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FinGPT/fingpt/FinGPT_Forecaster/A-share_data_preparation.ipynb

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{
"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",
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"================== symbol601985completed! ==================\n",
"8pages obtained for symbol: 601988\n",
"================== symbol601988completed! ==================\n",
"2pages obtained for symbol: 601989\n",
"================== symbol601989completed! ==================\n",
"6pages obtained for symbol: 601995\n",
"================== symbol601995completed! ==================\n",
"6pages obtained for symbol: 601998\n",
"================== symbol601998completed! ==================\n",
"6pages obtained for symbol: 603019\n",
"================== symbol603019completed! ==================\n",
"4pages obtained for symbol: 603185\n",
"================== symbol603185completed! ==================\n",
"5pages obtained for symbol: 603195\n",
"================== symbol603195completed! ==================\n",
"7pages obtained for symbol: 603259\n",
"================== symbol603259completed! ==================\n",
"5pages obtained for symbol: 603260\n",
"================== symbol603260completed! ==================\n",
"4pages obtained for symbol: 603288\n",
"================== symbol603288completed! ==================\n",
"4pages obtained for symbol: 603290\n",
"================== symbol603290completed! ==================\n",
"4pages obtained for symbol: 603369\n",
"================== symbol603369completed! ==================\n",
"6pages obtained for symbol: 603392\n",
"================== 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\"]<end_date)]\n",
" weekly_news = [\n",
" {\n",
" \"发布时间\": n[\"发布时间\"].strftime('%Y%m%d'),\n",
" \"新闻标题\": n['新闻标题'],\n",
" \"新闻内容\": n['新闻内容'],\n",
" } for a, n in weekly_news.iterrows()\n",
" ]\n",
" news_list.append(json.dumps(weekly_news,ensure_ascii=False))\n",
"\n",
" data[\"新闻\"] = news_list\n",
"\n",
" if with_basics:\n",
" data = get_basic(symbol=symbol, data=data, no_validrange=True)\n",
" data.to_csv(symbol+start_date+\"_\"+end_date+\".csv\")\n",
" else:\n",
" data['新闻'] = [json.dumps({})] * len(data)\n",
" data.to_csv(symbol+start_date+\"_\"+end_date+\"_nobasics.csv\")\n",
" \n",
" return data"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prompt Generation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_company_prompt(symbol):\n",
" \"\"\"\n",
" Get company information from API\n",
" \"\"\"\n",
" company_profile = dict(ak.stock_individual_info_em(symbol).values)\n",
" company_profile[\"上市时间\"] = pd.to_datetime(company_profile[\"上市时间\"]).strftime(\"%Y年%m月%d日\")\n",
"\n",
" template = \"[公司介绍]:\\n\\n{股票简称}是一家在{行业}行业的领先实体。自{上市时间}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，{股票简称}的总市值为{总市值}人民币，总股本数为{总股本}，流通市值为{流通市值}人民币，流通股数为{流通股}。\" \\\n",
" \"\\n\\n{股票简称}主要在中国运营，以股票代码{股票代码}在交易所进行交易。作为在{行业}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",
" formatted_profile = template.format(**company_profile)\n",
"\n",
" return formatted_profile\n",
"\n",
"def map_return_label(return_lb):\n",
" \"\"\"\n",
" Map abbrev in the raw data\n",
" Example:\n",
" 涨1 -- 上涨1%\n",
" 跌2 -- 下跌2%\n",
" \"\"\"\n",
" \n",
" lb = return_lb.replace('涨', '上涨')\n",
" lb = lb.replace('跌', '下跌')\n",
" lb = lb.replace('1', '0-1%')\n",
" lb = lb.replace('2', '1-2%')\n",
" lb = lb.replace('3', '2-3%')\n",
" lb = lb.replace('4', '3-4%')\n",
" if lb.endswith('+'):\n",
" lb = lb.replace('5+', '超过5%')\n",
" else:\n",
" lb = lb.replace('5', '4-5%')\n",
" \n",
" return lb\n",
"\n",
"def get_prompt_by_row(symbol, row):\n",
" \"\"\"\n",
" Generate prompt for each row in the raw data DF\n",
" Args:\n",
" symbol: str\n",
" stock ticker\n",
" row: pandas.Series\n",
" Return:\n",
" head: heading prompt\n",
" news: news info\n",
" basics: basic financial info\n",
" \"\"\"\n",
"\n",
" start_date = row['起始日期'] if isinstance(row['起始日期'], str) else row['起始日期'].strftime('%Y-%m-%d')\n",
" end_date = row['结算日期'] if isinstance(row['结算日期'], str) else row['结算日期'].strftime('%Y-%m-%d')\n",
" term = '上涨' if row['结算价'] > 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
}
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