checkagain.py

import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta
import csv
import os


# Function to fetch and display the current Bitcoin price with a timestamp
def check_btc_price():
    # Fetch the latest Bitcoin price data from Yahoo Finance
    btc_data = yf.download('BTC-USD', period='1d', interval='1m')  # Using 1-minute interval to get the latest data
    current_price = btc_data['Close'].iloc[-1]  # Get the most recent closing price

    # Get the current timestamp
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')

    # Output the current price and timestamp
    print(f"Timestamp: {timestamp}")
    print(f"Current Bitcoin price: {current_price:.2f} USD")

    return current_price


# Function to check the predicted price from 24 hours ago in the log file and calculate percentage difference
def check_prediction_24_hours_ago(log_file, accuracy_log_file):
    try:
        # Load the log file into a pandas DataFrame
        df = pd.read_csv(log_file)

        # Convert 'Timestamp' column to datetime format for accurate comparison
        df['Timestamp'] = pd.to_datetime(df['Timestamp'])

        # Calculate the target time window for 24 hours ago
        target_time_24_hours_ago = datetime.now() - timedelta(days=1)
        time_window_start = target_time_24_hours_ago - timedelta(hours=1)  # 1 hour before
        time_window_end = target_time_24_hours_ago + timedelta(hours=1)  # 1 hour after

        # Filter for entries within the time window
        filtered_df = df[(df['Timestamp'] >= time_window_start) & (df['Timestamp'] <= time_window_end)]

        if filtered_df.empty:
            print(f"No prediction found within 1 hour of 24 hours ago: {target_time_24_hours_ago}.")
            return None  # Return None if no prediction is found

        # Find the entry closest to the target time within the filtered data
        closest_row = filtered_df.iloc[(filtered_df['Timestamp'] - target_time_24_hours_ago).abs().argsort()[:1]]

        # Fetch the predicted price from the closest timestamp
        closest_timestamp = closest_row['Timestamp'].values[0]
        predicted_price_24_hours_ago = closest_row['Predicted Price'].values[0]

        # Fetch the current Bitcoin price
        current_price = check_btc_price()

        # Calculate the percentage difference
        percentage_difference = ((current_price - predicted_price_24_hours_ago) / predicted_price_24_hours_ago) * 100

        # Output the results
        print(f"Timestamp 24 Hours Ago (Closest): {closest_timestamp}")
        print(f"Predicted Bitcoin Price 24 Hours Ago: {predicted_price_24_hours_ago:.2f} USD")
        print(f"Current Bitcoin Price: {current_price:.2f} USD")
        print(f"Percentage Difference: {percentage_difference:.2f}%")

        # Log the results to the accuracy CSV file with the desired format
        header = ['Current Timestamp', 'Predicted Timestamp', 'Predicted Price', 'Actual Price', 'Percentage Difference']

        # Check if the accuracy log file exists and add the header if not
        file_exists = os.path.isfile(accuracy_log_file)
        with open(accuracy_log_file, 'a', newline='') as file:
            writer = csv.writer(file)
            if not file_exists or os.path.getsize(accuracy_log_file) == 0:
                writer.writerow(header)  # Write the header if the file doesn't exist or is empty
            writer.writerow([datetime.now().strftime('%Y-%m-%d %H:%M:%S'), closest_timestamp,
                             predicted_price_24_hours_ago, current_price, percentage_difference])

        return percentage_difference  # Return percentage difference for further use if needed

    except Exception as e:
        print(f"Error occurred: {str(e)}")
        return None


# Example usage: Check prediction made 24 hours ago
prediction_log_file = 'btc_price_prediction_log.csv'
accuracy_log_file = 'btc_price_accuracy_check-2.csv'

# Calculate percentage difference
percentage_difference = check_prediction_24_hours_ago(prediction_log_file, accuracy_log_file)