def jyfm_tools_futures_spread(type_1="RB",
type_2="RB",
code_1="01",
code_2="05",
headers="",
plot=True):
"""
交易法门-工具-套利分析-跨期价差(自由价差)
:param type_1: str
:param type_2: str
:param code_1: str
:param code_2: str
:param plot: Bool
:return: pandas.Series or pic
"""
csa_payload_his = csa_payload.copy()
csa_payload_his.update({"type1": type_1})
csa_payload_his.update({"type2": type_2})
csa_payload_his.update({"code1": code_1})
csa_payload_his.update({"code2": code_2})
res = requests.get(csa_url_spread, params=csa_payload_his, headers=headers)
data_json = res.json()
data_df = pd.DataFrame([data_json["category"], data_json["value"]]).T
data_df.index = pd.to_datetime(data_df.iloc[:, 0])
data_df = data_df.iloc[:, 1]
data_df.name = "value"
if plot:
data_df.plot()
plt.legend(loc="best")
plt.xlabel("date")
plt.ylabel("value")
plt.show()
return data_df
else:
return data_df
def jyfm_tools_futures_ratio(type_1="RB",
type_2="RB",
code_1="01",
code_2="05",
headers="",
plot=True):
"""
交易法门-工具-套利分析-自由价比
:param type_1: str
:param type_2: str
:param code_1: str
:param code_2: str
:param plot: Bool
:return: pandas.Series or pic
2013-01-04 -121
2013-01-07 -124
2013-01-08 -150
2013-01-09 -143
2013-01-10 -195
...
2019-10-21 116
2019-10-22 126
2019-10-23 123
2019-10-24 126
2019-10-25 134
"""
csa_payload_his = csa_payload.copy()
csa_payload_his.update({"type1": type_1})
csa_payload_his.update({"type2": type_2})
csa_payload_his.update({"code1": code_1})
csa_payload_his.update({"code2": code_2})
res = requests.get(csa_url_ratio, params=csa_payload_his, headers=headers)
data_json = res.json()
data_df = pd.DataFrame([data_json["category"], data_json["value"]]).T
data_df.index = pd.to_datetime(data_df.iloc[:, 0])
data_df = data_df.iloc[:, 1]
data_df.name = "value"
if plot:
data_df.plot()
plt.legend(loc="best")
plt.xlabel("date")
plt.ylabel("value")
plt.show()
return data_df
else:
return data_df
def get_futures_csa_seasonally(type_1="RB", type_2="RB", code_1="01", code_2="05", plot=True, headers=""):
"""
获取某两个合约的价差季节图
:param type_1: str
:param type_2: str
:param code_1: str
:param code_2: str
:param plot: Bool
:return: pd.DataFrame
year2019 year2018 year2017 year2016 year2015 year2014 \
dataCategory
01-02 452.0 406.0 NaN NaN NaN -262.0
01-03 408.0 409.0 90.0 NaN NaN -249.0
01-04 380.0 412.0 50.0 60.0 NaN NaN
01-05 NaN 377.0 67.0 56.0 20.0 NaN
01-06 NaN NaN 48.0 80.0 1.0 -242.0
... ... ... ... ... ...
12-27 NaN 490.0 298.0 -4.0 NaN NaN
12-28 NaN 448.0 383.0 NaN 77.0 NaN
12-29 NaN NaN 454.0 17.0 67.0 93.0
12-30 NaN NaN NaN 56.0 63.0 44.0
12-31 NaN NaN NaN NaN 61.0 1.0
year2013
dataCategory
01-02 NaN
01-03 NaN
01-04 -121.0
01-05 NaN
01-06 NaN
...
12-27 -174.0
12-28 NaN
12-29 NaN
12-30 -194.0
12-31 -223.0
"""
csa_payload_his = csa_payload.copy()
csa_payload_his.update({"type1": type_1})
csa_payload_his.update({"type2": type_2})
csa_payload_his.update({"code1": code_1})
csa_payload_his.update({"code2": code_2})
res = requests.get(csa_url_spread, params=csa_payload_his, headers=headers)
data_json = res.json()
data_df = pd.DataFrame([data_json["category"], data_json["value"]]).T
data_df.index = pd.to_datetime(data_df.iloc[:, 0])
data_df = data_df.iloc[:, 1]
data_df.name = "value"
big_df = pd.DataFrame()
name_list = []
for item in list(data_json.keys()):
if item not in ["category", "value"]:
name_list.append(item)
big_df = pd.concat([big_df, pd.DataFrame(data_json[item])], axis=1)
big_df.columns = name_list
big_df.index = big_df["dataCategory"]
del big_df["dataCategory"]
big_df.replace("NaN", np.nan, inplace=True)
if plot:
for item in big_df.columns:
temp_df = big_df[item].dropna()
temp_df.astype("float").plot()
plt.legend(loc="best")
plt.xlabel("date")
plt.ylabel("value")
plt.show()
return big_df
else:
return big_df
