-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathblack.py
306 lines (239 loc) · 13.8 KB
/
black.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
import os
import numpy as np
import pandas as pd
import datetime
import requests
import json
from utils import *
headers = {
"Accept": "*/*",
"Accept-Language": "zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2",
"Accept-Encoding": "gzip, deflate, br",
"Cache-Control": "no-cache",
"Host": "www.steelx2.com",
"Proxy-Connection": "keep-alive",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:106.0) Gecko/20100101 Firefox/106.0",
"Sec-Fetch-Site": "same-origin"
}
black_name_code = {
'钢铁原料成本价格': [[78, '废钢价格'], [178, '钢材成本指数'], [1006, '澳大利亚粉矿价格(56.5%，日照港)'],
[106, '澳大利亚粉矿价格(61.5%青岛港，元/吨)'], [107, '巴西粉矿价格(65% 日照港，元/吨)'],
[95, '西澳-北仑铁矿海运价'], [94, '巴西图巴朗-北仑铁矿海运价'],
[77, '波罗的海干散货指数(BDI)']],
'钢铁商品指数': [[1100, '中国(上海)自贸区进口铁矿石价格指数'], [65, '钢材指数'], [61, '铁矿指数'],
[64, '焦炭指数'], [1002, '煤炭指数'], [1003, '水泥指数']],
'钢铁行业PMI': [[118, '钢铁行业PMI'], [119, '钢铁行业PMI:生产'], [120, '钢铁行业PMI:新订单'],
[121, '钢铁行业PMI:新出口订单'], [122, '钢铁行业PMI:产成品库存'], [123, '钢铁行业PMI:原材料库存']],
'钢铁产量': [[124, '重点企业钢材库存量(旬)'], [161, '唐山地区钢坯库存量'], [43, '铁矿石港口存量'],
[67, '螺纹钢社会库存量'], [68, '线材社会库存量'], [69, '国内主要城市热轧卷板库存量'], [70, '国内主要城市冷轧卷板库存量'],
[117, '主要钢材品种库存总量'],
[177, '月度生铁产量'], [116, '月度中厚板产量'],
[35, '月度粗钢产量'], [36, '月度铁矿石原矿产量'], [38, '月度钢材出口量'], [39, '月度钢材进口量']],
}
def update_black_data():
BLACK_URL = 'https://www.steelx2.com/Handler2022/GetCommodityChartData.ashx?indicesids={}&datetype={}'
session = requests.session()
earlist_time = '2005-01-01'
current_time_dt = datetime.datetime.now()
# get cookies
url = BLACK_URL.format('78', 'Half')
r = session.get(url, verify=False, headers=headers)
cookies = r.cookies
for name in black_name_code:
path = os.path.join(data_dir, name+'.csv')
df = pd.DataFrame()
last_line_time = get_last_line_time(path, name, earlist_time, 10, '%Y-%m-%d')
start_time_dt = pd.to_datetime(last_line_time, format='%Y-%m-%d')
if (current_time_dt == start_time_dt):
continue
for codes_detail in black_name_code[name]:
if ((current_time_dt - start_time_dt) <= pd.Timedelta(days=360)):
url = BLACK_URL.format(str(codes_detail[0]), 'Year1')
elif ((current_time_dt - start_time_dt) <= pd.Timedelta(days=720)):
url = BLACK_URL.format(str(codes_detail[0]), 'Year2')
else:
url = BLACK_URL.format(str(codes_detail[0]), 'Full')
r = session.get(url, verify=False, headers=headers, cookies=cookies)
print(codes_detail)
if (r.status_code == 200):
s = r.content.decode('utf-8')
z = json.loads(s)
# print(z)
temp_df = pd.DataFrame(z["Data"])
if (len(temp_df) > 0):
temp_df.rename(columns={"PriceDate":"time", 'Price':codes_detail[1]}, inplace=True)
temp_df.drop(['IndicesId'], axis=1, inplace=True)
temp_df['time'] = temp_df['time'].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d'))
temp_df.sort_values(by = 'time', inplace=True)
temp_df['time'] = temp_df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
if (df.empty):
df = temp_df.copy()
else:
df = pd.merge(df, temp_df, on='time', how='outer')
# print(df)
if (len(df) > 0):
path = os.path.join(data_dir, name+'.csv')
if os.path.exists(path):
old_df = pd.read_csv(path)
old_df = pd.concat([old_df, df], axis=0)
old_df.drop_duplicates(subset=['time'], keep='last', inplace=True)
old_df['time'] = old_df['time'].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d'))
old_df.sort_values(by = 'time', inplace=True)
old_df['time'] = old_df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
old_df.to_csv(path, encoding='utf-8', index=False)
else:
df['time'] = df['time'].apply(lambda x:pd.to_datetime(x, format='%Y-%m-%d'))
df.sort_values(by = 'time', inplace=True)
df['time'] = df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
df.to_csv(path, encoding='utf-8', index=False)
def update_black_profit():
url = 'https://www.steelx2.com/Handler2022/GetHomePageChartData.ashx?type=gclr'
session = requests.session()
r = session.get(url, verify=False, headers=headers)
data = r.json()['Data']
data_json = json.loads(data)
df = pd.DataFrame(data_json)
df.rename(columns={'Date':'time', 'ElectricFurnaceProfit':'电炉利润', 'BlastFurnaceProfit':'高炉利润'}, inplace=True)
df['time'] = pd.to_datetime(df['time'], format='%Y-%m-%dT%H:%M:%S')
df['time'] = df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
path = os.path.join(data_dir, '钢厂利润'+'.csv')
if os.path.exists(path):
old_df = pd.read_csv(path)
old_df = pd.concat([old_df, df], axis=0)
old_df.drop_duplicates(subset=['time'], keep='last', inplace=True) # last
old_df['time'] = pd.to_datetime(old_df['time'], format='%Y-%m-%d')
old_df.sort_values(by='time', axis=0, ascending=True, inplace=True)
old_df['time'] = old_df['time'].apply(lambda x:datetime.datetime.strftime(x,'%Y-%m-%d'))
old_df.to_csv(path, encoding='utf-8', index=False)
else:
df.to_csv(path, encoding='utf-8', index=False)
def plot_steel_profit():
# 螺纹钢和线材的成本=（1.03吨粗钢+轧制费）*1.13，其中轧制费为150-300元/吨
# 粗钢成本=（0.96*生铁价格+0.15*废钢价格）/0.82
# 生铁、铁水制造成本=（1.6*铁矿石+0.5*焦炭）/0.9
path = os.path.join(data_dir, '钢铁原料成本价格'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
i1 = np.array(df['澳大利亚粉矿价格(56.5%，日照港)'], dtype=float)
i2 = np.array(df['澳大利亚粉矿价格(61.5%青岛港，元/吨)'], dtype=float)
i3 = np.array(df['巴西粉矿价格(65% 日照港，元/吨)'], dtype=float)
# cost = np.array(df['钢材成本指数'], dtype=float)
# fee1 = np.array(df['西澳-北仑铁矿海运价'], dtype=float)
# fee2 = np.array(df['巴西图巴朗-北仑铁矿海运价'], dtype=float)
path = os.path.join(data_dir, '钢厂利润'+'.csv')
df = pd.read_csv(path)
t1 = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
p1 = np.array(df['电炉利润'], dtype=float)
p2 = np.array(df['高炉利润'], dtype=float)
datas = [[[[t1, p1, '电炉利润', 'color=orange'],
[t1, p2, '高炉利润', 'color=blue']],
[],''],
[[[t, i1, '铁矿 56.5%', ''],
[t, i2, '铁矿 61.5%', ''],
[t, i3, '铁矿 65%', ''],],
[],''],
[[[t, i2-i1, '铁矿 61.5% - 56.5%', ''], [t, i3-i2, '铁矿 65% - 61.5%', '']],
[],'']]
plot_many_figure(datas)
pass
def plot_i_port_stock():
path = os.path.join(data_dir, '钢铁产量'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
# 库存
data3 = np.array(df['铁矿石港口存量'], dtype=float)
plot_seasonality(t, data3, start_year=2016, title='铁矿石港口存量')
def plot_steel_stock():
# 螺纹钢和线材的成本=（1.03吨粗钢+轧制费）*1.13，其中轧制费为150-300元/吨
# 粗钢成本=（0.96*生铁价格+0.15*废钢价格）/0.82
# 生铁、铁水制造成本=（1.6*铁矿石+0.5*焦炭）/0.9
path = os.path.join(data_dir, '钢铁产量'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
# 库存
data1 = np.array(df['重点企业钢材库存量(旬)'], dtype=float)
data2 = np.array(df['唐山地区钢坯库存量'], dtype=float)
data3 = np.array(df['铁矿石港口存量'], dtype=float)
data4 = np.array(df['螺纹钢社会库存量'], dtype=float)
data5 = np.array(df['线材社会库存量'], dtype=float)
data6 = np.array(df['国内主要城市热轧卷板库存量'], dtype=float)
data7 = np.array(df['国内主要城市冷轧卷板库存量'], dtype=float)
data8 = np.array(df['主要钢材品种库存总量'], dtype=float)
plot_seasonality(t, data1, start_year=2016, title='重点企业钢材库存量(旬)')
plot_seasonality(t, data2, start_year=2016, title='唐山地区钢坯库存量')
plot_seasonality(t, data3, start_year=2016, title='铁矿石港口存量')
plot_seasonality(t, data4, start_year=2016, title='螺纹钢社会库存量')
plot_seasonality(t, data5, start_year=2016, title='线材社会库存量')
plot_seasonality(t, data6, start_year=2016, title='国内主要城市热轧卷板库存量')
plot_seasonality(t, data7, start_year=2016, title='国内主要城市冷轧卷板库存量')
plot_seasonality(t, data8, start_year=2016, title='主要钢材品种库存总量')
# 产量
data1 = np.array(df['月度生铁产量'], dtype=float)
data2 = np.array(df['月度粗钢产量'], dtype=float)
data3 = np.array(df['月度中厚板产量'], dtype=float)
data4 = np.array(df['月度铁矿石原矿产量'], dtype=float)
plot_seasonality(t, data1, start_year=2016, title='月度生铁产量')
plot_seasonality(t, data2, start_year=2016, title='月度粗钢产量')
plot_seasonality(t, data3, start_year=2016, title='月度中厚板产量')
plot_seasonality(t, data4, start_year=2016, title='月度铁矿石原矿产量')
# 进出口
data1 = np.array(df['月度钢材出口量'], dtype=float)
data2 = np.array(df['月度钢材进口量'], dtype=float)
t1, data3 = data_sub(t, data1, t, data2)
plot_seasonality(t, data1, start_year=2012, title='月度钢材出口量')
plot_seasonality(t, data2, start_year=2012, title='月度钢材进口量')
plot_seasonality(t1, data3, start_year=2012, title='月度钢材净出口量')
def plot_corporate_profit():
path = os.path.join(nbs_dir, '按行业分工业企业主要经济指标'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m'))
data1 = np.array(df['黑色金属冶炼和压延加工业利润总额_累计值'], dtype=float)
data2 = np.array(df['黑色金属矿采选业利润总额_累计值'], dtype=float)
data3 = np.array(df['工业企业利润总额_累计值'], dtype=float)
plot_seasonality(t, data1, start_year=2012, title='黑色金属冶炼和压延加工业利润总额 累计值(亿元)')
plot_seasonality(t, data2, start_year=2012, title='黑色金属矿采选业利润总额 累计值(亿元)')
plot_seasonality(t, data3, start_year=2012, title='工业企业利润总额 累计值(亿元)')
def plot_steel_pmi():
path = os.path.join(data_dir, '钢铁行业PMI'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
pmi = np.array(df['钢铁行业PMI'], dtype=float)
pmi1 = np.array(df['钢铁行业PMI:生产'], dtype=float)
pmi2 = np.array(df['钢铁行业PMI:新订单'], dtype=float)
pmi3 = np.array(df['钢铁行业PMI:新出口订单'], dtype=float)
pmi4 = np.array(df['钢铁行业PMI:产成品库存'], dtype=float)
pmi5 = np.array(df['钢铁行业PMI:原材料库存'], dtype=float)
datas = [[[[t, pmi, '钢铁行业PMI', 'color=black'],],
[],''],
[[[t, pmi1, '钢铁行业PMI:生产', ''],
[t, pmi2, '钢铁行业PMI:新订单', ''],
[t, pmi3, '钢铁行业PMI:新出口订单', ''],],
[],''],
[[[t, pmi4, '钢铁行业PMI:产成品库存', ''],
[t, pmi5, '钢铁行业PMI:原材料库存', ''],],
[],''],
]
plot_many_figure(datas)
# 盈利 产能利用率
def test8():
path = os.path.join(data_dir, '钢铁利润'+'.csv')
df = pd.read_csv(path)
t = pd.DatetimeIndex(pd.to_datetime(df['time'], format='%Y-%m-%d'))
z1 = np.array(df['全国盈利钢厂比例'], dtype=float)
plot_seasonality(t, z1, start_year=2012, title='全国盈利钢厂比例')
z1 = np.array(df['唐山钢厂:产能利用率'], dtype=float)
plot_seasonality(t, z1, start_year=2012, title='唐山钢厂:产能利用率')
z1 = np.array(df['高炉炼铁产能利用率:样本钢厂'], dtype=float)
plot_seasonality(t, z1, start_year=2012, title='高炉炼铁产能利用率:样本钢厂')
z1 = np.array(df['高炉开工率:样本钢厂'], dtype=float)
plot_seasonality(t, z1, start_year=2012, title='高炉开工率:样本钢厂')
if __name__=="__main__":
update_black_profit()
update_black_data()
plot_steel_profit()
plot_steel_pmi()
plot_steel_stock()
plot_corporate_profit()
# 全国盈利钢厂比例, 产能利用率
test8()
pass