def stock_zh_kcb_daily(symbol="sh688008", factor=""):
"""
从新浪财经-A股获取某个股票的历史行情数据, 大量抓取容易封IP
:param symbol: str e.g., sh600000
:param factor: str 默认为空, 不复权; qfq, 前复权因子; hfq, 后复权因子;
:return: pandas.DataFrame
不复权数据
日期 开盘价 最高价 最低价 收盘价 成交 盘后量 盘后额
0 2019-07-22 91.300 97.200 66.300 74.920 58330685 40778 3055088
1 2019-07-23 70.020 78.880 70.000 74.130 23906020 43909 3254974
2 2019-07-24 74.130 76.550 72.500 75.880 21608530 23149 1756546
3 2019-07-25 75.000 79.980 74.600 78.000 24626920 66921 5219838
4 2019-07-26 76.780 76.780 70.300 71.680 16831530 49106 3519918
.. ... ... ... ... ... ... ... ...
67 2019-10-31 59.790 60.500 57.800 58.290 2886407 3846 224183
68 2019-11-01 57.900 59.960 57.600 59.250 2246059 0 0
69 2019-11-04 60.040 61.880 60.040 61.740 3945106 1782 110021
70 2019-11-05 61.100 62.780 60.850 62.160 4187105 400 24864
71 2019-11-06 62.320 62.620 60.900 61.130 2331354 1300 79469
后复权因子
date hfq_factor
0 2019-07-22 1.0000000000000000
1 1900-01-01 1.0000000000000000
前复权因子
date qfq_factor
0 2019-07-22 1.0000000000000000
1 1900-01-01 1.0000000000000000
"""
res = requests.get(
zh_sina_kcb_stock_hist_url.format(
symbol,
datetime.datetime.now().strftime("%Y_%m_%d"), symbol))
data_json = demjson.decode(
res.text[res.text.find("["):res.text.rfind("]") + 1])
data_df = pd.DataFrame(data_json)
data_df.columns = ["日期", "开盘价", "最高价", "最低价", "收盘价", "成交", "盘后量", "盘后额"]
if not factor:
return data_df
if factor == "hfq":
res = requests.get(zh_sina_kcb_stock_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"]
return hfq_factor_df
if factor == "qfq":
res = requests.get(zh_sina_kcb_stock_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_zh_kcb_daily(symbol="sh688399", adjust=""):
"""
从新浪财经-A股获取某个股票的历史行情数据, 大量抓取容易封IP
:param symbol: str e.g., sh600000
:param adjust: str 默认为空, 不复权; qfq, 前复权因子; hfq, 后复权因子;
:return: pandas.DataFrame
不复权数据
日期 开盘价 最高价 最低价 收盘价 成交 盘后量 盘后额
0 2019-07-22 91.300 97.200 66.300 74.920 58330685 40778 3055088
1 2019-07-23 70.020 78.880 70.000 74.130 23906020 43909 3254974
2 2019-07-24 74.130 76.550 72.500 75.880 21608530 23149 1756546
3 2019-07-25 75.000 79.980 74.600 78.000 24626920 66921 5219838
4 2019-07-26 76.780 76.780 70.300 71.680 16831530 49106 3519918
.. ... ... ... ... ... ... ... ...
67 2019-10-31 59.790 60.500 57.800 58.290 2886407 3846 224183
68 2019-11-01 57.900 59.960 57.600 59.250 2246059 0 0
69 2019-11-04 60.040 61.880 60.040 61.740 3945106 1782 110021
70 2019-11-05 61.100 62.780 60.850 62.160 4187105 400 24864
71 2019-11-06 62.320 62.620 60.900 61.130 2331354 1300 79469
后复权因子
date hfq_factor
0 2019-07-22 1.0000000000000000
1 1900-01-01 1.0000000000000000
前复权因子
date qfq_factor
0 2019-07-22 1.0000000000000000
1 1900-01-01 1.0000000000000000
"""
res = requests.get(
zh_sina_kcb_stock_hist_url.format(
symbol,
datetime.datetime.now().strftime("%Y_%m_%d"), symbol))
data_json = demjson.decode(
res.text[res.text.find("["):res.text.rfind("]") + 1])
data_df = pd.DataFrame(data_json)
data_df.index = pd.to_datetime(data_df["d"])
data_df.index.name = "date"
del data_df["d"]
r = requests.get(zh_sina_kcb_stock_amount_url.format(symbol, symbol))
amount_data_json = demjson.decode(
r.text[r.text.find("["):r.text.rfind("]") + 1])
amount_data_df = pd.DataFrame(amount_data_json)
amount_data_df.index = pd.to_datetime(amount_data_df.date)
del amount_data_df["date"]
temp_df = pd.merge(data_df,
amount_data_df,
left_index=True,
right_index=True,
how="left")
temp_df.fillna(method="ffill", inplace=True)
temp_df = temp_df.astype(float)
temp_df["amount"] = temp_df["amount"] * 10000
temp_df["turnover"] = temp_df["v"] / temp_df["amount"]
temp_df.columns = [
"open", "high", "low", "close", "volume", "after_volume",
"after_amount", "outstanding_share", "turnover"
]
if not adjust:
return temp_df
if adjust == "hfq":
res = requests.get(zh_sina_kcb_stock_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"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
temp_df = pd.merge(temp_df,
hfq_factor_df,
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["high"] = temp_df["high"] * temp_df["hfq_factor"]
temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"]
temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"]
return temp_df
if adjust == "qfq":
res = requests.get(zh_sina_kcb_stock_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_df = pd.merge(temp_df,
qfq_factor_df,
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"]
return temp_df
if adjust == "hfq-factor":
res = requests.get(zh_sina_kcb_stock_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"]
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(zh_sina_kcb_stock_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_zh_kcb_daily(symbol: str = "sh688399",
adjust: str = "") -> pd.DataFrame:
"""
新浪财经-科创板股票的历史行情数据, 大量抓取容易封IP
https://finance.sina.com.cn/realstock/company/sh688005/nc.shtml
:param symbol: 股票代码; 带市场标识的股票代码
:type symbol: str
:param adjust: 默认不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子
:type adjust: str
:return: 科创板股票的历史行情数据
:rtype: pandas.DataFrame
"""
res = requests.get(
zh_sina_kcb_stock_hist_url.format(
symbol,
datetime.datetime.now().strftime("%Y_%m_%d"), symbol))
data_json = demjson.decode(
res.text[res.text.find("["):res.text.rfind("]") + 1])
data_df = pd.DataFrame(data_json)
data_df.index = pd.to_datetime(data_df["d"])
data_df.index.name = "date"
del data_df["d"]
r = requests.get(zh_sina_kcb_stock_amount_url.format(symbol, symbol))
amount_data_json = demjson.decode(
r.text[r.text.find("["):r.text.rfind("]") + 1])
amount_data_df = pd.DataFrame(amount_data_json)
amount_data_df.index = pd.to_datetime(amount_data_df.date)
del amount_data_df["date"]
temp_df = pd.merge(data_df,
amount_data_df,
left_index=True,
right_index=True,
how="left")
temp_df.fillna(method="ffill", inplace=True)
temp_df = temp_df.astype(float)
temp_df["amount"] = temp_df["amount"] * 10000
temp_df["turnover"] = temp_df["v"] / temp_df["amount"]
temp_df.columns = [
"open",
"high",
"low",
"close",
"volume",
"after_volume",
"after_amount",
"outstanding_share",
"turnover",
]
if not adjust:
temp_df.reset_index(inplace=True)
temp_df['date'] = pd.to_datetime(temp_df['date']).dt.date
return temp_df
if adjust == "hfq":
res = requests.get(zh_sina_kcb_stock_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"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
temp_df = pd.merge(temp_df,
hfq_factor_df,
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["high"] = temp_df["high"] * temp_df["hfq_factor"]
temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"]
temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"]
temp_df = temp_df.iloc[:, :-1]
temp_df.reset_index(inplace=True)
temp_df['date'] = pd.to_datetime(temp_df['date']).dt.date
return temp_df
if adjust == "qfq":
res = requests.get(zh_sina_kcb_stock_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_df = pd.merge(temp_df,
qfq_factor_df,
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.iloc[:, :-1]
temp_df.reset_index(inplace=True)
temp_df['date'] = pd.to_datetime(temp_df['date']).dt.date
return temp_df
if adjust == "hfq-factor":
res = requests.get(zh_sina_kcb_stock_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"]
hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
del hfq_factor_df["date"]
hfq_factor_df.reset_index(inplace=True)
hfq_factor_df['date'] = pd.to_datetime(hfq_factor_df['date']).dt.date
return hfq_factor_df
if adjust == "qfq-factor":
res = requests.get(zh_sina_kcb_stock_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"]
qfq_factor_df.reset_index(inplace=True)
qfq_factor_df['date'] = pd.to_datetime(qfq_factor_df['date']).dt.date
return qfq_factor_df