| import pandas as pd
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| import numpy as np
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| from sklearn.preprocessing import MinMaxScaler
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| from pathlib import Path
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|
|
| def load_and_preprocess_data(filepath, window_size=30, features=None):
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|
|
| df = pd.read_csv(filepath)
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| print("🔍 Dados carregados:", df.shape)
|
|
|
|
|
| df['Date'] = pd.to_datetime(df['Date'])
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| df = df.sort_values(['Ticker', 'Date'])
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|
|
|
|
| if features is None:
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| features = ['Open', 'Close', 'Volume', 'Asset Turnover', 'Current Ratio',
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| 'Debt/Equity Ratio', 'Gross Margin', 'Net Profit Margin', 'ROA - Return On Assets']
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|
|
| df = df[['Ticker', 'Date'] + features].dropna()
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|
|
|
|
| scalers = {}
|
| grouped = df.groupby('Ticker')
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| sequences = []
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| tickers = []
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|
|
| for ticker, group in grouped:
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| scaler = MinMaxScaler()
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| values = scaler.fit_transform(group[features])
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| scalers[ticker] = scaler
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|
|
| closes = group['Close'].values
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|
|
|
|
| for i in range(len(values) - window_size):
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| seq = values[i:i+window_size]
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| label = closes[i+window_size]
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| sequences.append(seq)
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| tickers.append(ticker)
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|
|
|
|
|
|
| X = np.array([s for s, _ in sequences])
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| y = np.array([l for _, l in sequences])
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|
|
|
|
|
|
| X = np.array(sequences)
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| print(f"✅ Total de sequências geradas: {X.shape[0]} | Formato da entrada: {X.shape}")
|
| return X, tickers, scalers
|
|
|
| if __name__ == "__main__":
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| filepath = Path("sp500_ratios.csv")
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| X, tickers, scalers = load_and_preprocess_data(filepath)
|
|
|
