"""
V27 ORB Momentum Features Diagnostic — RSI-14 and Bollinger Band %B

Hypotheses:
  RSI-14 (prior day):
    Low RSI (<30 = oversold): post-selloff gap-up → strong reversion drive
    High RSI (>70 = overbought): momentum continuation → gap adds to trend
    Mid RSI (40-60): neutral — empirically uncertain
  BB %B (prior day):
    Low %B (<0.3): near lower band = compressed/reset before gap-up → explosive
    High %B (>0.7): near/above upper band = existing momentum + gap = continuation
    Direction: empirically determined, not assumed

Both features use daily bars from `prev_bars` (strictly before entry date — lookahead-free).
These are the next natural axes after:
  - V25: FINRA short-volume axis NULL (Pearson max 0.062)
  - V26: ORB tape ignition NULL (range_coil_orb Pearson=-0.128 but p~0.10, n=101 < 120)

Method:
  1. Run V24 simulation over 200d window → 101 trades with r_multiple
  2. For each trade, compute RSI-14 and BB %B from prev_bars (daily bars before entry)
  3. Also compute RSI-OBV interaction: RSI × obv_slope_20 / sqrt(2) — additive signal?
  4. Report Pearson, tercile WR/avg_R, gate outcomes
  5. Pairwise: vs obv_slope_20 and avg_daily_vol_14d (G5a/G5b)

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 = 200
FEATURE_LOOKBACK_TRADING = 30  # RSI-14 needs 15 prior bars; BB needs 20

_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_from_intraday(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_from_intraday(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


# ── Feature Computations (dict-based daily bars, lookahead-free) ──────────────

def compute_rsi_14(prev_bars: list[dict]) -> float | None:
    """RSI(14) from last 15 bars in prev_bars (14 price changes). Lookahead-free."""
    if len(prev_bars) < 15:
        return None
    recent = sorted(prev_bars, key=lambda b: b["date"])[-15:]
    gains, losses = [], []
    for i in range(14):
        chg = recent[i + 1]["close"] - recent[i]["close"]
        if chg >= 0:
            gains.append(chg); losses.append(0.0)
        else:
            gains.append(0.0); losses.append(abs(chg))
    avg_gain = sum(gains) / 14
    avg_loss = sum(losses) / 14
    if avg_loss == 0:
        return 100.0
    rs = avg_gain / avg_loss
    return 100.0 - (100.0 / (1.0 + rs))


def compute_bb_pct_b(prev_bars: list[dict], window: int = 20) -> float | None:
    """BB %B: (last_close - lower_band) / (upper_band - lower_band). >1 = above upper."""
    if len(prev_bars) < window:
        return None
    recent = sorted(prev_bars, key=lambda b: b["date"])[-window:]
    closes = [b["close"] for b in recent]
    sma = sum(closes) / window
    if sma <= 0:
        return None
    std = (sum((c - sma) ** 2 for c in closes) / window) ** 0.5
    if std <= 0:
        return 0.5
    band_w = 4 * std  # upper - lower = 4σ
    if band_w <= 0:
        return 0.5
    return (closes[-1] - (sma - 2 * std)) / band_w


# ── 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("=== V27 ORB Momentum Features Diagnostic (RSI-14 + BB %B) ===\n")

    # 1. Load V24 config
    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}")

    # 2. Determine 200d trading window
    today = dt.date(2026, 4, 21)
    all_td = trading_days_between(today - dt.timedelta(days=400), 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)

    # 3. Load universe
    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")

    # 4. Build daily bars + enrichment
    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")

    # 5. Pre-screen + load intraday
    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")

    # 6. Run V24 simulation
    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

    # 7. Compute features per trade
    print("\nComputing RSI-14 and BB %B 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] = {"rsi_14": 0, "bb_pct_b": 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)

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

        f_rsi = compute_rsi_14(prev_bars)
        f_bb = compute_bb_pct_b(prev_bars)
        f_obv = compute_obv_slope_approx(prev_bars, lookback=20)
        avg_vol = enrichment.get(ticker, {}).get(date, {}).get("avg_daily_vol_14d")

        for fname, fval in [("rsi_14", f_rsi), ("bb_pct_b", f_bb), ("obv_slope", f_obv)]:
            if fval is None:
                missing[fname] += 1

        annotated.append({
            "ticker": ticker, "date": date, "r": r, "win": r > 0,
            "rsi_14": f_rsi,
            "bb_pct_b": f_bb,
            "obv_slope": f_obv,
            "avg_daily_vol": avg_vol,
        })

    total = len(annotated)
    print(f"Annotated: {total} trades")
    for fname in ["rsi_14", "bb_pct_b", "obv_slope"]:
        print(f"  Missing {fname}: {missing[fname]}/{total}")

    # 8. Feature analysis — empirically determine best tercile direction
    feature_defs = [
        # (key, description, hypothesized best tercile — empirically verified)
        ("rsi_14",   "RSI-14 prior day: momentum state before gap-up",   "high"),
        ("bb_pct_b", "BB %B prior day: price position in Bollinger Band", "low"),
    ]

    print("\n" + "=" * 90)
    print("FEATURE ANALYSIS — V24 200d 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

        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}%")
        rho_abs = abs(rho) if rho is not None else 0.0
        print(f"  Pearson(feature, r_multiple) = {rho:.4f}" if rho is not None else "  Pearson = n/a")

        if tstat:
            h, m, lo = tstat["high"], tstat["mid"], tstat["low"]
            print(f"  Tercile breakdown (low→high feature value):")
            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}")

            # Empirically determine best tercile (higher avg_R wins)
            if h["avg_r"] >= lo["avg_r"]:
                best_tercile = "high"
                worst_tercile = "low"
            else:
                best_tercile = "low"
                worst_tercile = "high"
            direction_match = best_tercile == hypothesized_best
            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, "hypothesized_best": hypothesized_best,
            }

    # 9. 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[:18]:18s}, {ref_label}): {rho_x:+.4f}  {gate_name}: {'PASS ✓' if gate_pass else 'FAIL ✗'}")

    print("\n  Feature intercorrelation:")
    combined_rr = [
        (a["rsi_14"], a["bb_pct_b"]) for a in annotated
        if a["rsi_14"] is not None and a["bb_pct_b"] is not None
    ]
    if combined_rr:
        rho_cross = pearson([c[0] for c in combined_rr], [c[1] for c in combined_rr])
        print(f"  ρ(rsi_14, bb_pct_b): {rho_cross:.4f}" if rho_cross is not None else "  ρ(rsi_14, bb_pct_b): n/a")

    # 10. Summary
    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_momentum_signal in V27 config: {weight_sign * weight_magnitude:+.3f}")
        print(f"  → Wire '{best}' into enrich_daily_bars as momentum signal")
        print(f"  → Add weight_momentum_signal to V27 config (parent V24)")
    else:
        print(f"\nVERDICT: ABORT — RSI/BB momentum axis null on V24 200d trade set")
        print("  → V24 remains champion. Momentum overlay exhausted.")
        print("  → Next Ralph iteration: gravitational pull or gap z-score from libs/features/market_features.py")


if __name__ == "__main__":
    main()