"""
Automated wavelet signal detection for the watchlist — run once per cycle.

Ported (Python subset) from the trigger-signal detectors in
c:\\DataS\\InstrumentSimulator\\frontend\\src\\main.tsx / frontend/src/lib/waveletTrade.ts.
All 7 trigger kinds from the interactive Wavelets Simulation page are now
available here: extremum, level_threshold, trend_flatten, acceleration,
band_cross, ridge_shift (ssq only), energy_threshold (ssq only).

Parameters (engine + per-signal enable/thresholds) come from the "Technical
Desk" (services.database.get_ai_desk_by_type("technical"), config.signals.wavelet_*)
so they're editable from the existing AI Desks config UI — no hardcoded
defaults here beyond a safe fallback when the desk/key is absent. The
instrument scope stays get_instruments_watchlist() (the desk's own
`instruments` list is NOT used, to avoid reintroducing a second overlapping
instrument-list source).

Every (ticker, band) gets a row every cycle now — signal or not — so the AI
chat context always has fresh slope/energy/ridge state, not just firing
events (see ai_chat_context.py:_block_wavelet_signals).
"""
import json
from typing import Dict, List, Optional


def _compute_slope(series: List[float]) -> List[float]:
    n = len(series)
    slope = [0.0] * n
    for i in range(1, n):
        slope[i] = series[i] - series[i - 1]
    if n > 1:
        slope[0] = slope[1]
    return slope


def _compute_acceleration(slope: List[float]) -> List[float]:
    n = len(slope)
    accel = [0.0] * n
    for i in range(1, n):
        accel[i] = slope[i] - slope[i - 1]
    if n > 1:
        accel[0] = accel[1]
    return accel


def _avg_slope_range(slope: List[float], frm: int, to: int) -> Optional[float]:
    if frm < 0 or to > len(slope) - 1 or to <= frm:
        return None
    return sum(slope[frm + 1:to + 1]) / (to - frm)


def _build_extremum_signal(series: List[float], direction: str) -> List[bool]:
    n = len(series)
    raw = [False] * n
    for i in range(1, n - 1):
        prev_slope = series[i] - series[i - 1]
        next_slope = series[i + 1] - series[i]
        if direction == "up" and prev_slope > 0 and next_slope <= 0:
            raw[i] = True  # peak
        if direction == "down" and prev_slope < 0 and next_slope >= 0:
            raw[i] = True  # trough
    # raw[j] needs series[j+1] to confirm — shift by one day so the signal never
    # requires tomorrow's data.
    shifted = [False] * n
    for i in range(1, n):
        shifted[i] = raw[i - 1]
    return shifted


def _build_level_threshold_signal(series: List[float], direction: str, threshold_k: float) -> List[bool]:
    n = len(series)
    signal = [False] * n
    s = 0.0
    sq = 0.0
    for t in range(n):
        s += series[t]
        sq += series[t] * series[t]
        count = t + 1
        if count < 20:
            continue  # not enough history yet for a stable mean/std
        mean = s / count
        variance = max(0.0, sq / count - mean * mean)
        std = variance ** 0.5
        if direction == "up" and series[t] > mean + threshold_k * std:
            signal[t] = True
        if direction == "down" and series[t] < mean - threshold_k * std:
            signal[t] = True
    return signal


def _build_trend_flatten_signal(series: List[float], direction: str, trend_days: int, flatten_days: int,
                                 trend_threshold_k: float, flatten_threshold_k: float) -> List[bool]:
    n = len(series)
    slope = _compute_slope(series)
    signal = [False] * n
    s = 0.0
    sq = 0.0
    for t in range(1, n):
        s += slope[t]
        sq += slope[t] * slope[t]
        count = t
        if t < trend_days + flatten_days or count < 20:
            continue
        mean = s / count
        variance = max(0.0, sq / count - mean * mean)
        std = variance ** 0.5
        trend_thresh = trend_threshold_k * std
        flatten_thresh = flatten_threshold_k * std
        trend = _avg_slope_range(slope, t - flatten_days - trend_days, t - flatten_days)
        flat = _avg_slope_range(slope, t - flatten_days, t)
        if trend is None or flat is None:
            continue
        if direction == "up" and trend > trend_thresh and abs(flat) <= flatten_thresh:
            signal[t] = True
        if direction == "down" and trend < -trend_thresh and abs(flat) <= flatten_thresh:
            signal[t] = True
    return signal


def _build_acceleration_signal(series: List[float], direction: str, days: int, threshold_k: float) -> List[bool]:
    n = len(series)
    slope = _compute_slope(series)
    accel = _compute_acceleration(slope)
    signal = [False] * n
    s = 0.0
    sq = 0.0
    for t in range(2, n):
        s += accel[t]
        sq += accel[t] * accel[t]
        count = t - 1
        if t < days or count < 20:
            continue
        mean = s / count
        variance = max(0.0, sq / count - mean * mean)
        std = variance ** 0.5
        thresh = threshold_k * std
        if direction == "up":
            if slope[t] <= 0:
                continue
            ok = True
            for d in range(days):
                idx = t - d
                if idx < 0 or not (accel[idx] < -thresh):
                    ok = False
                    break
            signal[t] = ok
        else:
            if slope[t] >= 0:
                continue
            ok = True
            for d in range(days):
                idx = t - d
                if idx < 0 or not (accel[idx] > thresh):
                    ok = False
                    break
            signal[t] = ok
    return signal


def _build_band_cross_signal(primary: List[float], secondary: List[float], direction: str) -> List[bool]:
    n = min(len(primary), len(secondary))
    signal = [False] * n
    for t in range(1, n):
        prev_diff = primary[t - 1] - secondary[t - 1]
        curr_diff = primary[t] - secondary[t]
        if direction == "down" and prev_diff >= 0 and curr_diff < 0:
            signal[t] = True
        if direction == "up" and prev_diff <= 0 and curr_diff > 0:
            signal[t] = True
    return signal


def detect_extremum_signal(series: List[float]) -> Optional[str]:
    """Returns 'up' (confirmed peak) or 'down' (confirmed trough) if the most
    recent point is a signal, else None."""
    if len(series) < 3:
        return None
    if _build_extremum_signal(series, "up")[-1]:
        return "up"
    if _build_extremum_signal(series, "down")[-1]:
        return "down"
    return None


def detect_level_threshold_signal(series: List[float], threshold_k: float = 2.0) -> Optional[str]:
    """Returns 'up' (overbought) or 'down' (oversold) if the most recent point
    breaches a causal z-score threshold, else None."""
    if len(series) < 20:
        return None
    if _build_level_threshold_signal(series, "up", threshold_k)[-1]:
        return "up"
    if _build_level_threshold_signal(series, "down", threshold_k)[-1]:
        return "down"
    return None


def detect_trend_flatten_signal(series: List[float], trend_days: int = 10, flatten_days: int = 5,
                                 trend_threshold_k: float = 1.0, flatten_threshold_k: float = 0.3) -> Optional[str]:
    if len(series) < trend_days + flatten_days + 20:
        return None
    if _build_trend_flatten_signal(series, "up", trend_days, flatten_days, trend_threshold_k, flatten_threshold_k)[-1]:
        return "up"
    if _build_trend_flatten_signal(series, "down", trend_days, flatten_days, trend_threshold_k, flatten_threshold_k)[-1]:
        return "down"
    return None


def detect_acceleration_signal(series: List[float], accel_days: int = 3, accel_threshold_k: float = 1.5) -> Optional[str]:
    if len(series) < accel_days + 20:
        return None
    if _build_acceleration_signal(series, "up", accel_days, accel_threshold_k)[-1]:
        return "up"
    if _build_acceleration_signal(series, "down", accel_days, accel_threshold_k)[-1]:
        return "down"
    return None


def detect_band_cross_signal(primary: List[float], secondary: List[float]) -> Optional[str]:
    if len(primary) < 2 or len(secondary) < 2:
        return None
    if _build_band_cross_signal(primary, secondary, "up")[-1]:
        return "up"
    if _build_band_cross_signal(primary, secondary, "down")[-1]:
        return "down"
    return None


def _technical_desk_wavelet_config() -> Dict:
    from services.database import get_ai_desk_by_type
    desk = get_ai_desk_by_type("technical") or {}
    return (desk.get("config") or {}).get("signals") or {}


def scan_watchlist_wavelet_signals() -> List[Dict]:
    """Compute a causal (no-look-ahead) band decomposition for each watchlist
    instrument. Every (ticker, band) gets a row every cycle — current slope/
    value/energy state always, plus signal_kind/direction/params_json when one
    of the enabled trigger kinds fires on the most recent point (first match
    wins, evaluated extremum -> level_threshold -> trend_flatten ->
    acceleration -> band_cross -> energy_threshold). ridge_shift is evaluated
    once per ticker (not per band — the ridge is a single track for the whole
    decomposition) and stored as an extra band_label="ridge" row."""
    from services.database import get_instruments_watchlist
    from services.data_fetcher import get_historical
    from services.wavelet_engine import rolling_causal_bands, rolling_causal_bands_ssq

    sig_cfg = _technical_desk_wavelet_config()
    engine_cfg = sig_cfg.get("wavelet_engine") or {}
    if not engine_cfg.get("enabled", True):
        return []
    num_levels = int(engine_cfg.get("num_levels", 4))
    wavelet = engine_cfg.get("wavelet", "gmw")
    method = engine_cfg.get("method", "cwt")
    lookback = int(engine_cfg.get("lookback_days", 120))

    extremum_cfg = sig_cfg.get("wavelet_extremum") or {"enabled": True}
    level_cfg = sig_cfg.get("wavelet_level_threshold") or {"enabled": True, "threshold_k": 2.0}
    trend_cfg = sig_cfg.get("wavelet_trend_flatten") or {"enabled": False}
    accel_cfg = sig_cfg.get("wavelet_acceleration") or {"enabled": False}
    cross_cfg = sig_cfg.get("wavelet_band_cross") or {"enabled": False}
    ridge_cfg = sig_cfg.get("wavelet_ridge_shift") or {"enabled": False}
    energy_cfg = sig_cfg.get("wavelet_energy_threshold") or {"enabled": False}

    results: List[Dict] = []
    decomposer = rolling_causal_bands_ssq if method == "ssq" else rolling_causal_bands

    for item in get_instruments_watchlist():
        ticker = item["ticker"]
        try:
            hist = get_historical(ticker, period="1y", interval="1d")
            if len(hist) < lookback + 32:
                continue
            values = [h["close"] for h in hist]
            dates = [h["date"] for h in hist]
            start_idx = max(lookback, len(values) - 60)
            decomposed = decomposer(
                values, dates,
                start_idx=start_idx, lookback=lookback,
                num_levels=num_levels, wavelet=wavelet, step=1,
            )
            if not decomposed["dates"]:
                continue
            price_at_signal = decomposed["original"][-1]
            bands = decomposed["bands"]

            for i, band in enumerate(bands):
                series = band["series"]
                if not series:
                    continue
                slope = _compute_slope(series)
                energy = band.get("energy")

                kind: Optional[str] = None
                direction: Optional[str] = None
                params: Optional[Dict] = None

                if extremum_cfg.get("enabled", True):
                    direction = detect_extremum_signal(series)
                    kind = "extremum" if direction else None
                if not direction and level_cfg.get("enabled", True):
                    threshold_k = level_cfg.get("threshold_k", 2.0)
                    direction = detect_level_threshold_signal(series, threshold_k)
                    if direction:
                        kind, params = "level_threshold", {"threshold_k": threshold_k}
                if not direction and trend_cfg.get("enabled"):
                    direction = detect_trend_flatten_signal(
                        series,
                        trend_cfg.get("trend_days", 10), trend_cfg.get("flatten_days", 5),
                        trend_cfg.get("trend_threshold_k", 1.0), trend_cfg.get("flatten_threshold_k", 0.3),
                    )
                    if direction:
                        kind = "trend_flatten"
                        params = {k: trend_cfg.get(k) for k in ("trend_days", "flatten_days", "trend_threshold_k", "flatten_threshold_k")}
                if not direction and accel_cfg.get("enabled"):
                    accel_days = accel_cfg.get("accel_days", 3)
                    accel_threshold_k = accel_cfg.get("accel_threshold_k", 1.5)
                    direction = detect_acceleration_signal(series, accel_days, accel_threshold_k)
                    if direction:
                        kind, params = "acceleration", {"accel_days": accel_days, "accel_threshold_k": accel_threshold_k}
                if not direction and cross_cfg.get("enabled"):
                    sec_idx = int(cross_cfg.get("secondary_band", 1))
                    if 0 <= sec_idx < len(bands) and sec_idx != i:
                        direction = detect_band_cross_signal(series, bands[sec_idx]["series"])
                        if direction:
                            kind, params = "band_cross", {"secondary_band": sec_idx}
                if not direction and energy_cfg.get("enabled") and energy:
                    threshold_k = energy_cfg.get("threshold_k", 2.0)
                    direction = detect_level_threshold_signal(energy, threshold_k)
                    if direction:
                        kind, params = "energy_threshold", {"threshold_k": threshold_k}

                results.append({
                    "ticker": ticker,
                    "band_label": band["label"],
                    "period_low_days": band.get("period_low_days"),
                    "period_high_days": band.get("period_high_days"),
                    "signal_kind": kind,
                    "direction": direction,
                    "price_at_signal": price_at_signal,
                    "slope": slope[-1],
                    "value": series[-1],
                    "energy": energy[-1] if energy else None,
                    "ridge_period_days": None,
                    "params_json": json.dumps(params) if params else None,
                })

            # Ridge — one row per ticker (ssq only), not per band
            if method == "ssq" and decomposed.get("ridge_period_days"):
                ridge_series = [v for v in decomposed["ridge_period_days"] if v is not None]
                if ridge_series:
                    ridge_kind = None
                    ridge_direction = None
                    ridge_params = None
                    if ridge_cfg.get("enabled"):
                        threshold_k = ridge_cfg.get("threshold_k", 2.0)
                        ridge_direction = detect_level_threshold_signal(ridge_series, threshold_k)
                        if ridge_direction:
                            ridge_kind, ridge_params = "ridge_shift", {"threshold_k": threshold_k}
                    results.append({
                        "ticker": ticker,
                        "band_label": "ridge",
                        "period_low_days": None,
                        "period_high_days": None,
                        "signal_kind": ridge_kind,
                        "direction": ridge_direction,
                        "price_at_signal": price_at_signal,
                        "slope": None,
                        "value": None,
                        "energy": None,
                        "ridge_period_days": ridge_series[-1],
                        "params_json": json.dumps(ridge_params) if ridge_params else None,
                    })
        except Exception:
            continue  # one bad ticker must not abort the whole scan

    return results


def compute_and_save_wavelet_signals(run_id: str) -> List[Dict]:
    from services.database import save_wavelet_signals
    results = scan_watchlist_wavelet_signals()
    save_wavelet_signals(run_id, results)
    return results