exp_1143

FORMULA_NAME = "Contrarian 52-Week Recovery with Quality-Regime Momentum Arbitration (v1143)"
LOGIC_VARIANT_COUNT = 3
NOTES = """mode=explore; family=contrarian-52w-recovery-quality-regime-momentum-hybrid
This hybrid combines three orthogonal sleeves from the strongest parents, but only after explicit arbitration. In bullish tapes it prefers repaired momentum leaders that are still not fully extended versus the 52-week high. In mixed tapes it hunts deep 52-week drawdown names only when price repair, quality durability, and tradability all confirm together. In defensive tapes it suppresses low-quality rebounds and rotates toward lower-volatility, valuation-supported, quality names that still retain basic trend integrity.
Deliberate metric coverage: actively use momentum/return via return_3m_pct, return_6m_pct, momentum_12_1_pct; trend/recovery via from_52w_high_pct and from_200d_ma_pct; volatility via realized_vol_3m; liquidity via avg_daily_dollar_volume_3m and trading_days_3m; income via dividend_yield_ttm_pct; valuation via peg and forward_pe; growth via revenue_growth_pct and operating_income_growth_pct; quality via free_cash_flow_margin_pct and operating_margin_pct; size via market_cap. Deliberately weight return_1m_pct, return_12m_pct, avg_daily_volume_3m, dividend_ttm, pe, eps_growth_pct, forward_eps, free_cash_flow_growth_pct, shares_outstanding, close, adj_close, high_52w, ma_200d, eps_ttm, revenue_ttm, operating_income_ttm, and free_cash_flow_ttm at zero this run to avoid collapsing back into any parent recipe while still covering the full metric menu consciously. Sparse fundamentals are normalized by present weights before sleeve arbitration so missing data does not automatically zero a stock unless multiple confirmation blocks are absent."""

TREND_BULL = {
    "momentum_12_1_pct": (0.20, +1),
    "return_6m_pct": (0.18, +1),
    "from_200d_ma_pct": (0.16, +1),
    "from_52w_high_pct": (0.10, -1),
    "realized_vol_3m": (0.08, -1),
    "avg_daily_dollar_volume_3m": (0.07, +1),
    "operating_margin_pct": (0.07, +1),
    "revenue_growth_pct": (0.06, +1),
    "forward_pe": (0.04, -1),
    "market_cap": (0.04, -1),
}

WASHOUT_REPAIR = {
    "from_52w_high_pct": (0.24, -1),
    "from_200d_ma_pct": (0.18, +1),
    "return_3m_pct": (0.14, +1),
    "momentum_12_1_pct": (0.08, +1),
    "free_cash_flow_margin_pct": (0.10, +1),
    "operating_income_growth_pct": (0.07, +1),
    "avg_daily_dollar_volume_3m": (0.07, +1),
    "trading_days_3m": (0.05, +1),
    "realized_vol_3m": (0.04, -1),
    "market_cap": (0.03, -1),
}

DEFENSIVE_QUALITY = {
    "realized_vol_3m": (0.20, -1),
    "from_200d_ma_pct": (0.16, +1),
    "from_52w_high_pct": (0.08, -1),
    "operating_margin_pct": (0.12, +1),
    "free_cash_flow_margin_pct": (0.10, +1),
    "peg": (0.10, -1),
    "forward_pe": (0.06, -1),
    "dividend_yield_ttm_pct": (0.06, +1),
    "trading_days_3m": (0.07, +1),
    "return_3m_pct": (0.05, +1),
}

def score_universe(stocks, regime, ctx):
    def get_metric(stock, name):
        v = stock.get(name)
        if v is None:
            return None
        if isinstance(v, bool):
            return 1.0 if v else 0.0
        try:
            return float(v)
        except Exception:
            return None

    def rank_map(metric):
        present = []
        for stock in stocks:
            v = get_metric(stock, metric)
            if v is not None:
                present.append((stock.get("symbol"), v))
        present.sort(key=lambda x: x[1])
        n = len(present)
        out = {}
        if n == 0:
            return out
        if n == 1:
            out[present[0][0]] = 0.5
            return out
        i = 0
        while i < n:
            j = i
            total_rank = 0.0
            count = 0.0
            value = present[i][1]
            while j < n and present[j][1] == value:
                total_rank += j
                count += 1.0
                j += 1
            avg_rank = total_rank / count
            pct = avg_rank / float(n - 1)
            k = i
            while k < j:
                out[present[k][0]] = pct
                k += 1
            i = j
        return out

    metric_names = {}
    for group in (TREND_BULL, WASHOUT_REPAIR, DEFENSIVE_QUALITY):
        for name in group:
            metric_names[name] = 1

    ranks = {}
    for name in metric_names:
        ranks[name] = rank_map(name)

    def weighted_score(symbol, weights):
        total = 0.0
        used = 0.0
        for metric, spec in weights.items():
            weight = spec[0]
            direction = spec[1]
            rv = ranks.get(metric, {}).get(symbol)
            if rv is None:
                continue
            if direction < 0:
                rv = 1.0 - rv
            total += weight * rv
            used += weight
        if used <= 0.0:
            return None
        return total / used

    regime_name = ""
    if regime is None:
        regime_name = ""
    else:
        try:
            regime_name = str(regime).lower()
        except Exception:
            regime_name = ""

    is_bull = ("bull" in regime_name) or ("risk_on" in regime_name) or ("uptrend" in regime_name)
    is_bear = ("bear" in regime_name) or ("risk_off" in regime_name) or ("defensive" in regime_name) or ("stress" in regime_name)

    scores = {}
    for stock in stocks:
        symbol = stock.get("symbol")
        if not symbol:
            continue

        trend_score = weighted_score(symbol, TREND_BULL)
        repair_score = weighted_score(symbol, WASHOUT_REPAIR)
        defensive_score = weighted_score(symbol, DEFENSIVE_QUALITY)

        if trend_score is None:
            trend_score = 0.0
        if repair_score is None:
            repair_score = 0.0
        if defensive_score is None:
            defensive_score = 0.0

        drawdown_rank = ranks["from_52w_high_pct"].get(symbol)
        above_200_rank = ranks["from_200d_ma_pct"].get(symbol)
        mom_3m_rank = ranks["return_3m_pct"].get(symbol)
        mom_6m_rank = ranks["return_6m_pct"].get(symbol)
        mom_12_1_rank = ranks["momentum_12_1_pct"].get(symbol)
        vol_rank = ranks["realized_vol_3m"].get(symbol)
        liq_rank = ranks["avg_daily_dollar_volume_3m"].get(symbol)
        td_rank = ranks["trading_days_3m"].get(symbol)
        qual_rank = ranks["free_cash_flow_margin_pct"].get(symbol)
        opm_rank = ranks["operating_margin_pct"].get(symbol)

        deep_drawdown = drawdown_rank is not None and drawdown_rank <= 0.30
        repaired_trend = above_200_rank is not None and above_200_rank >= 0.52
        positive_momentum = (
            (mom_3m_rank is not None and mom_3m_rank >= 0.55) or
            (mom_6m_rank is not None and mom_6m_rank >= 0.55) or
            (mom_12_1_rank is not None and mom_12_1_rank >= 0.55)
        )
        quality_confirm = (
            (qual_rank is not None and qual_rank >= 0.45) or
            (opm_rank is not None and opm_rank >= 0.45)
        )
        tradable = (
            (liq_rank is not None and liq_rank >= 0.35) or
            (td_rank is not None and td_rank >= 0.45)
        )
        calm_profile = vol_rank is not None and vol_rank <= 0.45
        hot_momentum = (
            (mom_6m_rank is not None and mom_6m_rank >= 0.68) and
            (mom_12_1_rank is not None and mom_12_1_rank >= 0.62)
        )

        if is_bull:
            if deep_drawdown and repaired_trend and positive_momentum and quality_confirm and tradable:
                score = 0.58 * repair_score + 0.32 * trend_score + 0.10 * defensive_score
            elif hot_momentum and not deep_drawdown:
                score = 0.70 * trend_score + 0.20 * defensive_score + 0.10 * repair_score
            else:
                score = 0.52 * trend_score + 0.33 * repair_score + 0.15 * defensive_score
        elif is_bear:
            if deep_drawdown and not quality_confirm:
                score = 0.20 * repair_score + 0.80 * defensive_score
            elif calm_profile and quality_confirm:
                score = 0.18 * trend_score + 0.18 * repair_score + 0.64 * defensive_score
            else:
                score = 0.24 * trend_score + 0.26 * repair_score + 0.50 * defensive_score
        else:
            if deep_drawdown and repaired_trend and positive_momentum and quality_confirm:
                score = 0.62 * repair_score + 0.23 * defensive_score + 0.15 * trend_score
            elif hot_momentum and quality_confirm:
                score = 0.50 * trend_score + 0.20 * repair_score + 0.30 * defensive_score
            else:
                score = 0.40 * trend_score + 0.40 * repair_score + 0.20 * defensive_score

        penalty = 0.0
        if deep_drawdown and not repaired_trend:
            penalty += 0.08
        if deep_drawdown and not quality_confirm:
            penalty += 0.07
        if not tradable:
            penalty += 0.05
        if vol_rank is not None and vol_rank >= 0.90:
            penalty += 0.04

        bonus = 0.0
        if deep_drawdown and repaired_trend and positive_momentum and quality_confirm:
            bonus += 0.05
        if calm_profile and quality_confirm and tradable:
            bonus += 0.03

        scores[symbol] = score + bonus - penalty

    return scores