def macro_cons_gold_change():
"""
全球最大黄金ETF—SPDR Gold Trust持仓报告, 数据区间从20041118-至今
:return: pandas.Series
2004-11-18 0
2004-11-19 49.76
2004-11-22 29.24
2004-11-23 0.00
2004-11-24 9.33
...
2019-10-20 0.00
2019-10-21 0.00
2019-10-22 -4.98
2019-10-23 -1.18
2019-10-24 0.00
"""
t = time.time()
res = requests.get(
JS_CONS_GOLD_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"]["黄金"] 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 = "gold_change"
return temp_df
def macro_cons_gold_amount():
"""
全球最大黄金ETF—SPDR Gold Trust持仓报告, 数据区间从20041118-至今
:return: pandas.Series
2004-11-18 114920000.00
2004-11-19 828806907.20
2004-11-22 1253785205.50
2004-11-23 1254751438.19
2004-11-24 1390568824.08
...
2019-10-20 44286078486.23
2019-10-21 44333677232.68
2019-10-22 43907962483.56
2019-10-23 44120217405.82
2019-10-24 44120217405.82
"""
t = time.time()
res = requests.get(
JS_CONS_GOLD_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"]["黄金"] 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 = "gold_amount"
return temp_df
def macro_cons_gold_volume():
"""
全球最大黄金ETF—SPDR Gold Trust持仓报告, 数据区间从20041118-至今
:return: pandas.Series
2004-11-18 8.09
2004-11-19 57.85
2004-11-22 87.09
2004-11-23 87.09
2004-11-24 96.42
...
2019-10-20 924.64
2019-10-21 924.64
2019-10-22 919.66
2019-10-23 918.48
2019-10-24 918.48
"""
t = time.time()
res = requests.get(
JS_CONS_GOLD_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"]["黄金"] 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 = "gold_volume"
return temp_df
def macro_cons_gold_change():
"""
全球最大黄金ETF—SPDR Gold Trust持仓报告, 数据区间从20041118-至今
:return: pandas.Series
"""
t = time.time()
res = requests.get(
JS_CONS_GOLD_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"]["黄金"] 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)"]
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": "",
"category": "etf",
"attr_id": "1",
"_": 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
temp_df.name = "gold_change"
temp_df = temp_df.astype(float)
return temp_df