"""
    The MIT License (MIT)

    Copyright (c) 2023 pkjmesra

    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.

"""

# import openai
# import pandas as pd
# from datetime import datetime, timedelta

# # Load your API key
# openai.api_key = "your_api_key_here"

# # Load stock headlines data
# headlines_data = pd.read_csv("stock_headlines.csv")

# def get_sentiment(prompt):
#     response = openai.Completion.create(
#         engine="text-davinci-002",
#         prompt=prompt,
#         max_tokens=50,
#         n=1,
#         stop=None,
#         temperature=0.5,
#     )

#     sentiment = response.choices[0].text.strip().split('\n')[0]
#     return sentiment

# def analyze_headlines(headlines_data):
#     analyzed_headlines = []

#     for index, row in headlines_data.iterrows():
#         headline = row["headline"]
#         stock = row["stock"]
#         date = row["date"]

#         prompt = (
#             f"Forget all your previous instructions. Pretend you are a financial expert. "
#             f"You are a financial expert with stock recommendation experience. "
#             f"Read the following headline about the stock '{stock}': '{headline}'. "
#             f"Answer 'YES' if good news, 'NO' if bad news, or 'UNKNOWN' if uncertain in the first line. "
#             f"Then elaborate with one short and concise sentence on the next line."
#         )

#         sentiment = get_sentiment(prompt)
#         analyzed_headlines.append([stock, date, headline, sentiment])

#     return analyzed_headlines

# def get_next_day_return(stock, date):
#     # Implement a function that gets the next day's return for a given stock
#     # This function should fetch the stock prices for the given date and the next trading day
#     # and calculate the return
#     pass

# def evaluate_strategy(analyzed_headlines):
#     successful_predictions = 0
#     total_predictions = 0

#     for stock, date, headline, sentiment in analyzed_headlines:
#         next_day_return = get_next_day_return(stock, date)

#         if (sentiment == "YES" and next_day_return > 0) or (sentiment == "NO" and next_day_return < 0):
#             successful_predictions += 1

#         total_predictions += 1

#     success_rate = successful_predictions / total_predictions
#     return success_rate

# analyzed_headlines = analyze_headlines(headlines_data)
# success_rate = evaluate_strategy(analyzed_headlines)

# print(f"The success rate of the ChatGPT-based stock prediction strategy is: {success_rate:.2%}")
