From 5b89607aeb428a3c104637140095c2fb582a305c Mon Sep 17 00:00:00 2001 From: mac Date: Wed, 24 Jan 2024 22:17:14 -0500 Subject: [PATCH] Add a notebook for A-share stock news data preparation --- .../A-share_data_preparation.ipynb | 1189 +++++++++++++++++ 1 file changed, 1189 insertions(+) create mode 100644 fingpt/FinGPT_Forecaster/A-share_data_preparation.ipynb diff --git a/fingpt/FinGPT_Forecaster/A-share_data_preparation.ipynb b/fingpt/FinGPT_Forecaster/A-share_data_preparation.ipynb new file mode 100644 index 0000000..46b2793 --- /dev/null +++ b/fingpt/FinGPT_Forecaster/A-share_data_preparation.ipynb @@ -0,0 +1,1189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pip install akshare --upgrade" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/mac/Desktop/FinGPT_Forecasting_Project'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "HS300_stock = pd.read_csv(\"HS300Index.csv\", header=None)\n", + "HS_index = HS300_stock[0].tolist()\n", + "HS_index = [\"0\"*(6-len(str(i)))+str(i) for i in HS_index]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# with open(\"HS300_Index.txt\", 'w') as f:\n", + "# write_item = \"\\n\".join(HS_index)\n", + "# f.write(write_item)\n", + "# f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11pages obtained for symbol: 000063\n", + "================== symbol000063completed! ==================\n", + "6pages obtained for symbol: 000069\n", + "================== 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symbol603392completed! ==================\n", + "3pages obtained for symbol: 603486\n", + "================== symbol603486completed! ==================\n", + "6pages obtained for symbol: 603501\n", + "================== symbol603501completed! ==================\n", + "4pages obtained for symbol: 603659\n", + "================== symbol603659completed! ==================\n", + "5pages obtained for symbol: 603799\n", + "================== symbol603799completed! ==================\n", + "3pages obtained for symbol: 603806\n", + "================== symbol603806completed! ==================\n", + "5pages obtained for symbol: 603833\n", + "================== symbol603833completed! ==================\n", + "2pages obtained for symbol: 603899\n", + "================== symbol603899completed! ==================\n", + "5pages obtained for symbol: 603986\n", + "================== symbol603986completed! ==================\n", + "4pages obtained for symbol: 603993\n", + "================== symbol603993completed! ==================\n", + "5pages obtained for symbol: 605117\n", + "================== symbol605117completed! ==================\n", + "7pages obtained for symbol: 605499\n", + "================== symbol605499completed! ==================\n", + "6pages obtained for symbol: 688005\n", + "================== symbol688005completed! ==================\n", + "5pages obtained for symbol: 688008\n", + "================== symbol688008completed! ==================\n", + "9pages obtained for symbol: 688012\n", + "================== symbol688012completed! ==================\n", + "8pages obtained for symbol: 688036\n", + "================== symbol688036completed! ==================\n", + "3pages obtained for symbol: 688065\n", + "================== symbol688065completed! ==================\n", + "11pages obtained for symbol: 688111\n", + "================== symbol688111completed! ==================\n", + "4pages obtained for symbol: 688126\n", + "================== symbol688126completed! ==================\n", + "3pages obtained for symbol: 688187\n", + "================== symbol688187completed! ==================\n", + "10pages obtained for symbol: 688223\n", + "================== symbol688223completed! ==================\n", + "6pages obtained for symbol: 688303\n", + "================== symbol688303completed! ==================\n", + "6pages obtained for symbol: 688363\n", + "================== symbol688363completed! ==================\n", + "3pages obtained for symbol: 688396\n", + "================== symbol688396completed! ==================\n", + "5pages obtained for symbol: 688561\n", + "================== symbol688561completed! ==================\n", + "10pages obtained for symbol: 688599\n", + "================== symbol688599completed! ==================\n", + "8pages obtained for symbol: 688981\n", + "================== symbol688981completed! ==================\n" + ] + } + ], + "source": [ + "# Data Aquisition Pipeline\n", + "from datetime import date\n", + "import akshare as ak\n", + "name_dir = \"HS300_news_data\" + str(date.today().strftime(\"%Y%m%d\"))\n", + "# os.makedirs(name_dir, exist_ok=True)\n", + "os.chdir(name_dir)\n", + "for i in HS_index[2:]:\n", + " file_name = \"news_data\" + i + \".csv\"\n", + " df_list = []\n", + " for j in range(1, 20):\n", + " try:\n", + " df_list.append(ak.stock_news_em(symbol=i, page=j))\n", + " except KeyError:\n", + " print(str(j) + \"pages obtained for symbol: \" + i)\n", + " break\n", + " news_df_i = pd.concat(df_list, ignore_index=True)\n", + " news_df_i.to_csv(file_name)\n", + " print(\"================== symbol\"+i+\"completed! ==================\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Aquisition" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import akshare as ak\n", + "import pandas as pd\n", + "import time\n", + "import math\n", + "import json\n", + "import random\n", + "start_date = \"20230406\"\n", + "end_date = \"20230502\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def return_transform(ret):\n", + " \n", + " up_down = '涨' if ret >= 0 else '跌'\n", + " integer = math.ceil(abs(100 * ret))\n", + " \n", + " return up_down + (str(integer) if integer <= 5 else '5+')\n", + "\n", + "def get_return(symbol, start_date = start_date, end_date = end_date, adjust=\"hfq\"):\n", + " \"\"\"\n", + " Get stock return data.\n", + "\n", + " Args:\n", + " symbol: str\n", + " A-share market stock symbol\n", + " start_date, end_date: str\n", + " format: yyyymmdd\n", + " adjust: str (\"qfq\", \"hfq\")\n", + " price ajustment\n", + " default = \"hfq\" 后复权\n", + " \n", + " Return:\n", + " weekly forward filled return data\n", + " \"\"\"\n", + " \n", + " # load data\n", + " return_data = ak.stock_zh_a_hist(symbol=symbol, period=\"daily\", start_date=start_date, end_date=end_date, adjust=adjust)\n", + " \n", + " # process timestamp\n", + " return_data[\"日期\"] = pd.to_datetime(return_data[\"日期\"])\n", + " return_data.set_index(\"日期\", inplace=True)\n", + "\n", + " # resample and filled with forward data\n", + " weekly_data = return_data[\"收盘\"].resample(\"W\").ffill()\n", + " weekly_returns = weekly_data.pct_change()[1:]\n", + " weekly_start_prices = weekly_data[:-1]\n", + " weekly_end_prices = weekly_data[1:]\n", + " weekly_data = pd.DataFrame({\n", + " '起始日期': weekly_start_prices.index,\n", + " '起始价': weekly_start_prices.values,\n", + " '结算日期': weekly_end_prices.index,\n", + " '结算价': weekly_end_prices.values,\n", + " '周收益': weekly_returns.values\n", + " })\n", + " weekly_data[\"简化周收益\"] = weekly_data[\"周收益\"].map(return_transform)\n", + " \n", + " return weekly_data\n", + "\n", + "# get basics\n", + "def get_basic(symbol, data, no_validrange = False):\n", + " \"\"\"\n", + " Get and match basic data to news dataframe.\n", + "\n", + " Args:\n", + " symbol: str\n", + " A-share market stock symbol\n", + " data: DataFrame\n", + " dated news data\n", + " no_validrange: bool\n", + " False to set range for the valid basic_info.\n", + " default valid range is 3 weeks after the basic_info reported\n", + " \n", + " Return:\n", + " financial news dataframe with matched basic_financial info\n", + " \"\"\"\n", + "\n", + " # load quarterly basic data\n", + " basic_quarter_financials = ak.stock_financial_abstract_ths(symbol = symbol, indicator=\"按单季度\")\n", + "\n", + " basic_fin_dict = basic_quarter_financials.to_dict(\"index\")\n", + " basic_fin_list = [dict([(key, val) for key, val in basic_fin_dict[i].items() if val]) for i in range(len(basic_fin_dict))]\n", + "\n", + " # match basic financial data to news dataframe\n", + " matched_basic_fin = []\n", + " for i, row in data.iterrows():\n", + "\n", + " newsweek_enddate = row['结算日期'].strftime(\"%Y-%m-%d\")\n", + " \n", + " # set 3 weeks for the valid time of quarterly basic_financial info \n", + " check_date = start_date if i<2 else data.loc[i-2, '起始日期'].strftime(\"%Y-%m-%d\")\n", + "\n", + " matched_basic = {}\n", + " for basic in basic_fin_list:\n", + " if (no_validrange and basic[\"报告期\"] < newsweek_enddate) or (check_date <= basic[\"报告期\"] < newsweek_enddate):\n", + " matched_basic = basic\n", + " break\n", + " matched_basic_fin.append(json.dumps(matched_basic, ensure_ascii=False))\n", + "\n", + " data['基本面'] = matched_basic_fin\n", + "\n", + " return data\n", + "\n", + "def raw_financial_data(symbol, with_basics = True):\n", + " \n", + " # get return data from API\n", + " data = get_return(symbol=symbol)\n", + " \n", + " # get news data from local\n", + " file_name = \"news_data\" + symbol + \".csv\"\n", + " news_df = pd.read_csv(\"HS300_news_data20240118/\"+file_name, index_col=0)\n", + " news_df[\"发布时间\"] = pd.to_datetime(news_df[\"发布时间\"], exact=False, format=\"%Y-%m-%d\")\n", + " news_df.sort_values(by=[\"发布时间\"], inplace=True)\n", + " \n", + " # match weekly news for return data\n", + " news_list = []\n", + " for a, row in data.iterrows():\n", + " start_date = row['起始日期'].strftime('%Y-%m-%d')\n", + " end_date = row['结算日期'].strftime('%Y-%m-%d')\n", + " print(symbol, ': ', start_date, ' - ', end_date)\n", + " \n", + " weekly_news = news_df.loc[(news_df[\"发布时间\"]>start_date) & (news_df[\"发布时间\"] row['起始价'] else '下跌'\n", + " head = \"自{}至{}，{}的股票价格由{:.2f}{}至{:.2f}。在此期间的公司新闻如下所列:\\n\\n\".format(\n", + " start_date, end_date, symbol, row['起始价'], term, row['结算价'])\n", + "\n", + " news = json.loads(row[\"新闻\"])\n", + " news = [\"[新闻标题]：{}\\n[新闻内容]：{}\\n\".format(\n", + " n['新闻标题'], n['新闻内容']) for n in news if n['发布时间'][:8] <= end_date.replace('-', '')]\n", + "\n", + " basics = json.loads(row['基本面'])\n", + " if basics:\n", + " basics = \"如下所列为{}近期的一些金融基本面信息，记录时间为{}:\\n\\n[金融基本面]:\\n\\n\".format(\n", + " symbol, basics['报告期']) + \"\\n\".join(f\"{k}: {v}\" for k, v in basics.items() if k != 'period')\n", + " else:\n", + " basics = \"[金融基本面]:\\n\\n 无金融基本面记录\"\n", + "\n", + " return head, news, basics\n", + "\n", + "def sample_news(news, k=5):\n", + " \"\"\"\n", + " Ramdomly select past news.\n", + "\n", + " Args:\n", + " news:\n", + " newslist in the timerange\n", + " k: int\n", + " the number of selected news\n", + " \"\"\"\n", + " return [news[i] for i in sorted(random.sample(range(len(news)), k))]\n", + "\n", + "def get_all_prompts(symbol, min_past_week=1, max_past_weeks=3, with_basics=True):\n", + " \"\"\"\n", + " Generate prompt\n", + " Args:\n", + " symbol: str\n", + " stock ticker\n", + " min_past_week: int\n", + " max_past_week: int\n", + " with_basics: bool\n", + " If true, add basic infomation to the prompt\n", + " Return:\n", + " Prompts for the daterange\n", + " \"\"\"\n", + "\n", + " # Load Data\n", + " df = raw_financial_data(symbol, with_basics)\n", + " \n", + " company_prompt = get_company_prompt(symbol)\n", + "\n", + " prev_rows = []\n", + " all_prompts = []\n", + "\n", + " for row_idx, row in df.iterrows():\n", + "\n", + " prompt = \"\"\n", + "\n", + " # judge for available history news \n", + " if len(prev_rows) >= min_past_week:\n", + "\n", + " # randomly set retrieve weeks\n", + " idx = min(random.choice(range(min_past_week, max_past_weeks+1)), len(prev_rows))\n", + " for i in range(-idx, 0):\n", + " # Add Head\n", + " prompt += \"\\n\" + prev_rows[i][0]\n", + " # Add History News (with numbers constraint)\n", + " sampled_news = sample_news(\n", + " prev_rows[i][1],\n", + " min(5, len(prev_rows[i][1]))\n", + " )\n", + " if sampled_news:\n", + " prompt += \"\\n\".join(sampled_news)\n", + " else:\n", + " prompt += \"无有关新闻报告\"\n", + " \n", + " head, news, basics = get_prompt_by_row(symbol, row)\n", + " \n", + " prev_rows.append((head, news, basics))\n", + "\n", + " if len(prev_rows) > max_past_weeks:\n", + " prev_rows.pop(0)\n", + " \n", + " # set this to make sure there is history news for each considered date\n", + " if not prompt:\n", + " continue\n", + "\n", + " prediction = map_return_label(row['简化周收益'])\n", + "\n", + " prompt = company_prompt + '\\n' + prompt + '\\n' + basics\n", + " prompt += f\"\\n\\n基于在{row['起始日期']}之前的所有信息，让我们首先分析{symbol}的积极发展和潜在担忧。保持陈述的简洁，分别提出2-4个最重要的因素。大部分所提及的因素应该从公司的相关新闻中推断出来。\" \\\n", + " f\"那么让我们假设你对于下一周({row['起始日期']}至{row['结算日期']})的预测是{prediction}。提供一个总结分析来支持你的预测。预测结果需要从你最后的分析中推断出来，因此不作为你分析的基础因素。\"\n", + "\n", + " all_prompts.append(prompt.strip())\n", + "\n", + " return all_prompts" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "000001 : 2023-04-09 - 2023-04-16\n", + "000001 : 2023-04-16 - 2023-04-23\n", + "000001 : 2023-04-23 - 2023-04-30\n" + ] + }, + { + "data": { + "text/plain": [ + "['[公司介绍]:\\n\\n平安银行是一家在银行行业的领先实体。自1970年01月01日成立并公开交易以来，该公司已确立其作为市场主要参与者之一的声誉。截止今天，平安银行的总市值为178534447421.59998人民币，总股本数为19405918198.0，流通市值为178531031940.0人民币，流通股数为19405546950.0。\\n\\n平安银行主要在中国运营，以股票代码000001在交易所进行交易。作为在银行行业的一个主导力量，该公司持续创新，不断推动行业进步。\\n\\n自2023-04-09至2023-04-16，000001的股票价格由2257.00上涨至2268.38。在此期间的公司新闻如下所列:\\n\\n[新闻标题]：金融业高薪招聘AI训练师 有AI产品负责人年薪超百万\\n[新闻内容]：优先考虑具有拥有虚拟偶像、数字人相关产品工作经验者等。 BOSS直聘官网截图 也有一些相对“平易近人”的工作岗位。 如平安银行000001.SZ)网金及财富管理事业部招聘AI业务策略训练师，薪资待遇为1万-1.5万元/月。 平安银行介绍称，该岗位负责业务策略训练，将业务策略在智慧经营平台做落地实施，将策略与各触达方式做串联部署\\n\\n[新闻标题]：平安银行王蓉晖：矩阵式产品服务实现“真小微 真信用 真普惠”\\n[新闻内容]：就业多则经济活。 今年3月，在平安银行开放日上，平安银行董事长谢永林表示，全新的开放银行不仅是支持零售转型战略的新打法，而且通过服务平台上的海量小微客户，平安银行在服务小微，践行普惠金融方面也探索出了新举措。 平安银行财报显示，截至2022年末，平安银行普惠型小微企业贷款累计户数105\\n\\n[新闻标题]：20股特大单净流入资金超2亿元\\n[新闻内容]：491.562.30建筑装饰300120经纬辉开8.7819.952.28电子002051中工国际12.5010.042.25建筑装饰000001平安银行12.691.042.24银行601138工业富联18.072.322.09电子300014亿纬锂能68.904.242.07电力设备 特大单净流出资金排名\\n\\n如下所列为000001近期的一些金融基本面信息，记录时间为2023-03-31:\\n\\n[金融基本面]:\\n\\n报告期: 2023-03-31\\n净利润: 146.02亿\\n净利润同比增长率: 13.63%\\n扣非净利润: 144.69亿\\n扣非净利润同比增长率: 12.40%\\n营业总收入: 450.98亿\\n营业总收入同比增长率: -2.40%\\n基本每股收益: 0.6500\\n每股净资产: 19.42\\n每股资本公积金: 4.16\\n每股未分配利润: 10.25\\n每股经营现金流: 5.62\\n销售净利率: 32.38%\\n净资产收益率: 3.39%\\n净资产收益率-摊薄: 3.27%\\n产权比率: 11.21\\n资产负债率: 91.81%\\n\\n基于在2023-04-16 00:00:00之前的所有信息，让我们首先分析000001的积极发展和潜在担忧。保持陈述的简洁，分别提出2-4个最重要的因素。大部分所提及的因素应该从公司的相关新闻中推断出来。那么让我们假设你对于下一周(2023-04-16 00:00:00至2023-04-23 00:00:00)的预测是下跌1-2%。提供一个总结分析来支持你的预测。预测结果需要从你最后的分析中推断出来，因此不作为你分析的基础因素。',\n", + " '[公司介绍]:\\n\\n平安银行是一家在银行行业的领先实体。自1970年01月01日成立并公开交易以来，该公司已确立其作为市场主要参与者之一的声誉。截止今天，平安银行的总市值为178534447421.59998人民币，总股本数为19405918198.0，流通市值为178531031940.0人民币，流通股数为19405546950.0。\\n\\n平安银行主要在中国运营，以股票代码000001在交易所进行交易。作为在银行行业的一个主导力量，该公司持续创新，不断推动行业进步。\\n\\n自2023-04-16至2023-04-23，000001的股票价格由2268.38下跌至2237.50。在此期间的公司新闻如下所列:\\n\\n[新闻标题]：北向资金昨日净买入22.64亿元，光伏设备增持超10亿元（名单）\\n[新闻内容]：北向资金净卖出昆仑万维(300418.SZ)、金山办公(688111.SH)、平安银行000001.SZ)、华友钴业(603799.SH)、天齐锂业(002466.SZ)居前，其中，昆仑万维被净卖出4.30亿元，金山办公被净卖出3.65亿元，平安银行被净卖出3.60亿元，华友钴业被净卖出2.76亿元，天齐锂业被净卖出2\\n\\n如下所列为000001近期的一些金融基本面信息，记录时间为2023-03-31:\\n\\n[金融基本面]:\\n\\n报告期: 2023-03-31\\n净利润: 146.02亿\\n净利润同比增长率: 13.63%\\n扣非净利润: 144.69亿\\n扣非净利润同比增长率: 12.40%\\n营业总收入: 450.98亿\\n营业总收入同比增长率: -2.40%\\n基本每股收益: 0.6500\\n每股净资产: 19.42\\n每股资本公积金: 4.16\\n每股未分配利润: 10.25\\n每股经营现金流: 5.62\\n销售净利率: 32.38%\\n净资产收益率: 3.39%\\n净资产收益率-摊薄: 3.27%\\n产权比率: 11.21\\n资产负债率: 91.81%\\n\\n基于在2023-04-23 00:00:00之前的所有信息，让我们首先分析000001的积极发展和潜在担忧。保持陈述的简洁，分别提出2-4个最重要的因素。大部分所提及的因素应该从公司的相关新闻中推断出来。那么让我们假设你对于下一周(2023-04-23 00:00:00至2023-04-30 00:00:00)的预测是上涨0-1%。提供一个总结分析来支持你的预测。预测结果需要从你最后的分析中推断出来，因此不作为你分析的基础因素。']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_all_prompts(\"000001\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "你是一个经验丰富的股票市场分析师。你的任务是根据过去几周的相关新闻和基本财务状况，列出公司的积极发展和潜在担忧，然后对公司未来一周的股价走势提供分析和预测。你的回答格式应该如下：\n", + "\n", + "[积极发展]：\n", + "1. ...\n", + "\n", + "[潜在担忧]：\n", + "1. ...\n", + "\n", + "[预测和分析]：\n", + "...\n", + "\n" + ] + } + ], + "source": [ + "SYSTEM_PROMPT = \"你是一个经验丰富的股票市场分析师。你的任务是根据过去几周的相关新闻和基本财务状况，列出公司的积极发展和潜在担忧，然后对公司未来一周的股价走势提供分析和预测。\" \\\n", + " \"你的回答格式应该如下：\\n\\n[积极发展]：\\n1. ...\\n\\n[潜在担忧]：\\n1. ...\\n\\n[预测和分析]：\\n...\\n\"\n", + "print(SYSTEM_PROMPT)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "000001 : 2023-04-09 - 2023-04-16\n", + "000001 : 2023-04-16 - 2023-04-23\n", + "000001 : 2023-04-23 - 2023-04-30\n" + ] + } + ], + "source": [ + "pmt = get_all_prompts(\"000001\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "client = OpenAI(api_key = '')\n", + "completion = client.chat.completions.create(\n", + " model = \"gpt-3.5-turbo\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\":SYSTEM_PROMPT},\n", + " {\"role\": \"user\", \"content\": pmt[1]}\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "请求失败，状态码: 429\n", + "请求失败，body: {\"error\":{\"code\":\"insufficient_quota\",\"param\":null,\"type\":\"insufficient_quota\",\"message\":\"You exceeded your current quota, please check your plan and billing details.\"}}\n", + "请求失败，X-BC-Request-Id: cfcdb74d-9416-47e4-98d2-b6149fa1765f\n" + ] + } + ], + "source": [ + "import requests\n", + "import json\n", + "\n", + "def do_request():\n", + " url = \"https://api.baichuan-ai.com/v1/chat/completions\"\n", + " api_key = \"sk-7c765c13dddfc800953163c1b98b9304\"\n", + "\n", + " data = {\n", + " \"model\": \"Baichuan2-Turbo\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"世界第一高峰是\"\n", + " }\n", + " ],\n", + " \"stream\": True\n", + " }\n", + "\n", + " json_data = json.dumps(data)\n", + "\n", + " headers = {\n", + " \"Content-Type\": \"application/json\",\n", + " \"Authorization\": \"Bearer \" + api_key\n", + " }\n", + "\n", + " response = requests.post(url, data=json_data, headers=headers, timeout=60)\n", + "\n", + " if response.status_code == 200:\n", + " print(\"请求成功！\")\n", + " print(\"响应body:\", response.text)\n", + " print(\"请求成功，X-BC-Request-Id:\", response.headers.get(\"X-BC-Request-Id\"))\n", + " else:\n", + " print(\"请求失败，状态码:\", response.status_code)\n", + " print(\"请求失败，body:\", response.text)\n", + " print(\"请求失败，X-BC-Request-Id:\", response.headers.get(\"X-BC-Request-Id\"))\n", + "\n", + "if __name__ == \"__main__\":\n", + " do_request()\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}