Initial commit: cc-cursor 全链路量化研究平台
7 Sprints 全部完成: Sprint 0: 基础设施 (DataManager + MariaDB) Sprint 1: 因子引擎 (34因子/12分类) Sprint 2: VectorBT 回测 (5策略+截面) Sprint 3: Optuna 优化 (+Walk-Forward) Sprint 4: ML 模型 (LightGBM+CatBoost) Sprint 5: Qwen 情绪因子 (三源新闻+日期对齐) Sprint 6: Agent 系统 (4Agent+日报.md/.html) 生产加固 (15项): Tushare双源fallback, SSH自动恢复, pool_pre_ping, save_daily先删后插, load_dotenv绝对路径, 日报5d/20d修复, RiskAgent改上证指数, 昨日对比+数据截止, mac_report utf8mb4, CLAUDE-*.md 9条已知Bug, demo全参数化, djapi数据源归一化, indexDatas API修正 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.7
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"""
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AkShare 数据源封装。
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统一封装 AkShare 调用，返回标准化的 DataFrame。
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所有网络请求都在这一层处理，包含重试和容错。
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支持个股和指数两种数据接口。
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"""
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# 指数代码识别：.SH 后缀为主板指数，399xxx 为深证指数
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# 注意区分：000001.SZ 是平安银行(个股)，000001.SH 是上证指数
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def is_index_code(ts_code: str) -> bool:
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"""判断是否为指数代码。排除 000xxx.SZ 个股。"""
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if not ts_code:
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return False
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code = ts_code.upper()
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# .SH 开头 000 是指数
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if code.endswith(".SH") and (code.startswith("000") or code.startswith("399")):
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return True
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# 深交所 399xxx 指数
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if code.startswith("399"):
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return True
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# 无后缀的纯数字 000xxx（上证指数常见写法）
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if code == "000001":
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return True
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return False
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import time
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import pandas as pd
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import akshare as ak
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from config.settings import AKSHARE_CONFIG
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class AkShareSource:
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"""AkShare 数据源。"""
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def __init__(self):
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self._timeout = AKSHARE_CONFIG["request_timeout"]
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self._retry = AKSHARE_CONFIG["retry_times"]
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self._delay = AKSHARE_CONFIG["retry_delay"]
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def _retry_call(self, fn, name: str, **kwargs):
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"""带重试的 API 调用包装。重试间隔递增。"""
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last_err = None
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for i in range(self._retry):
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try:
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return fn(**kwargs)
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except Exception as e:
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last_err = e
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wait = self._delay * (i + 1)
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print(f" [retry] {name} 失败 ({e})，{wait}s 后重试 ({i + 1}/{self._retry})...")
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if i < self._retry - 1:
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time.sleep(wait)
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raise last_err # type: ignore
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# ── 股票列表 ──────────────────────────────────────────
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def fetch_stock_list(self) -> pd.DataFrame:
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"""获取 A 股股票列表。"""
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df = self._retry_call(ak.stock_info_a_code_name, "stock_list")
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df = df.rename(columns={
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"code": "ts_code",
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"name": "name",
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})
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return df[["ts_code", "name"]]
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# ── 日线数据 ──────────────────────────────────────────
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def fetch_daily(
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self, ts_code: str, start: str, end: str | None = None
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) -> pd.DataFrame:
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"""
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获取单只股票日线行情。
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参数:
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ts_code: 股票代码，如 '000001'（纯数字格式，AkShare 要求）
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start: 起始日期 'YYYYMMDD'
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end: 结束日期 'YYYYMMDD'，None 表示今天
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"""
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symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
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end = end or time.strftime("%Y%m%d")
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df = self._retry_call(
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ak.stock_zh_a_hist,
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"daily",
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symbol=symbol,
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period="daily",
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start_date=start,
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end_date=end,
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adjust="qfq", # 前复权
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)
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if df.empty:
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return df
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df = df.rename(columns={
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"日期": "trade_date",
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"开盘": "open",
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"收盘": "close",
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"最高": "high",
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"最低": "low",
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"成交量": "vol",
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"成交额": "amount",
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"振幅": "amplitude",
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"涨跌幅": "pct_chg",
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"涨跌额": "change",
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"换手率": "turnover_rate",
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})
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df["ts_code"] = ts_code
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# AkShare 返回 'YYYY-MM-DD'，统一转为 'YYYYMMDD'
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df["trade_date"] = df["trade_date"].astype(str).str.replace("-", "")
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return df
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# ── 指数日线 ──────────────────────────────────────────
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def fetch_index_daily(
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self, ts_code: str, start: str, end: str | None = None
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) -> pd.DataFrame:
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"""
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获取指数日线行情。
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AkShare index_zh_a_hist 接口，symbol 为纯数字（如 '000001'）。
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"""
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symbol = ts_code.replace(".SH", "").replace(".SZ", "").replace(".BJ", "")
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end = end or time.strftime("%Y%m%d")
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df = self._retry_call(
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ak.index_zh_a_hist,
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"index_daily",
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symbol=symbol, period="daily",
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start_date=start, end_date=end,
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)
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if df.empty:
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return df
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df = df.rename(columns={
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"日期": "trade_date",
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"开盘": "open",
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"收盘": "close",
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"最高": "high",
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"最低": "low",
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"成交量": "vol",
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"成交额": "amount",
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"涨跌幅": "pct_chg",
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"涨跌额": "change",
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})
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df["ts_code"] = ts_code
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df["trade_date"] = df["trade_date"].astype(str)
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return df
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# ── 财务数据 ──────────────────────────────────────────
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def fetch_financial(self, ts_code: str) -> pd.DataFrame:
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"""获取单只股票核心财务指标（同花顺接口）。"""
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symbol = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
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try:
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df = ak.stock_financial_abstract_ths(symbol=symbol)
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if df.empty:
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return pd.DataFrame()
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df = df.rename(columns={
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"报告期": "end_date",
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"净利润": "net_profit",
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"净利润同比增长率": "net_profit_yoy",
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"扣非净利润": "deducted_net_profit",
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"扣非净利润同比增长率": "deducted_net_profit_yoy",
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"营业总收入": "total_revenue",
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"营业总收入同比增长率": "total_revenue_yoy",
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"基本每股收益": "eps",
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"每股净资产": "bvps",
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"每股资本公积金": "capital_reserve_ps",
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"每股未分配利润": "undistributed_profit_ps",
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"每股经营现金流": "ocf_ps",
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"销售净利率": "net_profit_margin",
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"净资产收益率": "roe",
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"净资产收益率-摊薄": "roe_diluted",
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"营业周期": "operating_cycle",
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"应收账款周转天数": "receivables_days",
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"流动比率": "current_ratio",
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"速动比率": "quick_ratio",
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"保守速动比率": "conservative_quick_ratio",
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"产权比率": "equity_ratio",
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"资产负债率": "debt_to_assets",
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})
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df["ts_code"] = ts_code
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# 日期格式统一
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df["end_date"] = df["end_date"].astype(str).str.replace("-", "")
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# 数值列清洗：去掉 万/亿/% 等单位
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for col in df.columns:
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if col in ("ts_code", "end_date"):
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continue
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df[col] = self._parse_financial_value(df[col])
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return df
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except Exception:
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return pd.DataFrame()
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@staticmethod
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def _parse_financial_value(series: pd.Series) -> pd.Series:
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"""解析财务数值字符串 '4302.00万', '64.75%', 'False' → float"""
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def _parse(v):
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if v is None or v == "False" or v == "":
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return None
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if isinstance(v, (int, float)):
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return float(v)
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s = str(v).strip()
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if not s:
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return None
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try:
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if s.endswith("%"):
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return float(s[:-1])
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if "万" in s:
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return float(s.replace("万", "")) * 1e4
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if "亿" in s:
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return float(s.replace("亿", "")) * 1e8
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return float(s)
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except ValueError:
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return None
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return series.apply(_parse).astype("float64")
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@@ -0,0 +1,139 @@
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"""
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Tushare 数据源封装。
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与 AkShareSource 保持相同接口，作为平替/备份数据源。
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"""
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import os
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import time
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import pandas as pd
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import tushare as ts
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# 确保 .env 已加载（无论从哪个路径导入）
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import config.settings # noqa: F401
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class TushareSource:
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"""Tushare 数据源。"""
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def __init__(self, token: str | None = None):
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token = token or os.getenv("TUSHARE_TOKEN", "")
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if not token:
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self._pro = None
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else:
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ts.set_token(token)
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self._pro = ts.pro_api()
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@property
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def available(self) -> bool:
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return self._pro is not None
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def _ensure_pro(self):
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if self._pro is None:
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raise RuntimeError("Tushare token 未配置，请在 .env 中设置 TUSHARE_TOKEN")
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# ── 股票列表 ──────────────────────────────────────────
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def fetch_stock_list(self) -> pd.DataFrame:
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"""获取 A 股股票列表。"""
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self._ensure_pro()
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fields = "ts_code,name,area,industry,market,list_date,is_hs"
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df = self._pro.stock_basic(
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exchange="", list_status="L",
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fields=fields,
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)
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if df is None or df.empty:
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return pd.DataFrame()
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return df[["ts_code", "name"]]
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# ── 日线数据 ──────────────────────────────────────────
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def fetch_daily(
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self, ts_code: str, start: str, end: str | None = None
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) -> pd.DataFrame:
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"""
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获取单只股票日线行情。
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参数:
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ts_code: 如 '000001.SZ'
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start: 'YYYYMMDD'
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end: 'YYYYMMDD'
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"""
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self._ensure_pro()
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end = end or time.strftime("%Y%m%d")
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df = self._pro.daily(
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ts_code=ts_code,
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start_date=start,
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end_date=end,
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fields="ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount,turnover_rate",
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)
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if df is None or df.empty:
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return pd.DataFrame()
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# 前复权因子
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try:
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adj = self._pro.adj_factor(ts_code=ts_code, start_date=start, end_date=end)
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if adj is not None and not adj.empty:
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df = df.merge(adj[["trade_date", "adj_factor"]], on="trade_date", how="left")
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for col in ["open", "high", "low", "close", "pre_close"]:
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if col in df.columns and "adj_factor" in df.columns:
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df[col] = (df[col] * df["adj_factor"]).round(2)
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df = df.drop(columns=["adj_factor"])
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except Exception:
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pass
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df["trade_date"] = df["trade_date"].astype(str)
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return df
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# ── 指数日线 ──────────────────────────────────────────
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def fetch_index_daily(
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self, ts_code: str, start: str, end: str | None = None
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) -> pd.DataFrame:
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"""
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获取指数日线行情。
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Tushare index_daily 接口。
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"""
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self._ensure_pro()
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end = end or time.strftime("%Y%m%d")
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df = self._pro.index_daily(
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ts_code=ts_code,
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start_date=start, end_date=end,
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fields="ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount",
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)
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if df is None or df.empty:
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return pd.DataFrame()
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df["trade_date"] = df["trade_date"].astype(str)
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return df
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# ── 财务数据 ──────────────────────────────────────────
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def fetch_financial(self, ts_code: str) -> pd.DataFrame:
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"""获取单只股票核心财务指标。"""
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self._ensure_pro()
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fields = (
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"ts_code,end_date,roe,roa,grossprofit_margin,netprofit_margin,"
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"debt_to_assets,eps,dt_eps,bps,pe,pb,"
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"total_revenue,revenue_yoy,n_income,n_income_yoy"
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)
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df = self._pro.fina_indicator(
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ts_code=ts_code,
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fields=fields,
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)
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if df is None or df.empty:
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return pd.DataFrame()
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df = df.rename(columns={
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"grossprofit_margin": "gross_profit_margin",
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"netprofit_margin": "net_profit_margin",
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"dt_eps": "eps_diluted",
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"bps": "bvps",
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"revenue_yoy": "total_revenue_yoy",
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"n_income": "net_profit",
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"n_income_yoy": "net_profit_yoy",
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})
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df["end_date"] = df["end_date"].astype(str)
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return df
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Block a user