def macro_cons_silver_change():
"""
全球最大白银ETF--iShares Silver Trust持仓报告, 数据区间从20060429-至今
:return: pandas.Series
2006-04-29 0
2006-05-02 0.00
2006-05-03 342.11
2006-05-04 202.15
2006-05-05 108.86
...
2019-10-17 -58.16
2019-10-18 0.00
2019-10-21 -34.89
2019-10-22 -61.06
2019-10-23 0.00
"""
t = time.time()
res = requests.get(
JS_CONS_SLIVER_ETF_URL.format(str(int(round(t * 1000))),
str(int(round(t * 1000)) + 90)))
json_data = json.loads(res.text[res.text.find("{"):res.text.rfind("}") +
1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["白银"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["增持/减持(吨)"]
temp_df.name = "silver_change"
return temp_df
def macro_cons_silver_amount():
"""
全球最大白银ETF--iShares Silver Trust持仓报告, 数据区间从20060429-至今
:return: pandas.Series
2006-04-29 263651152
2006-05-02 263651152
2006-05-03 445408550
2006-05-04 555123947
2006-05-05 574713264
...
2019-10-17 Show All
2019-10-18 Show All
2019-10-21 Show All
2019-10-22 Show All
2019-10-23 Show All
"""
t = time.time()
res = requests.get(
JS_CONS_SLIVER_ETF_URL.format(str(int(round(t * 1000))),
str(int(round(t * 1000)) + 90)))
json_data = json.loads(res.text[res.text.find("{"):res.text.rfind("}") +
1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["白银"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["总价值(美元)"]
temp_df.name = "silver_amount"
return temp_df
def macro_cons_silver_volume():
"""
全球最大白银ETF--iShares Silver Trust持仓报告, 数据区间从20060429-至今
:return: pandas.Series
2006-04-29 653.17
2006-05-02 653.17
2006-05-03 995.28
2006-05-04 1197.43
2006-05-05 1306.29
...
2019-10-17 11847.91
2019-10-18 11847.91
2019-10-21 11813.02
2019-10-22 11751.96
2019-10-23 11751.96
"""
t = time.time()
res = requests.get(
JS_CONS_SLIVER_ETF_URL.format(str(int(round(t * 1000))),
str(int(round(t * 1000)) + 90)))
json_data = json.loads(res.text[res.text.find("{"):res.text.rfind("}") +
1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["白银"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["总库存(吨)"]
temp_df.name = "silver_volume"
return temp_df
def macro_cons_silver_amount():
"""
全球最大白银ETF--iShares Silver Trust持仓报告, 数据区间从20060429-至今
:return: pandas.Series
2006-04-29 263651152
2006-05-02 263651152
2006-05-03 445408550
2006-05-04 555123947
2006-05-05 574713264
...
2019-10-17 Show All
2019-10-18 Show All
2019-10-21 Show All
2019-10-22 Show All
2019-10-23 Show All
"""
t = time.time()
res = requests.get(
JS_CONS_SLIVER_ETF_URL.format(str(int(round(t * 1000))),
str(int(round(t * 1000)) + 90)))
json_data = json.loads(res.text[res.text.find("{"):res.text.rfind("}") +
1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["白银"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["总价值(美元)"]
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": "",
"category": "etf",
"attr_id": "2",
"_": str(int(round(t * 1000))),
}
headers = {
"accept": "*/*",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"cache-control": "no-cache",
"origin": "https://datacenter.jin10.com",
"pragma": "no-cache",
"referer":
"https://datacenter.jin10.com/reportType/dc_usa_michigan_consumer_sentiment",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-site",
"user-agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36",
"x-app-id": "rU6QIu7JHe2gOUeR",
"x-csrf-token": "",
"x-version": "1.0.0",
}
r = requests.get(url, params=params, headers=headers)
temp_se = pd.DataFrame(r.json()["data"]["values"]).iloc[:, [0, 3]]
temp_se.index = pd.to_datetime(temp_se.iloc[:, 0])
temp_se = temp_se.iloc[:, 1]
temp_df = temp_df.append(temp_se)
temp_df.dropna(inplace=True)
temp_df.sort_index(inplace=True)
temp_df = temp_df.reset_index()
temp_df.drop_duplicates(subset="index", keep="last", inplace=True)
temp_df.set_index("index", inplace=True)
temp_df = temp_df.squeeze()
temp_df.index.name = None
temp_df.name = "silver_amount"
url = "https://cdn.jin10.com/data_center/reports/etf_2.json"
r = requests.get(url)
data_json = r.json()
append_temp_df = pd.DataFrame(data_json["values"]).T
append_temp_df.columns = [item["name"] for item in data_json["keys"]]
temp_append_df = append_temp_df["总价值"]
temp_append_df.name = "silver_amount"
temp_df = temp_df.reset_index()
temp_df["index"] = temp_df["index"].astype(str)
temp_df = temp_df.append(temp_append_df.reset_index())
temp_df.drop_duplicates(subset=["index"], keep="last", inplace=True)
temp_df.index = pd.to_datetime(temp_df["index"])
del temp_df["index"]
temp_df = temp_df[temp_df != 'Show All']
temp_df.sort_index(inplace=True)
temp_df = temp_df.astype(float)
return temp_df
def macro_cons_silver_change():
"""
全球最大白银ETF--iShares Silver Trust持仓报告, 数据区间从20060429-至今
:return: pandas.Series
2006-04-29 0
2006-05-02 0.00
2006-05-03 342.11
2006-05-04 202.15
2006-05-05 108.86
...
2019-10-17 -58.16
2019-10-18 0.00
2019-10-21 -34.89
2019-10-22 -61.06
2019-10-23 0.00
"""
t = time.time()
res = requests.get(
JS_CONS_SLIVER_ETF_URL.format(str(int(round(t * 1000))),
str(int(round(t * 1000)) + 90)))
json_data = json.loads(res.text[res.text.find("{"):res.text.rfind("}") +
1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["白银"] for item in json_data["list"]]
value_df = pd.DataFrame(value_list)
value_df.columns = json_data["kinds"]
value_df.index = pd.to_datetime(date_list)
temp_df = value_df["增持/减持(吨)"]
temp_df.name = "silver_change"
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": "",
"category": "etf",
"attr_id": "2",
"_": str(int(round(t * 1000))),
}
headers = {
"accept": "*/*",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"cache-control": "no-cache",
"origin": "https://datacenter.jin10.com",
"pragma": "no-cache",
"referer":
"https://datacenter.jin10.com/reportType/dc_usa_michigan_consumer_sentiment",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-site",
"user-agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36",
"x-app-id": "rU6QIu7JHe2gOUeR",
"x-csrf-token": "",
"x-version": "1.0.0",
}
r = requests.get(url, params=params, headers=headers)
temp_se = pd.DataFrame(r.json()["data"]["values"]).iloc[:, [0, 2]]
temp_se.index = pd.to_datetime(temp_se.iloc[:, 0])
temp_se = temp_se.iloc[:, 1]
temp_df = temp_df.append(temp_se)
temp_df.dropna(inplace=True)
temp_df.sort_index(inplace=True)
temp_df = temp_df.reset_index()
temp_df.drop_duplicates(subset="index", keep="last", inplace=True)
temp_df.set_index("index", inplace=True)
temp_df = temp_df.squeeze()
temp_df.index.name = None
return temp_df
