知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
140 lines
4.2 KiB
Python
140 lines
4.2 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2026/2/27
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Desc: 同花顺-新发基金
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https://fund.10jqka.com.cn/datacenter/xfjj/
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"""
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import json
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import pandas as pd
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import requests
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def fund_new_found_ths(symbol: str = "全部") -> pd.DataFrame:
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"""
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同花顺-基金数据-新发基金
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https://fund.10jqka.com.cn/datacenter/xfjj/
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:param symbol: 选择基金类型; choice of {"全部", "发行中", "将发行"}
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:type symbol: str
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:return: 新发基金数据
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:rtype: pandas.DataFrame
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"""
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url = "https://fund.10jqka.com.cn/datacenter/xfjj/"
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r = requests.get(url, timeout=15)
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r.encoding = "utf-8"
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# 从页面中提取 jsonData
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# 找到 jsonData= 的位置
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start_idx = r.text.find("jsonData=")
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if start_idx == -1:
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raise ValueError("未找到 jsonData，可能页面结构已变化")
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# 找到第一个 {
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start_bracket = r.text.find("{", start_idx)
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if start_bracket == -1:
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raise ValueError("未找到 JSON 开始括号")
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# 通过计数括号找到完整的JSON对象
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count = 0
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end_idx = start_bracket
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for i in range(start_bracket, len(r.text)):
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if r.text[i] == "{":
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count += 1
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elif r.text[i] == "}":
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count -= 1
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if count == 0:
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end_idx = i + 1
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break
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if end_idx == start_bracket:
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raise ValueError("未找到完整的 JSON 对象")
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json_str = r.text[start_bracket:end_idx]
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data_json = json.loads(json_str)
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# 转换为 DataFrame
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temp_df = pd.DataFrame(data_json).T
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temp_df.reset_index(inplace=True, drop=True)
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# 根据 symbol 筛选数据
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if symbol == "发行中":
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# 发行中: zzfx=1
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temp_df = temp_df[temp_df["zzfx"] == 1]
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elif symbol == "将发行":
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# 将发行: zzfx != 1 (即 buy=0 且起始日在未来)
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temp_df = temp_df[temp_df["zzfx"] != 1]
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# 提取 manager 字段（可能是数组）
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if "manager" in temp_df.columns:
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temp_df["manager"] = temp_df["manager"].apply(
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lambda x: x[0] if isinstance(x, list) and len(x) > 0 else (x if pd.notna(x) else "")
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)
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# 重命名列
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temp_df.rename(
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columns={
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"code": "基金代码",
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"name": "基金名称",
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"type": "投资类型",
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"jjlx": "基金类型",
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"tzfg": "投资风格",
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"start": "募集起始日",
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"end": "募集终止日",
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"orgname": "管理人",
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"manager": "基金经理",
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"zgrgfl": "认购费率",
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"zdrg": "最低认购",
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"zdje": "认购金额",
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"zzfx": "发行中",
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"buy": "可购买",
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},
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inplace=True,
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)
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# 选择需要的列
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columns_order = [
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"基金代码",
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"基金名称",
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"投资类型",
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"募集起始日",
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"募集终止日",
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"管理人",
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"基金经理",
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"认购费率",
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"最低认购",
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"基金类型",
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"投资风格",
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]
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# 只保留存在的列
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existing_columns = [col for col in columns_order if col in temp_df.columns]
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temp_df = temp_df[existing_columns]
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# 数据类型转换
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if "募集起始日" in temp_df.columns:
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temp_df["募集起始日"] = pd.to_datetime(temp_df["募集起始日"], errors="coerce").dt.date
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if "募集终止日" in temp_df.columns:
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temp_df["募集终止日"] = pd.to_datetime(temp_df["募集终止日"], errors="coerce").dt.date
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if "认购费率" in temp_df.columns:
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temp_df["认购费率"] = pd.to_numeric(temp_df["认购费率"], errors="coerce")
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if "最低认购" in temp_df.columns:
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temp_df["最低认购"] = pd.to_numeric(temp_df["最低认购"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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# 测试获取全部新发基金
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fund_new_found_ths_df = fund_new_found_ths(symbol="全部")
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print(fund_new_found_ths_df)
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# 测试获取发行中的基金
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fund_new_found_ths_issue_df = fund_new_found_ths(symbol="发行中")
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print(fund_new_found_ths_issue_df)
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# 测试获取将发行的基金
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fund_new_found_ths_future_df = fund_new_found_ths(symbol="将发行")
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print(fund_new_found_ths_future_df)
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