diff --git a/config_jingbo.py b/config_jingbo.py index 48f5a77..7374b09 100644 --- a/config_jingbo.py +++ b/config_jingbo.py @@ -214,14 +214,13 @@ warning_data = { } query_data_list_item_nos_data = { - "funcModule":'数据项管理', - "funcOperation":'查询数据项编码', - "data":{ - "dataItemNoList":['Brent活跃合约',''], - "dateEnd":'', - "dateStart":'2023-01-01' - - } + "funcModule": "数据项", + "funcOperation": "查询", + "data": { + "dateStart":"20200101", + "dateEnd":"20241231", + "dataItemNoList":["Brentzdj","Brentzgj"] + } } @@ -238,6 +237,7 @@ table_name = 'v_tbl_crude_oil_warning' is_train = False # 是否训练 is_debug = False # 是否调试 is_eta = True # 是否使用eta接口 +is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 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 diff --git a/data.csv b/data.csv new file mode 100644 index 0000000..90073b0 --- /dev/null +++ b/data.csv @@ -0,0 +1,2567 @@ +,dataDate,Brentzdj,Brentzgj +0,20240709,84.53, 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raise Exception(f'Error: {response.status_code}, {response.text}') +def get_market_data(end_time,df): + """ + 获取市场数据，拼接到df中 + """ + # 获取token + token = get_head_auth_report() + # 定义请求参数 + query_data_list_item_nos_data['data']['dateEnd'] = end_time.replace('-','') + # 发送请求 + headers = {"Authorization": token} + items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35)) + json_data = json.loads(items_res.text) + df = pd.DataFrame(json_data['data']) + # 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe + df2 = pd.DataFrame() + for i in df['dataItemNo'].unique(): + df1 = df[df['dataItemNo'] == i] + df1 = df1[['dataDate', 'dataValue']] + df1 = df1.rename(columns={'dataValue': i}) + df2 = pd.concat([df2, df1], axis=0) + df2 = df2.rename(columns={'dataDate': 'ds'}) + # 20240101 转换为 2024-01-01 + df2['ds'] = pd.to_datetime(df2['ds'], format='%Y%m%d') + df2['ds'] = df2['ds'].dt.strftime('%Y-%m-%d') + df = pd.merge(df, df2, how='outer',on='ds') + return df + # 时间特征，年，月，一年的多少天，周几，第几周，第几季度，每月的第几天， 每季度的第几天，是否每月的第一天，是否每月的最后一天，是否每季度的第一天，是否每季度的最后一天，是否每年的第一天，是否每年的最后一天 def addtimecharacteristics(df,dataset): """ diff --git a/main_yuanyou.py b/main_yuanyou.py index d15475c..83d3a48 100644 --- a/main_yuanyou.py +++ b/main_yuanyou.py @@ -74,6 +74,10 @@ def predict_main(): ) df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理 + if is_market: + logger.info('从市场信息平台获取数据...') + df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju) + # 数据处理 df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, end_time=end_time) @@ -94,11 +98,11 @@ def predict_main(): # 取第一行数据存储到数据库中 first_row = df[['ds', 'y']].tail(1) # 判断ds是否与ent_time 一致且 y 不为空 - if len(first_row) > 0 and first_row['y'].values[0] is not None: + if first_row['ds'].values[0] == end_time and not np.isnan(first_row['y'].values[0]): pass else: - logger.info('{end_time}预测目标数据为空，跳过') - return + logger.info(f'{end_time}预测目标数据为空，跳过') + return None # 将最新真实值保存到数据库 if not sqlitedb.check_table_exists('trueandpredict'): first_row.to_sql('trueandpredict', sqlitedb.connection, index=False) @@ -190,7 +194,6 @@ def predict_main(): except: logger.info('上传预警信息到数据库失败') - if is_corr: df = corr_feature(df=df) @@ -263,7 +266,7 @@ if __name__ == '__main__': global end_time is_on = True # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 - for i_time in pd.date_range('2024-12-25', '2024-12-26', freq='B'): + for i_time in pd.date_range('2024-12-24', '2024-12-26', freq='B'): end_time = i_time.strftime('%Y-%m-%d') predict_main() if is_on: diff --git a/测试环境获取市场信息平台数据项.ipynb b/测试环境获取市场信息平台数据项.ipynb index 5b9306b..c3d2afd 100644 --- a/测试环境获取市场信息平台数据项.ipynb +++ b/测试环境获取市场信息平台数据项.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "id": "31c0e11d-c87a-4e95-92a0-d1d09625e255", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 37, "id": "83c81b9e", "metadata": {}, "outputs": [ @@ -25,7 +25,7 @@ "'http://192.168.100.53:8080/jingbo-dev/api/server/login'" ] }, - "execution_count": 26, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 38, "id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f", "metadata": {}, "outputs": [ @@ -59,7 +59,7 @@ " 'funcOperation': '获取token'}" ] }, - "execution_count": 27, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, "id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9", "metadata": {}, "outputs": [], @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 40, "id": "22c0c7c4", "metadata": {}, "outputs": [ @@ -95,7 +95,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzUxNDkzMzYsImp0aSI6IjczYzJkOGJjYzQ2NzQwYjNiYWQxZmI3NjMzODM4YTcxIn0.zLVuyCEbg-x9lRXuJDYbdiwzo_nhEQGCCInnJKfQcd8', 'md5Token': '39413fe9e3e93f717d8d2713c4487172'}, 'status': True}\n" + "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzUyMjYxOTMsImp0aSI6IjdjNzllYTc2NTY0MTQxODdiOTg1ZTJlODVjMDIyZGE2In0.IOhOgFiEHKA1VdQx4dYubLmZ2ZwpHppm4XbJkpZXIJg', 'md5Token': 'b939d2aed088dbff085dac1863f1eea4'}, 'status': True}\n" ] } ], @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 41, "id": "12077ead", "metadata": {}, "outputs": [], @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 42, "id": "a7ae21d1", "metadata": {}, "outputs": [], @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "54942e1a", "metadata": {}, "outputs": [ @@ -157,7 +157,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"confirmFlg\":false,\"data\":[],\"status\":true}\n" + 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"dataDate\":\"20241114\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":73.22000000},{\"dataDate\":\"20240326\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":86.42000000},{\"dataDate\":\"20240820\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":78.35000000},{\"dataDate\":\"20240814\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":81.44000000},{\"dataDate\":\"20240806\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":77.85000000},{\"dataDate\":\"20240807\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":78.87000000},{\"dataDate\":\"20240808\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":79.37000000},{\"dataDate\":\"20240426\",\"dataItemNo\":\"Brentzgj\",\"dataValue\":88.64000000}],\"status\":true}\n" ] } ], @@ -166,9 +166,9 @@ " \"funcModule\": \"数据项\",\n", " \"funcOperation\": \"查询\",\n", " \"data\": {\n", - " \"dateStart\":\"20240101\",\n", + " \"dateStart\":\"20200101\",\n", " \"dateEnd\":\"20241231\",\n", - " \"dataItemNoList\":[\"EXCHANGE|RATE|MIDDLE_PRICE\",\"/|250290802|PRICE\"]\n", + " \"dataItemNoList\":[\"Brentzdj\",\"Brentzgj\"]\n", " }\n", "}\n", "\n", @@ -176,6 +176,61 @@ "items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35))\n", "print(items_res.text)" ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5272c84b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dataDate Brentzdj Brentzgj\n", + "0 20240709 84.53 NaN\n", + "1 20241224 72.46 NaN\n", + "2 20200102 65.72 NaN\n", + "3 20200103 66.22 NaN\n", + "4 20200106 68.21 NaN\n", + "... ... ... ...\n", + "2561 20240814 NaN 81.44\n", + "2562 20240806 NaN 77.85\n", + "2563 20240807 NaN 78.87\n", + "2564 20240808 NaN 79.37\n", + "2565 20240426 NaN 88.64\n", + "\n", + "[2566 rows x 3 columns]\n" + ] + } + ], + "source": [ + "# 遍历items.text 获取data ,整理成 ds itemno value 的形式\n", + "import json\n", + "import pandas as pd\n", + "json_data = json.loads(items_res.text)\n", + "df = pd.DataFrame(json_data['data'])\n", + "# 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe\n", + "df2 = pd.DataFrame()\n", + "for i in df['dataItemNo'].unique():\n", + " df1 = df[df['dataItemNo'] == i]\n", + " df1 = df1[['dataDate', 'dataValue']]\n", + " df1 = df1.rename(columns={'dataValue': i})\n", + " df2 = pd.concat([df2, df1], axis=0)\n", + "print(df2)\n", + "\n", + "\n", + "\n", + "df2.to_csv('data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b1de00f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {