"""
Advanced Mathematical Features for Pattern Recognition.

Institutional-grade feature engineering beyond standard technical indicators.
These features capture deep market microstructure that traditional indicators miss.

Features:
  - Fourier Transform (spectral analysis, dominant frequencies)
  - Fractal Dimension (market roughness via Higuchi method)
  - Hurst Exponent (trending vs mean-reverting via R/S analysis)
  - Shannon Entropy (market randomness/uncertainty)
  - Autocorrelation Decay (momentum persistence)
  - Volume-Price Correlation (smart money detection)
  - Trend Strength Index (custom composite)
  - Market Microstructure Features (tick patterns, gap analysis)
"""

from __future__ import annotations

import logging
from typing import Any, Dict, List

import numpy as np
import pandas as pd

logger = logging.getLogger(__name__)


class AdvancedFeatureEngine:
    """Compute advanced mathematical features from OHLCV data."""

    def compute_all(self, df: pd.DataFrame) -> Dict[str, Any]:
        """
        Compute all advanced features and return as a flat dict.

        Returns both raw feature values and a features DataFrame
        that can be used for ML model input.
        """
        if df.empty or len(df) < 30:
            return {"error": "Insufficient data (need 30+ bars)", "features": {}}

        close = df["Close"].values.astype(float)
        high = df["High"].values.astype(float)
        low = df["Low"].values.astype(float)
        volume = df["Volume"].values.astype(float) if "Volume" in df.columns else np.ones(len(close))

        features: Dict[str, Any] = {}

        # 1. Fourier Transform Analysis
        try:
            ft = self._fourier_analysis(close)
            features.update(ft)
        except Exception as e:
            logger.debug("Fourier analysis failed: %s", e)

        # 2. Fractal Dimension (Higuchi)
        try:
            features["fractal_dimension"] = self._higuchi_fractal_dimension(close)
        except Exception as e:
            logger.debug("Fractal dimension failed: %s", e)

        # 3. Hurst Exponent
        try:
            features["hurst_exponent"] = self._hurst_exponent(close)
            features["hurst_regime"] = (
                "trending" if features["hurst_exponent"] > 0.55
                else "mean_reverting" if features["hurst_exponent"] < 0.45
                else "random_walk"
            )
        except Exception as e:
            logger.debug("Hurst exponent failed: %s", e)

        # 4. Shannon Entropy
        try:
            features["entropy_returns"] = self._shannon_entropy(close, bins=20)
            features["entropy_volume"] = self._shannon_entropy(volume, bins=20)
        except Exception as e:
            logger.debug("Entropy failed: %s", e)

        # 5. Autocorrelation Decay
        try:
            acf = self._autocorrelation_profile(close)
            features.update(acf)
        except Exception as e:
            logger.debug("Autocorrelation failed: %s", e)

        # 6. Volume-Price Correlation
        try:
            vpc = self._volume_price_analysis(close, volume)
            features.update(vpc)
        except Exception as e:
            logger.debug("Volume-price analysis failed: %s", e)

        # 7. Trend Strength Index
        try:
            features["trend_strength"] = self._trend_strength_index(close, high, low)
        except Exception as e:
            logger.debug("Trend strength failed: %s", e)

        # 8. Gap Analysis
        try:
            gap = self._gap_analysis(df)
            features.update(gap)
        except Exception as e:
            logger.debug("Gap analysis failed: %s", e)

        # 9. Price Efficiency Ratio
        try:
            features["price_efficiency"] = self._price_efficiency_ratio(close)
        except Exception as e:
            logger.debug("Price efficiency failed: %s", e)

        # 10. Kurtosis & Skewness of returns
        try:
            returns = np.diff(np.log(close + 1e-10))
            if len(returns) >= 10:
                features["return_skewness"] = float(pd.Series(returns).skew())
                features["return_kurtosis"] = float(pd.Series(returns).kurtosis())
        except Exception as e:
            logger.debug("Moments failed: %s", e)

        return features

    def compute_feature_series(self, df: pd.DataFrame, window: int = 20) -> pd.DataFrame:
        """
        Compute rolling advanced features as a DataFrame for ML training.
        Returns one row per bar with multiple feature columns.
        """
        result = df.copy()
        close = df["Close"].values.astype(float)
        volume = df["Volume"].values.astype(float) if "Volume" in df.columns else np.ones(len(close))

        n = len(df)
        hurst_vals = np.full(n, np.nan)
        fractal_vals = np.full(n, np.nan)
        entropy_vals = np.full(n, np.nan)
        efficiency_vals = np.full(n, np.nan)
        trend_vals = np.full(n, np.nan)

        for i in range(window, n):
            segment = close[i - window:i + 1]
            vol_seg = volume[i - window:i + 1]

            try:
                hurst_vals[i] = self._hurst_exponent(segment)
            except Exception:
                pass
            try:
                fractal_vals[i] = self._higuchi_fractal_dimension(segment)
            except Exception:
                pass
            try:
                entropy_vals[i] = self._shannon_entropy(segment, bins=10)
            except Exception:
                pass
            try:
                efficiency_vals[i] = self._price_efficiency_ratio(segment)
            except Exception:
                pass
            try:
                high_seg = df["High"].values[i - window:i + 1].astype(float)
                low_seg = df["Low"].values[i - window:i + 1].astype(float)
                trend_vals[i] = self._trend_strength_index(segment, high_seg, low_seg)
            except Exception:
                pass

        result["hurst_exponent"] = hurst_vals
        result["fractal_dimension"] = fractal_vals
        result["entropy"] = entropy_vals
        result["price_efficiency"] = efficiency_vals
        result["trend_strength"] = trend_vals

        # Return-based distribution features (rolling)
        returns = pd.Series(np.log(df["Close"] / df["Close"].shift(1)))
        result["return_skew_20"] = returns.rolling(window).skew()
        result["return_kurtosis_20"] = returns.rolling(window).apply(
            lambda x: float(pd.Series(x).kurtosis()), raw=False
        )

        return result

    # ── Fourier Analysis ─────────────────────────────────────────────────

    def _fourier_analysis(self, prices: np.ndarray, top_k: int = 5) -> Dict[str, Any]:
        """FFT spectral analysis of price series."""
        log_prices = np.log(prices + 1e-10)
        detrended = log_prices - np.linspace(log_prices[0], log_prices[-1], len(log_prices))

        fft_vals = np.fft.rfft(detrended)
        magnitudes = np.abs(fft_vals)
        freqs = np.fft.rfftfreq(len(detrended))

        # Skip DC component (index 0)
        if len(magnitudes) > 1:
            magnitudes = magnitudes[1:]
            freqs = freqs[1:]

        if len(magnitudes) == 0:
            return {}

        # Top dominant frequencies
        top_indices = np.argsort(magnitudes)[-top_k:][::-1]
        total_energy = np.sum(magnitudes ** 2)

        dominant_periods = []
        for idx in top_indices:
            if freqs[idx] > 0:
                period = 1.0 / freqs[idx]
                energy_pct = (magnitudes[idx] ** 2) / total_energy * 100 if total_energy > 0 else 0
                dominant_periods.append({
                    "period_bars": round(period, 1),
                    "energy_pct": round(energy_pct, 2),
                })

        # Spectral energy ratios
        low_freq = magnitudes[:max(1, len(magnitudes)//4)]
        high_freq = magnitudes[len(magnitudes)//4:]
        low_energy = np.sum(low_freq ** 2)
        high_energy = np.sum(high_freq ** 2)

        return {
            "fft_dominant_periods": dominant_periods[:3],
            "fft_spectral_ratio": round(low_energy / (high_energy + 1e-10), 4),
            "fft_total_energy": round(float(total_energy), 4),
        }

    # ── Fractal Dimension (Higuchi) ──────────────────────────────────────

    def _higuchi_fractal_dimension(self, x: np.ndarray, k_max: int = 10) -> float:
        """Higuchi fractal dimension — measures market roughness."""
        n = len(x)
        if n < k_max + 1:
            k_max = max(2, n // 2)

        lk = np.zeros(k_max)
        for k in range(1, k_max + 1):
            lm_sum = 0.0
            for m in range(1, k + 1):
                indices = np.arange(0, (n - m) // k) * k + m - 1
                if len(indices) < 2:
                    continue
                segment = x[indices.astype(int)]
                length = np.sum(np.abs(np.diff(segment))) * (n - 1) / (k * len(segment))
                lm_sum += length
            lk[k - 1] = lm_sum / k if k > 0 else 0

        # Fit log-log regression
        valid = lk > 0
        if np.sum(valid) < 2:
            return 1.5  # default

        ks = np.arange(1, k_max + 1)[valid]
        log_k = np.log(1.0 / ks)
        log_lk = np.log(lk[valid])

        slope = np.polyfit(log_k, log_lk, 1)[0]
        return round(float(slope), 4)

    # ── Hurst Exponent (R/S Analysis) ────────────────────────────────────

    def _hurst_exponent(self, prices: np.ndarray) -> float:
        """
        Rescaled range (R/S) Hurst exponent.
        H > 0.5: trending, H < 0.5: mean-reverting, H ≈ 0.5: random walk
        """
        returns = np.diff(np.log(prices + 1e-10))
        n = len(returns)
        if n < 20:
            return 0.5

        max_k = min(n // 2, 100)
        divisions = [d for d in range(10, max_k + 1, max(1, max_k // 20))]
        if len(divisions) < 3:
            return 0.5

        rs_values = []
        sizes = []
        for d in divisions:
            n_segments = n // d
            if n_segments < 1:
                continue
            rs_list = []
            for seg in range(n_segments):
                segment = returns[seg * d:(seg + 1) * d]
                mean_seg = np.mean(segment)
                cumdev = np.cumsum(segment - mean_seg)
                r = np.max(cumdev) - np.min(cumdev)
                s = np.std(segment, ddof=1)
                if s > 0:
                    rs_list.append(r / s)
            if rs_list:
                rs_values.append(np.mean(rs_list))
                sizes.append(d)

        if len(sizes) < 3:
            return 0.5

        log_sizes = np.log(np.array(sizes, dtype=float))
        log_rs = np.log(np.array(rs_values, dtype=float))
        slope = np.polyfit(log_sizes, log_rs, 1)[0]
        return round(float(np.clip(slope, 0, 1)), 4)

    # ── Shannon Entropy ──────────────────────────────────────────────────

    def _shannon_entropy(self, data: np.ndarray, bins: int = 20) -> float:
        """Shannon entropy of data distribution. Higher = more random."""
        if len(data) < 5:
            return 0.0
        hist, _ = np.histogram(data, bins=bins, density=True)
        hist = hist[hist > 0]
        if len(hist) == 0:
            return 0.0
        hist = hist / hist.sum()  # normalize
        return round(float(-np.sum(hist * np.log2(hist + 1e-12))), 4)

    # ── Autocorrelation Profile ──────────────────────────────────────────

    def _autocorrelation_profile(self, prices: np.ndarray) -> Dict[str, float]:
        """Compute autocorrelation at multiple lags."""
        returns = np.diff(np.log(prices + 1e-10))
        if len(returns) < 20:
            return {}

        result = {}
        for lag in [1, 3, 5, 10, 20]:
            if lag < len(returns):
                acf = np.corrcoef(returns[lag:], returns[:-lag])[0, 1]
                result[f"acf_lag_{lag}"] = round(float(acf), 4) if np.isfinite(acf) else 0.0

        # Decay rate: how fast autocorrelation drops
        acf_values = [result.get(f"acf_lag_{l}", 0) for l in [1, 5, 10, 20]]
        result["acf_decay_rate"] = round(
            float(np.polyfit(range(len(acf_values)), acf_values, 1)[0]), 6
        )

        return result

    # ── Volume-Price Analysis ────────────────────────────────────────────

    def _volume_price_analysis(
        self, prices: np.ndarray, volume: np.ndarray
    ) -> Dict[str, float]:
        """Analyze volume-price relationship for smart money detection."""
        returns = np.diff(np.log(prices + 1e-10))
        vol = volume[1:]  # align with returns

        if len(returns) < 10:
            return {}

        # Volume-return correlation
        corr = np.corrcoef(returns, vol)[0, 1]
        abs_corr = np.corrcoef(np.abs(returns), vol)[0, 1]

        # Volume on up vs down days
        up_mask = returns > 0
        down_mask = returns < 0
        avg_up_vol = np.mean(vol[up_mask]) if up_mask.sum() > 0 else 0
        avg_down_vol = np.mean(vol[down_mask]) if down_mask.sum() > 0 else 0
        vol_asymmetry = (avg_up_vol - avg_down_vol) / (avg_up_vol + avg_down_vol + 1e-10)

        return {
            "volume_return_corr": round(float(corr), 4) if np.isfinite(corr) else 0,
            "volume_abs_return_corr": round(float(abs_corr), 4) if np.isfinite(abs_corr) else 0,
            "volume_asymmetry": round(float(vol_asymmetry), 4),
        }

    # ── Trend Strength Index ─────────────────────────────────────────────

    def _trend_strength_index(
        self, close: np.ndarray, high: np.ndarray, low: np.ndarray
    ) -> float:
        """
        Custom composite trend strength (0 = no trend, 1 = strong trend).
        Combines ADX-like directional movement with price efficiency.
        """
        n = len(close)
        if n < 14:
            return 0.5

        # Directional movement
        dm_plus = np.maximum(np.diff(high), 0)
        dm_minus = np.maximum(-np.diff(low), 0)

        # Nullify weaker direction
        both = dm_plus > dm_minus
        dm_plus[~both] = 0
        dm_minus[both] = 0

        # Smoothed (14-period average)
        period = min(14, len(dm_plus))
        avg_dm_plus = np.mean(dm_plus[-period:])
        avg_dm_minus = np.mean(dm_minus[-period:])
        total_dm = avg_dm_plus + avg_dm_minus

        if total_dm == 0:
            return 0.0

        dx = abs(avg_dm_plus - avg_dm_minus) / total_dm

        # Combine with efficiency
        efficiency = self._price_efficiency_ratio(close)

        return round(float((dx + efficiency) / 2), 4)

    # ── Price Efficiency Ratio ───────────────────────────────────────────

    def _price_efficiency_ratio(self, prices: np.ndarray) -> float:
        """
        Kaufman Efficiency Ratio: net movement / total path length.
        1.0 = perfect trend, 0.0 = pure noise.
        """
        if len(prices) < 5:
            return 0.5
        net_change = abs(prices[-1] - prices[0])
        total_path = np.sum(np.abs(np.diff(prices)))
        return round(float(net_change / (total_path + 1e-10)), 4)

    # ── Gap Analysis ─────────────────────────────────────────────────────

    def _gap_analysis(self, df: pd.DataFrame) -> Dict[str, Any]:
        """Analyze price gaps for institutional activity detection."""
        if len(df) < 10:
            return {}

        opens = df["Open"].values
        prev_closes = df["Close"].shift(1).values
        gaps = (opens[1:] - prev_closes[1:]) / (prev_closes[1:] + 1e-10)

        gap_ups = gaps[gaps > 0.005]
        gap_downs = gaps[gaps < -0.005]

        return {
            "gap_up_count_20": int(np.sum(gaps[-20:] > 0.005)) if len(gaps) >= 20 else 0,
            "gap_down_count_20": int(np.sum(gaps[-20:] < -0.005)) if len(gaps) >= 20 else 0,
            "avg_gap_size": round(float(np.mean(np.abs(gaps[-20:])) * 100), 4) if len(gaps) >= 20 else 0,
            "max_gap_pct": round(float(np.max(np.abs(gaps[-20:])) * 100), 4) if len(gaps) >= 20 else 0,
        }


# Module singleton
advanced_feature_engine = AdvancedFeatureEngine()