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import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from pathlib import Path
def load_and_preprocess_data(filepath, window_size=30, features=None):
# 1. Carregar os dados
df = pd.read_csv(filepath)
print("🔍 Dados carregados:", df.shape)
# 2. Conversão de data
df['Date'] = pd.to_datetime(df['Date'])
df = df.sort_values(['Ticker', 'Date'])
# 3. Seleção de colunas
if features is None:
features = ['Open', 'Close', 'Volume', 'Asset Turnover', 'Current Ratio',
'Debt/Equity Ratio', 'Gross Margin', 'Net Profit Margin', 'ROA - Return On Assets']
df = df[['Ticker', 'Date'] + features].dropna()
# 4. Normalização por Ticker
scalers = {}
grouped = df.groupby('Ticker')
sequences = []
tickers = []
for ticker, group in grouped:
scaler = MinMaxScaler()
values = scaler.fit_transform(group[features])
scalers[ticker] = scaler
closes = group['Close'].values # Para prever retorno futuro
# 5. Janela deslizante para sequências temporais
for i in range(len(values) - window_size):
seq = values[i:i+window_size]
label = closes[i+window_size] # Preço após a janela
sequences.append(seq)
tickers.append(ticker)
X = np.array([s for s, _ in sequences])
y = np.array([l for _, l in sequences])
X = np.array(sequences)
print(f"✅ Total de sequências geradas: {X.shape[0]} | Formato da entrada: {X.shape}")
return X, tickers, scalers
if __name__ == "__main__":
filepath = Path("sp500_ratios.csv")
X, tickers, scalers = load_and_preprocess_data(filepath)
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