113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
from openai import OpenAI
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import time
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import ujson
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import sqlite3
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import requests
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import os
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from dotenv import load_dotenv
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from tqdm import tqdm
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from datetime import datetime
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# Load environment variables
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load_dotenv()
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# Initialize OpenAI client
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benzinga_api_key = os.getenv('BENZINGA_API_KEY')
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openai_api_key = os.getenv('OPENAI_API_KEY')
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org_id = os.getenv('OPENAI_ORG')
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client = OpenAI(
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api_key=openai_api_key,
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organization=org_id,
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)
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headers = {"accept": "application/json"}
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url = "https://api.benzinga.com/api/v1/analyst/insights"
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def save_json(symbol, data):
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with open(f"json/analyst/insight/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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def get_analyst_insight(ticker):
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res_dict = {}
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try:
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querystring = {"token": benzinga_api_key,"symbols": ticker}
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response = requests.request("GET", url, params=querystring)
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output = ujson.loads(response.text)['analyst-insights'][0] #get the latest insight only
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# Extracting required fields
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res_dict = {
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'insight': output['analyst_insights'],
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'id': output['id'],
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'date': datetime.strptime(output['date'], "%Y-%m-%d").strftime("%b %d, %Y")
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}
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except:
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pass
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return res_dict
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# Function to summarize the text using GPT-3.5-turbo
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def get_summary(data):
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# Define the data to be summarized
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# Format the data as a string
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data_string = (
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f"Insights: {data['insight']}"
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)
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response = client.chat.completions.create(
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model="gpt-3.5-turbo-0125",
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messages=[
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{"role": "system", "content": "Summarize analyst insights clearly and concisely in under 400 characters. Ensure the summary is professional and easy to understand. Conclude with whether the report is bullish or bearish."},
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{"role": "user", "content": data_string}
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],
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max_tokens=150,
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temperature=0.7
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)
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summary = response.choices[0].message.content
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data = {
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'insight': summary,
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'id': data['id'],
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'date': data['date']
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}
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return data
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try:
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stock_con = sqlite3.connect('stocks.db')
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stock_cursor = stock_con.cursor()
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stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%'")
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stock_symbols = [row[0] for row in stock_cursor.fetchall()]
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stock_con.close()
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for symbol in tqdm(stock_symbols):
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try:
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data = get_analyst_insight(symbol)
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new_report_id = data.get('id', '')
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try:
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with open(f"json/analyst/insight/{symbol}.json", 'r') as file:
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old_report_id = ujson.load(file).get('id', '')
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except:
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old_report_id = ''
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#check first if new report id exist already to save money before sending it to closedai company
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if new_report_id != old_report_id and len(data['insight']) > 0:
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res = get_summary(data)
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save_json(symbol, res)
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else:
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print('skipped')
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except:
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pass
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except Exception as e:
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print(e)
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