232 lines
9.1 KiB
Python
232 lines
9.1 KiB
Python
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import asyncio
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import httpx
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import math
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import pandas as pd
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from datetime import datetime, timezone, timedelta
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import logging
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logging.getLogger("httpx").setLevel(logging.WARNING)
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HEADERS = {
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"User-Agent": (
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"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
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"AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/122.0.0.0 Safari/537.36"
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),
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"Referer": "https://quote.eastmoney.com/center/gridlist.html#fund_etf", # 合理来源页
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"Accept": "application/json, text/plain, */*",
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"Accept-Language": "zh-CN,zh;q=0.9",
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"Connection": "keep-alive",
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}
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async def fetch_page(client: httpx.AsyncClient, url: str, params: dict) -> pd.DataFrame:
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try:
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resp = await client.get(url, params=params, timeout=10)
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data_json = resp.json()
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if "data" in data_json and data_json["data"].get("diff"):
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return pd.DataFrame(data_json["data"]["diff"])
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except Exception as e:
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print(f"Error on page {params.get('pn')}: {e}")
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return pd.DataFrame()
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async def fund_etf_spot_em_async() -> pd.DataFrame:
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url = "https://88.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": "1",
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"pz": "100",
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"po": "1",
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"np": "1",
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"ut": "bd1d9ddb04089700cf9c27f6f7426281",
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"fltt": "2",
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"invt": "2",
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"wbp2u": "|0|0|0|web",
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"fid": "f12",
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"fs": "b:MK0021,b:MK0022,b:MK0023,b:MK0024,b:MK0827",
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"fields": (
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"f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,"
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"f12,f13,f14,f15,f16,f17,f18,f20,f21,"
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"f23,f24,f25,f22,f11,f30,f31,f32,f33,"
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"f34,f35,f38,f62,f63,f64,f65,f66,f69,"
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"f72,f75,f78,f81,f84,f87,f115,f124,f128,"
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"f136,f152,f184,f297,f402,f441"
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),
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}
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async with httpx.AsyncClient(headers=HEADERS, http2=True) as client:
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# 获取首页数据，确认总页数
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resp = await client.get(url, params=params)
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data_json = resp.json()
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if "data" not in data_json or not data_json["data"].get("diff"):
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return pd.DataFrame()
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total = data_json["data"]["total"]
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per_page = len(data_json["data"]["diff"])
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total_pages = math.ceil(total / per_page)
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tasks = []
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for page in range(1, total_pages + 1):
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new_params = params.copy()
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new_params["pn"] = str(page)
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tasks.append(fetch_page(client, url, new_params))
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dfs = await asyncio.gather(*tasks)
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temp_df = pd.concat([df for df in dfs if not df.empty], ignore_index=True)
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temp_df.rename(
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columns={
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"f12": "代码",
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"f14": "名称",
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"f2": "最新价",
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"f4": "涨跌额",
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"f3": "涨跌幅",
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"f5": "成交量",
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"f6": "成交额",
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"f7": "振幅",
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"f17": "开盘价",
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"f15": "最高价",
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"f16": "最低价",
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"f18": "昨收",
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"f8": "换手率",
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"f10": "量比",
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"f30": "现手",
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"f31": "买一",
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"f32": "卖一",
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"f33": "委比",
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"f34": "外盘",
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"f35": "内盘",
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"f62": "主力净流入-净额",
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"f184": "主力净流入-净占比",
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"f66": "超大单净流入-净额",
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"f69": "超大单净流入-净占比",
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"f72": "大单净流入-净额",
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"f75": "大单净流入-净占比",
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"f78": "中单净流入-净额",
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"f81": "中单净流入-净占比",
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"f84": "小单净流入-净额",
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"f87": "小单净流入-净占比",
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"f38": "最新份额",
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"f21": "流通市值",
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"f20": "总市值",
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"f402": "基金折价率",
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"f441": "IOPV实时估值",
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"f297": "数据日期",
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"f124": "更新时间",
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},
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inplace=True,
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)
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temp_df = temp_df[
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[
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"代码",
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"名称",
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"最新价",
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"IOPV实时估值",
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"基金折价率",
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"涨跌额",
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"涨跌幅",
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"成交量",
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"成交额",
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"开盘价",
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"最高价",
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"最低价",
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"昨收",
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"振幅",
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"换手率",
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"量比",
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"委比",
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"外盘",
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"内盘",
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"主力净流入-净额",
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"主力净流入-净占比",
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"超大单净流入-净额",
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"超大单净流入-净占比",
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"大单净流入-净额",
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"大单净流入-净占比",
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"中单净流入-净额",
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"中单净流入-净占比",
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"小单净流入-净额",
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"小单净流入-净占比",
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"现手",
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"买一",
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"卖一",
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"最新份额",
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"流通市值",
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"总市值",
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"数据日期",
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"更新时间",
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]
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].copy()
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temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce")
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temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce")
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temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce")
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temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
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temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
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temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
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temp_df["委比"] = pd.to_numeric(temp_df["委比"], errors="coerce")
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temp_df["外盘"] = pd.to_numeric(temp_df["外盘"], errors="coerce")
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temp_df["内盘"] = pd.to_numeric(temp_df["内盘"], errors="coerce")
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temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
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temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
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temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
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temp_df["现手"] = pd.to_numeric(temp_df["现手"], errors="coerce")
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temp_df["买一"] = pd.to_numeric(temp_df["买一"], errors="coerce")
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temp_df["卖一"] = pd.to_numeric(temp_df["卖一"], errors="coerce")
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temp_df["最新份额"] = pd.to_numeric(temp_df["最新份额"], errors="coerce")
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temp_df["IOPV实时估值"] = pd.to_numeric(temp_df["IOPV实时估值"], errors="coerce")
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temp_df["基金折价率"] = pd.to_numeric(temp_df["基金折价率"], errors="coerce")
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temp_df["主力净流入-净额"] = pd.to_numeric(
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temp_df["主力净流入-净额"], errors="coerce"
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)
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temp_df["主力净流入-净占比"] = pd.to_numeric(
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temp_df["主力净流入-净占比"], errors="coerce"
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)
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temp_df["超大单净流入-净额"] = pd.to_numeric(
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temp_df["超大单净流入-净额"], errors="coerce"
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)
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temp_df["超大单净流入-净占比"] = pd.to_numeric(
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temp_df["超大单净流入-净占比"], errors="coerce"
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)
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temp_df["大单净流入-净额"] = pd.to_numeric(
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temp_df["大单净流入-净额"], errors="coerce"
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)
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temp_df["大单净流入-净占比"] = pd.to_numeric(
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temp_df["大单净流入-净占比"], errors="coerce"
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)
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temp_df["中单净流入-净额"] = pd.to_numeric(
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temp_df["中单净流入-净额"], errors="coerce"
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)
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temp_df["中单净流入-净占比"] = pd.to_numeric(
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temp_df["中单净流入-净占比"], errors="coerce"
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)
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temp_df["小单净流入-净额"] = pd.to_numeric(
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temp_df["小单净流入-净额"], errors="coerce"
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)
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temp_df["小单净流入-净占比"] = pd.to_numeric(
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temp_df["小单净流入-净占比"], errors="coerce"
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)
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temp_df["数据日期"] = pd.to_datetime(
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temp_df["数据日期"], format="%Y%m%d", errors="coerce"
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)
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temp_df["更新时间"] = (
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pd.to_datetime(temp_df["更新时间"], unit="s", errors="coerce")
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.dt.tz_localize("UTC")
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.dt.tz_convert("Asia/Shanghai")
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)
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return temp_df
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# 示例同步调用封装（如需在同步代码中使用）
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def fund_etf_spot_em() -> pd.DataFrame:
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return asyncio.run(fund_etf_spot_em_async())
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if __name__ == "__main__":
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df = fund_etf_spot_em()
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print(df.head())
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