147 lines
4.4 KiB
Python
147 lines
4.4 KiB
Python
import os
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from openai import OpenAI
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from .config import get_config
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def get_stock_news_openai(query, start_date, end_date):
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"""
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使用 OpenAI 模型搜索社交媒體上的股票新聞。
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Args:
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query (str): 搜索查詢。
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start_date (str): 開始日期。
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end_date (str): 結束日期。
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Returns:
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str: 模型的文字回應。
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"""
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config = get_config()
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# Get the OpenAI API key from environment variable
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openai_api_key = os.getenv("OPENAI_API_KEY")
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client = OpenAI(base_url=config["backend_url"], api_key=openai_api_key)
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"您能從 {start_date} 到 {end_date} 在社交媒體上搜索關於 {query} 的資訊嗎？請確保您只獲取在該期間內發布的數據。",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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def get_global_news_openai(curr_date, look_back_days=7, limit=5):
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"""
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使用 OpenAI 模型搜索全球宏觀經濟新聞。
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Args:
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curr_date (str): 當前日期。
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look_back_days (int): 回溯天數。
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limit (int): 結果數量限制。
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Returns:
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str: 模型的文字回應。
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"""
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config = get_config()
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# Get the OpenAI API key from environment variable
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openai_api_key = os.getenv("OPENAI_API_KEY")
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client = OpenAI(base_url=config["backend_url"], api_key=openai_api_key)
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"您能從 {curr_date} 前回溯 {look_back_days} 天到 {curr_date} 期間，搜索對交易有參考價值的全球或宏觀經濟新聞嗎？請確保您只獲取在該期間內發布的數據。將結果限制在 {limit} 篇文章。",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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def get_fundamentals_openai(ticker, curr_date):
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"""
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使用 OpenAI 模型搜索公司的基本面數據。
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Args:
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ticker (str): 股票代碼。
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curr_date (str): 當前日期。
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Returns:
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str: 模型的文字回應。
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"""
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config = get_config()
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# Get the OpenAI API key from environment variable
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openai_api_key = os.getenv("OPENAI_API_KEY")
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client = OpenAI(base_url=config["backend_url"], api_key=openai_api_key)
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"您能搜索關於 {ticker} 在 {curr_date} 前一個月到 {curr_date} 當月的討論中的基本面數據嗎？請確保您只獲取在該期間內發布的數據。以表格形式列出，包含本益比/市銷率/現金流等資訊。",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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