# src/data/indicators.py

import pandas as pd
import numpy as np
import ta

class TechnicalIndicators:
    """Engineers a rich set of features from price data, including adaptive indicators."""

    @staticmethod
    def adaptive_laguerre_rsi(df: pd.DataFrame, gamma: float = 0.5) -> pd.Series:
        """ATR-adaptive Laguerre RSI."""
        atr = ta.volatility.average_true_range(df['high'], df['low'], df['close'])
        # Normalize ATR to adjust gamma
        atr_norm = atr / atr.rolling(20).mean()
        adaptive_gamma = gamma * (1 + (atr_norm - 1).clip(-0.3, 0.3))

        price = df['close']
        l0 = l1 = l2 = l3 = 0.0
        l0s = []
        for i in range(len(price)):
            gamma_val = adaptive_gamma.iloc[i] if not pd.isna(adaptive_gamma.iloc[i]) else gamma
            l0 = (1 - gamma_val) * price.iloc[i] + gamma_val * l1
            l1 = (1 - gamma_val) * l0 + gamma_val * l2
            l2 = (1 - gamma_val) * l1 + gamma_val * l3
            l3 = (1 - gamma_val) * l2 + gamma_val * l3
            l0s.append(l0)
        l0_series = pd.Series(l0s, index=price.index)
        # Calculate RSI from Laguerre filter
        cu = (l0_series - l0_series.shift(1)).clip(lower=0).rolling(14).sum()
        cd = (l0_series.shift(1) - l0_series).clip(lower=0).rolling(14).sum()
        rsi = 100 - (100 / (1 + cu / cd))
        return rsi

    @staticmethod
    def add_adaptive_indicators(df: pd.DataFrame) -> pd.DataFrame:
        df = df.copy()
        df['adaptive_laguerre_rsi'] = TechnicalIndicators.adaptive_laguerre_rsi(df)
        # Volatility-adjusted momentum
        df['vol_adj_momentum'] = df['close'].pct_change(10) / df['atr']
        return df

    @staticmethod
    def add_all_indicators(df: pd.DataFrame) -> pd.DataFrame:
        df = df.copy()

        # Trend Indicators
        df['sma_20'] = ta.trend.sma_indicator(df['close'], window=20)
        df['sma_50'] = ta.trend.sma_indicator(df['close'], window=50)
        df['ema_20'] = ta.trend.ema_indicator(df['close'], window=20)
        df['macd'] = ta.trend.macd_diff(df['close'])

        # Momentum Indicators
        df['rsi'] = ta.momentum.rsi(df['close'])
        df['stoch_k'] = ta.momentum.stoch(df['high'], df['low'], df['close'])

        # Volatility Indicators
        df['bb_high'] = ta.volatility.bollinger_hband(df['close'])
        df['bb_low'] = ta.volatility.bollinger_lband(df['close'])
        df['atr'] = ta.volatility.average_true_range(df['high'], df['low'], df['close'])

        # Volume Indicators
        df['obv'] = ta.volume.on_balance_volume(df['close'], df['volume'])

        # Price-derived features
        df['returns'] = df['close'].pct_change()
        df['log_returns'] = np.log(df['close'] / df['close'].shift(1))
        df['high_low_ratio'] = (df['high'] - df['low']) / df['close']

        # Add adaptive indicators
        df = TechnicalIndicators.add_adaptive_indicators(df)

        return df.dropna()