I’m learning how to use the KNN algorithm in python to forecast stock market prices. The problem I have is that the test_and_predict method which is used to test the model on historical data gives great results, but the train_and_predict method which is used to forecast into the future gives strange results. I have no idea what can cause this behavior.

Below is the part of the application responsible for data preparation, testing and forecasting

def prepare_data(df, weeks_ahead, features, history):
    matching_features = find_matching_columns(df, features)
    
    X = []
    y = []
    dates = []
    
    closing_price_col = get_close_matches('Kurs zamknięcia', df.columns, n=1, cutoff=0.6)[0]
    date_col = get_close_matches('Data', df.columns, n=1, cutoff=0.6)[0]

    df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
    for i in range(len(df) - max(history, weeks_ahead)):
        if i + history + weeks_ahead >= len(df):
            X.append(df[matching_features].iloc[i:i+history].values)
        else:
            X.append(df[matching_features].iloc[i:i+history].values)
            y.append(df[closing_price_col].iloc[i+history+weeks_ahead-1])
            dates.append(df[date_col].iloc[i+history+weeks_ahead-1])
    
    
    return np.array(X), np.array(y), np.array(dates)

def test_and_predict(X, y, dates, weeks, k, classifier, random_state, test_size=0.2):
    
    if len(X.shape) == 3:
        n_samples, n_time_steps, n_features = X.shape
        X = X.reshape(n_samples, n_time_steps*n_features)  
   
    x_testing = X[:len(X)-weeks]
    y_testing = y[:len(y)]
    
    scaler = StandardScaler()
    X = scaler.fit_transform(X)
    if k > len(X):
        
        return None, None, None

    mape_scores = []
    all_y_true = []
    all_y_pred = []
    all_dates = []
    
   
    lenX = len(x_testing)
    lenY = len(y_testing)
    for i in range(1,weeks):
        X_train, X_test = x_testing[:lenX-i], x_testing[lenX-i:lenX-i+1] 
        y_train, y_test = y_testing[:lenY-i], y_testing[lenY-i:lenY-i+1]
        dates_test = dates[lenX-i:lenX-i+1]
        classifier.fit(X_train, y_train)
        y_pred = classifier.predict(X_test)
        all_y_true.extend(y_test)
        all_y_pred.extend(y_pred)
        all_dates.extend(dates_test)
      
  
    mape = mean_absolute_percentage_error(y_test, y_pred)
    mape_scores.append(mape)
        

    accuracy_scores = [1 - mape for mape in mape_scores]  

    results = {
        "weeks": weeks,
        "cross_val_scores": accuracy_scores,
        "accuracy_scores": accuracy_scores,
        "mean_accuracy": np.mean(accuracy_scores),
        "std_accuracy": np.std(accuracy_scores),
        "random_state": random_state,
        "y_true": all_y_true,
        "y_pred": all_y_pred,
        "dates": all_dates
    }

    plt.figure(figsize=(10, 6))
    plt.plot(all_dates, all_y_true, label='True Values', marker='o')
    plt.plot(all_dates, all_y_pred, label='Predicted Values', marker='x')
    plt.xlabel('Date')
    plt.ylabel('Values')
    plt.title('True vs. Predicted Values Over Time')
    plt.legend()
    plt.show()

    return classifier, scaler, results

def train_and_predict(X, y, dates, weeks, k, classifier, random_state, test_size=0.2):
    if len(X.shape) == 3:
        n_samples, n_time_steps, n_features = X.shape
        X = X.reshape(n_samples, n_time_steps*n_features)  
    

    scaler = StandardScaler()
    X = scaler.fit_transform(X)
    if k > len(X):
    
        return None, None, None

    mape_scores = []
    all_y_true = []
    all_y_pred = []
    all_dates = []
    
    for i in range(1,weeks):
        X_train, X_test = X[:len(X)-weeks], X[len(X)-i:len(X)-i+1] 
        print(X_test)
        y_train = y[:len(y)]
        dates_test = dates[len(y)-i:len(y)-i+1]
        classifier.fit(X_train, y_train)
        y_pred = classifier.predict(X_test)
      
        all_y_pred.extend(y_pred)
        
    plt.figure(figsize=(10, 6))

    plt.plot( all_y_pred, label='Predicted Values', marker='x')
    plt.xlabel('Date')
    plt.ylabel('Values')
    plt.title('Predicted Values Over Time')
    plt.legend()
    plt.show()

Charts: The first graph shows the results of testing, the second graph shows the results of forecasting:
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enter image description here