check dataset, make predictions, sees mistakes, does not learn from mistakes

import time
import requests
from bs4 import BeautifulSoup
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# URL of the webpage containing the chart
url = 'https://example.com/chart'

# Initialize an empty DataFrame to store the historical chart data
historical_data = pd.DataFrame(columns=['Timestamp', 'Value'])

# Continuously monitor the website and extract chart data
while True:
    # Send a GET request to the webpage
    response = requests.get(url)

    # Parse the HTML content of the webpage using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')

    # Extract the chart data from the webpage using appropriate HTML selectors and attributes
    chart_data = []
    # ...

    # Process the chart data and append it to the historical data DataFrame
    df = pd.DataFrame(chart_data, columns=['Timestamp', 'Value'])
    historical_data = pd.concat([historical_data, df], ignore_index=True)

    # Perform data preprocessing (e.g., cleaning, feature engineering, etc.) on the historical data
    # ...

    # Create target variable 'Direction' indicating price direction
    historical_data['Direction'] = historical_data['Value'].diff().apply(lambda x: 1 if x > 0 else 0)

    # Train a prediction model using the historical data
    X = historical_data.drop(columns=['Timestamp', 'Value', 'Direction'])  # Input features
    y = historical_data['Direction']  # Target variable

    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Train a prediction model (e.g., logistic regression)
    model = LogisticRegression()
    model.fit(X_train, y_train)

    # Make predictions on the testing set
    y_pred = model.predict(X_test)

    # Map the predicted outcomes to actions (Buy/Sell)
    predicted_actions = ['Buy' if pred == 1 else 'Sell' for pred in y_pred]

    # Extract the actual price values for the testing set
    actual_prices = historical_data.iloc[X_test.index]['Value']

    # Check for mistakes in the predictions based on price movement
    mistakes = [i for i, (predicted, actual) in enumerate(zip(predicted_actions, actual_prices)) if
                (predicted == 'Buy' and actual < actual_prices.iloc[i - 1]) or
                (predicted == 'Sell' and actual > actual_prices.iloc[i - 1])]

    # Print the predicted actions
    print("Predicted actions:")
    print(predicted_actions)

    # Print the mistakes made in the predictions
    print("Mistakes:")
    for idx in mistakes:
        print("Instance:", X_test.iloc[idx])
        print("Predicted action:", predicted_actions[idx])
        print("Actual price:", actual_prices.iloc[idx])
        print()

    # Delay for 30 seconds before extracting data again
    time.sleep(30)