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data/features_10m.csv +0 -1
data/features_120m.csv +0 -1
data/features_24m.csv +0 -1
data/features_60m.csv +0 -1
data/raw_PETR4_SA.csv +0 -0
data/target_10m.csv +0 -1
data/target_120m.csv +0 -1
data/target_24m.csv +0 -1
data/target_60m.csv +0 -1
pdm.lock +61 -1714
pyproject.toml +1 -1
src/feature_engineering.py +99 -31
src/model_training.py +74 -13

data/features_10m.csv DELETED Viewed

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data/features_120m.csv DELETED Viewed

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data/features_24m.csv DELETED Viewed

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data/features_60m.csv DELETED Viewed

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data/raw_PETR4_SA.csv CHANGED Viewed

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data/target_10m.csv DELETED Viewed

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data/target_120m.csv DELETED Viewed

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data/target_60m.csv DELETED Viewed

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 version = "2.6.3"
     {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
 ]
 [[package]]
 name = "websockets"
 version = "16.0"
     {file = "websockets-16.0.tar.gz", hash = "sha256:5f6261a5e56e8d5c42a4497b364ea24d94d9563e8fbd44e78ac40879c60179b5"},
 ]
 [[package]]
 name = "yfinance"
 version = "1.2.0"

pyproject.toml CHANGED Viewed

@@ -6,7 +6,7 @@ authors = [
     {name = "kauabarros-24", email = "martinsbarroskaua@gmail.com"},
 ]
 dependencies = [
-    "pandas>=2.0.0",
     "numpy>=1.24.0",
     "scipy>=1.10.0",
     "statsmodels>=0.14.0",

     {name = "kauabarros-24", email = "martinsbarroskaua@gmail.com"},
 ]
 dependencies = [
+    "pandas>=3.0.1",
     "numpy>=1.24.0",
     "scipy>=1.10.0",
     "statsmodels>=0.14.0",

src/feature_engineering.py CHANGED Viewed

@@ -2,48 +2,116 @@ import pandas as pd
 import numpy as np
 import os
 def load_data(ticker, data_dir='data'):
     ticker_clean = ticker.replace('.', '_')
     filename = f"raw_{ticker_clean}.csv"
     path = os.path.join(data_dir, filename)
-    df = pd.read_csv(path)
-    if 'Date' in df.columns:
-        df.set_index('Date', inplace=True)
-    elif 'Unnamed: 0' in df.columns:
-        df.set_index('Unnamed: 0', inplace=True)
     df.index = pd.to_datetime(df.index, errors='coerce')
-    df = df.dropna()
     return df
-def resample_monthly(df_daily):
-    monthly = df_daily.resample('ME').last()
-    return monthly
-def create_features_and_target(df_monthly, horizon_months):
-    df = df_monthly.copy()
     close = df["Close"]
-    for lag in [1, 2, 3, 4, 5]:
-        df[f"ret_{lag}m"] = close.pct_change(lag).shift(1)
-    df['ma6'] = close.rolling(6).mean()
-    df['ma12'] = close.rolling(12).mean()
-    df['close/ma6'] = close / df['ma6']
-    df['close/ma12'] = close / df['ma12']
-    future_close = close.shift(-horizon_months)
-    df['target'] = (future_close > close).astype(int)
     df.dropna(inplace=True)
     return df
-def prepare_data_for_all_horizons(ticker, horizons=[10, 24, 60, 120]):
-    os.makedirs('data', exist_ok=True)
-    df_daily = load_data(ticker)
-    print(f"Dados carregados: {len(df_daily)} linhas")
-    df_monthly = resample_monthly(df_daily)
-    print(f"Dados mensais: {len(df_monthly)} linhas")
     for h in horizons:
-        df_h = create_features_and_target(df_monthly, h)
-        feature_cols = [col for col in df_h.columns if col not in ['target', 'Close', 'Open', 'High', 'Low', 'Volume']]
         X = df_h[feature_cols]
-        y = df_h['target']
-        X.to_csv(f"data/features_{h}m.csv")
-        y.to_csv(f"data/target_{h}m.csv", header=['target'])
-        print(f"Horizonte {h} meses: {X.shape[0]} amostras salvas.")

 import numpy as np
 import os
 def load_data(ticker, data_dir='data'):
+    print("=================================")
+    print("Iniciando carregamento de dados")
     ticker_clean = ticker.replace('.', '_')
     filename = f"raw_{ticker_clean}.csv"
     path = os.path.join(data_dir, filename)
+    print(f"Carregando arquivo: {path}")
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Arquivo não encontrado: {path}")
+    df = pd.read_csv(path, index_col=0)
+    print("Convertendo índice para datetime...")
     df.index = pd.to_datetime(df.index, errors='coerce')
+    print("Removendo datas inválidas...")
+    df = df[~df.index.isna()]
+    if "Close" not in df.columns:
+        print("Colunas disponíveis:", df.columns)
+        raise ValueError("Coluna 'Close' não encontrada")
+    print("Dataset carregado:", df.shape)
     return df
+def create_features_and_target(df, horizon_days):
+    print(f"Criando features para horizonte {horizon_days} dias")
+    df = df.copy()
     close = df["Close"]
+    # retornos passados
+    for lag in [5, 10, 20, 50]:
+        df[f"ret_{lag}d"] = close.pct_change(lag).shift(1)
+    # médias móveis
+    df["ma20"] = close.rolling(20).mean()
+    df["ma50"] = close.rolling(50).mean()
+    # relação preço/média
+    df["close_ma20_ratio"] = close / df["ma20"]
+    df["close_ma50_ratio"] = close / df["ma50"]
+    # target futuro
+    future_close = close.shift(-horizon_days)
+    df["target"] = (future_close > close).astype(int)
     df.dropna(inplace=True)
+    print("Features criadas:", df.shape)
     return df
+def prepare_data_for_all_horizons(ticker, horizons=[30, 90, 180, 360]):
+    print("=================================")
+    print(f"Preparando dados para {ticker}")
+    print("=================================")
+    os.makedirs("data", exist_ok=True)
+    df = load_data(ticker)
+    print("Total de dados:", len(df))
     for h in horizons:
+        print("---------------------------------")
+        print(f"Horizonte: {h} dias")
+        df_h = create_features_and_target(df, h)
+        feature_cols = [
+            col for col in df_h.columns
+            if col not in [
+                "target",
+                "Close",
+                "Open",
+                "High",
+                "Low",
+                "Volume",
+                "Adj Close"
+            ]
+        ]
         X = df_h[feature_cols]
+        y = df_h["target"]
+        X_path = f"data/features_{h}d.csv"
+        y_path = f"data/target_{h}d.csv"
+        X.to_csv(X_path)
+        y.to_csv(y_path, header=["target"])
+        print("Features salvas:", X_path)
+        print("Target salvo:", y_path)
+        print("Amostras:", X.shape[0])
+if __name__ == "__main__":
+    prepare_data_for_all_horizons("PETR4.SA")

src/model_training.py CHANGED Viewed

@@ -5,44 +5,105 @@ from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score, classification_report
 import joblib
 import os
 def load_features_target(horizon):
     X = pd.read_csv(f"data/features_{horizon}m.csv", index_col=0)
     y = pd.read_csv(f"data/target_{horizon}m.csv", index_col=0).squeeze()
     return X, y
 def temporal_train_split(X, y, test_size=0.2):
     n = len(X)
     split_idx = int(n * (1 - test_size))
-    X_train, X_test = X.iloc[:split_idx], X.iloc[split_idx:]
-    y_train, y_test = y.iloc[:split_idx], y.iloc[split_idx:]
     return X_train, X_test, y_train, y_test
 def train_model(horizon, model_type='random_forest', save_model=True):
     X, y = load_features_target(horizon)
-    X_train, X_test, y_train, y_test = temporal_train_split(X, y, test_size=0.2)
     if model_type == 'logistic':
         model = LogisticRegression(max_iter=1000, random_state=42)
     else:
-        model = RandomForestClassifier(n_estimators=100, random_state=42)
     model.fit(X_train, y_train)
     y_pred = model.predict(X_test)
     acc = accuracy_score(y_test, y_pred)
-    report = classification_report(y_test, y_pred, target_names=['Queda', 'Sobe'])
-    print(f"\nHorizonte: {horizon} meses - {model_type}")
     print(f"Acurácia: {acc:.4f}")
     print(report)
     if save_model:
-        os.makedirs('models', exist_ok=True)
-        joblib.dump(model, f"models/{model_type}_{horizon}m.pkl")
-        print(f"Modelo salvo em models/{model_type}_{horizon}m.pkl")
-    return model, acc, report
 def train_all_horizons():
     horizons = [10, 24, 60, 120]
-    print("Treinando Random Forest...")
     for h in horizons:
         train_model(h, 'random_forest')
-    print("\nTreinando Regressão Logística...")
     for h in horizons:
-        train_model(h, 'logistic')

 from sklearn.metrics import accuracy_score, classification_report
 import joblib
 import os
+import logging
 def load_features_target(horizon):
+    print(f"{horizon} - HORIZON")
     X = pd.read_csv(f"data/features_{horizon}m.csv", index_col=0)
     y = pd.read_csv(f"data/target_{horizon}m.csv", index_col=0).squeeze()
+    print(f"Tô aqui: {X, y}")
     return X, y
 def temporal_train_split(X, y, test_size=0.2):
     n = len(X)
+    if n == 0:
+        return None, None, None, None
     split_idx = int(n * (1 - test_size))
+    X_train = X.iloc[:split_idx]
+    X_test = X.iloc[split_idx:]
+    y_train = y.iloc[:split_idx]
+    y_test = y.iloc[split_idx:]
     return X_train, X_test, y_train, y_test
 def train_model(horizon, model_type='random_forest', save_model=True):
     X, y = load_features_target(horizon)
+    print(f"\n========== Horizonte {horizon} ==========")
+    print("Shape X:", X.shape)
+    print("Shape y:", y.shape)
+    if len(X) == 0:
+        print("❌ Dataset vazio. Pulando treino.")
+        return None
+    X_train, X_test, y_train, y_test = temporal_train_split(X, y)
+    if X_train is None or len(X_train) == 0:
+        print("❌ Sem dados de treino.")
+        return None
     if model_type == 'logistic':
         model = LogisticRegression(max_iter=1000, random_state=42)
     else:
+        model = RandomForestClassifier(
+            n_estimators=100,
+            random_state=42
+        )
+    print("Treinando modelo...")
     model.fit(X_train, y_train)
+    if len(X_test) == 0:
+        print("⚠️ Sem dados de teste. Modelo treinado mas sem avaliação.")
+        return model
     y_pred = model.predict(X_test)
     acc = accuracy_score(y_test, y_pred)
     print(f"Acurácia: {acc:.4f}")
+    report = classification_report(
+        y_test,
+        y_pred,
+        target_names=['Queda', 'Sobe']
+    )
     print(report)
     if save_model:
+        os.makedirs("models", exist_ok=True)
+        path = f"models/{model_type}_{horizon}m.pkl"
+        joblib.dump(model, path)
+        print("Modelo salvo em:", path)
+    return model
 def train_all_horizons():
     horizons = [10, 24, 60, 120]
+    print("\n===== Random Forest =====")
     for h in horizons:
         train_model(h, 'random_forest')
+    print("\n===== Logistic Regression =====")
     for h in horizons:
+        train_model(h, 'logistic')