diff --git a/ETA获取数据.py b/ETA获取数据.py
index b6aaa66..4ae8f78 100644
--- a/ETA获取数据.py
+++ b/ETA获取数据.py
@@ -706,8 +706,36 @@ edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy cur
# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
]
+
+# eta自有数据指标编码
+modelsindex = {
+ 'NHITS': 'SELF0000001',
+ 'Informer':'SELF0000057',
+ 'LSTM':'SELF0000058',
+ 'iTransformer':'SELF0000059',
+ 'TSMixer':'SELF0000060',
+ 'TSMixerx':'SELF0000061',
+ 'PatchTST':'SELF0000062',
+ 'RNN':'SELF0000063',
+ 'GRU':'SELF0000064',
+ 'TCN':'SELF0000065',
+ 'BiTCN':'SELF0000066',
+ 'DilatedRNN':'SELF0000067',
+ 'MLP':'SELF0000068',
+ 'DLinear':'SELF0000069',
+ 'NLinear':'SELF0000070',
+ 'TFT':'SELF0000071',
+ 'FEDformer':'SELF0000072',
+ 'StemGNN':'SELF0000073',
+ 'MLPMultivariate':'SELF0000074',
+ 'TiDE':'SELF0000075',
+ 'DeepNPTS':'SELF0000076'
+ }
+
+edbcodelist = edbcodelist+list(modelsindex.values())
+
### 文件
-data_set = '原油指标数据.xlsx' # 数据集文件
+data_set = '历史预测结果.xlsx' # 数据集文件
# data_set = 'INE_OIL(1).csv'
### 文件夹
dataset = 'yuanyoudataset' # 数据集文件夹
diff --git a/config_jingbo.py b/config_jingbo.py
index d96483f..bacd157 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -90,54 +90,86 @@ data = {
ClassifyId = 1214
-### 报告上传配置
-# 变量定义--线上环境
-# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
-# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
-# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
+################################################################################################################ 变量定义--线上环境
+login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
+upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
+upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
-# login_data = {
-# "data": {
-# "account": "api_dev",
-# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
-# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
-# "terminal": "API"
-# },
-# "funcModule": "API",
-# "funcOperation": "获取token"
-# }
+login_data = {
+ "data": {
+ "account": "api_dev",
+ "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
+ "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+ "terminal": "API"
+ },
+ "funcModule": "API",
+ "funcOperation": "获取token"
+}
-# upload_data = {
-# "funcModule":'研究报告信息',
-# "funcOperation":'上传原油价格预测报告',
-# "data":{
-# "ownerAccount":'27663', #报告所属用户账号 27663 - 刘小朋
-# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
-# "fileName": '', #文件名称
-# "fileBase64": '' ,#文件内容base64
-# "categoryNo":'yyjgycbg', # 研究报告分类编码
-# "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
-# "reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
-# "reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部
-# "productGroupCode":"RAW_MATERIAL" # 商品分类
-# }
-# }
+upload_data = {
+ "funcModule":'研究报告信息',
+ "funcOperation":'上传原油价格预测报告',
+ "data":{
+ "ownerAccount":'27663', #报告所属用户账号 27663 - 刘小朋
+ "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
+ "fileName": '', #文件名称
+ "fileBase64": '' ,#文件内容base64
+ "categoryNo":'yyjgycbg', # 研究报告分类编码
+ "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
+ "reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
+ "reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部
+ "productGroupCode":"RAW_MATERIAL" # 商品分类
+ }
+}
-# warning_data = {
-# "funcModule":'原油特征停更预警',
-# "funcOperation":'原油特征停更预警',
-# "data":{
-# 'WARNING_TYPE_NAME':'特征数据停更预警',
-# 'WARNING_CONTENT':'',
-# 'WARNING_DATE':''
-# }
-# }
+warning_data = {
+ "funcModule":'原油特征停更预警',
+ "funcOperation":'原油特征停更预警',
+ "data":{
+ 'WARNING_TYPE_NAME':'特征数据停更预警',
+ 'WARNING_CONTENT':'',
+ 'WARNING_DATE':''
+ }
+}
+
+# 生产环境数据库
+# url: jdbc:mysql://rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com:3306/jingbo?autoReconnect=true&useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&transformedBitIsBoolean=true&useSSL=false&serverTimezone=GMT%2B8&nullCatalogMeansCurrent=true
+# username: jingbo
+# password: shihua@123
+# table-schema: jingbo
+# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
+# port = 3306
+# dbusername ='jingbo'
+# password = 'shihua@123'
+# dbname = 'jingbo'
+# table_name = 'v_tbl_crude_oil_warning'
+
+# 预生产环境
+# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
+# port = 3306
+# dbusername ='jingbo'
+# password = 'shihua@123'
+# dbname = 'jingbo-test'
+# table_name = 'v_tbl_crude_oil_warning'
-# # 变量定义--测试环境
+### 线上开关
+# is_train = True # 是否训练
+# is_debug = False # 是否调试
+# is_eta = True # 是否使用eta接口
+# is_timefurture = True # 是否使用时间特征
+# is_fivemodels = False # 是否使用之前保存的最佳的5个模型
+# is_edbcode = False # 特征使用edbcoding列表中的
+# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
+# is_update_eta = True # 预测结果上传到eta
+# is_update_report = True # 是否上传报告
+# is_update_warning_data = True if datetime.datetime.now().weekday() == 1 else False # 是否上传预警数据
+
+
+################################################################################################################ 变量定义--测试环境
login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
@@ -182,17 +214,13 @@ warning_data = {
}
}
-### 线上开关
-# is_train = True # 是否训练
-# is_debug = False # 是否调试
-# is_eta = True # 是否使用eta接口
-# is_timefurture = True # 是否使用时间特征
-# is_fivemodels = False # 是否使用之前保存的最佳的5个模型
-# is_edbcode = False # 特征使用edbcoding列表中的
-# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
-# is_update_eta = True # 预测结果上传到eta
-# is_update_report = True # 是否上传报告
-# is_update_warning_data = True if datetime.datetime.now().weekday() == 1 else False # 是否上传预警数据
+# 北京环境数据库
+host = '192.168.101.27'
+port = 3306
+dbusername ='root'
+password = '123456'
+dbname = 'jingbo_test'
+table_name = 'v_tbl_crude_oil_warning'
### 开关
@@ -204,28 +232,19 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
is_update_eta = False # 预测结果上传到eta
-is_update_report = False # 是否上传报告
-is_update_warning_data = False # 是否上传预警数据
+is_update_report = True # 是否上传报告
+is_update_warning_data = True # 是否上传预警数据
-### 北京环境数据库jbsh_test
-# url: jdbc:mysql://192.168.101.27:3306/jingbo_test?autoReconnect=true&useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&transformedBitIsBoolean=true&useSSL=false&serverTimezone=GMT%2B8&nullCatalogMeansCurrent=true
-
-host = '192.168.101.27'
-port = 3306
-dbusername ='root'
-password = '123456'
-dbname = 'jingbo_test'
-table_name = 'v_tbl_crude_oil_warning'
-
-db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
# 连接到数据库
+db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
db_mysql.connect()
+print("数据库连接成功",host,dbname,dbusername)
# 数据截取日期
-start_year = 2000 # 数据开始年份
-end_time = '2024-11-23' # 数据截取日期
+start_year = 2018 # 数据开始年份
+end_time = '' # 数据截取日期
freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
delweekenday = True if freq == 'B' else False # 是否删除周末数据
is_corr = False # 特征是否参与滞后领先提升相关系数
diff --git a/lib/dataread.py b/lib/dataread.py
index f4a19e6..09b4e59 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -259,7 +259,7 @@ def upload_warning_info(df_count):
# 更新预警数据中的日期和内容
warning_data['data']['WARNING_DATE'] = warning_date
- warning_data['data']['WARNING_CONTENT'] = content + '2'
+ warning_data['data']['WARNING_CONTENT'] = content
# 调用 upload_warning_data 函数上传预警数据
upload_warning_data(warning_data)
diff --git a/main_yuanyou.py b/main_yuanyou.py
index 4494307..d957d81 100644
--- a/main_yuanyou.py
+++ b/main_yuanyou.py
@@ -109,37 +109,38 @@ def predict_main():
import datetime
# 判断当前日期是不是周一
is_weekday = datetime.datetime.now().weekday() == 0
- # if is_weekday:
- # logger.info('今天是周一，更新预测模型')
- # # 计算最近60天预测残差最低的模型名称
- # model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
- # # 删除空值率为40%以上的列
- # if len(model_results) > 10:
- # model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
- # # 删除空行
- # model_results = model_results.dropna()
- # modelnames = model_results.columns.to_list()[2:]
- # for col in model_results[modelnames].select_dtypes(include=['object']).columns:
- # model_results[col] = model_results[col].astype(np.float32)
- # # 计算每个预测值与真实值之间的偏差率
- # for model in modelnames:
- # model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
- # # 获取每行对应的最小偏差率值
- # min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
- # # 获取每行对应的最小偏差率值对应的列名
- # min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
- # # 将列名索引转换为列名
- # min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
- # # 取出现次数最多的模型名称
- # most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
- # logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
- # # 保存结果到数据库
- # if not sqlitedb.check_table_exists('most_model'):
- # sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
- # sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
+ if is_weekday:
+ logger.info('今天是周一，更新预测模型')
+ # 计算最近60天预测残差最低的模型名称
+ model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
+ # 删除空值率为40%以上的列
+ if len(model_results) > 10:
+ model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
+ # 删除空行
+ model_results = model_results.dropna()
+ modelnames = model_results.columns.to_list()[2:]
+ for col in model_results[modelnames].select_dtypes(include=['object']).columns:
+ model_results[col] = model_results[col].astype(np.float32)
+ # 计算每个预测值与真实值之间的偏差率
+ for model in modelnames:
+ model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
+ # 获取每行对应的最小偏差率值
+ min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
+ # 获取每行对应的最小偏差率值对应的列名
+ min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
+ # 将列名索引转换为列名
+ min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
+ # 取出现次数最多的模型名称
+ most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
+ logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
+ # 保存结果到数据库
+ if not sqlitedb.check_table_exists('most_model'):
+ sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
+ sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
try:
- if is_weekday:
+ # if is_weekday:
+ if True:
logger.info('今天是周一，发送特征预警')
# 上传预警信息到数据库
warning_data_df = df_zhibiaoliebiao.copy()
@@ -147,6 +148,11 @@ def predict_main():
# 重命名列名
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
from sqlalchemy import create_engine
+ import urllib
+ global password
+ if '@' in password:
+ password = urllib.parse.quote_plus(password)
+
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
warning_data_df['TENANT_CODE'] = 'T0004'
diff --git a/up_week_dates.csv b/up_week_dates.csv
index 6782268..2bb7d4e 100644
--- a/up_week_dates.csv
+++ b/up_week_dates.csv
@@ -1,16 +1,16 @@
ds,ACCURACY,PREDICT_DATE,CREAT_DATE,HIGH_PRICE_y,LOW_PRICE_y,MIN_PRICE,MAX_PRICE,Ds_Week,Pre_Week
-2024-12-02,0.0,2024-12-02,2024-11-29,72.89,71.52,81.17999,81.17999,48,48
-2024-12-03,0.0,2024-12-03,2024-11-29,73.93,71.68,80.79155,80.79155,48,48
-2024-12-04,0.0,2024-12-04,2024-11-29,74.28,72.25,80.650894,80.650894,48,48
-2024-12-05,0.0,2024-12-05,2024-11-29,72.92,71.8,80.298,80.298,48,48
-2024-12-06,0.0,2024-12-06,2024-11-29,72.19,70.85,75.50838,80.264275,48,48
-2024-12-03,1.0,2024-12-03,2024-12-02,73.93,71.68,69.80779,75.83719,48,48
-2024-12-04,1.0,2024-12-04,2024-12-02,74.28,72.25,71.281296,76.24051,48,48
-2024-12-05,1.0,2024-12-05,2024-12-02,72.92,71.8,70.52623,75.95541,48,48
-2024-12-06,1.0,2024-12-06,2024-12-02,72.19,70.85,70.71932,75.99768,48,48
-2024-12-04,0.9432068965517233,2024-12-04,2024-12-03,74.28,72.25,72.36529,75.89061,48,48
-2024-12-05,0.6439196428571465,2024-12-05,2024-12-03,72.92,71.8,72.19881,75.72962,48,48
-2024-12-06,0.04017164179103775,2024-12-06,2024-12-03,72.19,70.85,72.13617,75.97762,48,48
-2024-12-05,0.0,2024-12-05,2024-12-04,72.92,71.8,82.40537,82.40537,48,48
-2024-12-06,0.0,2024-12-06,2024-12-04,72.19,70.85,82.0727,82.0727,48,48
-2024-12-06,0.0,2024-12-06,2024-12-05,72.19,70.85,83.367485,83.367485,48,48
+2024-11-26,1.0,2024-11-26,2024-11-25,73.8,71.63,71.071556,76.0069,47,47
+2024-11-27,1.0,2024-11-27,2024-11-25,72.85,71.71,71.003624,75.58056,47,47
+2024-11-28,0.7893243243243208,2024-11-28,2024-11-25,72.96,71.85,72.08385,76.20426,47,47
+2024-11-29,1.0,2024-11-29,2024-11-25,73.34,71.75,71.32973,75.70395,47,47
+2024-11-27,0.6068728070175414,2024-11-27,2024-11-26,72.85,71.71,72.158165,76.17365,47,47
+2024-11-28,0.8021441441441385,2024-11-28,2024-11-26,72.96,71.85,72.06962,76.447,47,47
+2024-11-29,0.6082389937106918,2024-11-29,2024-11-26,73.34,71.75,72.3729,76.08291,47,47
+2024-11-28,1.0,2024-11-28,2024-11-27,72.96,71.85,70.70975,75.20803,47,47
+2024-11-29,1.0,2024-11-29,2024-11-27,73.34,71.75,69.92311,75.423775,47,47
+2024-11-29,1.0,2024-11-29,2024-11-28,73.34,71.75,70.43276,75.48062,47,47
+2024-11-25,0.11832806324110515,2024-11-25,2024-11-22,74.83,72.3,74.53063,76.67314,47,47
+2024-11-26,0.0,2024-11-26,2024-11-22,73.8,71.63,74.44043,76.874565,47,47
+2024-11-27,0.0,2024-11-27,2024-11-22,72.85,71.71,74.66318,76.73413,47,47
+2024-11-28,0.0,2024-11-28,2024-11-22,72.96,71.85,74.70841,77.14105,47,47
+2024-11-29,0.0,2024-11-29,2024-11-22,73.34,71.75,74.70321,77.74617,47,47
diff --git a/原油价格预测准确率计算.ipynb b/原油价格预测准确率计算.ipynb
index 3d6a783..6bace69 100644
--- a/原油价格预测准确率计算.ipynb
+++ b/原油价格预测准确率计算.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 130,
+ "execution_count": 136,
"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
"metadata": {},
"outputs": [
@@ -10,9 +10,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "(50, 9)\n",
+ "(75, 9)\n",
"(20, 4)\n",
- "(50, 12)\n",
+ "(75, 12)\n",
" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x LOW_PRICE_x \\\n",
"0 1 2024-11-26 2024-11-25 71.071556 76.006900 \n",
"1 2 2024-11-27 2024-11-25 71.003624 75.580560 \n",
@@ -27,18 +27,18 @@
"3 2024-11-29 6.0 71.75 73.34 \n",
"4 2024-12-02 5.0 71.52 72.89 \n",
" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x \\\n",
- "45 46 2024-12-09 2024-12-06 68.894770 73.909450 \n",
- "46 47 2024-12-10 2024-12-06 68.845604 73.921120 \n",
- "47 48 2024-12-11 2024-12-06 69.181210 73.906260 \n",
- "48 49 2024-12-12 2024-12-06 69.075340 73.862946 \n",
- "49 50 2024-12-13 2024-12-06 69.076550 74.007740 \n",
+ "70 71 2024-11-25 2024-11-22 74.53063 76.673140 \n",
+ "71 72 2024-11-26 2024-11-22 74.44043 76.874565 \n",
+ "72 73 2024-11-27 2024-11-22 74.66318 76.734130 \n",
+ "73 74 2024-11-28 2024-11-22 74.70841 77.141050 \n",
+ "74 75 2024-11-29 2024-11-22 74.70321 77.746170 \n",
"\n",
- " LOW_PRICE_x RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
- "45 2024-12-09 NaN NaN NaN \n",
- "46 2024-12-10 NaN NaN NaN \n",
- "47 2024-12-11 NaN NaN NaN \n",
- "48 2024-12-12 NaN NaN NaN \n",
- "49 2024-12-13 NaN NaN NaN \n"
+ " LOW_PRICE_x RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
+ "70 2024-11-25 10.0 72.30 74.83 \n",
+ "71 2024-11-26 9.0 71.63 73.80 \n",
+ "72 2024-11-27 8.0 71.71 72.85 \n",
+ "73 2024-11-28 7.0 71.85 72.96 \n",
+ "74 2024-11-29 6.0 71.75 73.34 \n"
]
}
],
@@ -87,7 +87,7 @@
},
{
"cell_type": "code",
- "execution_count": 131,
+ "execution_count": 137,
"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
"metadata": {},
"outputs": [
@@ -101,51 +101,14 @@
"2 2024-11-28 0.789324 72.96 71.85 72.083850 76.204260\n",
"3 2024-11-29 1.000000 73.34 71.75 71.329730 75.703950\n",
"4 2024-12-02 0.853412 72.89 71.52 71.720825 76.264275\n",
- "5 2024-11-27 0.606873 72.85 71.71 72.158165 76.173650\n",
- "6 2024-11-28 0.802144 72.96 71.85 72.069620 76.447000\n",
- "7 2024-11-29 0.608239 73.34 71.75 72.372900 76.082910\n",
- "8 2024-12-02 0.457007 72.89 71.52 72.263900 76.198685\n",
- "9 2024-12-03 0.758302 73.93 71.68 72.223820 76.041725\n",
- "10 2024-11-28 1.000000 72.96 71.85 70.709750 75.208030\n",
- "11 2024-11-29 1.000000 73.34 71.75 69.923110 75.423775\n",
- "12 2024-12-02 1.000000 72.89 71.52 70.810356 75.786860\n",
- "13 2024-12-03 1.000000 73.93 71.68 70.814430 75.791115\n",
- "14 2024-12-04 1.000000 74.28 72.25 70.476650 75.623730\n",
- "15 2024-11-29 1.000000 73.34 71.75 70.432760 75.480620\n",
- "16 2024-12-02 1.000000 72.89 71.52 69.651825 75.270940\n",
- "17 2024-12-03 1.000000 73.93 71.68 70.536285 75.826430\n",
- "18 2024-12-04 1.000000 74.28 72.25 70.568340 75.770290\n",
- "19 2024-12-05 1.000000 72.92 71.80 70.222640 76.113310\n",
- "20 2024-12-02 0.000000 72.89 71.52 81.179990 81.179990\n",
- "21 2024-12-03 0.000000 73.93 71.68 80.791550 80.791550\n",
- "22 2024-12-04 0.000000 74.28 72.25 80.650894 80.650894\n",
- "23 2024-12-05 0.000000 72.92 71.80 80.298000 80.298000\n",
- "24 2024-12-06 0.000000 72.19 70.85 75.508380 80.264275\n",
- "25 2024-12-03 1.000000 73.93 71.68 69.807790 75.837190\n",
- "26 2024-12-04 1.000000 74.28 72.25 71.281296 76.240510\n",
- "27 2024-12-05 1.000000 72.92 71.80 70.526230 75.955410\n",
- "28 2024-12-06 1.000000 72.19 70.85 70.719320 75.997680\n",
- "29 2024-12-09 0.000000 NaN NaN 71.642590 75.828636\n",
- "30 2024-12-04 0.943207 74.28 72.25 72.365290 75.890610\n",
- "31 2024-12-05 0.643920 72.92 71.80 72.198810 75.729620\n",
- "32 2024-12-06 0.040172 72.19 70.85 72.136170 75.977620\n",
- "33 2024-12-09 0.000000 NaN NaN 72.165790 75.880104\n",
- "34 2024-12-10 0.000000 NaN NaN 72.000620 75.906100\n",
- "35 2024-12-05 0.000000 72.92 71.80 82.405370 82.405370\n",
- "36 2024-12-06 0.000000 72.19 70.85 82.072700 82.072700\n",
- "37 2024-12-09 0.000000 NaN NaN 82.652700 82.652700\n",
- "38 2024-12-10 0.000000 NaN NaN 81.920910 81.920910\n",
- "39 2024-12-11 0.000000 NaN NaN 81.091270 81.091270\n",
- "40 2024-12-06 0.000000 72.19 70.85 83.367485 83.367485\n",
- "41 2024-12-09 0.000000 NaN NaN 82.869705 82.869705\n",
- "42 2024-12-10 0.000000 NaN NaN 82.580550 82.580550\n",
- "43 2024-12-11 0.000000 NaN NaN 82.442154 82.442154\n",
- "44 2024-12-12 0.000000 NaN NaN 82.276510 82.276510\n",
- "45 2024-12-09 0.000000 NaN NaN 68.894770 73.909450\n",
- "46 2024-12-10 0.000000 NaN NaN 68.845604 73.921120\n",
- "47 2024-12-11 0.000000 NaN NaN 69.181210 73.906260\n",
- "48 2024-12-12 0.000000 NaN NaN 69.075340 73.862946\n",
- "49 2024-12-13 0.000000 NaN NaN 69.076550 74.007740\n"
+ ".. ... ... ... ... ... ...\n",
+ "70 2024-11-25 0.118328 74.83 72.30 74.530630 76.673140\n",
+ "71 2024-11-26 0.000000 73.80 71.63 74.440430 76.874565\n",
+ "72 2024-11-27 0.000000 72.85 71.71 74.663180 76.734130\n",
+ "73 2024-11-28 0.000000 72.96 71.85 74.708410 77.141050\n",
+ "74 2024-11-29 0.000000 73.34 71.75 74.703210 77.746170\n",
+ "\n",
+ "[75 rows x 6 columns]\n"
]
}
],
@@ -176,7 +139,7 @@
},
{
"cell_type": "code",
- "execution_count": 132,
+ "execution_count": 138,
"id": "0f942c69",
"metadata": {},
"outputs": [],
@@ -189,7 +152,7 @@
},
{
"cell_type": "code",
- "execution_count": 133,
+ "execution_count": 139,
"id": "a7b05510",
"metadata": {},
"outputs": [
@@ -293,592 +256,86 @@
" | 48 | \n",
" \n",
" \n",
- " | 5 | \n",
- " 2024-11-27 | \n",
- " 0.606873 | \n",
- " 2024-11-27 | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 70 | \n",
+ " 2024-11-25 | \n",
+ " 0.118328 | \n",
+ " 2024-11-25 | \n",
+ " 2024-11-22 | \n",
+ " 74.83 | \n",
+ " 72.30 | \n",
+ " 74.530630 | \n",
+ " 76.673140 | \n",
+ " 47 | \n",
+ " 47 | \n",
+ "
\n",
+ " \n",
+ " | 71 | \n",
" 2024-11-26 | \n",
+ " 0.000000 | \n",
+ " 2024-11-26 | \n",
+ " 2024-11-22 | \n",
+ " 73.80 | \n",
+ " 71.63 | \n",
+ " 74.440430 | \n",
+ " 76.874565 | \n",
+ " 47 | \n",
+ " 47 | \n",
+ "
\n",
+ " \n",
+ " | 72 | \n",
+ " 2024-11-27 | \n",
+ " 0.000000 | \n",
+ " 2024-11-27 | \n",
+ " 2024-11-22 | \n",
" 72.85 | \n",
" 71.71 | \n",
- " 72.158165 | \n",
- " 76.173650 | \n",
+ " 74.663180 | \n",
+ " 76.734130 | \n",
" 47 | \n",
" 47 | \n",
"
\n",
" \n",
- " | 6 | \n",
+ " 73 | \n",
" 2024-11-28 | \n",
- " 0.802144 | \n",
+ " 0.000000 | \n",
" 2024-11-28 | \n",
- " 2024-11-26 | \n",
+ " 2024-11-22 | \n",
" 72.96 | \n",
" 71.85 | \n",
- " 72.069620 | \n",
- " 76.447000 | \n",
+ " 74.708410 | \n",
+ " 77.141050 | \n",
" 47 | \n",
" 47 | \n",
"
\n",
" \n",
- " | 7 | \n",
+ " 74 | \n",
" 2024-11-29 | \n",
- " 0.608239 | \n",
+ " 0.000000 | \n",
" 2024-11-29 | \n",
- " 2024-11-26 | \n",
+ " 2024-11-22 | \n",
" 73.34 | \n",
" 71.75 | \n",
- " 72.372900 | \n",
- " 76.082910 | \n",
+ " 74.703210 | \n",
+ " 77.746170 | \n",
" 47 | \n",
" 47 | \n",
"
\n",
- " \n",
- " | 8 | \n",
- " 2024-12-02 | \n",
- " 0.457007 | \n",
- " 2024-12-02 | \n",
- " 2024-11-26 | \n",
- " 72.89 | \n",
- " 71.52 | \n",
- " 72.263900 | \n",
- " 76.198685 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " 2024-12-03 | \n",
- " 0.758302 | \n",
- " 2024-12-03 | \n",
- " 2024-11-26 | \n",
- " 73.93 | \n",
- " 71.68 | \n",
- " 72.223820 | \n",
- " 76.041725 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 10 | \n",
- " 2024-11-28 | \n",
- " 1.000000 | \n",
- " 2024-11-28 | \n",
- " 2024-11-27 | \n",
- " 72.96 | \n",
- " 71.85 | \n",
- " 70.709750 | \n",
- " 75.208030 | \n",
- " 47 | \n",
- " 47 | \n",
- "
\n",
- " \n",
- " | 11 | \n",
- " 2024-11-29 | \n",
- " 1.000000 | \n",
- " 2024-11-29 | \n",
- " 2024-11-27 | \n",
- " 73.34 | \n",
- " 71.75 | \n",
- " 69.923110 | \n",
- " 75.423775 | \n",
- " 47 | \n",
- " 47 | \n",
- "
\n",
- " \n",
- " | 12 | \n",
- " 2024-12-02 | \n",
- " 1.000000 | \n",
- " 2024-12-02 | \n",
- " 2024-11-27 | \n",
- " 72.89 | \n",
- " 71.52 | \n",
- " 70.810356 | \n",
- " 75.786860 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 13 | \n",
- " 2024-12-03 | \n",
- " 1.000000 | \n",
- " 2024-12-03 | \n",
- " 2024-11-27 | \n",
- " 73.93 | \n",
- " 71.68 | \n",
- " 70.814430 | \n",
- " 75.791115 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 14 | \n",
- " 2024-12-04 | \n",
- " 1.000000 | \n",
- " 2024-12-04 | \n",
- " 2024-11-27 | \n",
- " 74.28 | \n",
- " 72.25 | \n",
- " 70.476650 | \n",
- " 75.623730 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 15 | \n",
- " 2024-11-29 | \n",
- " 1.000000 | \n",
- " 2024-11-29 | \n",
- " 2024-11-28 | \n",
- " 73.34 | \n",
- " 71.75 | \n",
- " 70.432760 | \n",
- " 75.480620 | \n",
- " 47 | \n",
- " 47 | \n",
- "
\n",
- " \n",
- " | 16 | \n",
- " 2024-12-02 | \n",
- " 1.000000 | \n",
- " 2024-12-02 | \n",
- " 2024-11-28 | \n",
- " 72.89 | \n",
- " 71.52 | \n",
- " 69.651825 | \n",
- " 75.270940 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 17 | \n",
- " 2024-12-03 | \n",
- " 1.000000 | \n",
- " 2024-12-03 | \n",
- " 2024-11-28 | \n",
- " 73.93 | \n",
- " 71.68 | \n",
- " 70.536285 | \n",
- " 75.826430 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 18 | \n",
- " 2024-12-04 | \n",
- " 1.000000 | \n",
- " 2024-12-04 | \n",
- " 2024-11-28 | \n",
- " 74.28 | \n",
- " 72.25 | \n",
- " 70.568340 | \n",
- " 75.770290 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 19 | \n",
- " 2024-12-05 | \n",
- " 1.000000 | \n",
- " 2024-12-05 | \n",
- " 2024-11-28 | \n",
- " 72.92 | \n",
- " 71.80 | \n",
- " 70.222640 | \n",
- " 76.113310 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 20 | \n",
- " 2024-12-02 | \n",
- " 0.000000 | \n",
- " 2024-12-02 | \n",
- " 2024-11-29 | \n",
- " 72.89 | \n",
- " 71.52 | \n",
- " 81.179990 | \n",
- " 81.179990 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 21 | \n",
- " 2024-12-03 | \n",
- " 0.000000 | \n",
- " 2024-12-03 | \n",
- " 2024-11-29 | \n",
- " 73.93 | \n",
- " 71.68 | \n",
- " 80.791550 | \n",
- " 80.791550 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 22 | \n",
- " 2024-12-04 | \n",
- " 0.000000 | \n",
- " 2024-12-04 | \n",
- " 2024-11-29 | \n",
- " 74.28 | \n",
- " 72.25 | \n",
- " 80.650894 | \n",
- " 80.650894 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 23 | \n",
- " 2024-12-05 | \n",
- " 0.000000 | \n",
- " 2024-12-05 | \n",
- " 2024-11-29 | \n",
- " 72.92 | \n",
- " 71.80 | \n",
- " 80.298000 | \n",
- " 80.298000 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 24 | \n",
- " 2024-12-06 | \n",
- " 0.000000 | \n",
- " 2024-12-06 | \n",
- " 2024-11-29 | \n",
- " 72.19 | \n",
- " 70.85 | \n",
- " 75.508380 | \n",
- " 80.264275 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 25 | \n",
- " 2024-12-03 | \n",
- " 1.000000 | \n",
- " 2024-12-03 | \n",
- " 2024-12-02 | \n",
- " 73.93 | \n",
- " 71.68 | \n",
- " 69.807790 | \n",
- " 75.837190 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 26 | \n",
- " 2024-12-04 | \n",
- " 1.000000 | \n",
- " 2024-12-04 | \n",
- " 2024-12-02 | \n",
- " 74.28 | \n",
- " 72.25 | \n",
- " 71.281296 | \n",
- " 76.240510 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 27 | \n",
- " 2024-12-05 | \n",
- " 1.000000 | \n",
- " 2024-12-05 | \n",
- " 2024-12-02 | \n",
- " 72.92 | \n",
- " 71.80 | \n",
- " 70.526230 | \n",
- " 75.955410 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 28 | \n",
- " 2024-12-06 | \n",
- " 1.000000 | \n",
- " 2024-12-06 | \n",
- " 2024-12-02 | \n",
- " 72.19 | \n",
- " 70.85 | \n",
- " 70.719320 | \n",
- " 75.997680 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 29 | \n",
- " 2024-12-09 | \n",
- " 0.000000 | \n",
- " 2024-12-09 | \n",
- " 2024-12-02 | \n",
- " NaN | \n",
- " NaN | \n",
- " 71.642590 | \n",
- " 75.828636 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 30 | \n",
- " 2024-12-04 | \n",
- " 0.943207 | \n",
- " 2024-12-04 | \n",
- " 2024-12-03 | \n",
- " 74.28 | \n",
- " 72.25 | \n",
- " 72.365290 | \n",
- " 75.890610 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 31 | \n",
- " 2024-12-05 | \n",
- " 0.643920 | \n",
- " 2024-12-05 | \n",
- " 2024-12-03 | \n",
- " 72.92 | \n",
- " 71.80 | \n",
- " 72.198810 | \n",
- " 75.729620 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 32 | \n",
- " 2024-12-06 | \n",
- " 0.040172 | \n",
- " 2024-12-06 | \n",
- " 2024-12-03 | \n",
- " 72.19 | \n",
- " 70.85 | \n",
- " 72.136170 | \n",
- " 75.977620 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 33 | \n",
- " 2024-12-09 | \n",
- " 0.000000 | \n",
- " 2024-12-09 | \n",
- " 2024-12-03 | \n",
- " NaN | \n",
- " NaN | \n",
- " 72.165790 | \n",
- " 75.880104 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 34 | \n",
- " 2024-12-10 | \n",
- " 0.000000 | \n",
- " 2024-12-10 | \n",
- " 2024-12-03 | \n",
- " NaN | \n",
- " NaN | \n",
- " 72.000620 | \n",
- " 75.906100 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 35 | \n",
- " 2024-12-05 | \n",
- " 0.000000 | \n",
- " 2024-12-05 | \n",
- " 2024-12-04 | \n",
- " 72.92 | \n",
- " 71.80 | \n",
- " 82.405370 | \n",
- " 82.405370 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 36 | \n",
- " 2024-12-06 | \n",
- " 0.000000 | \n",
- " 2024-12-06 | \n",
- " 2024-12-04 | \n",
- " 72.19 | \n",
- " 70.85 | \n",
- " 82.072700 | \n",
- " 82.072700 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 37 | \n",
- " 2024-12-09 | \n",
- " 0.000000 | \n",
- " 2024-12-09 | \n",
- " 2024-12-04 | \n",
- " NaN | \n",
- " NaN | \n",
- " 82.652700 | \n",
- " 82.652700 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 38 | \n",
- " 2024-12-10 | \n",
- " 0.000000 | \n",
- " 2024-12-10 | \n",
- " 2024-12-04 | \n",
- " NaN | \n",
- " NaN | \n",
- " 81.920910 | \n",
- " 81.920910 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 39 | \n",
- " 2024-12-11 | \n",
- " 0.000000 | \n",
- " 2024-12-11 | \n",
- " 2024-12-04 | \n",
- " NaN | \n",
- " NaN | \n",
- " 81.091270 | \n",
- " 81.091270 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 40 | \n",
- " 2024-12-06 | \n",
- " 0.000000 | \n",
- " 2024-12-06 | \n",
- " 2024-12-05 | \n",
- " 72.19 | \n",
- " 70.85 | \n",
- " 83.367485 | \n",
- " 83.367485 | \n",
- " 48 | \n",
- " 48 | \n",
- "
\n",
- " \n",
- " | 41 | \n",
- " 2024-12-09 | \n",
- " 0.000000 | \n",
- " 2024-12-09 | \n",
- " 2024-12-05 | \n",
- " NaN | \n",
- " NaN | \n",
- " 82.869705 | \n",
- " 82.869705 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 42 | \n",
- " 2024-12-10 | \n",
- " 0.000000 | \n",
- " 2024-12-10 | \n",
- " 2024-12-05 | \n",
- " NaN | \n",
- " NaN | \n",
- " 82.580550 | \n",
- " 82.580550 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 43 | \n",
- " 2024-12-11 | \n",
- " 0.000000 | \n",
- " 2024-12-11 | \n",
- " 2024-12-05 | \n",
- " NaN | \n",
- " NaN | \n",
- " 82.442154 | \n",
- " 82.442154 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 44 | \n",
- " 2024-12-12 | \n",
- " 0.000000 | \n",
- " 2024-12-12 | \n",
- " 2024-12-05 | \n",
- " NaN | \n",
- " NaN | \n",
- " 82.276510 | \n",
- " 82.276510 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 45 | \n",
- " 2024-12-09 | \n",
- " 0.000000 | \n",
- " 2024-12-09 | \n",
- " 2024-12-06 | \n",
- " NaN | \n",
- " NaN | \n",
- " 68.894770 | \n",
- " 73.909450 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 46 | \n",
- " 2024-12-10 | \n",
- " 0.000000 | \n",
- " 2024-12-10 | \n",
- " 2024-12-06 | \n",
- " NaN | \n",
- " NaN | \n",
- " 68.845604 | \n",
- " 73.921120 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 47 | \n",
- " 2024-12-11 | \n",
- " 0.000000 | \n",
- " 2024-12-11 | \n",
- " 2024-12-06 | \n",
- " NaN | \n",
- " NaN | \n",
- " 69.181210 | \n",
- " 73.906260 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 48 | \n",
- " 2024-12-12 | \n",
- " 0.000000 | \n",
- " 2024-12-12 | \n",
- " 2024-12-06 | \n",
- " NaN | \n",
- " NaN | \n",
- " 69.075340 | \n",
- " 73.862946 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
- " \n",
- " | 49 | \n",
- " 2024-12-13 | \n",
- " 0.000000 | \n",
- " 2024-12-13 | \n",
- " 2024-12-06 | \n",
- " NaN | \n",
- " NaN | \n",
- " 69.076550 | \n",
- " 74.007740 | \n",
- " 49 | \n",
- " 49 | \n",
- "
\n",
" \n",
"\n",
+ "75 rows × 10 columns
\n",
""
],
"text/plain": [
@@ -888,51 +345,12 @@
"2 2024-11-28 0.789324 2024-11-28 2024-11-25 72.96 71.85 \n",
"3 2024-11-29 1.000000 2024-11-29 2024-11-25 73.34 71.75 \n",
"4 2024-12-02 0.853412 2024-12-02 2024-11-25 72.89 71.52 \n",
- "5 2024-11-27 0.606873 2024-11-27 2024-11-26 72.85 71.71 \n",
- "6 2024-11-28 0.802144 2024-11-28 2024-11-26 72.96 71.85 \n",
- "7 2024-11-29 0.608239 2024-11-29 2024-11-26 73.34 71.75 \n",
- "8 2024-12-02 0.457007 2024-12-02 2024-11-26 72.89 71.52 \n",
- "9 2024-12-03 0.758302 2024-12-03 2024-11-26 73.93 71.68 \n",
- "10 2024-11-28 1.000000 2024-11-28 2024-11-27 72.96 71.85 \n",
- "11 2024-11-29 1.000000 2024-11-29 2024-11-27 73.34 71.75 \n",
- "12 2024-12-02 1.000000 2024-12-02 2024-11-27 72.89 71.52 \n",
- "13 2024-12-03 1.000000 2024-12-03 2024-11-27 73.93 71.68 \n",
- "14 2024-12-04 1.000000 2024-12-04 2024-11-27 74.28 72.25 \n",
- "15 2024-11-29 1.000000 2024-11-29 2024-11-28 73.34 71.75 \n",
- "16 2024-12-02 1.000000 2024-12-02 2024-11-28 72.89 71.52 \n",
- "17 2024-12-03 1.000000 2024-12-03 2024-11-28 73.93 71.68 \n",
- "18 2024-12-04 1.000000 2024-12-04 2024-11-28 74.28 72.25 \n",
- "19 2024-12-05 1.000000 2024-12-05 2024-11-28 72.92 71.80 \n",
- "20 2024-12-02 0.000000 2024-12-02 2024-11-29 72.89 71.52 \n",
- "21 2024-12-03 0.000000 2024-12-03 2024-11-29 73.93 71.68 \n",
- "22 2024-12-04 0.000000 2024-12-04 2024-11-29 74.28 72.25 \n",
- "23 2024-12-05 0.000000 2024-12-05 2024-11-29 72.92 71.80 \n",
- "24 2024-12-06 0.000000 2024-12-06 2024-11-29 72.19 70.85 \n",
- "25 2024-12-03 1.000000 2024-12-03 2024-12-02 73.93 71.68 \n",
- "26 2024-12-04 1.000000 2024-12-04 2024-12-02 74.28 72.25 \n",
- "27 2024-12-05 1.000000 2024-12-05 2024-12-02 72.92 71.80 \n",
- "28 2024-12-06 1.000000 2024-12-06 2024-12-02 72.19 70.85 \n",
- "29 2024-12-09 0.000000 2024-12-09 2024-12-02 NaN NaN \n",
- "30 2024-12-04 0.943207 2024-12-04 2024-12-03 74.28 72.25 \n",
- "31 2024-12-05 0.643920 2024-12-05 2024-12-03 72.92 71.80 \n",
- "32 2024-12-06 0.040172 2024-12-06 2024-12-03 72.19 70.85 \n",
- "33 2024-12-09 0.000000 2024-12-09 2024-12-03 NaN NaN \n",
- "34 2024-12-10 0.000000 2024-12-10 2024-12-03 NaN NaN \n",
- "35 2024-12-05 0.000000 2024-12-05 2024-12-04 72.92 71.80 \n",
- "36 2024-12-06 0.000000 2024-12-06 2024-12-04 72.19 70.85 \n",
- "37 2024-12-09 0.000000 2024-12-09 2024-12-04 NaN NaN \n",
- "38 2024-12-10 0.000000 2024-12-10 2024-12-04 NaN NaN \n",
- "39 2024-12-11 0.000000 2024-12-11 2024-12-04 NaN NaN \n",
- "40 2024-12-06 0.000000 2024-12-06 2024-12-05 72.19 70.85 \n",
- "41 2024-12-09 0.000000 2024-12-09 2024-12-05 NaN NaN \n",
- "42 2024-12-10 0.000000 2024-12-10 2024-12-05 NaN NaN \n",
- "43 2024-12-11 0.000000 2024-12-11 2024-12-05 NaN NaN \n",
- "44 2024-12-12 0.000000 2024-12-12 2024-12-05 NaN NaN \n",
- "45 2024-12-09 0.000000 2024-12-09 2024-12-06 NaN NaN \n",
- "46 2024-12-10 0.000000 2024-12-10 2024-12-06 NaN NaN \n",
- "47 2024-12-11 0.000000 2024-12-11 2024-12-06 NaN NaN \n",
- "48 2024-12-12 0.000000 2024-12-12 2024-12-06 NaN NaN \n",
- "49 2024-12-13 0.000000 2024-12-13 2024-12-06 NaN NaN \n",
+ ".. ... ... ... ... ... ... \n",
+ "70 2024-11-25 0.118328 2024-11-25 2024-11-22 74.83 72.30 \n",
+ "71 2024-11-26 0.000000 2024-11-26 2024-11-22 73.80 71.63 \n",
+ "72 2024-11-27 0.000000 2024-11-27 2024-11-22 72.85 71.71 \n",
+ "73 2024-11-28 0.000000 2024-11-28 2024-11-22 72.96 71.85 \n",
+ "74 2024-11-29 0.000000 2024-11-29 2024-11-22 73.34 71.75 \n",
"\n",
" MIN_PRICE MAX_PRICE Ds_Week Pre_Week \n",
"0 71.071556 76.006900 47 47 \n",
@@ -940,54 +358,17 @@
"2 72.083850 76.204260 47 47 \n",
"3 71.329730 75.703950 47 47 \n",
"4 71.720825 76.264275 48 48 \n",
- "5 72.158165 76.173650 47 47 \n",
- "6 72.069620 76.447000 47 47 \n",
- "7 72.372900 76.082910 47 47 \n",
- "8 72.263900 76.198685 48 48 \n",
- "9 72.223820 76.041725 48 48 \n",
- "10 70.709750 75.208030 47 47 \n",
- "11 69.923110 75.423775 47 47 \n",
- "12 70.810356 75.786860 48 48 \n",
- "13 70.814430 75.791115 48 48 \n",
- "14 70.476650 75.623730 48 48 \n",
- "15 70.432760 75.480620 47 47 \n",
- "16 69.651825 75.270940 48 48 \n",
- "17 70.536285 75.826430 48 48 \n",
- "18 70.568340 75.770290 48 48 \n",
- "19 70.222640 76.113310 48 48 \n",
- "20 81.179990 81.179990 48 48 \n",
- "21 80.791550 80.791550 48 48 \n",
- "22 80.650894 80.650894 48 48 \n",
- "23 80.298000 80.298000 48 48 \n",
- "24 75.508380 80.264275 48 48 \n",
- "25 69.807790 75.837190 48 48 \n",
- "26 71.281296 76.240510 48 48 \n",
- "27 70.526230 75.955410 48 48 \n",
- "28 70.719320 75.997680 48 48 \n",
- "29 71.642590 75.828636 49 49 \n",
- "30 72.365290 75.890610 48 48 \n",
- "31 72.198810 75.729620 48 48 \n",
- "32 72.136170 75.977620 48 48 \n",
- "33 72.165790 75.880104 49 49 \n",
- "34 72.000620 75.906100 49 49 \n",
- "35 82.405370 82.405370 48 48 \n",
- "36 82.072700 82.072700 48 48 \n",
- "37 82.652700 82.652700 49 49 \n",
- "38 81.920910 81.920910 49 49 \n",
- "39 81.091270 81.091270 49 49 \n",
- "40 83.367485 83.367485 48 48 \n",
- "41 82.869705 82.869705 49 49 \n",
- "42 82.580550 82.580550 49 49 \n",
- "43 82.442154 82.442154 49 49 \n",
- "44 82.276510 82.276510 49 49 \n",
- "45 68.894770 73.909450 49 49 \n",
- "46 68.845604 73.921120 49 49 \n",
- "47 69.181210 73.906260 49 49 \n",
- "48 69.075340 73.862946 49 49 \n",
- "49 69.076550 74.007740 49 49 "
+ ".. ... ... ... ... \n",
+ "70 74.530630 76.673140 47 47 \n",
+ "71 74.440430 76.874565 47 47 \n",
+ "72 74.663180 76.734130 47 47 \n",
+ "73 74.708410 77.141050 47 47 \n",
+ "74 74.703210 77.746170 47 47 \n",
+ "\n",
+ "[75 rows x 10 columns]"
]
},
- "execution_count": 133,
+ "execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
@@ -998,7 +379,7 @@
},
{
"cell_type": "code",
- "execution_count": 134,
+ "execution_count": 140,
"id": "1374e354",
"metadata": {},
"outputs": [
@@ -1006,14 +387,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n",
+ "['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"(15, 10)\n"
]
}
],
"source": [
"# 取结束日期上一周的日期\n",
- "endtime = '2024-12-9'\n",
+ "endtime = '2024-12-3'\n",
"endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')\n",
"up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)\n",
"up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]\n",
@@ -1030,7 +417,7 @@
},
{
"cell_type": "code",
- "execution_count": 135,
+ "execution_count": 141,
"id": "8aa47e90",
"metadata": {},
"outputs": [
@@ -1038,22 +425,22 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2024-12-02 00:00:00\n",
+ "2024-11-25 00:00:00\n",
"权重： 0.07\n",
- "准确率： 0.0\n",
- "2024-12-03 00:00:00\n",
+ "准确率： 1.7749209486165771\n",
+ "2024-11-26 00:00:00\n",
"权重： 0.13\n",
"准确率： 7.5\n",
- "2024-12-04 00:00:00\n",
+ "2024-11-27 00:00:00\n",
"权重： 0.2\n",
- "准确率： 9.716034482758616\n",
- "2024-12-05 00:00:00\n",
+ "准确率： 8.034364035087705\n",
+ "2024-11-28 00:00:00\n",
"权重： 0.27\n",
- "准确率： 6.1646986607143\n",
- "2024-12-06 00:00:00\n",
+ "准确率： 9.718006756756724\n",
+ "2024-11-29 00:00:00\n",
"权重： 0.33\n",
- "准确率： 3.1205149253731137\n",
- "26.50124806884603\n"
+ "准确率： 10.824716981132076\n",
+ "37.85200872159308\n"
]
}
],
@@ -1069,14 +456,6 @@
"\n",
"print(accuracy_rote)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3e69cacc",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {