def stock_us_daily(symbol: str = "AAPL", adjust: str = "") -> pd.DataFrame:
"""
新浪财经-美股
http://finance.sina.com.cn/stock/usstock/sector.shtml
备注：CIEN 新浪复权因子错误
:param symbol: 可以使用 get_us_stock_name 获取
:type symbol: str
:param adjust: "": 返回未复权的数据 ; qfq: 返回前复权后的数据; qfq-factor: 返回前复权因子和调整;
:type adjust: str
:return: 指定 adjust 的数据
:rtype: pandas.DataFrame
"""
res = requests.get(f"https://finance.sina.com.cn/staticdata/us/{symbol}")
js_code = py_mini_racer.MiniRacer()
js_code.eval(zh_js_decode)
dict_list = js_code.call(
"d", res.text.split("=")[1].split(";")[0].replace('"', "")
) # 执行js解密代码
data_df = pd.DataFrame(dict_list)
data_df.index = pd.to_datetime(data_df["date"])
del data_df["amount"]
del data_df["date"]
data_df = data_df.astype("float")
res = requests.get(us_sina_stock_hist_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split("=")[1].split("\n")[0])["data"])
qfq_factor_df.rename(columns={"c": "adjust", "d": "date", "f": "qfq_factor", }, inplace=True)
qfq_factor_df.index = pd.to_datetime(qfq_factor_df["date"])
del qfq_factor_df["date"]
# 处理复权因子
temp_date_range = pd.date_range("1900-01-01", qfq_factor_df.index[0].isoformat())
temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range)
new_range = pd.merge(
temp_df, qfq_factor_df, left_index=True, right_index=True, how="left"
)
new_range = new_range.fillna(method="ffill")
new_range = new_range.iloc[:, [1, 2]]
if adjust == "qfq":
if len(new_range) == 1:
new_range.index.values[0] = pd.to_datetime(str(data_df.index.date[0]))
temp_df = pd.merge(
data_df, new_range, left_index=True, right_index=True, how="left"
)
temp_df.fillna(method="ffill", inplace=True)
temp_df.fillna(method="bfill", inplace=True)
temp_df = temp_df.astype(float)
temp_df["open"] = temp_df["open"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df["high"] = temp_df["high"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df["close"] = temp_df["close"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df["low"] = temp_df["low"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df = temp_df.apply(lambda x: round(x, 4))
temp_df = temp_df.astype("float")
return temp_df.iloc[:, :-2]
if adjust == "qfq-factor":
return qfq_factor_df
if adjust == "":
return data_df
def stock_us_daily(symbol: str = "AAPL", adjust: str = "") -> pd.DataFrame:
"""
新浪财经-美股-个股的历史行情数据
:param symbol: 可以使用 get_us_stock_name 获取
:type symbol: str
:param adjust: "": 返回未复权的数据 ; qfq: 返回前复权后的数据; qfq-factor: 返回前复权因子和调整;
:type adjust: str
:return: 指定 adjust 的数据
:rtype: pandas.DataFrame
"""
res = requests.get(us_sina_stock_hist_url.format(symbol))
js_code = execjs.compile(hk_js_decode)
dict_list = js_code.call(
"d", res.text.split("=")[1].split(";")[0].replace('"', "")
) # 执行js解密代码
data_df = pd.DataFrame(dict_list)
data_df["date"] = data_df["date"].str.split("T", expand=True).iloc[:, 0]
data_df.index = pd.to_datetime(data_df["date"])
del data_df["date"]
data_df = data_df.astype("float")
res = requests.get(us_sina_stock_hist_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split("=")[1].split("\n")[0])["data"])
qfq_factor_df.columns = ["date", "qfq_factor", "adjust"]
qfq_factor_df = qfq_factor_df
qfq_factor_df.index = pd.to_datetime(qfq_factor_df["date"])
del qfq_factor_df["date"]
# 处理复权因子
temp_date_range = pd.date_range("1900-01-01", qfq_factor_df.index[0].isoformat())
temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range)
new_range = pd.merge(
temp_df, qfq_factor_df, left_index=True, right_index=True, how="left"
)
new_range = new_range.fillna(method="ffill")
new_range = new_range.iloc[:, [1, 2]]
if adjust == "qfq":
temp_df = pd.merge(
data_df, new_range, left_index=True, right_index=True, how="left"
)
temp_df.fillna(method="ffill", inplace=True)
temp_df.fillna(method="bfill", inplace=True)
temp_df = temp_df.astype(float)
temp_df["open"] = temp_df["open"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df["high"] = temp_df["high"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df["close"] = temp_df["close"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df["low"] = temp_df["low"] * temp_df["qfq_factor"] + temp_df["adjust"]
temp_df = temp_df.apply(lambda x: round(x, 4))
temp_df = temp_df.astype("float")
return temp_df.iloc[:, :-2]
if adjust == "qfq-factor":
return qfq_factor_df
if adjust == "":
return data_df
def stock_us_daily(symbol="BRK.A", factor=""):
res = requests.get(us_sina_stock_hist_url.format(symbol))
js_code = execjs.compile(hk_js_decode)
dict_list = js_code.call(
"d", res.text.split("=")[1].split(";")[0].replace('"', "")
) # 执行js解密代码
data_df = pd.DataFrame(dict_list)
data_df["date"] = data_df["date"].str.split("T", expand=True).iloc[:, 0]
data_df.index = pd.to_datetime(data_df["date"])
del data_df["date"]
data_df.astype("float")
res = requests.get(us_sina_stock_hist_qfq_url.format(symbol))
qfq_factor_df = pd.DataFrame(eval(res.text.split("=")[1].split("\n")[0])["data"])
qfq_factor_df.columns = ["date", "qfq_factor", "-"]
if factor == "qfq":
return qfq_factor_df.iloc[:, :2]
else:
return data_df
