ai-news-analyzer / external /FinGPT /fingpt /FinGPT_Others /shares_news_sentiment_classify.py
Sigrid De los Santos
Remove remaining binary file for Hugging Face
9df4cc0
import os.path
import json
import http.client
import urllib.parse
import pandas as pd
import tushare as ts
import os
import openai
def get_news_from_tushare(api_key: str, data_path: str = 'finance_news_from_tushare.csv') -> str:
start_date = '2023-02-01'
end_date = '2023-02-02'
limit_line = 200
if_news_or_reports = False
if os.path.exists(data_path):
df = pd.read_csv(data_path)
else:
pro = ts.pro_api(api_key)
if if_news_or_reports:
df = pro.news(**{
"start_date": start_date,
"end_date": end_date,
"src": "sina",
"limit": limit_line,
"offset": 0
}, fields=[
"datetime",
"title"
"content",
])
else:
df = pro.jinse(**{
"start_date": start_date,
"end_date": end_date,
"limit": limit_line,
"offset": 0
}, fields=[
"datetime",
"title",
"content",
])
df.to_csv(data_path)
max_num_news = 4
max_len_title = 32
max_len_content = 0
data_str = ""
for i in df.index[:max_num_news]:
row = df.iloc[i]
title = row['title'][1:max_len_title]
content = row['content'][:max_len_content]
data_str += f"{title}, {content}\n"
return data_str
def get_news_from_market_aux(api_key: str, data_path: str = 'finance_news_from_market_aux.txt'):
limit_line = 4
if os.path.exists(data_path):
with open(data_path, 'r') as f:
data = json.load(f)
else:
conn = http.client.HTTPSConnection('api.marketaux.com')
params = urllib.parse.urlencode({
'api_token': api_key,
"found": 8,
"returned": 3,
"limit": limit_line,
"page": 1,
"source_id": "adweek.com-1",
"domain": "adweek.com",
"language": "en",
})
conn.request('GET', '/v1/news/all?{}'.format(params))
data = conn.getresponse()
data = data.read().decode('utf-8')
data = json.loads(data)
with open(data_path, 'w') as f:
f.write(json.dumps(data, indent=2))
assert isinstance(data, dict)
'''concert dict to string (Title: ... Content: ...)'''
max_num_news = 8
max_len_title = 32 * 4
max_len_content = 0 * 4
data = data['data']
data_str = ""
for item in data[:max_num_news]:
title = item['title'][:max_len_title]
content = item['description'][:max_len_content]
data_str += f"{title}, {content}\n"
return data_str
def get_result_from_openai_davinci(api_key: str, prompt_str: str):
max_tokens = 64
# openai.api_key =
openai.api_key = api_key # os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt_str,
temperature=0,
max_tokens=max_tokens,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
return response
API_KEY_Tushare = '' # https://www.tushare.pro/user/token
API_KEY_MarketAUX = '' # https://www.marketaux.com/account/dashboard
API_KEY_OpenAI = '' # https://platform.openai.com/account/api-keys
def run_news_in_chinese():
prompt_str = "读下方新闻,列举3个可能受影响的美国股票,分别简短地判断积极或消极:\n\n"
prompt_str += get_news_from_tushare(api_key=API_KEY_Tushare)
print(f"\n====\n{prompt_str}")
result_str = get_result_from_openai_davinci(api_key=API_KEY_OpenAI, prompt_str=prompt_str)
print(f"\n====\n{result_str}")
def run_news_in_english():
prompt_str = "Read following news and list 3 stocks that may be affected, " \
"briefly judge each one 'positive' or 'negative':\n\n"
prompt_str += get_news_from_market_aux(api_key=API_KEY_MarketAUX)
print(f"\n====\n{prompt_str}")
result_str = get_result_from_openai_davinci(api_key=API_KEY_OpenAI, prompt_str=prompt_str)
print(f"\n====\n{result_str}")
if __name__ == '__main__':
run_news_in_chinese()
run_news_in_english()