"""
V28 ORB Volatility/Compression Features Diagnostic

Tests three features already computed in enrich_daily_bars — no new data needed:

  gap_zscore_20d         : How unusual is today's gap vs prior 20d gap history.
                           High = anomalous gap-up = fresh institutional demand?
                           V24 uses raw gap_pct (weight_gap=0.20) but not z-score.

  range_compression_10_60: avg_range_10 / avg_range_60. Low = compressed (coiling)
                           before breakout at the DAILY level. Orthogonal to ORB
                           tape ignition (1-min range coil, V26 diagnostic).

  atr_ratio_10_60        : ATR(10) / ATR(60). Low = recent volatility below long-term.
                           Measures whether stock has "calmed down" before the gap event.

Context: V25 (FINRA short-vol) FAILED. V26 (tape ignition) FAILED. V27 (RSI/BB) FAILED —
RSI-14 redundant with OBV-slope (ρ=0.726). Next: orthogonal axes that avoid volume/momentum.

Gates:
  G1: |Pearson| ≥ 0.07 on ≥ 120 trades (relaxed to 60 if coverage < 80%)
  G2: |top − bottom tercile avg_R| ≥ 0.30R
  G3: |top − bottom tercile WR| ≥ 5pp
  G4: feature coverage ≥ 50%
  G5a: |ρ(feature, obv_slope_20)| < 0.70
  G5b: |ρ(feature, avg_daily_vol_14d)| < 0.70
"""
from __future__ import annotations

import concurrent.futures
import datetime as dt
import os
import sys
from pathlib import Path

import pyarrow.parquet as pq
import yaml
from zoneinfo import ZoneInfo

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")))

from libs.common.time_utils import trading_days_between
from libs.intraday.domain import ORBStrategyParams
from libs.intraday.features import compute_obv_slope_approx, enrich_daily_bars
from libs.intraday.orb_simulator import ORBSimulationState, run_orb_simulation_with_state
from libs.intraday.screener import orb_pre_screen_candidates

# ── Config ──────────────────────────────────────────────────────────────────
V24_CONFIG = "configs/intraday/strategies/orb_gainers_v24_quality_overlay.yaml"
UNIVERSE_FILE = "configs/symbols_midlarge_snapshot_exact.yaml"
INTRADAY_CACHE_DIR = "data/cache/intraday"
LOOKBACK_DAYS = 400
FEATURE_LOOKBACK_TRADING = 30

_ET = ZoneInfo("America/New_York")
_MKT_OPEN = dt.time(9, 30)
_MKT_CLOSE = dt.time(16, 0)


# ── Daily Bar Builder ─────────────────────────────────────────────────────────

def _parse_ts(ts_raw: object) -> dt.datetime:
    s = str(ts_raw)
    if s.endswith("Z"):
        s = s[:-1] + "+00:00"
    return dt.datetime.fromisoformat(s).astimezone(_ET)


def _build_daily_bar(path: Path, date: str) -> dict | None:
    try:
        table = pq.read_table(str(path))
        rows = table.to_pydict()
    except Exception:
        return None
    opens, highs, lows, closes, vols = [], [], [], [], []
    for i, ts_raw in enumerate(rows.get("timestamp", [])):
        try:
            ts = _parse_ts(ts_raw)
        except Exception:
            continue
        if _MKT_OPEN <= ts.time() < _MKT_CLOSE:
            opens.append(float(rows["open"][i] or 0))
            highs.append(float(rows["high"][i] or 0))
            lows.append(float(rows["low"][i] or 0))
            closes.append(float(rows["close"][i] or 0))
            vols.append(float(rows["volume"][i] or 0))
    if not opens:
        return None
    return {
        "date": date, "open": opens[0], "high": max(highs),
        "low": min(lows), "close": closes[-1], "volume": sum(vols),
    }


def build_daily_bars(tickers: list[str], dates: list[str], workers: int = 8) -> dict[str, list[dict]]:
    root = Path(INTRADAY_CACHE_DIR)

    def _load(ticker: str) -> tuple[str, list[dict]]:
        d_path = root / ticker
        if not d_path.is_dir():
            return ticker, []
        bars: list[dict] = []
        for date in dates:
            p = d_path / f"{date}.parquet"
            if not p.exists():
                continue
            bar = _build_daily_bar(p, date)
            if bar and bar["close"] > 0:
                bars.append(bar)
        return ticker, bars

    result: dict[str, list[dict]] = {}
    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as ex:
        for ticker, bars in ex.map(_load, tickers):
            if bars:
                result[ticker] = bars
    return result


def load_intraday_bulk(candidates: dict[str, list[str]]) -> dict[str, dict[str, list[dict]]]:
    import pandas as pd
    result: dict[str, dict[str, list[dict]]] = {}
    for date, tickers in candidates.items():
        day_bars: dict[str, list[dict]] = {}
        for ticker in tickers:
            p = Path(INTRADAY_CACHE_DIR) / ticker / f"{date}.parquet"
            if not p.exists():
                continue
            try:
                df = pd.read_parquet(str(p))
                if not df.empty and len(df) >= 5:
                    day_bars[ticker] = df.to_dict("records")
            except Exception:
                pass
        if day_bars:
            result[date] = day_bars
    return result


# ── Stats Helpers ─────────────────────────────────────────────────────────────

def pearson(xs: list[float], ys: list[float]) -> float | None:
    if len(xs) != len(ys) or len(xs) < 2:
        return None
    n = len(xs)
    xm, ym = sum(xs) / n, sum(ys) / n
    num = sum((xs[i] - xm) * (ys[i] - ym) for i in range(n))
    dx = (sum((x - xm) ** 2 for x in xs)) ** 0.5
    dy = (sum((y - ym) ** 2 for y in ys)) ** 0.5
    if dx <= 0 or dy <= 0:
        return None
    return num / (dx * dy)


def tercile_stats(vals: list[float], outcomes_r: list[float]) -> dict:
    if len(vals) < 6:
        return {}
    pairs = sorted(zip(vals, outcomes_r), key=lambda p: p[0])
    n = len(pairs)
    t = n // 3

    def stats(sub):
        ys = [p[1] for p in sub]
        wr = sum(1 for y in ys if y > 0) / len(ys) if ys else 0.0
        avg = sum(ys) / len(ys) if ys else 0.0
        return {"n": len(ys), "wr": wr, "avg_r": avg}

    return {"low": stats(pairs[:t]), "mid": stats(pairs[t:2*t]), "high": stats(pairs[2*t:])}


# ── Main ─────────────────────────────────────────────────────────────────────

def main() -> None:
    print("=== V28 ORB Volatility/Compression Features Diagnostic ===\n")

    with open(V24_CONFIG) as f:
        raw = yaml.safe_load(f)
    params = ORBStrategyParams(**raw["orb_strategy"])
    print(f"V24 config loaded. weight_obv_slope={params.weight_obv_slope}")

    today = dt.date(2026, 4, 21)
    all_td = trading_days_between(today - dt.timedelta(days=700), today)
    trading_days_list = [d.isoformat() for d in all_td[-LOOKBACK_DAYS:]]
    print(f"Window: {trading_days_list[0]} → {trading_days_list[-1]} ({len(trading_days_list)} trading days)")

    extended_td = [d.isoformat() for d in all_td[-(LOOKBACK_DAYS + FEATURE_LOOKBACK_TRADING + 10):]]
    first_cal = dt.date.fromisoformat(extended_td[0])
    last_cal = dt.date.fromisoformat(trading_days_list[-1])
    needed_dates: list[str] = []
    d = first_cal
    while d <= last_cal:
        needed_dates.append(d.isoformat())
        d += dt.timedelta(days=1)

    with open(UNIVERSE_FILE) as f:
        udata = yaml.safe_load(f)
    universe = udata.get("symbols", udata) if isinstance(udata, dict) else udata
    if "QQQ" not in universe:
        universe = list(universe) + ["QQQ"]
    print(f"Universe: {len(universe)} tickers")

    print(f"\nBuilding daily bars ({len(needed_dates)} calendar days)...")
    daily_bars = build_daily_bars(universe, needed_dates)
    print(f"Built daily bars for {len(daily_bars)} tickers")
    print("Computing enrichment...")
    enrichment = enrich_daily_bars(daily_bars, trading_days_list)
    print(f"Enrichment for {len(enrichment)} tickers")

    candidates = orb_pre_screen_candidates(
        daily_bars, trading_days_list, enrichment,
        min_price=params.min_price, min_atr=params.min_atr_14,
        min_avg_dollar_vol=params.min_avg_dollar_volume, max_per_day=None,
    )
    total_pairs = sum(len(v) for v in candidates.values())
    print(f"Pre-screened: {total_pairs} ticker-days")
    print("Loading intraday bars...")
    all_intraday = load_intraday_bulk(candidates)
    print(f"Loaded: {sum(len(v) for v in all_intraday.values())} ticker-days")

    print("\nRunning V24 simulation...")
    state = ORBSimulationState(equity=params.initial_capital)
    day_results, _ = run_orb_simulation_with_state(
        all_intraday, trading_days_list, params, enrichment, state=state,
    )
    all_trades = [t for dr in day_results for t in dr.trades]
    trades_with_r = [t for t in all_trades if getattr(t, "r_multiple_at_exit", None) is not None]
    print(f"Total trades: {len(all_trades)}, with r_multiple: {len(trades_with_r)}")

    if len(trades_with_r) < 20:
        print("ABORT: fewer than 20 trades with r_multiple")
        return

    # Features from enrichment — zero extra computation needed
    print("\nExtracting enrichment-based features per trade...")
    sorted_daily: dict[str, list[dict]] = {
        t: sorted(bars, key=lambda b: b["date"]) for t, bars in daily_bars.items()
    }

    annotated: list[dict] = []
    missing: dict[str, int] = {
        "gap_zscore_20d": 0, "range_compression_10_60": 0,
        "atr_ratio_10_60": 0, "obv_slope": 0,
    }

    for trade in trades_with_r:
        ticker = trade.ticker
        date = str(trade.date)[:10]
        r = float(trade.r_multiple_at_exit)

        enrich_day = enrichment.get(ticker, {}).get(date, {})
        f_gap_z = enrich_day.get("gap_zscore_20d")
        f_range_comp = enrich_day.get("range_compression_10_60")
        f_atr_ratio = enrich_day.get("atr_ratio_10_60")
        avg_vol = enrich_day.get("avg_daily_vol_14d")

        bars_t = sorted_daily.get(ticker, [])
        prev_bars = [b for b in bars_t if b["date"][:10] < date]
        f_obv = compute_obv_slope_approx(prev_bars, lookback=20)

        for fname, fval in [
            ("gap_zscore_20d", f_gap_z),
            ("range_compression_10_60", f_range_comp),
            ("atr_ratio_10_60", f_atr_ratio),
            ("obv_slope", f_obv),
        ]:
            if fval is None:
                missing[fname] += 1

        annotated.append({
            "ticker": ticker, "date": date, "r": r, "win": r > 0,
            "gap_zscore_20d": f_gap_z,
            "range_compression_10_60": f_range_comp,
            "atr_ratio_10_60": f_atr_ratio,
            "obv_slope": f_obv,
            "avg_daily_vol": avg_vol,
        })

    total = len(annotated)
    print(f"Annotated: {total} trades")
    for fname in ["gap_zscore_20d", "range_compression_10_60", "atr_ratio_10_60"]:
        print(f"  Missing {fname}: {missing[fname]}/{total}")

    # Feature analysis
    feature_defs = [
        ("gap_zscore_20d", "Today's gap z-score vs 20d history — anomalous gap = fresh demand?", "high"),
        ("range_compression_10_60", "avg_range(10d) / avg_range(60d) — low = coiling before breakout", "low"),
        ("atr_ratio_10_60", "ATR(10) / ATR(60) — low = volatility compressed below long-term avg", "low"),
    ]

    print("\n" + "=" * 90)
    print(f"FEATURE ANALYSIS — V24 {LOOKBACK_DAYS}d trade set")
    print("=" * 90)

    results: dict[str, dict | None] = {}
    for feat_name, description, hypothesized_best in feature_defs:
        valid = [(a[feat_name], a["r"]) for a in annotated if a[feat_name] is not None]
        if len(valid) < 20:
            print(f"\n{feat_name}: SKIP — only {len(valid)} valid trades (need ≥20)")
            results[feat_name] = None
            continue

        vals = [v[0] for v in valid]
        rs = [v[1] for v in valid]
        overall_wr = sum(1 for v in valid if v[1] > 0) / len(valid)
        rho = pearson(vals, rs)
        tstat = tercile_stats(vals, rs)
        coverage_pct = len(valid) / total
        rho_abs = abs(rho) if rho is not None else 0.0

        # Empirically determine best tercile
        if tstat:
            best_tercile = "high" if tstat["high"]["avg_r"] >= tstat["low"]["avg_r"] else "low"
            worst_tercile = "low" if best_tercile == "high" else "high"
            direction_match = best_tercile == hypothesized_best

            print(f"\n{'─'*60}")
            print(f"FEATURE: {feat_name}")
            print(f"  Description: {description}")
            print(f"  n={len(valid)}/{total} ({coverage_pct*100:.0f}% coverage), overall WR={overall_wr*100:.1f}%")
            print(f"  Pearson(feature, r_multiple) = {rho:.4f}" if rho is not None else "  Pearson = n/a")
            print(f"  Tercile breakdown (low→high feature value):")
            h, m, lo = tstat["high"], tstat["mid"], tstat["low"]
            print(f"    Bottom: n={lo['n']}, WR={lo['wr']*100:.1f}%, avg_R={lo['avg_r']:+.3f}")
            print(f"    Middle: n={m['n']}, WR={m['wr']*100:.1f}%, avg_R={m['avg_r']:+.3f}")
            print(f"    Top:    n={h['n']}, WR={h['wr']*100:.1f}%, avg_R={h['avg_r']:+.3f}")
            print(f"  Empirical best: '{best_tercile}' tercile (hypothesis: '{hypothesized_best}' → {'✓ confirmed' if direction_match else '✗ INVERTED'})")

            best = tstat[best_tercile]
            worst = tstat[worst_tercile]
            min_trades = 120 if coverage_pct >= 0.80 else 60
            g1 = rho_abs >= 0.07 and len(valid) >= min_trades
            g2 = best["avg_r"] - worst["avg_r"] >= 0.30
            g3 = best["wr"] >= worst["wr"] + 0.05
            g4 = coverage_pct >= 0.50
            n_failed = sum([not g1, not g2, not g3, not g4])
            overall_pass = n_failed == 0

            print(f"  Gates (best='{best_tercile}' tercile):")
            print(f"    G1 |Pearson|≥0.07 + n≥{min_trades}: {rho_abs:.4f}, n={len(valid)}  → {'PASS ✓' if g1 else 'FAIL ✗'}")
            print(f"    G2 avg_R gap ≥ 0.30R: {best['avg_r'] - worst['avg_r']:+.3f}  → {'PASS ✓' if g2 else 'FAIL ✗'}")
            print(f"    G3 WR gap ≥ 5pp: {(best['wr'] - worst['wr'])*100:+.1f}pp  → {'PASS ✓' if g3 else 'FAIL ✗'}")
            print(f"    G4 coverage ≥ 50%: {coverage_pct*100:.0f}%  → {'PASS ✓' if g4 else 'FAIL ✗'}")
            print(f"  VERDICT: {'ALL GATES PASS → PROCEED TO PHASE 2' if overall_pass else f'FAIL ({n_failed} gate(s) failed)'}")

            results[feat_name] = {
                "pass": overall_pass, "pearson": rho, "stats": tstat,
                "n": len(valid), "coverage": coverage_pct, "best_tercile": best_tercile,
            }

    # Pairwise correlations
    print(f"\n{'─'*60}")
    print("PAIRWISE CORRELATIONS:")
    for feat_name in [fd[0] for fd in feature_defs]:
        for ref_key, ref_label, gate_name in [
            ("obv_slope", "obv_slope_20", "G5a"),
            ("avg_daily_vol", "avg_daily_vol_14d", "G5b"),
        ]:
            combined = [
                (a[feat_name], a[ref_key]) for a in annotated
                if a[feat_name] is not None and a[ref_key] is not None
            ]
            if len(combined) >= 10:
                rho_x = pearson([c[0] for c in combined], [c[1] for c in combined])
                if rho_x is not None:
                    gate_pass = abs(rho_x) < 0.70
                    print(f"  ρ({feat_name[:28]:28s}, {ref_label}): {rho_x:+.4f}  {gate_name}: {'PASS ✓' if gate_pass else 'FAIL ✗'}")

    print("\n  Inter-feature correlations:")
    feat_keys = [fd[0] for fd in feature_defs]
    for i, f1 in enumerate(feat_keys):
        for f2 in feat_keys[i+1:]:
            combined = [(a[f1], a[f2]) for a in annotated if a[f1] is not None and a[f2] is not None]
            if len(combined) >= 10:
                rho_x = pearson([c[0] for c in combined], [c[1] for c in combined])
                if rho_x is not None:
                    print(f"    ρ({f1[:26]:26s}, {f2[:26]:26s}): {rho_x:+.4f}")

    passing = [name for name, r in results.items() if r is not None and r["pass"]]
    failed = [name for name, r in results.items() if r is not None and not r["pass"]]
    skipped = [name for name, r in results.items() if r is None]

    print(f"\n{'='*90}")
    print("SUMMARY")
    print(f"{'='*90}")
    print(f"Features passing all gates: {passing if passing else 'NONE'}")
    print(f"Features failing gates:     {failed if failed else 'NONE'}")
    print(f"Features skipped:           {skipped if skipped else 'NONE'}")

    if passing:
        best = max(passing, key=lambda n: abs(results[n]["pearson"] or 0))
        pearson_val = results[best]["pearson"]
        obv_pearson_ref = 0.2349
        weight_magnitude = round(0.05 * min(1.0, abs(pearson_val) / obv_pearson_ref), 2)
        weight_magnitude = max(weight_magnitude, 0.02)
        best_direction = results[best]["best_tercile"]
        weight_sign = +1 if best_direction == "high" else -1

        print(f"\nVERDICT: PROCEED TO PHASE 2")
        print(f"  Best feature: {best}")
        print(f"  Pearson: {pearson_val:.4f}")
        print(f"  Direction: '{best_direction}' tercile is best → weight = {weight_sign * weight_magnitude:+.3f}")
        print(f"  Suggested weight_vol_signal in V28 config: {weight_sign * weight_magnitude:+.3f}")
        print(f"  → '{best}' is already in enrich_daily_bars — wire directly into orb_simulator scoring")
    else:
        print(f"\nVERDICT: ABORT — Volatility/compression axis null on V24 200d trade set")
        print("  → V24 remains champion.")
        print("  → Next Ralph iteration: gravitational pull from libs/features/market_features.py")
        print("    or expand to 400d window to increase n above 120 threshold")


if __name__ == "__main__":
    main()