check dataset, make predictions, learn from mistakes

import time
import requests
from bs4 import BeautifulSoup
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler

# 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'])

# Initialize the model
model = RandomForestClassifier()

# 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
    # ...

    # Add a moving average feature
    window_size = 5
    historical_data['MovingAverage'] = historical_data['Value'].rolling(window=window_size).mean()

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

    # Split the historical data into features and target variable
    X = historical_data[['Value', 'MovingAverage']]
    y = historical_data['Direction']

    # Scale the features
    scaler = MinMaxScaler()
    X_scaled = scaler.fit_transform(X)

    # Update the model with the current data
    model.partial_fit(X_scaled, y, classes=[0, 1])

    # Make predictions on the current data
    y_pred = model.predict(X_scaled)

    # Calculate the model's accuracy
    accuracy = accuracy_score(y, y_pred)
    print("Model Accuracy:", accuracy)

    # Extract the actual price values
    actual_prices = historical_data['Value']

    # Compare the predicted actions with the actual price movements
    mistakes = []
    for i, (predicted, actual, timestamp) in enumerate(zip(y_pred, actual_prices, historical_data['Timestamp'])):
        if (predicted == 1 and actual < actual_prices.iloc[i - 1]) or (predicted == 0 and actual > actual_prices.iloc[i - 1]):
            mistakes.append((timestamp, predicted, actual))

    # Print the predicted actions
    predicted_actions = ['Buy' if pred == 1 else 'Sell' for pred in y_pred]
    print("Predicted actions:")
    for timestamp, action in zip(historical_data['Timestamp'], predicted_actions):
        print(timestamp, action)

    # Print the mistakes made in the predictions
    print("Mistakes:")
    for timestamp, action, actual in mistakes:
        print("Timestamp:", timestamp)
        print("Action:", action)
        print("Actual price:", actual)
        print()

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