import pandas as pd
import pandas_ta as ta
import numpy as np
from .fracdiff import frac_diff_ffd
from .fractals import get_fractal_features

def build_advanced_features(data: pd.DataFrame) -> pd.DataFrame:
    """
    Constructs an advanced set of features for financial machine learning models.
    """
    close = data["close"]
    high = data["high"]
    low = data["low"]
    volume = data.get("volume", data.get("tick_volume", pd.Series(1, index=data.index)))
    
    features = pd.DataFrame(index=data.index)
    
    # 1. Basic Returns & Momentum
    features["ret_1"] = close.pct_change(1)
    features["ret_5"] = close.pct_change(5)
    features["ret_20"] = close.pct_change(20)
    features["mom_10"] = ta.mom(close, length=10) / close
    features["rsi_14"] = ta.rsi(close, length=14) / 100.0
    
    # 2. Fractional Differentiation (Preserving Memory)
    features["ffd_04"] = frac_diff_ffd(close, d=0.4, thres=1e-4)
    
    # 3. Bollinger Bands (Relative)
    bbands = ta.bbands(close, length=20, std=2)
    if bbands is not None:
        # Find columns by position or name pattern
        mid_col = [c for c in bbands.columns if 'BBM' in c][0]
        upper_col = [c for c in bbands.columns if 'BBU' in c][0]
        lower_col = [c for c in bbands.columns if 'BBL' in c][0]
        
        # Distance from middle band as %
        features["bb_mid_rel"] = (close - bbands[mid_col]) / bbands[mid_col]
        # Bandwidth
        features["bb_width"] = (bbands[upper_col] - bbands[lower_col]) / bbands[mid_col]
        # %B (Position within bands)
        features["bb_pct_b"] = (close - bbands[lower_col]) / (bbands[upper_col] - bbands[lower_col]).replace(0, np.nan)

    # 4. MACD (Trend & Momentum)
    macd = ta.macd(close, fast=12, slow=26, signal=9)
    if macd is not None:
        hist_col = [c for c in macd.columns if 'MACDh' in c][0]
        macd_col = [c for c in macd.columns if 'MACD' in c and 'h' not in c and 's' not in c][0]
        features["macd_hist"] = macd[hist_col] / close
        features["macd_rel"] = macd[macd_col] / close

    # 5. Volatility & Risk
    atr = ta.atr(high, low, close, length=14)
    features["atr_rel"] = atr / close
    features["vol_20"] = close.pct_change().rolling(20).std()
    
    # 6. Fractal Features (Advanced structural points)
    # Note: Fractal features might be slow, using small lookback
    fractal_features = get_fractal_features(data, volatility=atr, n=2)
    # Select only most relevant fractal features to avoid dimensionality blowup
    selected_fractals = [
        "fractal_trend_strength", "fractal_trend_direction", 
        "distance_to_resistance", "distance_to_support"
    ]
    features = features.join(fractal_features[selected_fractals])
    
    # 8. Volume Dynamics
    features["vpt"] = ta.pvt(close, volume) # Price Volume Trend
    features["vpt_roc"] = features["vpt"].pct_change(5)
    
    # Clean up
    return features.replace([np.inf, -np.inf], np.nan).ffill().dropna()