feat: add volatility labeling scheme

- Removed requirements.txt file
- Added volatility label function
- Created pyproject.toml
- Added uv.lock file

features/labeling_schemes.py

@@ -143,3 +143,33 @@ def create_labels_regime_detection(df, short_window=20, long_window=50):
     df_copy.dropna(subset=["ma_short", "ma_long"], inplace=True)
     
     return df_copy
+
+
+def create_labels_volatility(df: pd.DataFrame, returns_window: int = 1, vol_window: int = 20) -> pd.DataFrame:
+    """
+    Creates labels based on volatility and future returns.
+    
+    The function calculates the future returns and the rolling volatility, then
+    assigns labels based on the following conditions:
+    -  1: if future return > volatility
+    - -1: if future return < -volatility
+    -  0: otherwise
+
+    Args:
+        df (pd.DataFrame): DataFrame containing the 'close' column.
+        returns_window (int): Horizon for calculating future returns.
+        vol_window (int): Rolling window for calculating volatility.
+
+    Returns:
+        pd.DataFrame: A new DataFrame with a 'volatility_label' column in {-1, 0, +1}.
+    """
+    df_copy = df.copy()
+    df_copy = calculate_future_returns(df_copy, horizon=returns_window)
+    df_copy["volatility"] = df_copy["future_returns"].rolling(vol_window, min_periods=1).std()
+
+    df_copy["volatility_label"] = 0
+    df_copy.loc[df_copy["future_returns"] > df_copy["volatility"], "volatility_label"] = 1
+    df_copy.loc[df_copy["future_returns"] < -df_copy["volatility"], "volatility_label"] = -1
+
+    df_copy.dropna(subset=["volatility", "future_returns"], inplace=True)
+    return df_copy

pyproject.toml

@@ -0,0 +1,37 @@
+[project]
+name = "alphaflow-trading-lab"
+version = "0.1.0"
+description = "AlphaFlow ML & DL Trading Bot is an end-to-end machine learning and deep learning trading framework for MetaTrader 5. It covers data loading, feature engineering, model training/tuning, backtesting with vectorbt, and live deployment—all in one repository."
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "apscheduler>=3.11.0",
+    "ipykernel>=6.30.1",
+    "joblib>=1.5.2",
+    "lightgbm==4.6.0",
+    "matplotlib>=3.10.6",
+    "matplotlib-inline>=0.1.7",
+    "metatrader5>=5.0.4874 ; sys_platform == 'win32'",
+    "nbformat>=5.10.4",
+    "numpy>=1.23.5",
+    "optuna>=4.5.0",
+    "pandas>=2.3.3",
+    "plotly>=5.24.1",
+    "psutil>=7.1.0",
+    "scikit-learn>=1.7.1",
+    "scipy>=1.15.3",
+    "seaborn>=0.13.2",
+    "six>=1.17.0",
+    "statsmodels>=0.14.5",
+    "streamlit>=1.50.0",
+    "streamlit-autorefresh>=1.0.1",
+    "ta>=0.11.0",
+    "ta-lib>=0.6.7",
+    "tensorboard>=2.20.0",
+    "tensorboard-data-server>=0.7.2",
+    "tensorflow>=2.20.0",
+    "tensorflow-intel>=0.0.1",
+    "tensorflow-io-gcs-filesystem<=0.31.0",
+    "vectorbt<=0.28.0",
+    "xgboost==2.1.4",
+]