| name | global-stock-data |
| description | 美股港股全栈数据工具包 — 覆盖行情(新浪+腾讯+东财push2)、K线(新浪+Yahoo)、技术指标(MA/MACD/RSI/KDJ/布林带)、基本面(东财datacenter三表+GMAININDICATOR+Yahoo quoteSummary+SEC XBRL)、资金面(东财push2his日级资金流)、期权(Yahoo)、SEC Filing(EDGAR)、搜索与工具(东财search+Yahoo+SEC CIK+全市场列表)八层数据源，内嵌全部调用代码，自包含零依赖外部文件。适用于美股港股个股分析、全市场筛选、财报解读、期权策略、SEC文件检索、资金流追踪、机构持仓分析等场景。 |
| origin | custom |
| version | 1.0.1 |
📦 项目主页：https://github.com/simonlin1212/global-stock-data — 更新、反馈、支持作者
作者：Simon 林 · 抖音「Simon林」· 公众号「硅基世纪」
美股港股全栈数据工具包 V1.0.1
八层数据架构，18 个端点，5 个数据源，全部零鉴权，实测可用（2026-05-20 验证）。
使用方式： 将本文件放入 ~/.claude/skills/global-stock-data/SKILL.md，Claude Code 会自动识别并在美股/港股相关对话中激活。
行情层（实时/延时）
├── 新浪财经 → 美股 gb_XXXX 36字段 / 港股 rt_hkXXXXX 25字段
├── 腾讯财经 → 美股 usXXXX 71字段 / 港股 r_hkXXXXX 78字段
└── 东财 push2 → 美股/港股 secid 实时行情，含中文名/涨跌幅/换手率
K线层（日/周/月/分钟）
├── 新浪 → 美股日K (回溯至1984年)
└── Yahoo chart → 美股+港股 (v8 API, 零crumb)
技术指标层（纯计算，零额外依赖）
└── MA/EMA + MACD + RSI + KDJ + 布林带 基于K线OHLCV，纯Python计算
基本面层
├── 东财 datacenter → 美股/港股三表(资产负债+利润+现金流) + GMAININDICATOR(关键指标)
├── Yahoo crumb → 23个模块(财务数据+关键指标+分析师+机构持仓)
└── SEC EDGAR XBRL → 美股503个GAAP指标 (仅美股)
资金面层
└── 东财 push2his → 日级资金流(主力/大单/中单/小单) 美股+港股
期权层（仅美股）
└── Yahoo crumb → 期权链(calls+puts, 所有到期日) 仅美股(港股期权不在Yahoo覆盖范围)
SEC Filing层（仅美股）
├── EDGAR submissions → 10-K/10-Q/8-K 完整Filing列表
└── EDGAR XBRL → 结构化财务指标(营收/净利/EPS等)
工具层
├── 东财 search → 股票搜索(中英文, 含市场代码映射)
├── 东财 push2 → 全市场股票列表(涨跌幅/成交量排名, 美股5925只+港股18000+只)
├── Yahoo search → 新闻资讯(按股票代码)
└── SEC CIK mapping → ticker↔CIK 映射 (仅美股)
When to Activate
- 用户要查美股/港股行情（价格/涨跌幅/成交量）
- 用户要拉 K 线（日线/周线/月线/分钟线）
- 用户要看财报（资产负债表/利润表/现金流量表）
- 用户要看关键财务指标（PE/PB/ROE/利润率/目标价）
- 用户要看分析师预期（EPS预测/评级/目标价区间）
- 用户要看机构持仓（前十大机构/持股比例）
- 用户要看资金流向（主力/大单/中单/小单净流入）
- 用户要查期权链（calls/puts/到期日/Greeks）
- 用户要查 SEC Filing（10-K/10-Q/8-K/年报/季报）
- 用户要做美股财报量化分析（从 XBRL 拉多年营收/净利/EPS 趋势）
- 用户要搜索股票（中英文均可）
- 用户要看美股/港股新闻
- 用户要看全市场涨跌幅排名（当日涨幅/跌幅最大的股票）
- 用户要做全市场筛选（遍历美股/港股列表做初筛）
- 用户要看关键财务指标概览（营收/净利/EPS/ROE/ROA/资产负债率 中文版）
- 用户要看技术指标（MACD/RSI/KDJ/布林带/均线）
- 用户要判断金叉死叉/超买超卖/变盘信号
- 关键词：美股、港股、AAPL、苹果、腾讯、00700、TSLA、特斯拉、BABA、阿里巴巴、行情、K线、财报、PE、PB、ROE、分析师、目标价、期权、call、put、SEC、10-K、年报、季报、资金流、主力、机构持仓、新闻、涨幅排名、全市场、筛选、关键指标、MACD、RSI、KDJ、布林带、均线、金叉、死叉、超买、超卖、技术分析
Prerequisites
pip install requests
| 依赖 | 版本要求 | 用途 |
|---|
| requests | any | 所有 HTTP API 直连 |
极简依赖： 仅需 requests，所有数据源均为直连 HTTP API，零第三方数据封装。
市场代码规则
东财 secid 前缀（push2/push2his 用）
| 前缀 | 市场 | 示例 |
|---|
| 105 | 美股 NASDAQ | 105.AAPL, 105.TSLA |
| 106 | 美股 NYSE | 106.BABA, 106.JD |
| 107 | 美股 ETF/其他 | 107.CRSH |
| 116 | 港股 | 116.00700, 116.09988 |
如何判断 105/106/107？ 调 stock_search() 获取 MktNum 字段自动映射。
Yahoo Finance 代码格式
| 市场 | 格式 | 示例 |
|---|
| 美股 | 直接 ticker | AAPL, TSLA, BABA |
| 港股 | 四/五位数字 + .HK | 0700.HK, 9988.HK |
东财 datacenter SECUCODE 格式
| 市场 | 格式 | 示例 |
|---|
| 美股 NASDAQ | TICKER.O | AAPL.O, TSLA.O |
| 美股 NYSE | TICKER.N | BABA.N, JD.N |
| 港股 | CODE.HK | 00700.HK, 09988.HK |
共用 Helper 函数
Yahoo Finance crumb 管理器
Yahoo quoteSummary/options 等 v7/v10 接口需要 cookie+crumb。以下 helper 自动获取并缓存：
import requests
_yahoo_session = None
def get_yahoo_session() -> requests.Session:
"""获取带 crumb 的 Yahoo Finance session（自动缓存）"""
global _yahoo_session
if _yahoo_session and hasattr(_yahoo_session, '_crumb'):
return _yahoo_session
s = requests.Session()
s.headers['User-Agent'] = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36'
s.get('https://fc.yahoo.com', timeout=10)
r = s.get('https://query2.finance.yahoo.com/v1/test/getcrumb', timeout=10)
r.raise_for_status()
s._crumb = r.text
_yahoo_session = s
return s
def yahoo_quote_summary(symbol: str, modules: list[str]) -> dict:
"""Yahoo quoteSummary 统一查询"""
s = get_yahoo_session()
r = s.get(f'https://query2.finance.yahoo.com/v10/finance/quoteSummary/{symbol}', params={
'modules': ','.join(modules),
'crumb': s._crumb,
}, timeout=15)
r.raise_for_status()
results = r.json().get('quoteSummary', {}).get('result', [{}])
return results[0] if results else {}
东财数据中心统一查询
UA = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"
DATACENTER_URL = "https://datacenter-web.eastmoney.com/api/data/v1/get"
def eastmoney_datacenter(report_name: str, columns: str = "ALL",
filter_str: str = "", page_size: int = 50,
sort_columns: str = "", sort_types: str = "-1") -> list[dict]:
"""东财数据中心统一查询"""
params = {
"reportName": report_name, "columns": columns,
"filter": filter_str, "pageNumber": "1", "pageSize": str(page_size),
"sortColumns": sort_columns, "sortTypes": sort_types,
"source": "WEB", "client": "WEB",
}
r = requests.get(DATACENTER_URL, params=params, headers={"User-Agent": UA}, timeout=15)
d = r.json()
if d.get("result") and d["result"].get("data"):
return d["result"]["data"]
return []
Layer 1: 行情层
1.1 美股实时行情 — 新浪 + 腾讯
两个独立数据源，任一可用即可。新浪字段侧重价格成交，腾讯字段更全（含52周高低/市值/PE）。
import requests, re
def us_stock_quote_sina(ticker: str) -> dict:
"""
新浪美股行情 — 36字段
ticker: 纯字母，如 "AAPL", "TSLA", "BABA"
"""
url = f"https://hq.sinajs.cn/list=gb_{ticker.lower()}"
r = requests.get(url, headers={
"Referer": "https://finance.sina.com.cn/",
"User-Agent": UA,
}, timeout=10)
r.encoding = "gbk"
text = r.text
m = re.search(r'"(.+)"', text)
if not m:
return {}
fields = m.group(1).split(",")
if len(fields) < 30:
return {}
return {
"name": fields[0],
"price": float(fields[1]),
"change_pct": float(fields[2]),
"timestamp": fields[3],
"prev_close": float(fields[26]),
"open": float(fields[5]),
"high": float(fields[6]),
"low": float(fields[7]),
"volume": float(fields[10]) if fields[10] else 0,
"high_52w": float(fields[8]) if fields[8] else 0,
"low_52w": float(fields[9]) if fields[9] else 0,
"market_cap": float(fields[12]) if fields[12] else 0,
"eps": float(fields[13]) if fields[13] else 0,
"pe": float(fields[14]) if fields[14] else 0,
}
def us_stock_quote_tencent(ticker: str) -> dict:
"""
腾讯美股行情 — 71字段
ticker: 纯字母，如 "AAPL"
"""
url = f"https://qt.gtimg.cn/q=us{ticker.upper()}"
r = requests.get(url, timeout=10)
r.encoding = "gbk"
text = r.text
m = re.search(r'"(.+)"', text)
if not m:
return {}
fields = m.group(1).split("~")
if len(fields) < 50:
return {}
return {
"name": fields[1],
"name_en": fields[27],
"price": float(fields[3]) if fields[3] else 0,
"prev_close": float(fields[4]) if fields[4] else 0,
"open": float(fields[5]) if fields[5] else 0,
"volume": int(fields[6]) if fields[6] else 0,
"high": float(fields[33]) if fields[33] else 0,
"low": float(fields[34]) if fields[34] else 0,
"high_52w": float(fields[35]) if fields[35] else 0,
"low_52w": float(fields[36]) if fields[36] else 0,
"change_pct": float(fields[32]) if fields[32] else 0,
"market_cap": float(fields[44]) if fields[44] else 0,
"pe": float(fields[53]) if fields[53] else 0,
"pb": float(fields[56]) if fields[56] else 0,
"timestamp": fields[30],
}
1.2 港股实时行情 — 腾讯 + 新浪
def hk_stock_quote_tencent(code: str) -> dict:
"""
腾讯港股行情 — 78字段（最全）
code: 五位数字，如 "00700", "09988"
"""
url = f"https://qt.gtimg.cn/q=r_hk{code}"
r = requests.get(url, timeout=10)
r.encoding = "gbk"
text = r.text
m = re.search(r'"(.+)"', text)
if not m:
return {}
fields = m.group(1).split("~")
if len(fields) < 50:
return {}
return {
"name": fields[1],
"name_en": fields[2],
"price": float(fields[3]) if fields[3] else 0,
"prev_close": float(fields[4]) if fields[4] else 0,
"open": float(fields[5]) if fields[5] else 0,
"high": float(fields[33]) if fields[33] else 0,
"low": float(fields[34]) if fields[34] else 0,
"volume": int(fields[6]) if fields[6] else 0,
"amount": float(fields[37]) if fields[37] else 0,
"change_pct": float(fields[32]) if fields[32] else 0,
"pe": float(fields[39]) if fields[39] else 0,
"pb": float(fields[56]) if fields[56] else 0,
"high_52w": float(fields[35]) if fields[35] else 0,
"low_52w": float(fields[36]) if fields[36] else 0,
"market_cap": float(fields[44]) if fields[44] else 0,
"timestamp": fields[30],
}
def hk_stock_quote_sina(code: str) -> dict:
"""
新浪港股行情 — 25字段
code: 五位数字，如 "00700"
"""
url = f"https://hq.sinajs.cn/list=rt_hk{code}"
r = requests.get(url, headers={
"Referer": "https://finance.sina.com.cn/",
"User-Agent": UA,
}, timeout=10)
r.encoding = "gbk"
text = r.text
m = re.search(r'"(.+)"', text)
if not m:
return {}
fields = m.group(1).split(",")
if len(fields) < 15:
return {}
return {
"name_en": fields[0],
"name": fields[1],
"open": float(fields[2]) if fields[2] else 0,
"prev_close": float(fields[3]) if fields[3] else 0,
"high": float(fields[4]) if fields[4] else 0,
"low": float(fields[5]) if fields[5] else 0,
"price": float(fields[6]) if fields[6] else 0,
"change": float(fields[7]) if fields[7] else 0,
"change_pct": float(fields[8]) if fields[8] else 0,
"volume": float(fields[12]) if fields[12] else 0,
"amount": float(fields[11]) if fields[11] else 0,
}
1.3 东财 push2 实时行情 — 美股 + 港股
东财 push2 接口，通过 secid 统一查询美股/港股实时行情。优点：有中文名、换手率、涨跌幅，且 secid 可由 stock_search() 自动获取。
def stock_quote_eastmoney(ticker_or_code: str, secid_prefix: int = 105) -> dict:
"""
东财 push2 实时行情 — 美股+港股统一接口
美股: stock_quote_eastmoney("AAPL", 105) # NASDAQ
stock_quote_eastmoney("BABA", 106) # NYSE
港股: stock_quote_eastmoney("00700", 116)
返回: 最新价/开高低收/成交量/成交额/换手率/涨跌幅/中文名
secid_prefix 说明: 105=NASDAQ, 106=NYSE, 107=US_ETF, 116=港股
如不确定前缀，先调 stock_search() 获取 mkt_num
"""
url = "https://push2.eastmoney.com/api/qt/stock/get"
params = {
"secid": f"{secid_prefix}.{ticker_or_code}",
"fields": "f43,f44,f45,f46,f47,f48,f55,f57,f58,f59,f60,f170",
}
r = requests.get(url, params=params, timeout=10)
d = r.json().get("data")
if not d:
return {}
dec = d.get("f59", 3)
divisor = 10 ** dec
def _p(key):
v = d.get(key)
if v is None or v == "-":
return None
return round(v / divisor, dec)
return {
"code": d.get("f57"),
"name": d.get("f58"),
"price": _p("f43"),
"high": _p("f44"),
"low": _p("f45"),
"open": _p("f46"),
"volume": d.get("f47"),
"amount": d.get("f48"),
"turnover_rate": d.get("f55"),
"prev_close": _p("f60"),
"change_pct": round(d["f170"] / 100, 2) if d.get("f170") is not None else None,
}
Layer 2: K线层
2.1 美股 K 线 — 新浪（主）+ Yahoo（备）
两个独立数据源。新浪最长可回溯到 1984 年；Yahoo 适合需要复权数据的场景。
注意： 东财 push2his kline/get 端点实测不返回美股/港股数据（2026-05-20 验证），仅支持 A 股。美股/港股 K 线用新浪和 Yahoo。
def us_stock_kline_sina(ticker: str, num: int = 120) -> list[dict]:
"""
新浪美股日K — 可回溯到1984年
ticker: 如 "AAPL"
返回: [{date, open, high, low, close, volume}, ...]
"""
url = "https://stock.finance.sina.com.cn/usstock/api/jsonp.php/var/US_MinKService.getDailyK"
params = {"symbol": ticker.upper(), "num": num}
r = requests.get(url, params=params, headers={"Referer": "https://finance.sina.com.cn/"}, timeout=15)
text = r.text
import json
m = re.search(r'\((\[.+\])\)', text)
if not m:
return []
items = json.loads(m.group(1))
result = []
for item in items:
result.append({
"date": item.get("d"),
"open": float(item.get("o", 0)),
"high": float(item.get("h", 0)),
"low": float(item.get("l", 0)),
"close": float(item.get("c", 0)),
"volume": int(item.get("v", 0)),
})
return result
def stock_kline_yahoo(symbol: str, interval: str = "1d",
range_: str = "6mo") -> list[dict]:
"""
Yahoo Finance chart API — 美股+港股通用，零crumb
symbol: "AAPL" (美股) 或 "0700.HK" (港股)
interval: "1d", "1wk", "1mo", "5m", "15m", "1h"
range_: "1d", "5d", "1mo", "3mo", "6mo", "1y", "5y", "max"
返回: [{date, open, high, low, close, volume}, ...]
"""
url = f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}"
params = {"interval": interval, "range": range_}
r = requests.get(url, params=params, headers={
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"
}, timeout=15)
r.raise_for_status()
d = r.json()
chart = d.get("chart", {}).get("result", [{}])[0]
timestamps = chart.get("timestamp", [])
quote = chart.get("indicators", {}).get("quote", [{}])[0]
from datetime import datetime
result = []
for i, ts in enumerate(timestamps):
result.append({
"date": datetime.fromtimestamp(ts).strftime("%Y-%m-%d %H:%M") if "m" in interval or "h" in interval else datetime.fromtimestamp(ts).strftime("%Y-%m-%d"),
"open": round(quote["open"][i], 2) if quote["open"][i] else 0,
"high": round(quote["high"][i], 2) if quote["high"][i] else 0,
"low": round(quote["low"][i], 2) if quote["low"][i] else 0,
"close": round(quote["close"][i], 2) if quote["close"][i] else 0,
"volume": int(quote["volume"][i]) if quote["volume"][i] else 0,
})
return result
2.2 港股 K 线 — Yahoo（唯一可用源）
港股 K 线只有 Yahoo 一个可用源（新浪港股K线已失效，东财 push2his 不返回港股K线数据）。
Layer 3: 技术指标层
基于 K 线 OHLCV 数据的纯 Python 技术指标计算，零额外依赖。
使用方式： 先调 K 线函数获取数据，再传入技术指标函数：
klines = us_stock_kline_sina("AAPL", 120)
macd = calc_macd(klines)
rsi = calc_rsi(klines)
3.1 移动平均线 MA / EMA
def _ema(values: list[float], period: int) -> list[float]:
"""EMA 指数移动平均（内部辅助）"""
result = [values[0]]
k = 2 / (period + 1)
for v in values[1:]:
result.append(v * k + result[-1] * (1 - k))
return result
def calc_ma(klines: list[dict], periods: list[int] = None) -> list[dict]:
"""
移动平均线 MA + EMA
klines: K线数据 [{date, open, high, low, close, volume}, ...]
periods: 周期列表，默认 [5, 10, 20, 60]
返回: [{date, close, ma5, ma10, ma20, ma60, ema12, ema26}, ...]
"""
if periods is None:
periods = [5, 10, 20, 60]
closes = [k["close"] for k in klines]
ema12 = _ema(closes, 12)
ema26 = _ema(closes, 26)
result = []
for i, k in enumerate(klines):
row = {"date": k["date"], "close": k["close"]}
for p in periods:
if i >= p - 1:
row[f"ma{p}"] = round(sum(closes[i - p + 1:i + 1]) / p, 4)
else:
row[f"ma{p}"] = None
row["ema12"] = round(ema12[i], 4)
row["ema26"] = round(ema26[i], 4)
result.append(row)
return result
3.2 MACD
def calc_macd(klines: list[dict], fast: int = 12, slow: int = 26,
signal: int = 9) -> list[dict]:
"""
MACD (Moving Average Convergence Divergence)
klines: K线数据
fast/slow/signal: 快线/慢线/信号线周期（默认 12/26/9）
返回: [{date, close, dif, dea, macd_hist}, ...]
dif = EMA(fast) - EMA(slow) 金叉/死叉看 dif 穿越 dea
dea = EMA(signal) of dif 信号线
macd_hist = (dif - dea) * 2 柱状图（红涨绿跌）
"""
closes = [k["close"] for k in klines]
ema_fast = _ema(closes, fast)
ema_slow = _ema(closes, slow)
dif = [round(f - s, 4) for f, s in zip(ema_fast, ema_slow)]
dea = _ema(dif, signal)
result = []
for i, k in enumerate(klines):
result.append({
"date": k["date"],
"close": k["close"],
"dif": round(dif[i], 4),
"dea": round(dea[i], 4),
"macd_hist": round((dif[i] - dea[i]) * 2, 4),
})
return result
3.3 RSI
def calc_rsi(klines: list[dict],
periods: list[int] = None) -> list[dict]:
"""
RSI (Relative Strength Index)
klines: K线数据
periods: 周期列表（默认 [6, 12, 24]）
返回: [{date, close, rsi6, rsi12, rsi24}, ...]
RSI > 70 超买区（可能回调）
RSI < 30 超卖区（可能反弹）
"""
if periods is None:
periods = [6, 12, 24]
closes = [k["close"] for k in klines]
changes = [0.0] + [closes[i] - closes[i - 1] for i in range(1, len(closes))]
gains = [max(c, 0) for c in changes]
losses = [max(-c, 0) for c in changes]
result = []
for i, k in enumerate(klines):
row = {"date": k["date"], "close": k["close"]}
for p in periods:
if i < p:
row[f"rsi{p}"] = None
continue
avg_gain = sum(gains[i - p + 1:i + 1]) / p
avg_loss = sum(losses[i - p + 1:i + 1]) / p
if avg_loss == 0:
row[f"rsi{p}"] = 100.0
else:
rs = avg_gain / avg_loss
row[f"rsi{p}"] = round(100 - 100 / (1 + rs), 2)
result.append(row)
return result
3.4 KDJ
def calc_kdj(klines: list[dict], n: int = 9,
m1: int = 3, m2: int = 3) -> list[dict]:
"""
KDJ 随机指标
klines: K线数据
n: RSV 周期（默认9）
m1/m2: K/D 平滑系数（默认3/3）
返回: [{date, close, k, d, j}, ...]
K/D > 80 超买，K/D < 20 超卖
J > 100 或 J < 0 为极端信号
金叉: K 上穿 D；死叉: K 下穿 D
"""
k_val, d_val = 50.0, 50.0
result = []
for i, kline in enumerate(klines):
if i < n - 1:
result.append({"date": kline["date"], "close": kline["close"],
"k": None, "d": None, "j": None})
continue
window = klines[i - n + 1:i + 1]
high_n = max(w["high"] for w in window)
low_n = min(w["low"] for w in window)
rsv = (kline["close"] - low_n) / (high_n - low_n) * 100 if high_n != low_n else 50.0
k_val = (1 / m1) * rsv + (1 - 1 / m1) * k_val
d_val = (1 / m2) * k_val + (1 - 1 / m2) * d_val
j_val = 3 * k_val - 2 * d_val
result.append({
"date": kline["date"],
"close": kline["close"],
"k": round(k_val, 2),
"d": round(d_val, 2),
"j": round(j_val, 2),
})
return result
3.5 布林带
def calc_boll(klines: list[dict], period: int = 20,
num_std: float = 2.0) -> list[dict]:
"""
布林带 (Bollinger Bands)
klines: K线数据
period: 中轨 MA 周期（默认20）
num_std: 标准差倍数（默认2）
返回: [{date, close, upper, middle, lower, bandwidth}, ...]
价格触及 upper → 可能超买
价格触及 lower → 可能超卖
bandwidth 收窄 → 即将变盘
"""
closes = [k["close"] for k in klines]
result = []
for i, k in enumerate(klines):
if i < period - 1:
result.append({"date": k["date"], "close": k["close"],
"upper": None, "middle": None, "lower": None,
"bandwidth": None})
continue
window = closes[i - period + 1:i + 1]
ma = sum(window) / period
std = (sum((x - ma) ** 2 for x in window) / period) ** 0.5
upper = ma + num_std * std
lower = ma - num_std * std
result.append({
"date": k["date"],
"close": k["close"],
"upper": round(upper, 4),
"middle": round(ma, 4),
"lower": round(lower, 4),
"bandwidth": round((upper - lower) / ma * 100, 2) if ma else None,
})
return result
Layer 4: 基本面层
4.1 财报三表 — 东财 datacenter
东财 datacenter 提供美股/港股的资产负债表、利润表、现金流量表，中文字段名，按科目行展开。
def financial_statements_eastmoney(secucode: str, statement: str = "balance",
page_size: int = 200) -> list[dict]:
"""
东财 datacenter 财报三表
secucode: "AAPL.O" (NASDAQ) / "BABA.N" (NYSE) / "00700.HK" (港股)
statement: "balance" / "income" / "cashflow"
返回: [{ITEM_NAME, AMOUNT, YOY_RATIO, REPORT, REPORT_DATE, ...}, ...]
注意: 数据按科目行展开，每行一个科目（如"流动资产合计"、"营业收入"等），
同一期报告有多行。用 REPORT_DATE 分组可还原整张报表。
"""
report_map = {
"balance": {"us": "RPT_USF10_FN_BALANCE", "hk": "RPT_HKF10_FN_BALANCE"},
"income": {"us": "RPT_USF10_FN_INCOME", "hk": "RPT_HKF10_FN_INCOME"},
"cashflow": {"us": "RPT_USSK_FN_CASHFLOW", "hk": "RPT_HKSK_FN_CASHFLOW"},
}
market = "hk" if secucode.endswith(".HK") else "us"
report_name = report_map[statement][market]
return eastmoney_datacenter(
report_name=report_name,
filter_str=f'(SECUCODE="{secucode}")',
page_size=page_size,
sort_columns="REPORT_DATE",
sort_types="-1",
)
4.2 关键财务指标(中文) — 东财 GMAININDICATOR
东财 datacenter 的 GMAININDICATOR 报表，提供中文关键财务指标概览。美股 49 字段、港股 75 字段，包含 ROE/ROA/EPS/毛利率/资产负债率/流动比率等，按季度报告。
def key_indicators_eastmoney(secucode: str, page_size: int = 4) -> list[dict]:
"""
东财 GMAININDICATOR 关键财务指标（中文）
secucode: "AAPL.O" (NASDAQ) / "BABA.N" (NYSE) / "00700.HK" (港股)
page_size: 返回最近几期报告（默认4期=一年）
返回: [{REPORT_DATE, OPERATE_INCOME, BASIC_EPS, ROE_AVG, ROA, ...}, ...]
美股核心字段(49): OPERATE_INCOME(营收), GROSS_PROFIT(毛利), GROSS_PROFIT_RATIO(毛利率%),
PARENT_HOLDER_NETPROFIT(归母净利), NET_PROFIT_RATIO(净利率%), BASIC_EPS, DILUTED_EPS,
ROE_AVG(平均ROE%), ROA(%), CURRENT_RATIO(流动比率), DEBT_ASSET_RATIO(资产负债率%),
OPERATE_INCOME_YOY(营收同比%), BASIC_EPS_YOY(EPS同比%)
港股额外字段(75): BPS(每股净资产), ROIC(投入资本回报率), EQUITY_RATIO(产权比率),
HOLDER_PROFIT(股东应占溢利), OCF_SALES(经营现金流/营收%), DPS_HKD(每股股息),
DIVI_RATIO(股息率%), PER_NETCASH_OPERATE(每股经营现金流)
"""
market = "hk" if secucode.endswith(".HK") else "us"
report_name = f"RPT_{'HK' if market == 'hk' else 'US'}F10_FN_GMAININDICATOR"
return eastmoney_datacenter(
report_name=report_name,
filter_str=f'(SECUCODE="{secucode}")',
page_size=page_size,
sort_columns="REPORT_DATE",
sort_types="-1",
)
4.3 关键财务指标(英文) — Yahoo quoteSummary
Yahoo quoteSummary 的 financialData + defaultKeyStatistics 模块提供最核心的估值指标。
def key_statistics(symbol: str) -> dict:
"""
Yahoo 关键财务指标
symbol: "AAPL" (美股) 或 "0700.HK" (港股)
返回: PE/PB/EV/EBITDA/利润率/目标价/ROE/Beta 等
"""
data = yahoo_quote_summary(symbol, ["financialData", "defaultKeyStatistics", "summaryDetail"])
fd = data.get("financialData", {})
ks = data.get("defaultKeyStatistics", {})
sd = data.get("summaryDetail", {})
def _val(d, key):
v = d.get(key, {})
return v.get("raw") if isinstance(v, dict) else v
return {
"current_price": _val(fd, "currentPrice"),
"target_high": _val(fd, "targetHighPrice"),
"target_low": _val(fd, "targetLowPrice"),
"target_mean": _val(fd, "targetMeanPrice"),
"recommendation": fd.get("recommendationKey"),
"trailing_pe": _val(sd, "trailingPE"),
"forward_pe": _val(ks, "forwardPE"),
"peg_ratio": _val(ks, "pegRatio"),
"price_to_book": _val(ks, "priceToBook"),
"enterprise_value": _val(ks, "enterpriseValue"),
"ev_to_ebitda": _val(ks, "enterpriseToEbitda"),
"ev_to_revenue": _val(ks, "enterpriseToRevenue"),
"profit_margin": _val(ks, "profitMargins"),
"operating_margin": _val(fd, "operatingMargins"),
"gross_margin": _val(fd, "grossMargins"),
"return_on_equity": _val(fd, "returnOnEquity"),
"return_on_assets": _val(fd, "returnOnAssets"),
"earnings_growth": _val(fd, "earningsGrowth"),
"revenue_growth": _val(fd, "revenueGrowth"),
"beta": _val(ks, "beta"),
"short_ratio": _val(ks, "shortRatio"),
"dividend_yield": _val(sd, "dividendYield"),
"payout_ratio": _val(ks, "payoutRatio"),
"market_cap": _val(sd, "marketCap"),
"total_revenue": _val(fd, "totalRevenue"),
"total_cash": _val(fd, "totalCash"),
"total_debt": _val(fd, "totalDebt"),
}
4.4 分析师预期与评级 — Yahoo quoteSummary
def analyst_estimates(symbol: str) -> dict:
"""
Yahoo 分析师预期 — EPS预测/评级趋势/升降级历史
symbol: "AAPL" 或 "0700.HK"
"""
data = yahoo_quote_summary(symbol, [
"earningsTrend", "recommendationTrend", "upgradeDowngradeHistory",
"earnings", "earningsHistory",
])
et = data.get("earningsTrend", {}).get("trend", [])
eps_trend = []
for t in et:
eps_trend.append({
"period": t.get("period"),
"end_date": t.get("endDate"),
"eps_estimate": t.get("earningsEstimate", {}).get("avg", {}).get("raw"),
"eps_high": t.get("earningsEstimate", {}).get("high", {}).get("raw"),
"eps_low": t.get("earningsEstimate", {}).get("low", {}).get("raw"),
"revenue_estimate": t.get("revenueEstimate", {}).get("avg", {}).get("raw"),
"num_analysts": t.get("earningsEstimate", {}).get("numberOfAnalysts", {}).get("raw"),
})
rt = data.get("recommendationTrend", {}).get("trend", [])
rating_trend = []
for r_ in rt:
rating_trend.append({
"period": r_.get("period"),
"strong_buy": r_.get("strongBuy"),
"buy": r_.get("buy"),
"hold": r_.get("hold"),
"sell": r_.get("sell"),
"strong_sell": r_.get("strongSell"),
})
udh = data.get("upgradeDowngradeHistory", {}).get("history", [])[:20]
upgrades = []
for u in udh:
upgrades.append({
"date": u.get("epochGradeDate"),
"firm": u.get("firm"),
"to_grade": u.get("toGrade"),
"from_grade": u.get("fromGrade"),
"action": u.get("action"),
})
return {
"eps_trend": eps_trend,
"rating_trend": rating_trend,
"upgrade_downgrade": upgrades,
}
4.5 机构持仓 — Yahoo quoteSummary
def institutional_holders(symbol: str) -> dict:
"""
Yahoo 机构持仓 — 前10大机构 + 内部人持股比例
symbol: "AAPL" 或 "0700.HK"
"""
data = yahoo_quote_summary(symbol, ["institutionOwnership", "majorHoldersBreakdown"])
mhb = data.get("majorHoldersBreakdown", {})
def _val(d, key):
v = d.get(key, {})
return v.get("raw") if isinstance(v, dict) else v
overview = {
"insiders_pct": _val(mhb, "insidersPercentHeld"),
"institutions_pct": _val(mhb, "institutionsPercentHeld"),
"institutions_float_pct": _val(mhb, "institutionsFloatPercentHeld"),
"institutions_count": _val(mhb, "institutionsCount"),
}
io = data.get("institutionOwnership", {}).get("ownershipList", [])
top_holders = []
for h in io[:10]:
top_holders.append({
"name": h.get("organization"),
"shares": _val(h, "position"),
"value": _val(h, "value"),
"pct_held": _val(h, "pctHeld"),
"report_date": h.get("reportDate", {}).get("fmt") if isinstance(h.get("reportDate"), dict) else None,
})
return {"overview": overview, "top_holders": top_holders}
4.6 年度/季度财报明细 — Yahoo quoteSummary
东财 datacenter 按科目行展开，Yahoo 直接返回完整报表结构，两个互补。
def financial_statements_yahoo(symbol: str,
quarterly: bool = False) -> dict:
"""
Yahoo 财报三表 — 结构化完整报表
symbol: "AAPL" 或 "0700.HK"
quarterly: False=年度, True=季度
返回: {"income": [...], "balance": [...], "cashflow": [...]}
"""
suffix = "Quarterly" if quarterly else ""
data = yahoo_quote_summary(symbol, [
f"incomeStatementHistory{suffix}",
f"balanceSheetHistory{suffix}",
f"cashflowStatementHistory{suffix}",
])
def _extract(statements):
result = []
for stmt in statements:
row = {}
for k, v in stmt.items():
if isinstance(v, dict) and "raw" in v:
row[k] = v["raw"]
elif isinstance(v, dict) and "fmt" in v:
row[k] = v["fmt"]
else:
row[k] = v
result.append(row)
return result
income_key = f"incomeStatementHistory{suffix}"
balance_key = f"balanceSheetHistory{suffix}"
cashflow_key = f"cashflowStatementHistory{suffix}"
return {
"income": _extract(data.get(income_key, {}).get("incomeStatementHistory", [])),
"balance": _extract(data.get(balance_key, {}).get("balanceSheetStatements", [])),
"cashflow": _extract(data.get(cashflow_key, {}).get("cashflowStatements", [])),
}
Layer 5: 资金面层
5.1 日级资金流 — 东财 push2his
def fund_flow_daily(ticker_or_code: str, secid_prefix: int = 105,
limit: int = 100) -> list[dict]:
"""
东财 push2his 日级资金流 — 主力/大单/中单/小单净流入
美股: fund_flow_daily("AAPL", 105) # NASDAQ
fund_flow_daily("BABA", 106) # NYSE
港股: fund_flow_daily("00700", 116)
返回: [{date, main_net, big_net, mid_net, small_net, main_pct, ...}, ...]
"""
url = "https://push2his.eastmoney.com/api/qt/stock/fflow/daykline/get"
params = {
"secid": f"{secid_prefix}.{ticker_or_code}",
"klt": 101,
"fields1": "f1,f2,f3,f7",
"fields2": "f51,f52,f53,f54,f55,f56,f57",
"lmt": limit,
}
r = requests.get(url, params=params, timeout=15)
d = r.json()
data = d.get("data")
if not data or not data.get("klines"):
return []
result = []
for line in data["klines"]:
parts = line.split(",")
result.append({
"date": parts[0],
"main_net": float(parts[1]),
"small_net": float(parts[2]),
"mid_net": float(parts[3]),
"big_net": float(parts[4]),
"super_big_net": float(parts[5]),
"main_pct": float(parts[6]) if len(parts) > 6 and parts[6] else 0,
})
return result
Layer 6: 期权层
6.1 期权链 — Yahoo Finance
def options_chain(symbol: str, expiration: int = None) -> dict:
"""
Yahoo 期权链 — calls + puts 完整数据（仅美股）
symbol: "AAPL", "TSLA" 等美股 ticker
⚠️ 港股(如0700.HK)期权不在Yahoo覆盖范围，调用会返回空列表
expiration: Unix timestamp (不传则返回最近到期日 + 所有到期日列表)
返回: {"expiration_dates": [...], "calls": [...], "puts": [...]}
"""
s = get_yahoo_session()
params = {"crumb": s._crumb}
if expiration:
params["date"] = expiration
r = s.get(f"https://query2.finance.yahoo.com/v7/finance/options/{symbol}",
params=params, timeout=15)
r.raise_for_status()
oc = r.json().get("optionChain", {}).get("result", [{}])[0]
exp_dates = oc.get("expirationDates", [])
options = oc.get("options", [{}])[0] if oc.get("options") else {}
def _parse_options(opts):
result = []
for o in opts:
def _val(key):
v = o.get(key, {})
return v.get("raw") if isinstance(v, dict) else v
result.append({
"strike": _val("strike"),
"last_price": _val("lastPrice"),
"bid": _val("bid"),
"ask": _val("ask"),
"volume": _val("volume"),
"open_interest": _val("openInterest"),
"implied_volatility": _val("impliedVolatility"),
"in_the_money": o.get("inTheMoney"),
"expiration": o.get("expiration", {}).get("fmt") if isinstance(o.get("expiration"), dict) else None,
"contract_symbol": o.get("contractSymbol"),
})
return result
return {
"expiration_dates": exp_dates,
"calls": _parse_options(options.get("calls", [])),
"puts": _parse_options(options.get("puts", [])),
"underlying_price": oc.get("quote", {}).get("regularMarketPrice"),
}
Layer 7: SEC Filing 层（仅美股）
7.1 SEC Filing 列表 — EDGAR submissions
SEC_HEADERS = {"User-Agent": "SimonLin global-stock-data/1.0 (contact@example.com)"}
def sec_filings(cik: str, form_type: str = None) -> dict:
"""
SEC EDGAR Filing 列表
cik: CIK号（10位补零），如 "0000320193" (Apple)
可通过 ticker_to_cik() 从 ticker 转换
form_type: 筛选类型，如 "10-K", "10-Q", "8-K"（不传返回全部）
返回: {"company_name": ..., "filings": [{form, date, accession_number, primary_document}, ...]}
"""
url = f"https://data.sec.gov/submissions/CIK{cik}.json"
r = requests.get(url, headers=SEC_HEADERS, timeout=15)
r.raise_for_status()
data = r.json()
recent = data.get("filings", {}).get("recent", {})
forms = recent.get("form", [])
dates = recent.get("filingDate", [])
accessions = recent.get("accessionNumber", [])
primary_docs = recent.get("primaryDocument", [])
descriptions = recent.get("primaryDocDescription", [])
filings = []
for i in range(len(forms)):
if form_type and forms[i] != form_type:
continue
filings.append({
"form": forms[i],
"date": dates[i],
"accession_number": accessions[i],
"primary_document": primary_docs[i] if i < len(primary_docs) else "",
"description": descriptions[i] if i < len(descriptions) else "",
"url": f"https://www.sec.gov/Archives/edgar/data/{int(cik)}/{accessions[i].replace('-', '')}/{primary_docs[i]}" if i < len(primary_docs) and primary_docs[i] else "",
})
return {
"company_name": data.get("name"),
"cik": cik,
"ticker": data.get("tickers", [""])[0] if data.get("tickers") else "",
"filings": filings[:50],
}
7.2 SEC XBRL 结构化财务数据 — EDGAR companyfacts
覆盖 503 个 GAAP 指标，可精确提取多年营收/净利/EPS/资产/负债等。
def sec_xbrl_facts(cik: str, metrics: list[str] = None) -> dict:
"""
SEC EDGAR XBRL 结构化财务数据
cik: CIK号（10位补零）
metrics: 要提取的指标名，如 ["RevenueFromContractWithCustomerExcludingAssessedTax",
"NetIncomeLoss", "EarningsPerShareDiluted"]
不传则返回所有可用指标名列表
返回: {"company": ..., "metrics": {"Revenue": [{end, val, form, filed}, ...], ...}}
"""
url = f"https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json"
r = requests.get(url, headers=SEC_HEADERS, timeout=15)
r.raise_for_status()
facts = r.json()
us_gaap = facts.get("facts", {}).get("us-gaap", {})
if not metrics:
available = []
for k, v in us_gaap.items():
label = v.get("label", k)
units = list(v.get("units", {}).keys())
available.append({"name": k, "label": label, "units": units})
return {
"company": facts.get("entityName"),
"total_metrics": len(available),
"available_metrics": available,
}
result = {}
for metric_name in metrics:
metric = us_gaap.get(metric_name, {})
if not metric:
result[metric_name] = []
continue
units = metric.get("units", {})
unit_key = "USD" if "USD" in units else list(units.keys())[0] if units else None
if not unit_key:
result[metric_name] = []
continue
entries = units[unit_key]
filtered = [e for e in entries if e.get("form") in ("10-K", "10-Q")]
result[metric_name] = [{
"end": e.get("end"),
"val": e.get("val"),
"form": e.get("form"),
"filed": e.get("filed"),
"fy": e.get("fy"),
"fp": e.get("fp"),
} for e in filtered[-20:]]
return {
"company": facts.get("entityName"),
"metrics": result,
}
常用 XBRL 指标名速查：
| 指标 | XBRL 名 |
|---|
| 营业收入 | RevenueFromContractWithCustomerExcludingAssessedTax 或 Revenues |
| 净利润 | NetIncomeLoss |
| 稀释 EPS | EarningsPerShareDiluted |
| 基本 EPS | EarningsPerShareBasic |
| 总资产 | Assets |
| 总负债 | Liabilities |
| 股东权益 | StockholdersEquity |
| 经营现金流 | NetCashProvidedByOperatingActivities |
| 研发费用 | ResearchAndDevelopmentExpense |
| 股份回购 | PaymentsForRepurchaseOfCommonStock |
| 股息支付 | PaymentsOfDividends |
Layer 8: 工具层
8.1 股票搜索 — 东财 search API
def stock_search(keyword: str, count: int = 10) -> list[dict]:
"""
东财股票搜索 — 支持中英文，返回代码+市场+中文名
keyword: "AAPL" / "苹果" / "Tencent" / "00700" / "特斯拉"
返回: [{code, name, mkt_num, market_name, security_type}, ...]
mkt_num 即 push2/push2his 的 secid 前缀:
105=NASDAQ, 106=NYSE, 107=美股ETF, 116=港股
"""
url = "https://searchapi.eastmoney.com/api/suggest/get"
params = {
"input": keyword,
"type": 14,
"token": "D43BF722C8E33BDC906FB84D85E326E8",
"count": count,
}
r = requests.get(url, params=params, timeout=10)
d = r.json()
suggestions = d.get("QuotationCodeTable", {}).get("Data", [])
result = []
for s in suggestions:
mkt = s.get("MktNum", "")
if str(mkt) not in ("105", "106", "107", "116"):
continue
market_map = {"105": "NASDAQ", "106": "NYSE", "107": "US_OTHER", "116": "HK"}
result.append({
"code": s.get("Code"),
"name": s.get("Name"),
"mkt_num": int(mkt),
"market_name": market_map.get(str(mkt), str(mkt)),
"security_type": s.get("SecurityTypeName"),
})
return result
8.2 股票新闻 — Yahoo Finance search
def stock_news(keyword: str, count: int = 10) -> list[dict]:
"""
Yahoo Finance 新闻搜索
keyword: 股票代码或关键词，如 "AAPL", "Tesla", "0700.HK"
返回: [{title, publisher, link, publish_time, thumbnail}, ...]
注意: 需要先获取 Yahoo cookie 才能调用，否则返回 400
"""
s = requests.Session()
s.headers["User-Agent"] = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"
s.get("https://fc.yahoo.com", timeout=10)
url = "https://query2.finance.yahoo.com/v1/finance/search"
params = {"q": keyword, "quotesCount": 0, "newsCount": count}
r = s.get(url, params=params, timeout=10)
r.raise_for_status()
news = r.json().get("news", [])
result = []
for n in news:
result.append({
"title": n.get("title"),
"publisher": n.get("publisher"),
"link": n.get("link"),
"publish_time": n.get("providerPublishTime"),
"thumbnail": n.get("thumbnail", {}).get("resolutions", [{}])[0].get("url") if n.get("thumbnail") else None,
})
return result
8.3 Ticker → CIK 映射 — SEC EDGAR（仅美股）
_cik_cache = None
def ticker_to_cik(ticker: str) -> dict:
"""
SEC EDGAR ticker → CIK 映射
ticker: 如 "AAPL", "TSLA", "MSFT"
返回: {"ticker": "AAPL", "cik": "0000320193", "company": "Apple Inc."}
首次调用下载完整映射表(~10KB JSON, 10000+公司)并缓存。
"""
global _cik_cache
if not _cik_cache:
r = requests.get("https://www.sec.gov/files/company_tickers.json",
headers=SEC_HEADERS, timeout=15)
r.raise_for_status()
_cik_cache = r.json()
ticker_upper = ticker.upper()
for _, v in _cik_cache.items():
if v.get("ticker") == ticker_upper:
cik_str = str(v["cik_str"]).zfill(10)
return {
"ticker": ticker_upper,
"cik": cik_str,
"company": v.get("title"),
}
return {}
8.4 全市场股票列表 — 东财 push2
def market_stock_list(market: str = "us_nasdaq", sort_field: str = "f3",
sort_desc: bool = True, page: int = 1,
page_size: int = 20) -> dict:
"""
东财 push2 全市场股票列表 — 涨跌幅/成交量/成交额排名
market: "us_nasdaq" (m:105), "us_nyse" (m:106), "hk" (m:116)
sort_field: 排序字段
f3=涨跌幅, f5=成交量, f6=成交额, f2=最新价, f7=振幅, f15=最高, f16=最低
sort_desc: True=降序(默认), False=升序
page/page_size: 分页（默认第1页，每页20条）
返回: {"total": 5925, "stocks": [{code, name, price, change_pct, volume, ...}, ...]}
典型用途:
- 今日涨幅 TOP 20: market_stock_list("us_nasdaq", "f3", True)
- 今日跌幅 TOP 20: market_stock_list("us_nasdaq", "f3", False)
- 成交量 TOP 20: market_stock_list("hk", "f5", True)
- 遍历全市场: 循环 page=1..N, 每页100条做筛选
"""
market_map = {"us_nasdaq": "m:105", "us_nyse": "m:106", "us_etf": "m:107", "hk": "m:116"}
fs = market_map.get(market, market)
url = "https://push2.eastmoney.com/api/qt/clist/get"
params = {
"fs": fs,
"fields": "f2,f3,f4,f5,f6,f7,f12,f14,f15,f16,f17,f18",
"pn": page,
"pz": page_size,
"fid": sort_field,
"po": 1 if sort_desc else 0,
}
r = requests.get(url, params=params, timeout=15)
d = r.json()
data = d.get("data", {})
total = data.get("total", 0)
diff = data.get("diff", [])
if isinstance(diff, dict):
diff = list(diff.values())
stocks = []
for item in diff:
stocks.append({
"code": item.get("f12"),
"name": item.get("f14"),
"price": item.get("f2"),
"change_pct": round(item["f3"] / 100, 2) if item.get("f3") is not None else None,
"change_amount": item.get("f4"),
"volume": item.get("f5"),
"amount": item.get("f6"),
"amplitude": round(item["f7"] / 100, 2) if item.get("f7") is not None else None,
"high": item.get("f15"),
"low": item.get("f16"),
"open": item.get("f17"),
"prev_close": item.get("f18"),
})
return {"total": total, "stocks": stocks}
数据源优先级
| 场景 | 第一优先 | 备选 | 说明 |
|---|
| 美股行情 | 新浪 gb_XXXX | 腾讯 / 东财 push2 | 新浪有中文名+EPS+PE |
| 港股行情 | 腾讯 r_hkXXXXX | 新浪 / 东财 push2 | 腾讯字段最全(78个) |
| 美股K线 | 新浪 | Yahoo chart | 新浪回溯至1984年；Yahoo支持多周期 |
| 港股K线 | Yahoo chart | — | 新浪港股K线已失效；push2his不返回港股K线 |
| 财报三表(中文) | 东财 datacenter | — | 中文科目名，按行展开 |
| 财报三表(结构化) | Yahoo quoteSummary | — | 英文，完整报表结构 |
| 关键指标(中文) | 东财 GMAININDICATOR | — | ROE/ROA/EPS/毛利率/资产负债率 (美49/港75字段) |
| 关键指标(英文) | Yahoo quoteSummary | — | PE/PB/EV/利润率/目标价 |
| 分析师预期 | Yahoo quoteSummary | — | EPS预测+评级+升降级 |
| 机构持仓 | Yahoo quoteSummary | — | 前10大机构+内部人 |
| 资金流 | 东财 push2his | — | 日级主力/大单/中单/小单 |
| 期权链 | Yahoo options | — | 仅美股；港股期权需港交所专有接口 |
| SEC Filing | EDGAR | — | 官方数据，仅美股 |
| XBRL财务 | EDGAR | — | 503个GAAP指标 |
| 搜索 | 东财 search | Yahoo search | 东财有 secid 映射 |
| 新闻 | Yahoo search | — | 唯一稳定的新闻源 |
| 全市场列表 | 东财 push2 clist | — | 涨跌幅/成交量排名，美股5925+港股18000+ |
数据源汇总
| 数据源 | 协议 | 鉴权 | 覆盖 |
|---|
| 东财 push2 | HTTPS | 零 | 美股+港股 实时行情+全市场列表 |
| 东财 push2his | HTTPS | 零 | 美股+港股 资金流（K线仅A股，不覆盖美股/港股） |
| 东财 datacenter | HTTPS | 零 | 美股+港股 财报三表+GMAININDICATOR关键指标 |
| 东财 search API | HTTPS | 零 | 全球股票搜索+secid映射 |
| Yahoo Finance | HTTPS | cookie+crumb(自动) | 美股+港股 全品类 |
| 新浪财经 | HTTP | 零 | 美股+港股 行情、美股K线 |
| 腾讯财经 | HTTPS | 零 | 美股+港股 行情 |
| SEC EDGAR | HTTPS | 零(需UA) | 美股 Filing+XBRL |
📦 https://github.com/simonlin1212/global-stock-data — Star ⭐ 是最好的支持
