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shares_news_sentiment_classify.py
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shares_news_sentiment_classify.py
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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()