def stock_hk_daily(symbol="00001", factor=""):
res = requests.get(hk_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")
if not factor:
return data_df
if factor == "hfq":
res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])['data'])
hfq_factor_df.columns = ["date", "hfq_factor", "cash"]
return hfq_factor_df
if factor == "qfq":
res = requests.get(hk_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"]
return qfq_factor_df
def stock_hk_daily(symbol: str = "00772", adjust: str = "qfq") -> pd.DataFrame:
"""
新浪财经-港股-个股的历史行情数据
https://stock.finance.sina.com.cn/hkstock/quotes/02912.html
:param symbol: 可以使用 stock_hk_spot 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(hk_sina_stock_hist_url.format(symbol))
js_code = py_mini_racer.MiniRacer()
js_code.eval(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.index = pd.to_datetime(data_df["date"])
del data_df["date"]
data_df = data_df.astype("float")
if adjust == "":
return data_df
if adjust == "hfq":
res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol))
try:
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])['data'])
if len(hfq_factor_df) == 1:
return data_df
except SyntaxError as e:
return data_df
hfq_factor_df.columns = ["date", "hfq_factor", "cash"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
# 处理复权因子
temp_date_range = pd.date_range("1900-01-01",
hfq_factor_df.index[0].isoformat())
temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range)
new_range = pd.merge(temp_df,
hfq_factor_df,
left_index=True,
right_index=True,
how="left")
new_range = new_range.fillna(method="ffill")
new_range = new_range.iloc[:, [1, 2]]
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 = temp_df.astype(float)
temp_df[
"open"] = temp_df["open"] * temp_df["hfq_factor"] + temp_df["cash"]
temp_df[
"high"] = temp_df["high"] * temp_df["hfq_factor"] + temp_df["cash"]
temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] + temp_df[
"cash"]
temp_df[
"low"] = temp_df["low"] * temp_df["hfq_factor"] + temp_df["cash"]
temp_df = temp_df.apply(lambda x: round(x, 2))
return temp_df.iloc[:, :-2]
if adjust == "qfq":
res = requests.get(hk_sina_stock_hist_qfq_url.format(symbol))
try:
qfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])['data'])
if len(qfq_factor_df) == 1:
return data_df
except SyntaxError as e:
return data_df
qfq_factor_df.columns = ["date", "qfq_factor"]
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df["date"]
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]]
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 = temp_df.astype(float)
temp_df["open"] = temp_df["open"] * temp_df["qfq_factor"]
temp_df["high"] = temp_df["high"] * temp_df["qfq_factor"]
temp_df["close"] = temp_df["close"] * temp_df["qfq_factor"]
temp_df["low"] = temp_df["low"] * temp_df["qfq_factor"]
temp_df = temp_df.apply(lambda x: round(x, 2))
return temp_df.iloc[:, :-1]
if adjust == "hfq-factor":
res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])['data'])
hfq_factor_df.columns = ["date", "hfq_factor", "cash"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
return hfq_factor_df
if adjust == "qfq-factor":
res = requests.get(hk_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"]
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df["date"]
return qfq_factor_df
def stock_hk_daily(symbol: str = "00700", adjust: str = "") -> pd.DataFrame:
"""
新浪财经-港股-个股的历史行情数据
:param symbol: 可以使用 stock_hk_spot 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(hk_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")
if adjust == "":
return data_df
if adjust == "hfq":
res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])["data"])
hfq_factor_df.columns = ["date", "hfq_factor", "cash"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
# 处理复权因子
temp_date_range = pd.date_range("1900-01-01",
hfq_factor_df.index[0].isoformat())
temp_df = pd.DataFrame(range(len(temp_date_range)), temp_date_range)
new_range = pd.merge(temp_df,
hfq_factor_df,
left_index=True,
right_index=True,
how="left")
new_range = new_range.fillna(method="ffill")
new_range = new_range.iloc[:, [1, 2]]
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 = temp_df.astype(float)
temp_df[
"open"] = temp_df["open"] * temp_df["hfq_factor"] + temp_df["cash"]
temp_df[
"high"] = temp_df["high"] * temp_df["hfq_factor"] + temp_df["cash"]
temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] + temp_df[
"cash"]
temp_df[
"low"] = temp_df["low"] * temp_df["hfq_factor"] + temp_df["cash"]
temp_df = temp_df.apply(lambda x: round(x, 2))
return temp_df.iloc[:, :-2]
if adjust == "qfq":
res = requests.get(hk_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"]
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df["date"]
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]]
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 = temp_df.astype(float)
temp_df["open"] = temp_df["open"] * temp_df["qfq_factor"]
temp_df["high"] = temp_df["high"] * temp_df["qfq_factor"]
temp_df["close"] = temp_df["close"] * temp_df["qfq_factor"]
temp_df["low"] = temp_df["low"] * temp_df["qfq_factor"]
temp_df = temp_df.apply(lambda x: round(x, 2))
return temp_df.iloc[:, :-1]
if adjust == "hfq-factor":
res = requests.get(hk_sina_stock_hist_hfq_url.format(symbol))
hfq_factor_df = pd.DataFrame(
eval(res.text.split("=")[1].split("\n")[0])["data"])
hfq_factor_df.columns = ["date", "hfq_factor", "cash"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
return hfq_factor_df
if adjust == "qfq-factor":
res = requests.get(hk_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"]
qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
del qfq_factor_df["date"]
return qfq_factor_df
