core/utils.py

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import yfinance as yf
import os

def download_yahoo_data(ticker, start_date, end_date):
    df = yf.download(ticker, start=start_date, end=end_date)
    df = df[['Close']].dropna()
    df.reset_index(inplace=True)
    return df

def load_offline_csv(file):
    df = pd.read_csv(file)
    if 'Date' in df.columns:
        df['Date'] = pd.to_datetime(df['Date'])
        df.set_index('Date', inplace=True)
    return df

def prepare_sequences(data, window_size):
    X, y = [], []
    for i in range(len(data) - window_size):
        X.append(data[i:i + window_size])
        y.append(data[i + window_size])
    return np.array(X), np.array(y)

def scale_data(df, column='Close'):
    scaler = StandardScaler()
    scaled = scaler.fit_transform(df[[column]])
    return scaled, scaler

def inverse_transform(scaler, data):
    return scaler.inverse_transform(data)

def compute_metrics(y_true, y_pred):
    return {
        "MSE": mean_squared_error(y_true, y_pred),
        "MAE": mean_absolute_error(y_true, y_pred),
        "R²": r2_score(y_true, y_pred)
    }

def rolling_backtest(model, X, y, step=5):
    predictions = []
    for i in range(0, len(X) - step, step):
        X_batch = X[i:i + step]
        pred = model.predict(X_batch)
        predictions.extend(pred.flatten())
    return predictions

# core/utils.py

import matplotlib.pyplot as plt

def plot_loss_curve(train_losses, val_losses=None):
    plt.figure(figsize=(10, 5))
    plt.plot(train_losses, label='Train Loss', color='blue')
    if val_losses is not None:
        plt.plot(val_losses, label='Validation Loss', color='orange')
    plt.title('Training Loss Curve')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.savefig("loss_curve.png")  # Saves the plot
    plt.close()