +### 8. ETF数据 + +| 方法 | 说明 | +|------|------| +| `get_etf_pcf(codes)` | ETF申赎数据 | +| `get_fund_share(codes, start_date, end_date)` | 基金份额 | + +### 9. 可转债数据 + +| 方法 | 说明 | +|------|------| +| `get_kzz_issuance(codes)` | 可转债发行数据 | + +## 证券类型枚举 + +```python +from amazing_data_adapter import SecurityType + +SecurityType.STOCK_A # 沪深A股 +SecurityType.STOCK_A_SH_SZ # 沪深A股（沪深） +SecurityType.INDEX_A # 沪深指数 +SecurityType.ETF # ETF +SecurityType.FUTURE # 期货 +SecurityType.KZZ # 可转债 +SecurityType.GLRA # 逆回购 +SecurityType.HKT # 港股通 +SecurityType.ETF_OP # ETF期权 +``` + +## 使用示例 + +### 获取历史K线数据 + +```python +# 获取多只股票日线数据 +kline_data = adapter.get_kline( + codes=['000001.SZ', '600000.SH'], + start_date='20240101', + end_date='20241231', + period=Period.DAILY +) + +for code, df in kline_data.items(): + print(f"{code}: {len(df)} 条数据") + print(df.head()) +``` + +### 获取财务报表 + +```python +# 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:") + print(df[['REPORTING_PERIOD', 'TOTAL_ASSETS', 'TOTAL_CUR_ASSETS']]) +``` + +### 获取指数成分股 + +```python +# 获取沪深300成分股 +constituents = adapter.get_index_constituents(['000300.SH']) +df = constituents['000300.SH'] +print(f"沪深300成分股数量: {len(df)}") +print(df[['CON_CODE', 'INDATE', 'INDEX_NAME']].head()) +``` + +### 批量数据处理 + +```python +# 获取所有A股代码 +all_codes = adapter.get_code_list(SecurityType.STOCK_A) + +# 分批处理避免超时 +batch_size = 50 +for i in range(0, len(all_codes), batch_size): + batch_codes = all_codes[i:i+batch_size] + data = adapter.get_balance_sheet(batch_codes) + # 处理数据... +``` + +### 结合复权因子计算真实价格 + +```python +# 获取K线和复权因子 +kline = adapter.get_kline(['000001.SZ'], '20240101', '20241231') +adj_factor = adapter.get_backward_factor(['000001.SZ']) + +df = kline['000001.SZ'] +# 合并并计算复权价格 +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') +df['adj_close'] = df['close'] * df['000001.SZ'] +``` + +## 数据缓存 + +适配器支持本地数据缓存，可大幅提升重复查询的速度： + +```python +adapter = create_adapter( + username='xxx', + password='xxx', + host='xxx', + port=8080, + local_path='./my_data_cache/', # 缓存目录 + use_local_cache=True # 默认启用缓存 +) + +# 强制从服务器获取最新数据 +adapter.get_kline(codes, start_date, end_date, is_local=False) +``` + +## 注意事项 + +1. **账号权限**: 使用本适配器需要先向中国银河证券申请开通星耀数智平台权限 + +2. **日期格式**: 支持多种日期格式： + - `int`: 20240101 + - `str`: "2024-01-01" 或 "20240101" + - `date`: datetime.date(2024, 1, 1) + +3. **错误处理**: 所有方法在连接断开会抛出 `RuntimeError`，建议在外层做好异常处理 + +4. **资源释放**: 使用完毕后请调用 `adapter.disconnect()` 断开连接 + +## 文件说明 + +- `amazing_data_adapter.py` - 适配器主代码 +- `amazing_data_examples.py` - 详细使用示例 +- `README.md` - 本文档 + +## API参考 + +详细的SDK接口文档请参考银河证券提供的《AmazingData开发手册》。 diff --git a/__pycache__/futures_data_collector.cpython-311.pyc b/__pycache__/futures_data_collector.cpython-311.pyc new file mode 100644 index 0000000..890c86b Binary files /dev/null and b/__pycache__/futures_data_collector.cpython-311.pyc differ diff --git a/amazing_data_adapter.py b/amazing_data_adapter.py new file mode 100644 index 0000000..0b1b802 --- /dev/null +++ b/amazing_data_adapter.py @@ -0,0 +1,840 @@ +""" +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 +""" + +from calendar import calendar + +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" + MIN3 = "min3" + MIN5 = "min5" + MIN10 = "min10" + MIN15 = "min15" + MIN30 = "min30" + MIN60 = "min60" + MIN120 = "min120" + DAILY = "daily" + WEEKLY = "weekly" + MONTHLY = "monthly" + SEASON = "season" + YEAR = "year" + + +@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): + """ + 初始化适配器 + + Args: + config: 数据源配置 + """ + 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 + + Returns: + bool: 是否连接成功 + """ + try: + import AmazingData as ad + self._ad = ad + + # 登录 + ad.login( + username=self.config.username, + password=self.config.password, + host=self.config.host, + port=self.config.port + ) + + # 初始化数据类 + 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 + + Returns: + 证券代码列表 + """ + 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 + + Args: + security_type: 证券类型 + + Returns: + DataFrame 包含证券基本信息 + """ + 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 + + Args: + market: 市场 + + Returns: + 交易日列表 (YYYYMMDD 格式) + """ + 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: + """ + 获取复权因子（单次复权） + + 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 + + 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_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: 证券代码或代码列表 + start_date: 开始日期 + end_date: 结束日期 + period: K线周期 + + Returns: + Dict[代码, DataFrame]，DataFrame包含OHLCV等字段 + """ + self._check_login() + + if isinstance(codes, str): + codes = [codes] + + start_date = self._format_date(start_date) + end_date = self._format_date(end_date) + + print(f"正在获取K线数据: 代码={codes}, 日期范围={start_date}~{end_date}, 周期={period.value}") + # 获取K线数据 + kline_dict = self._market_data.query_kline( + code_list=codes, + begin_date=20240302, + end_date=20240306, + period=self._ad.constant.Period.day.value) + print(f"成功获取K线数据: {len(kline_dict)} 只证券") + print(f"示例数据:\n{kline_dict[codes[0]].head()}") + return kline_dict + + 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（tick级别） + + Args: + codes: 证券代码或代码列表 + start_date: 开始日期 + end_date: 结束日期 + + Returns: + Dict[代码, DataFrame] + """ + self._check_login() + + if isinstance(codes, str): + codes = [codes] + + start_date = self._format_date(start_date) + end_date = self._format_date(end_date) + + snapshot_dict = self._market_data.query_snapshot( + code_list=codes, + begin_date=start_date, + end_date=end_date + ) + + return snapshot_dict + + # ==================== 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 + end_date: 结束报告期 + is_local: 是否使用本地缓存 + + Returns: + Dict[代码, DataFrame] + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始报告期 + end_date: 结束报告期 + is_local: 是否使用本地缓存 + + Returns: + Dict[代码, DataFrame] + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始报告期 + end_date: 结束报告期 + is_local: 是否使用本地缓存 + + Returns: + Dict[代码, DataFrame] + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始报告期 + end_date: 结束报告期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始报告期 + end_date: 结束报告期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + Dict[代码, DataFrame] + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + + Args: + codes: 股票代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + DataFrame + """ + 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 + is_local: 是否使用本地缓存 + + Returns: + Dict[指数代码, DataFrame] + """ + 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：上证50(000016.SH)、沪深300(000300.SH)、中证500(000905.SH)、 + 中证800(000906.SH)、中证1000(000852.SH) + + Args: + codes: 指数代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + Dict[指数代码, DataFrame] + """ + 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 + + Args: + codes: ETF代码列表 + + Returns: + (etf_info, etf_constituents) + """ + 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 + + Args: + codes: ETF代码列表 + start_date: 开始日期 + end_date: 结束日期 + is_local: 是否使用本地缓存 + + Returns: + Dict[代码, DataFrame] + """ + 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 + + Args: + codes: 可转债代码列表 + is_local: 是否使用本地缓存 + + Returns: + Dict[代码, DataFrame] + """ + 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): + """检查是否已登录""" + 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: + """统一日期格式为 YYYYMMDD""" + 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]: + """通用财务数据获取方法""" + 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 + ) + + +# ==================== 便捷函数 ==================== + +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 + + Args: + username: 用户名 + password: 密码 + host: 服务器地址 + port: 服务器端口 + local_path: 本地缓存路径 + use_local_cache: 是否使用本地缓存 + + Returns: + AmazingDataAdapter 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) + + +# ==================== 使用示例 ==================== + +if __name__ == "__main__": + # 示例代码 + print(""" +# 使用示例： + +# 1. 创建适配器 +adapter = create_adapter( + username='your_username', + password='your_password', + host='your_host', + port=your_port +) + +# 2. 连接数据源 +if adapter.connect(): + # 3. 获取沪深A股代码列表 + codes = adapter.get_code_list(SecurityType.STOCK_A) + print(f"获取到 {len(codes)} 只股票") + + # 4. 获取历史K线数据 + kline_data = adapter.get_kline( + codes=['000001.SZ', '600000.SH'], + start_date='20240101', + end_date='20241231', + period=Period.DAILY + ) + + # 5. 获取财务数据 + balance_sheet = adapter.get_balance_sheet( + codes=['000001.SZ', '600000.SH'], + start_date='20240101', + end_date='20241231' + ) + + # 6. 获取指数成分股 + constituents = adapter.get_index_constituents(['000300.SH']) # 沪深300 + + # 7. 断开连接 + adapter.disconnect() +""") diff --git a/data/000001_20260308_223844.json b/data/000001_20260308_223844.json new file mode 100644 index 0000000..74c7724 --- /dev/null +++ b/data/000001_20260308_223844.json @@ -0,0 +1,2616 @@ +{ + "symbol": "000001", + "type": "stock", + "current_price": 10.82, + "timestamp": "2026-03-08T22:38:41+08:00", + "timeframes": { + "60min": [ + { + "time": "2026-02-10 14:00:00", + "open": 11.05, + "high": 11.08, + "low": 11.05, + "close": 11.05, + "volume": 11331002, + "ma10": 11.05, + "ma20": 11.02, + "macd_dif": 0.03, + "macd_dea": 0.0284, + "macd_histogram": 0.0031 + }, + { + "time": "2026-02-10 15:00:00", + "open": 11.05, + "high": 11.07, + "low": 11.05, + "close": 11.06, + "volume": 9829853, + "ma10": 11.05, + "ma20": 11.03, + "macd_dif": 0.0293, + "macd_dea": 0.0286, + "macd_histogram": 0.0014 + }, + { + "time": "2026-02-11 10:30:00", + "open": 11.06, + "high": 11.08, + "low": 11.02, + "close": 11.05, + "volume": 18163700, + "ma10": 11.05, + "ma20": 11.04, + "macd_dif": 0.0276, + "macd_dea": 0.0284, + "macd_histogram": -0.0016 + }, + { + "time": "2026-02-11 11:30:00", + 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+#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +================================================================================ +期货/股票多周期数据获取与技术指标计算脚本 +================================================================================ + +【功能介绍】 +本脚本用于获取期货或股票的多周期K线数据，并自动计算技术指标，输出JSON格式， +可直接用于技术分析Skill的输入。 + +【核心功能】 +1. 多周期数据获取 + - 5分钟K线（至少50根） + - 15分钟K线（至少50根） + - 30分钟K线（至少50根） + - 60分钟K线（至少50根） + - 日K线（至少50根） + +2. 技术指标自动计算（每根K线都包含） + - MA10: 10日/周期移动平均线 + - MA20: 20日/周期移动平均线 + - MACD_DIF: MACD快线 + - MACD_DEA: MACD慢线（信号线） + - MACD_HISTOGRAM: MACD柱状图 + +3. 输出格式 + 每个周期为一个数组，数组中每项包含： + { + "time": "K线时间", + "open": 开盘价, + "high": 最高价, + "low": 最低价, + "close": 收盘价, + "volume": 成交量, + "ma10": MA10值, + "ma20": MA20值, + "macd_dif": DIF值, + "macd_dea": DEA值, + "macd_histogram": 柱状图值 + } + +【使用方法】 + +1. 安装依赖 + pip install akshare pandas + +2. 运行脚本 + # 获取期货数据（默认） + python futures_data_collector.py --symbol SN2504 --type futures + + # 获取股票数据 + python futures_data_collector.py --symbol 000001 --type stock + python futures_data_collector.py --symbol 600000 --type stock --output stock_data.json + +3. 参数说明 + --symbol: 代码（必填） + 期货格式：品种代码 + 年份 + 月份，如 SN2504(沪锡)、AG2506(沪银) + 股票格式：6位数字代码，如 000001(平安银行)、600000(浦发银行) + + --type: 数据类型（可选，默认 futures） + 可选值：futures(期货)、stock(股票) + + --output: 输出文件名（可选） + 默认格式：{代码}_{时间戳}.json + +【常见代码对照】 + 期货： + SN2504 - 沪锡（上海期货交易所） + AG2506 - 沪银（上海期货交易所） + LC2505 - 碳酸锂（广州期货交易所） + NI2505 - 沪镍（上海期货交易所） + + 股票： + 000001 - 平安银行 + 600000 - 浦发银行 + 000858 - 五粮液 + 600519 - 贵州茅台 + +【输出示例】 +{ + "symbol": "SN2504", + "current_price": 223500, + "timestamp": "2026-03-07T22:15:00+08:00", + "timeframes": { + "60min": [ + { + "time": "2026-03-07 14:00", + "open": 22100, + "high": 22300, + "low": 22050, + "close": 22250, + "volume": 12500, + "ma10": 22180.5, + "ma20": 22000.3, + "macd_dif": 0.0523, + "macd_dea": 0.0312, + "macd_histogram": 0.0422 + } + ], + "30min": [ ... ], + "15min": [ ... ], + "5min": [ ... ], + "daily": [ ... ] + } +} + +【注意事项】 +1. 数据源使用akshare，数据可能有延迟或频率限制 +2. 分钟数据受交易所限制，可能无法获取太多历史数据 +3. 如遇数据获取失败，请检查合约代码是否正确 +4. 脚本会自动过滤掉数据不足的周期 + +【作者】OpenClaw Assistant +【日期】2026-03-07 +================================================================================ +""" + +import akshare as ak +import pandas as pd +import json +import argparse +import os +from datetime import datetime, timedelta +from typing import Dict, List +import warnings +warnings.filterwarnings('ignore') +# 清除缓存 +ak.cache = {} + +# 数据目录配置 +DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data') +os.makedirs(DATA_DIR, exist_ok=True) + +def calculate_ma(df: pd.DataFrame, periods: List[int] = [10, 20]) -> pd.DataFrame: + """计算移动平均线""" + for period in periods: + df[f'MA{period}'] = df['close'].rolling(window=period, min_periods=1).mean() + return df + + +def calculate_macd(df: pd.DataFrame, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame: + """计算MACD指标""" + ema_fast = df['close'].ewm(span=fast, adjust=False).mean() + ema_slow = df['close'].ewm(span=slow, adjust=False).mean() + df['macd_dif'] = ema_fast - ema_slow + df['macd_dea'] = df['macd_dif'].ewm(span=signal, adjust=False).mean() + df['macd_histogram'] = (df['macd_dif'] - df['macd_dea']) * 2 + + # MACD信号判断 + df['macd_signal'] = df.apply(lambda row: + 'bullish' if row['macd_dif'] > row['macd_dea'] and row['macd_histogram'] > 0 + else 'bearish' if row['macd_dif'] < row['macd_dea'] and row['macd_histogram'] < 0 + else 'neutral', axis=1) + + return df + + +def get_current_time() -> datetime: + """获取当前北京时间（去除微秒）""" + return datetime.now().replace(microsecond=0) + + +def filter_future_data(df: pd.DataFrame, current_time: datetime = None) -> pd.DataFrame: + """ + 过滤掉未来数据 + + Args: + df: 包含datetime列的DataFrame + current_time: 当前时间，默认为系统当前时间 + + Returns: + 过滤后的DataFrame + """ + if current_time is None: + current_time = get_current_time() + + if 'datetime' not in df.columns: + return df + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤掉大于当前时间的数据（未来数据） + original_count = len(df) + df = df[df['datetime'] <= current_time].copy() + filtered_count = original_count - len(df) + + if filtered_count > 0: + print(f" 过滤了 {filtered_count} 条未来数据") + + return df + + +def extend_night_session_data(df: pd.DataFrame, symbol: str, period: str) -> pd.DataFrame: + """ + 尝试获取完整的夜盘数据 + 期货夜盘时间：21:00 - 02:30（次日） + """ + if df.empty or 'datetime' not in df.columns: + return df + + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 检查是否需要补充夜盘数据 + # 获取最后一条数据的时间 + last_time = df['datetime'].iloc[-1] + last_hour = last_time.hour + last_minute = last_time.minute + + # 如果最后数据在 21:00-23:59 或 00:00-02:30 范围内，可能需要补充 + # 夜盘结束时间是 02:30 + is_night_session = ( + (last_hour >= 21) or # 21:00 - 23:59 + (last_hour < 2) or # 00:00 - 01:59 + (last_hour == 2 and last_minute <= 30) # 02:00 - 02:30 + ) + + if not is_night_session: + return df + + # 检查是否包含 02:30 的数据（对于5分钟、15分钟等周期） + has_0230 = False + for dt in df['datetime']: + if dt.hour == 2 and dt.minute == 30: + has_0230 = True + break + + # 如果已经有 02:30 的数据，说明夜盘完整 + if has_0230: + return df + + # 尝试通过获取历史数据来补充夜盘 + # 由于akshare限制，我们记录警告信息 + print(f" 注意: 夜盘数据可能不完整（缺少02:30及之前的数据）") + + return df + + +def get_minute_data(symbol: str, period: str) -> pd.DataFrame: + """ + 获取期货分钟K线数据，过滤未来数据，确保夜盘完整 + + Args: + symbol: 合约代码，如 "SN2504" + period: 分钟周期，"5", "15", "30", "60" + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 使用akshare获取分钟数据 + df = ak.futures_zh_minute_sina(symbol=symbol, period=period) + + # 重命名列 + df = df.rename(columns={ + 'day': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤未来数据 + df = filter_future_data(df, current_time) + + # 尝试补充夜盘数据 + df = extend_night_session_data(df, symbol, period) + + # 确保至少50根K线 + if len(df) < 50: + print(f" 警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源") + + return df + + except Exception as e: + print(f" 获取{period}分钟数据失败: {e}") + return pd.DataFrame() + + +def get_daily_data(symbol: str, days: int = 60) -> pd.DataFrame: + """ + 获取期货日K线数据，过滤未来数据 + + Args: + symbol: 合约代码 + days: 获取天数 + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 获取日K数据（获取较多历史数据） + df = ak.futures_zh_daily_sina(symbol=symbol) + + # 重命名列 + df = df.rename(columns={ + 'date': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 排序 + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 过滤未来数据（只保留今天及之前的数据） + df = filter_future_data(df, current_time) + + # 取最近N天 + df = df.tail(days).reset_index(drop=True) + + return df + + except Exception as e: + print(f" 获取日K数据失败: {e}") + return pd.DataFrame() + + +def get_stock_minute_data(symbol: str, period: str) -> pd.DataFrame: + """ + 获取股票分钟K线数据 + + Args: + symbol: 股票代码，如 "000001" + period: 分钟周期，"5", "15", "30", "60" + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 使用akshare获取股票分钟数据 + # stock_zh_a_minute 需要 symbol 格式为 sh600000 或 sz000001 + if symbol.startswith('6'): + full_symbol = f"sh{symbol}" + else: + full_symbol = f"sz{symbol}" + + df = ak.stock_zh_a_minute(symbol=full_symbol, period=period) + + # 重命名列 + df = df.rename(columns={ + 'day': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤未来数据 + df = filter_future_data(df, current_time) + + # 确保至少50根K线 + if len(df) < 50: + print(f" 警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源") + + return df + + except Exception as e: + print(f" 获取{period}分钟数据失败: {e}") + return pd.DataFrame() + + +def get_stock_daily_data(symbol: str, days: int = 60) -> pd.DataFrame: + """ + 获取股票日K线数据 + + Args: + symbol: 股票代码，如 "000001" + days: 获取天数 + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 计算开始日期（获取足够的历史数据） + end_date = current_time.strftime('%Y%m%d') + start_date = (current_time - timedelta(days=days*2)).strftime('%Y%m%d') + + # 获取日K数据 + df = ak.stock_zh_a_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date) + + # 重命名列 + df = df.rename(columns={ + '日期': 'datetime', + '开盘': 'open', + '最高': 'high', + '最低': 'low', + '收盘': 'close', + '成交量': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 排序 + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 过滤未来数据（只保留今天及之前的数据） + df = filter_future_data(df, current_time) + + # 取最近N天 + df = df.tail(days).reset_index(drop=True) + + return df + + except Exception as e: + print(f" 获取日K数据失败: {e}") + return pd.DataFrame() + + +def process_data(df: pd.DataFrame, timeframe: str) -> List[Dict]: + """ + 处理数据，计算指标并格式化输出 + 每个周期返回一个数组，每项包含交易数据+计算指标 + + Args: + df: K线DataFrame + timeframe: 周期名称 + + Returns: + 格式化后的K线数组，每项包含指标 + """ + if df.empty or len(df) < 10: + return [] + + # 计算技术指标 + df = calculate_ma(df) + df = calculate_macd(df) + + # 格式化K线数据（取最近50根或全部） + candles = [] + df_tail = df.tail(50) if len(df) > 50 else df + + for _, row in df_tail.iterrows(): + candle = { + "time": str(row['datetime']), + "open": round(float(row['open']), 2), + "high": round(float(row['high']), 2), + "low": round(float(row['low']), 2), + "close": round(float(row['close']), 2), + "volume": int(row['volume']) if not pd.isna(row['volume']) else 0, + "ma10": round(float(row['MA10']), 2) if not pd.isna(row.get('MA10')) else None, + "ma20": round(float(row['MA20']), 2) if not pd.isna(row.get('MA20')) else None, + "macd_dif": round(float(row['macd_dif']), 4) if not pd.isna(row.get('macd_dif')) else 0, + "macd_dea": round(float(row['macd_dea']), 4) if not pd.isna(row.get('macd_dea')) else 0, + "macd_histogram": round(float(row['macd_histogram']), 4) if not pd.isna(row.get('macd_histogram')) else 0 + } + candles.append(candle) + + return candles + + +def collect_futures_data(symbol: str) -> Dict: + """ + 收集期货多周期完整数据 + + Args: + symbol: 合约代码，如 "SN2504" + + Returns: + 完整的JSON格式数据 + """ + print(f"\n正在获取期货 {symbol} 的多周期数据...") + print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}") + print("-" * 50) + + result = { + "symbol": symbol, + "type": "futures", + "current_price": None, + "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"), + "timeframes": {} + } + + # 获取各周期数据 + periods = [ + ("60min", "60"), + ("30min", "30"), + ("15min", "15"), + ("5min", "5") + ] + + for tf_name, tf_period in periods: + print(f"获取 {tf_name} 数据...") + try: + df = get_minute_data(symbol, tf_period) + if not df.empty and len(df) >= 50: + candles = process_data(df, tf_name) + if candles: + result["timeframes"][tf_name] = candles + # 设置当前价格为最新收盘价 + if result["current_price"] is None: + result["current_price"] = candles[-1]["close"] + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + # 获取日K数据 + print("获取 daily 数据...") + try: + df_daily = get_daily_data(symbol, days=60) + if not df_daily.empty and len(df_daily) >= 50: + candles = process_data(df_daily, "daily") + if candles: + result["timeframes"]["daily"] = candles + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + print("-" * 50) + return result + + +def collect_stock_data(symbol: str) -> Dict: + """ + 收集股票多周期完整数据 + + Args: + symbol: 股票代码，如 "000001" + + Returns: + 完整的JSON格式数据 + """ + print(f"\n正在获取股票 {symbol} 的多周期数据...") + print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}") + print("-" * 50) + + result = { + "symbol": symbol, + "type": "stock", + "current_price": None, + "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"), + "timeframes": {} + } + + # 获取各周期数据 + periods = [ + ("60min", "60"), + ("30min", "30"), + ("15min", "15"), + ("5min", "5") + ] + + for tf_name, tf_period in periods: + print(f"获取 {tf_name} 数据...") + try: + df = get_stock_minute_data(symbol, tf_period) + if not df.empty and len(df) >= 50: + candles = process_data(df, tf_name) + if candles: + result["timeframes"][tf_name] = candles + # 设置当前价格为最新收盘价 + if result["current_price"] is None: + result["current_price"] = candles[-1]["close"] + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + # 获取日K数据 + print("获取 daily 数据...") + try: + df_daily = get_stock_daily_data(symbol, days=60) + if not df_daily.empty and len(df_daily) >= 50: + candles = process_data(df_daily, "daily") + if candles: + result["timeframes"]["daily"] = candles + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + print("-" * 50) + return result + + +def main(): + parser = argparse.ArgumentParser(description='期货/股票多周期数据获取与技术指标计算') + parser.add_argument('--symbol', type=str, required=True, + help='代码，期货如 SN2504(沪锡), 股票如 000001(平安银行)') + parser.add_argument('--type', type=str, default='futures', choices=['futures', 'stock'], + help='数据类型：futures(期货)、stock(股票)，默认为 futures') + parser.add_argument('--output', type=str, default=None, + help='输出JSON文件名，默认为 代码_时间戳.json') + + args = parser.parse_args() + + # 根据类型获取数据 + if args.type == 'stock': + data = collect_stock_data(args.symbol) + else: + data = collect_futures_data(args.symbol) + + # 检查是否获取到数据 + if not data["timeframes"]: + print("\n错误: 未能获取到任何数据，请检查代码是否正确") + if args.type == 'stock': + print("常见股票代码示例:") + print(" 000001 - 平安银行") + print(" 600000 - 浦发银行") + print(" 000858 - 五粮液") + print(" 600519 - 贵州茅台") + else: + print("常见期货合约代码示例:") + print(" SN2504 - 沪锡2504") + print(" AG2506 - 沪银2506") + print(" LC2505 - 碳酸锂2505") + print(" NI2505 - 沪镍2505") + return + + # 打印JSON到控制台 + print("\n" + "="*60) + print("JSON 输出:") + print("="*60) + json_output = json.dumps(data, ensure_ascii=False, indent=2) + print(json_output) + + # 保存到文件（统一放到 data 目录） + if args.output: + # 如果用户指定了文件名，也放到 data 目录下 + filename = os.path.join(DATA_DIR, args.output) + else: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = os.path.join(DATA_DIR, f"{data['symbol']}_{timestamp}.json") + + with open(filename, 'w', encoding='utf-8') as f: + f.write(json_output) + + print("\n" + "="*60) + print(f"[OK] 数据已保存到: {filename}") + print(f"[OK] 共获取 {len(data['timeframes'])} 个周期数据") + print("="*60) + + +if __name__ == "__main__": + main() diff --git a/futures_data_collector.py b/futures_data_collector.py new file mode 100644 index 0000000..1d53a5e --- /dev/null +++ b/futures_data_collector.py @@ -0,0 +1,681 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +================================================================================ +期货/股票多周期数据获取与技术指标计算脚本 +================================================================================ + +【功能介绍】 +本脚本用于获取期货或股票的多周期K线数据，并自动计算技术指标，输出JSON格式， +可直接用于技术分析Skill的输入。 + +【核心功能】 +1. 多周期数据获取 + - 5分钟K线（至少50根） + - 15分钟K线（至少50根） + - 30分钟K线（至少50根） + - 60分钟K线（至少50根） + - 日K线（至少50根） + +2. 技术指标自动计算（每根K线都包含） + - MA10: 10日/周期移动平均线 + - MA20: 20日/周期移动平均线 + - MACD_DIF: MACD快线 + - MACD_DEA: MACD慢线（信号线） + - MACD_HISTOGRAM: MACD柱状图 + +3. 输出格式 + 每个周期为一个数组，数组中每项包含： + { + "time": "K线时间", + "open": 开盘价, + "high": 最高价, + "low": 最低价, + "close": 收盘价, + "volume": 成交量, + "ma10": MA10值, + "ma20": MA20值, + "macd_dif": DIF值, + "macd_dea": DEA值, + "macd_histogram": 柱状图值 + } + +【使用方法】 + +1. 安装依赖 + pip install akshare pandas + +2. 运行脚本 + # 获取期货数据（默认） + python futures_data_collector.py --symbol SN2504 --type futures + + # 获取股票数据 + python futures_data_collector.py --symbol 000001 --type stock + python futures_data_collector.py --symbol 600000 --type stock --output stock_data.json + +3. 参数说明 + --symbol: 代码（必填） + 期货格式：品种代码 + 年份 + 月份，如 SN2504(沪锡)、AG2506(沪银) + 股票格式：6位数字代码，如 000001(平安银行)、600000(浦发银行) + + --type: 数据类型（可选，默认 futures） + 可选值：futures(期货)、stock(股票) + + --output: 输出文件名（可选） + 默认格式：{代码}_{时间戳}.json + +【常见代码对照】 + 期货： + SN2504 - 沪锡（上海期货交易所） + AG2506 - 沪银（上海期货交易所） + LC2505 - 碳酸锂（广州期货交易所） + NI2505 - 沪镍（上海期货交易所） + + 股票： + 000001 - 平安银行 + 600000 - 浦发银行 + 000858 - 五粮液 + 600519 - 贵州茅台 + +【输出示例】 +{ + "symbol": "SN2504", + "current_price": 223500, + "timestamp": "2026-03-07T22:15:00+08:00", + "timeframes": { + "60min": [ + { + "time": "2026-03-07 14:00", + "open": 22100, + "high": 22300, + "low": 22050, + "close": 22250, + "volume": 12500, + "ma10": 22180.5, + "ma20": 22000.3, + "macd_dif": 0.0523, + "macd_dea": 0.0312, + "macd_histogram": 0.0422 + } + ], + "30min": [ ... ], + "15min": [ ... ], + "5min": [ ... ], + "daily": [ ... ] + } +} + +【注意事项】 +1. 数据源使用akshare，数据可能有延迟或频率限制 +2. 分钟数据受交易所限制，可能无法获取太多历史数据 +3. 如遇数据获取失败，请检查合约代码是否正确 +4. 脚本会自动过滤掉数据不足的周期 + +【作者】OpenClaw Assistant +【日期】2026-03-07 +================================================================================ +""" + +import akshare as ak +import pandas as pd +import json +import argparse +import os +from datetime import datetime, timedelta +from typing import Dict, List +import warnings +warnings.filterwarnings('ignore') +# 清除缓存 +ak.cache = {} + +# 数据目录配置 +DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data') +os.makedirs(DATA_DIR, exist_ok=True) + +def calculate_ma(df: pd.DataFrame, periods: List[int] = [10, 20]) -> pd.DataFrame: + """计算移动平均线""" + for period in periods: + df[f'MA{period}'] = df['close'].rolling(window=period, min_periods=1).mean() + return df + + +def calculate_macd(df: pd.DataFrame, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame: + """计算MACD指标""" + ema_fast = df['close'].ewm(span=fast, adjust=False).mean() + ema_slow = df['close'].ewm(span=slow, adjust=False).mean() + df['macd_dif'] = ema_fast - ema_slow + df['macd_dea'] = df['macd_dif'].ewm(span=signal, adjust=False).mean() + df['macd_histogram'] = (df['macd_dif'] - df['macd_dea']) * 2 + + # MACD信号判断 + df['macd_signal'] = df.apply(lambda row: + 'bullish' if row['macd_dif'] > row['macd_dea'] and row['macd_histogram'] > 0 + else 'bearish' if row['macd_dif'] < row['macd_dea'] and row['macd_histogram'] < 0 + else 'neutral', axis=1) + + return df + + +def get_current_time() -> datetime: + """获取当前北京时间（去除微秒）""" + return datetime.now().replace(microsecond=0) + + +def filter_future_data(df: pd.DataFrame, current_time: datetime = None) -> pd.DataFrame: + """ + 过滤掉未来数据 + + Args: + df: 包含datetime列的DataFrame + current_time: 当前时间，默认为系统当前时间 + + Returns: + 过滤后的DataFrame + """ + if current_time is None: + current_time = get_current_time() + + if 'datetime' not in df.columns: + return df + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤掉大于当前时间的数据（未来数据） + original_count = len(df) + df = df[df['datetime'] <= current_time].copy() + filtered_count = original_count - len(df) + + if filtered_count > 0: + print(f" 过滤了 {filtered_count} 条未来数据") + + return df + + +def extend_night_session_data(df: pd.DataFrame, symbol: str, period: str) -> pd.DataFrame: + """ + 尝试获取完整的夜盘数据 + 期货夜盘时间：21:00 - 02:30（次日） + """ + if df.empty or 'datetime' not in df.columns: + return df + + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 检查是否需要补充夜盘数据 + # 获取最后一条数据的时间 + last_time = df['datetime'].iloc[-1] + last_hour = last_time.hour + last_minute = last_time.minute + + # 如果最后数据在 21:00-23:59 或 00:00-02:30 范围内，可能需要补充 + # 夜盘结束时间是 02:30 + is_night_session = ( + (last_hour >= 21) or # 21:00 - 23:59 + (last_hour < 2) or # 00:00 - 01:59 + (last_hour == 2 and last_minute <= 30) # 02:00 - 02:30 + ) + + if not is_night_session: + return df + + # 检查是否包含 02:30 的数据（对于5分钟、15分钟等周期） + has_0230 = False + for dt in df['datetime']: + if dt.hour == 2 and dt.minute == 30: + has_0230 = True + break + + # 如果已经有 02:30 的数据，说明夜盘完整 + if has_0230: + return df + + # 尝试通过获取历史数据来补充夜盘 + # 由于akshare限制，我们记录警告信息 + print(f" 注意: 夜盘数据可能不完整（缺少02:30及之前的数据）") + + return df + + +def get_minute_data(symbol: str, period: str) -> pd.DataFrame: + """ + 获取期货分钟K线数据，过滤未来数据，确保夜盘完整 + + Args: + symbol: 合约代码，如 "SN2504" + period: 分钟周期，"5", "15", "30", "60" + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 使用akshare获取分钟数据 + df = ak.futures_zh_minute_sina(symbol=symbol, period=period) + + # 重命名列 + df = df.rename(columns={ + 'day': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤未来数据 + df = filter_future_data(df, current_time) + + # 尝试补充夜盘数据 + df = extend_night_session_data(df, symbol, period) + + # 确保至少50根K线 + if len(df) < 50: + print(f" 警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源") + + return df + + except Exception as e: + print(f" 获取{period}分钟数据失败: {e}") + return pd.DataFrame() + + +def get_daily_data(symbol: str, days: int = 60) -> pd.DataFrame: + """ + 获取期货日K线数据，过滤未来数据 + + Args: + symbol: 合约代码 + days: 获取天数 + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 获取日K数据（获取较多历史数据） + df = ak.futures_zh_daily_sina(symbol=symbol) + + # 重命名列 + df = df.rename(columns={ + 'date': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 排序 + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 过滤未来数据（只保留今天及之前的数据） + df = filter_future_data(df, current_time) + + # 取最近N天 + df = df.tail(days).reset_index(drop=True) + + return df + + except Exception as e: + print(f" 获取日K数据失败: {e}") + return pd.DataFrame() + + +def get_stock_minute_data(symbol: str, period: str) -> pd.DataFrame: + """ + 获取股票分钟K线数据 + + Args: + symbol: 股票代码，如 "000001" + period: 分钟周期，"5", "15", "30", "60" + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 使用akshare获取股票分钟数据 + # stock_zh_a_minute 需要 symbol 格式为 sh600000 或 sz000001 + if symbol.startswith('6'): + full_symbol = f"sh{symbol}" + else: + full_symbol = f"sz{symbol}" + + df = ak.stock_zh_a_minute(symbol=full_symbol, period=period) + + # 重命名列 + df = df.rename(columns={ + 'day': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤未来数据 + df = filter_future_data(df, current_time) + + # 确保至少50根K线 + if len(df) < 50: + print(f" 警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源") + + return df + + except Exception as e: + print(f" 获取{period}分钟数据失败: {e}") + return pd.DataFrame() + + +def get_stock_daily_data(symbol: str, days: int = 60) -> pd.DataFrame: + """ + 获取股票日K线数据 + + Args: + symbol: 股票代码，如 "000001" + days: 获取天数 + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 计算开始日期（获取足够的历史数据） + end_date = current_time.strftime('%Y%m%d') + start_date = (current_time - timedelta(days=days*2)).strftime('%Y%m%d') + + # 获取日K数据 + df = ak.stock_zh_a_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date) + + # 重命名列 + df = df.rename(columns={ + '日期': 'datetime', + '开盘': 'open', + '最高': 'high', + '最低': 'low', + '收盘': 'close', + '成交量': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 排序 + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 过滤未来数据（只保留今天及之前的数据） + df = filter_future_data(df, current_time) + + # 取最近N天 + df = df.tail(days).reset_index(drop=True) + + return df + + except Exception as e: + print(f" 获取日K数据失败: {e}") + return pd.DataFrame() + + +def process_data(df: pd.DataFrame, timeframe: str) -> List[Dict]: + """ + 处理数据，计算指标并格式化输出 + 每个周期返回一个数组，每项包含交易数据+计算指标 + + Args: + df: K线DataFrame + timeframe: 周期名称 + + Returns: + 格式化后的K线数组，每项包含指标 + """ + if df.empty or len(df) < 10: + return [] + + # 计算技术指标 + df = calculate_ma(df) + df = calculate_macd(df) + + # 格式化K线数据（取最近50根或全部） + candles = [] + df_tail = df.tail(50) if len(df) > 50 else df + + for _, row in df_tail.iterrows(): + candle = { + "time": str(row['datetime']), + "open": round(float(row['open']), 2), + "high": round(float(row['high']), 2), + "low": round(float(row['low']), 2), + "close": round(float(row['close']), 2), + "volume": int(row['volume']) if not pd.isna(row['volume']) else 0, + "ma10": round(float(row['MA10']), 2) if not pd.isna(row.get('MA10')) else None, + "ma20": round(float(row['MA20']), 2) if not pd.isna(row.get('MA20')) else None, + "macd_dif": round(float(row['macd_dif']), 4) if not pd.isna(row.get('macd_dif')) else 0, + "macd_dea": round(float(row['macd_dea']), 4) if not pd.isna(row.get('macd_dea')) else 0, + "macd_histogram": round(float(row['macd_histogram']), 4) if not pd.isna(row.get('macd_histogram')) else 0 + } + candles.append(candle) + + return candles + + +def collect_futures_data(symbol: str) -> Dict: + """ + 收集期货多周期完整数据 + + Args: + symbol: 合约代码，如 "SN2504" + + Returns: + 完整的JSON格式数据 + """ + print(f"\n正在获取期货 {symbol} 的多周期数据...") + print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}") + print("-" * 50) + + result = { + "symbol": symbol, + "type": "futures", + "current_price": None, + "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"), + "timeframes": {} + } + + # 获取各周期数据 + periods = [ + ("60min", "60"), + ("30min", "30"), + ("15min", "15"), + ("5min", "5") + ] + + for tf_name, tf_period in periods: + print(f"获取 {tf_name} 数据...") + try: + df = get_minute_data(symbol, tf_period) + if not df.empty and len(df) >= 50: + candles = process_data(df, tf_name) + if candles: + result["timeframes"][tf_name] = candles + # 设置当前价格为最新收盘价 + if result["current_price"] is None: + result["current_price"] = candles[-1]["close"] + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + # 获取日K数据 + print("获取 daily 数据...") + try: + df_daily = get_daily_data(symbol, days=60) + if not df_daily.empty and len(df_daily) >= 50: + candles = process_data(df_daily, "daily") + if candles: + result["timeframes"]["daily"] = candles + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + print("-" * 50) + return result + + +def collect_stock_data(symbol: str) -> Dict: + """ + 收集股票多周期完整数据 + + Args: + symbol: 股票代码，如 "000001" + + Returns: + 完整的JSON格式数据 + """ + print(f"\n正在获取股票 {symbol} 的多周期数据...") + print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}") + print("-" * 50) + + result = { + "symbol": symbol, + "type": "stock", + "current_price": None, + "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"), + "timeframes": {} + } + + # 获取各周期数据 + periods = [ + ("60min", "60"), + ("30min", "30"), + ("15min", "15"), + ("5min", "5") + ] + + for tf_name, tf_period in periods: + print(f"获取 {tf_name} 数据...") + try: + df = get_stock_minute_data(symbol, tf_period) + if not df.empty and len(df) >= 50: + candles = process_data(df, tf_name) + if candles: + result["timeframes"][tf_name] = candles + # 设置当前价格为最新收盘价 + if result["current_price"] is None: + result["current_price"] = candles[-1]["close"] + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + # 获取日K数据 + print("获取 daily 数据...") + try: + df_daily = get_stock_daily_data(symbol, days=60) + if not df_daily.empty and len(df_daily) >= 50: + candles = process_data(df_daily, "daily") + if candles: + result["timeframes"]["daily"] = candles + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + print("-" * 50) + return result + + +def main(): + parser = argparse.ArgumentParser(description='期货/股票多周期数据获取与技术指标计算') + parser.add_argument('--symbol', type=str, required=True, + help='代码，期货如 SN2504(沪锡), 股票如 000001(平安银行)') + parser.add_argument('--type', type=str, default='futures', choices=['futures', 'stock'], + help='数据类型：futures(期货)、stock(股票)，默认为 futures') + parser.add_argument('--output', type=str, default=None, + help='输出JSON文件名，默认为 代码_时间戳.json') + + args = parser.parse_args() + + # 根据类型获取数据 + if args.type == 'stock': + data = collect_stock_data(args.symbol) + else: + data = collect_futures_data(args.symbol) + + # 检查是否获取到数据 + if not data["timeframes"]: + print("\n错误: 未能获取到任何数据，请检查代码是否正确") + if args.type == 'stock': + print("常见股票代码示例:") + print(" 000001 - 平安银行") + print(" 600000 - 浦发银行") + print(" 000858 - 五粮液") + print(" 600519 - 贵州茅台") + else: + print("常见期货合约代码示例:") + print(" SN2504 - 沪锡2504") + print(" AG2506 - 沪银2506") + print(" LC2505 - 碳酸锂2505") + print(" NI2505 - 沪镍2505") + return + + # 打印JSON到控制台 + print("\n" + "="*60) + print("JSON 输出:") + print("="*60) + json_output = json.dumps(data, ensure_ascii=False, indent=2) + print(json_output) + + # 保存到文件（统一放到 data 目录） + if args.output: + # 如果用户指定了文件名，也放到 data 目录下 + filename = os.path.join(DATA_DIR, args.output) + else: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = os.path.join(DATA_DIR, f"{data['symbol']}_{timestamp}.json") + + with open(filename, 'w', encoding='utf-8') as f: + f.write(json_output) + + print("\n" + "="*60) + print(f"[OK] 数据已保存到: {filename}") + print(f"[OK] 共获取 {len(data['timeframes'])} 个周期数据") + print("="*60) + + +if __name__ == "__main__": + main() diff --git a/test_amazing_data_adapter.py b/test_amazing_data_adapter.py new file mode 100644 index 0000000..e430821 --- /dev/null +++ b/test_amazing_data_adapter.py @@ -0,0 +1,748 @@ +""" +AmazingData 数据源适配器 - 完整测试套件 +每个接口都有独立的测试用例和入口 +""" + +import sys +import traceback +from datetime import datetime +from amazing_data_adapter import ( + AmazingDataAdapter, + DataSourceConfig, + SecurityType, + Market, + Period, + create_adapter +) + +# ============== 配置信息 ============== +USERNAME = '11200008169' +PASSWORD = '11200008169@2026' +HOST = '140.206.44.234' +PORT = 8600 +LOCAL_PATH = './amazing_data_cache/' + + +def create_test_adapter() -> AmazingDataAdapter: + """创建测试用的适配器实例""" + return create_adapter( + username=USERNAME, + password=PASSWORD, + host=HOST, + port=PORT, + local_path=LOCAL_PATH, + use_local_cache=True + ) + + +def run_test(test_name: str, test_func): + """运行单个测试并输出结果""" + print(f"\n{'='*60}") + print(f"测试: {test_name}") + print('='*60) + + adapter = create_test_adapter() + + try: + if not adapter.connect(): + print("❌ 连接失败") + return False + + test_func(adapter) + print("✅ 测试通过") + return True + + except Exception as e: + print(f"❌ 测试失败: {e}") + traceback.print_exc() + return False + + finally: + adapter.disconnect() + + +# ============== 基础数据接口测试 ============== + +def test_get_code_list(adapter: AmazingDataAdapter): + """测试: 获取代码列表""" + print("\n--- 测试获取各类证券代码列表 ---") + + # 沪深A股 + stock_codes = adapter.get_code_list(SecurityType.STOCK_A) + print(f"沪深A股数量: {len(stock_codes)}") + if stock_codes: + print(f"示例代码: {stock_codes[:5]}") + + # ETF + etf_codes = adapter.get_code_list(SecurityType.ETF) + print(f"ETF数量: {len(etf_codes)}") + if etf_codes: + print(f"示例代码: {etf_codes[:3]}") + + # 期货 + future_codes = adapter.get_code_list(SecurityType.FUTURE) + print(f"期货数量: {len(future_codes)}") + if future_codes: + print(f"示例代码: {future_codes[:3]}") + + # 可转债 + kzz_codes = adapter.get_code_list(SecurityType.KZZ) + print(f"可转债数量: {len(kzz_codes)}") + if kzz_codes: + print(f"示例代码: {kzz_codes[:3]}") + + # 指数 + index_codes = adapter.get_code_list(SecurityType.INDEX_A) + print(f"沪深指数数量: {len(index_codes)}") + if index_codes: + print(f"示例代码: {index_codes[:3]}") + + +def test_get_code_info(adapter: AmazingDataAdapter): + """测试: 获取证券信息""" + print("\n--- 测试获取证券基本信息 ---") + + stock_info = adapter.get_code_info(SecurityType.STOCK_A) + print(f"证券信息形状: {stock_info.shape}") + print(f"列名: {stock_info.columns.tolist()[:10]}") + print("\n前3条数据:") + print(stock_info.head(3)) + + +def test_get_trading_calendar(adapter: AmazingDataAdapter): + """测试: 获取交易日历""" + print("\n--- 测试获取交易日历 ---") + + # 上海市场 + sh_calendar = adapter.get_trading_calendar(Market.SH) + print(f"上海市场交易日数量: {len(sh_calendar)}") + print(f"最近5个交易日: {sh_calendar[-5:]}") + + # 深圳市场 + sz_calendar = adapter.get_trading_calendar(Market.SZ) + print(f"深圳市场交易日数量: {len(sz_calendar)}") + + # 北京市场 + bj_calendar = adapter.get_trading_calendar(Market.BJ) + print(f"北京市场交易日数量: {len(bj_calendar)}") + + +def test_get_adj_factor(adapter: AmazingDataAdapter): + """测试: 获取复权因子""" + print("\n--- 测试获取复权因子 ---") + + codes = ['000001.SZ', '600000.SH'] + adj_factor = adapter.get_adj_factor(codes, is_local=False) + + print(f"复权因子形状: {adj_factor.shape}") + print(f"列名: {adj_factor.columns.tolist()}") + print("\n前5条数据:") + print(adj_factor.head()) + + +def test_get_backward_factor(adapter: AmazingDataAdapter): + """测试: 获取后复权因子""" + print("\n--- 测试获取后复权因子 ---") + + codes = ['000001.SZ', '600000.SH', '000858.SZ'] + backward_factor = adapter.get_backward_factor(codes) + + print(f"后复权因子形状: {backward_factor.shape}") + print(f"列名: {backward_factor.columns.tolist()}") + print("\n前5条数据:") + print(backward_factor.head()) + + +# ============== 历史行情数据接口测试 ============== + +def test_get_kline(adapter: AmazingDataAdapter): + """测试: 获取历史K线数据""" + print("\n--- 测试获取历史K线数据 ---") + + # 获取沪深A股代码列表 + stock_codes = adapter.get_code_list(SecurityType.STOCK_A) + sample_codes = stock_codes[:3] + + # 日K线 + print("\n>>> 日K线数据") + kline_daily = adapter.get_kline( + codes=sample_codes, + start_date='20241201', + end_date='20241231', + period=Period.DAILY + ) + for code, df in list(kline_daily.items())[:2]: + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + print(f" 列名: {df.columns.tolist()}") + if not df.empty: + print(f" 最新数据:\n{df.tail(2)}") + + # 分钟K线 + print("\n>>> 60分钟K线数据") + kline_min = adapter.get_kline( + codes=['000001.SZ'], + start_date='20241201', + end_date='20241231', + period=Period.MIN60 + ) + for code, df in kline_min.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 前2条数据:\n{df.head(2)}") + + +def test_get_snapshot(adapter: AmazingDataAdapter): + """测试: 获取历史快照数据（tick级别）""" + print("\n--- 测试获取历史快照数据 ---") + + snapshot_data = adapter.get_snapshot( + codes=['000001.SZ'], + start_date='20241201', + end_date='20241201' # 只取一天的数据，避免数据量过大 + ) + print(f"快照数据形状: {snapshot_data}") + + for code, df in snapshot_data.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()[:15]}") # 显示前15列 + print(f" 前3条数据:\n{df.head(3)}") + else: + print(" 数据为空") + + +# ============== 财务数据接口测试 ============== + +def test_get_balance_sheet(adapter: AmazingDataAdapter): + """测试: 获取资产负债表""" + print("\n--- 测试获取资产负债表 ---") + + codes = ['000001.SZ', '600000.SH'] + balance_sheet = adapter.get_balance_sheet( + codes=codes, + start_date=20250101, + end_date=20260311 + ) + + for code, df in balance_sheet.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名(前10): {df.columns.tolist()[:10]}") + print(f" 数据预览:\n{df.head(2)}") + + # 获取最后一个资产负债表并打印最近日期的 TOT_SHARE + print("\n--- 最后一个资产负债表详情 ---") + last_code = list(balance_sheet.keys())[-1] + last_df = balance_sheet[last_code] + if not last_df.empty: + print(f"股票代码: {last_code}") + print(f"资产负债表:\n{last_df}") + if 'TOT_SHARE' in last_df.columns: + # 按 REPORTING_PERIOD 降序排序，获取最近日期的一行 + if 'REPORTING_PERIOD' in last_df.columns: + # 输出日期和总股本对应关系 + print("\n日期与总股本对应关系:") + for idx, row in last_df.iterrows(): + print(f" {row['REPORTING_PERIOD']}: {row['TOT_SHARE']}") + + # 获取最近日期的数据 + latest_row = last_df.sort_values('REPORTING_PERIOD', ascending=False).iloc[0] + latest_date = latest_row['REPORTING_PERIOD'] + else: + # 如果没有 REPORTING_PERIOD 列，取最后一行 + print("\n日期与总股本对应关系:") + for idx, row in last_df.iterrows(): + print(f" {idx}: {row['TOT_SHARE']}") + latest_row = last_df.iloc[-1] + latest_date = latest_row.name if hasattr(latest_row, 'name') else '未知' + latest_tot_share = latest_row['TOT_SHARE'] + print(f"\n>>> 最近报告期 {latest_date} 的总股本: {latest_tot_share}") + else: + print(f"\n可用列: {last_df.columns.tolist()}") + print("未找到 TOT_SHARE 列") + else: + print(f"{last_code} 的资产负债表为空") + + +def test_get_cash_flow(adapter: AmazingDataAdapter): + """测试: 获取现金流量表""" + print("\n--- 测试获取现金流量表 ---") + + codes = ['000001.SZ', '600000.SH'] + cash_flow = adapter.get_cash_flow( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + for code, df in cash_flow.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名(前10): {df.columns.tolist()[:10]}") + print(f" 数据预览:\n{df.head(2)}") + + +def test_get_income_statement(adapter: AmazingDataAdapter): + """测试: 获取利润表""" + print("\n--- 测试获取利润表 ---") + + codes = ['000001.SZ', '600000.SH'] + income = adapter.get_income_statement( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + for code, df in income.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名(前10): {df.columns.tolist()[:10]}") + print(f" 数据预览:\n{df.head(2)}") + + +def test_get_profit_express(adapter: AmazingDataAdapter): + """测试: 获取业绩快报""" + print("\n--- 测试获取业绩快报 ---") + + codes = ['000001.SZ', '600000.SH', '000858.SZ'] + profit_express = adapter.get_profit_express( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + print(f"数据形状: {profit_express.shape}") + if not profit_express.empty: + print(f"列名: {profit_express.columns.tolist()}") + print(f"\n前5条数据:\n{profit_express.head()}") + else: + print("数据为空") + + +def test_get_profit_notice(adapter: AmazingDataAdapter): + """测试: 获取业绩预告""" + print("\n--- 测试获取业绩预告 ---") + + codes = ['000001.SZ', '600000.SH'] + profit_notice = adapter.get_profit_notice( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + print(f"数据形状: {profit_notice.shape}") + if not profit_notice.empty: + print(f"列名: {profit_notice.columns.tolist()}") + print(f"\n前5条数据:\n{profit_notice.head()}") + else: + print("数据为空") + + +# ============== 股东股本数据接口测试 ============== + +def test_get_top10_shareholders(adapter: AmazingDataAdapter): + """测试: 获取十大股东数据""" + print("\n--- 测试获取十大股东数据 ---") + + codes = ['000001.SZ', '600000.SH'] + top10 = adapter.get_top10_shareholders( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + print(f"数据形状: {top10.shape}") + if not top10.empty: + print(f"列名: {top10.columns.tolist()}") + print(f"\n前5条数据:\n{top10.head()}") + else: + print("数据为空") + + +def test_get_shareholder_count(adapter: AmazingDataAdapter): + """测试: 获取股东户数数据""" + print("\n--- 测试获取股东户数数据 ---") + + codes = ['000001.SZ', '600000.SH'] + holder_count = adapter.get_shareholder_count( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + print(f"数据形状: {holder_count.shape}") + if not holder_count.empty: + print(f"列名: {holder_count.columns.tolist()}") + print(f"\n前5条数据:\n{holder_count.head()}") + else: + print("数据为空") + + +def test_get_equity_structure(adapter: AmazingDataAdapter): + """测试: 获取股本结构数据""" + print("\n--- 测试获取股本结构数据 ---") + + codes = ['600000.SH'] + equity = adapter.get_equity_structure( + codes=codes, + start_date=20250101, + end_date=20251231 + ) + + print(f"数据形状: {equity.shape}") + if not equity.empty: + print(f"列名: {equity.columns.tolist()}") + print(f"\n前5条数据:\n{equity.head()}") + + # 按日期和 TOT_A_SHARE 输出 + if 'TOT_A_SHARE' in equity.columns: + print("\n日期与流通A股(TOT_A_SHARE)对应关系:") + if 'REPORTING_PERIOD' in equity.columns: + for idx, row in equity.iterrows(): + print(f" {row['REPORTING_PERIOD']}: {row['TOT_A_SHARE']}") + # 获取最近日期的数据 + latest_row = equity.sort_values('REPORTING_PERIOD', ascending=False).iloc[0] + latest_date = latest_row['REPORTING_PERIOD'] + else: + for idx, row in equity.iterrows(): + print(f" {idx}: {row['TOT_A_SHARE']}") + latest_row = equity.iloc[-1] + latest_date = latest_row.name if hasattr(latest_row, 'name') else '未知' + print(f"\n>>> 最近报告期 {latest_date} 的流通A股: {latest_row['TOT_A_SHARE']}") + else: + print(f"\n可用列: {equity.columns.tolist()}") + print("未找到 TOT_A_SHARE 列") + else: + print("数据为空") + + +# ============== 融资融券数据接口测试 ============== + +def test_get_margin_summary(adapter: AmazingDataAdapter): + """测试: 获取融资融券成交汇总""" + print("\n--- 测试获取融资融券成交汇总 ---") + + margin_summary = adapter.get_margin_summary( + start_date=20240101, + end_date=20241231 + ) + + print(f"数据形状: {margin_summary.shape}") + if not margin_summary.empty: + print(f"列名: {margin_summary.columns.tolist()}") + print(f"\n前5条数据:\n{margin_summary.head()}") + else: + print("数据为空") + + +def test_get_margin_detail(adapter: AmazingDataAdapter): + """测试: 获取融资融券交易明细""" + print("\n--- 测试获取融资融券交易明细 ---") + + codes = ['000001.SZ', '600000.SH'] + margin_detail = adapter.get_margin_detail( + codes=codes, + start_date=20241201, + end_date=20241231 + ) + + for code, df in margin_detail.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()[:10]}") + print(f" 数据预览:\n{df.head(2)}") + + +# ============== 交易异动数据接口测试 ============== + +def test_get_longhu_bang(adapter: AmazingDataAdapter): + """测试: 获取龙虎榜数据""" + print("\n--- 测试获取龙虎榜数据 ---") + + codes = ['000001.SZ', '600000.SH'] + longhu = adapter.get_longhu_bang( + codes=codes, + start_date=20241201, + end_date=20241231 + ) + + print(f"数据形状: {longhu.shape}") + if not longhu.empty: + print(f"列名: {longhu.columns.tolist()}") + print(f"\n前5条数据:\n{longhu.head()}") + else: + print("数据为空") + + +def test_get_block_trading(adapter: AmazingDataAdapter): + """测试: 获取大宗交易数据""" + print("\n--- 测试获取大宗交易数据 ---") + + codes = ['000001.SZ', '600000.SH'] + block_trade = adapter.get_block_trading( + codes=codes, + start_date=20241201, + end_date=20241231 + ) + + print(f"数据形状: {block_trade.shape}") + if not block_trade.empty: + print(f"列名: {block_trade.columns.tolist()}") + print(f"\n前5条数据:\n{block_trade.head()}") + else: + print("数据为空") + + +# ============== 指数数据接口测试 ============== + +def test_get_index_constituents(adapter: AmazingDataAdapter): + """测试: 获取指数成分股""" + print("\n--- 测试获取指数成分股 ---") + + index_codes = ['000300.SH', '000905.SH', '000016.SH'] + constituents = adapter.get_index_constituents(index_codes) + + for code, df in constituents.items(): + print(f"\n{code}:") + print(f" 成分股数量: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()}") + print(f" 前5只成分股:\n{df.head()}") + + +def test_get_index_weights(adapter: AmazingDataAdapter): + """测试: 获取指数成分股权重""" + print("\n--- 测试获取指数成分股权重 ---") + + index_codes = ['000300.SH', '000905.SH'] + weights = adapter.get_index_weights( + codes=index_codes, + start_date=20241201, + end_date=20241231 + ) + + for code, df in weights.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()}") + print(f" 权重最大的5只股票:\n{df.nlargest(5, 'WEIGHT') if 'WEIGHT' in df.columns else df.head()}") + + +# ============== ETF数据接口测试 ============== + +def test_get_etf_pcf(adapter: AmazingDataAdapter): + """测试: 获取ETF申赎数据""" + print("\n--- 测试获取ETF申赎数据 ---") + + etf_codes = ['510050.SH', '510300.SH'] + etf_info, etf_constituents = adapter.get_etf_pcf(etf_codes) + + print(f"ETF基本信息形状: {etf_info.shape}") + if not etf_info.empty: + print(f"列名: {etf_info.columns.tolist()}") + print(f"\nETF基本信息:\n{etf_info.head()}") + + print(f"\nETF成分股数据数量: {len(etf_constituents)}") + for code, df in list(etf_constituents.items())[:2]: + print(f"\n{code} 成分股:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()[:10]}") + print(f" 前3条:\n{df.head(3)}") + + +def test_get_fund_share(adapter: AmazingDataAdapter): + """测试: 获取基金份额数据""" + print("\n--- 测试获取基金份额数据 ---") + + codes = ['510050.SH', '510300.SH'] + fund_share = adapter.get_fund_share( + codes=codes, + start_date=20240101, + end_date=20241231 + ) + + for code, df in fund_share.items(): + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()}") + print(f" 数据预览:\n{df.head()}") + + +# ============== 可转债数据接口测试 ============== + +def test_get_kzz_issuance(adapter: AmazingDataAdapter): + """测试: 获取可转债发行数据""" + print("\n--- 测试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: + sample_codes = kzz_codes[:5] + print(f"测试代码: {sample_codes}") + + kzz_issuance = adapter.get_kzz_issuance(sample_codes) + + for code, df in list(kzz_issuance.items())[:3]: + print(f"\n{code}:") + print(f" 数据条数: {len(df)}") + if not df.empty: + print(f" 列名: {df.columns.tolist()[:10]}") + print(f" 数据预览:\n{df.head()}") + + +# ============== 测试入口 ============== + +# 所有测试用例映射 +TEST_CASES = { + # 基础数据接口 (5个) + 'code_list': test_get_code_list, + 'code_info': test_get_code_info, + 'trading_calendar': test_get_trading_calendar, + 'adj_factor': test_get_adj_factor, + 'backward_factor': test_get_backward_factor, + + # 历史行情数据接口 (2个) + 'kline': test_get_kline, + 'snapshot': test_get_snapshot, + + # 财务数据接口 (5个) + 'balance_sheet': test_get_balance_sheet, + 'cash_flow': test_get_cash_flow, + 'income_statement': test_get_income_statement, + 'profit_express': test_get_profit_express, + 'profit_notice': test_get_profit_notice, + + # 股东股本数据接口 (3个) + 'top10_shareholders': test_get_top10_shareholders, + 'shareholder_count': test_get_shareholder_count, + 'equity_structure': test_get_equity_structure, + + # 融资融券数据接口 (2个) + 'margin_summary': test_get_margin_summary, + 'margin_detail': test_get_margin_detail, + + # 交易异动数据接口 (2个) + 'longhu_bang': test_get_longhu_bang, + 'block_trading': test_get_block_trading, + + # 指数数据接口 (2个) + 'index_constituents': test_get_index_constituents, + 'index_weights': test_get_index_weights, + + # ETF数据接口 (2个) + 'etf_pcf': test_get_etf_pcf, + 'fund_share': test_get_fund_share, + + # 可转债数据接口 (1个) + 'kzz_issuance': test_get_kzz_issuance, +} + + +def print_usage(): + """打印使用说明""" + print(""" +使用方法: + python test_amazing_data_adapter.py + +测试用例列表: + 基础数据接口: + code_list - 获取代码列表 + code_info - 获取证券信息 + trading_calendar - 获取交易日历 + adj_factor - 获取复权因子 + backward_factor - 获取后复权因子 + + 历史行情数据接口: + kline - 获取历史K线数据 + snapshot - 获取历史快照数据 + + 财务数据接口: + balance_sheet - 获取资产负债表 + cash_flow - 获取现金流量表 + income_statement - 获取利润表 + profit_express - 获取业绩快报 + profit_notice - 获取业绩预告 + + 股东股本数据接口: + top10_shareholders - 获取十大股东数据 + shareholder_count - 获取股东户数数据 + equity_structure - 获取股本结构数据 + + 融资融券数据接口: + margin_summary - 获取融资融券成交汇总 + margin_detail - 获取融资融券交易明细 + + 交易异动数据接口: + longhu_bang - 获取龙虎榜数据 + block_trading - 获取大宗交易数据 + + 指数数据接口: + index_constituents - 获取指数成分股 + index_weights - 获取指数成分股权重 + + ETF数据接口: + etf_pcf - 获取ETF申赎数据 + fund_share - 获取基金份额数据 + + 可转债数据接口: + kzz_issuance - 获取可转债发行数据 + + 特殊命令: + all - 运行所有测试 + list - 列出所有测试用例 +""") + + +def run_all_tests(): + """运行所有测试""" + print("\n" + "="*60) + print(f"运行所有测试用例: {list(TEST_CASES.keys())}") + print("="*60) + + results = {} + for name, func in TEST_CASES.items(): + print(f"\n--- 测试: {name} ---") + results[name] = run_test(name, func) + print(f"\n--- 测试: {name} 完成 ---") + + # 打印汇总 + print("\n" + "="*60) + print("测试结果汇总") + print("="*60) + passed = sum(results.values()) + total = len(results) + for name, result in results.items(): + status = "✅ 通过" if result else "❌ 失败" + print(f"{name:20s} {status}") + print("-"*60) + print(f"总计: {passed}/{total} 通过") + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print_usage() + sys.exit(1) + + test_name = sys.argv[1].lower() + + if test_name == 'list': + print_usage() + elif test_name == 'all': + run_all_tests() + elif test_name in TEST_CASES: + run_test(test_name, TEST_CASES[test_name]) + else: + print(f"未知测试用例: {test_name}") + print_usage() + sys.exit(1) diff --git a/tgw-1.0.8.5-py3-none-any.whl b/tgw-1.0.8.5-py3-none-any.whl new file mode 100644 index 0000000..f6a8a02 Binary files /dev/null and b/tgw-1.0.8.5-py3-none-any.whl differ