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+#!/usr/bin/env python3
+# trading_setup.py
+import os, math, warnings
+from dataclasses import dataclass
+from typing import List, Optional, Dict
+import numpy as np
+import pandas as pd
+import yfinance as yf
+import ta
+from scipy.stats import rankdata
+
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# ---------------------------
+# Config
+# ---------------------------
+DEFAULT_LOOKBACK_YEARS = 2
+MIN_HISTORY_DAYS = 252
+MOM_WINDOW_DAYS = {"1m":21, "3m":63, "6m":126, "12m":252}
+TECH_EMAS = (20, 50, 200)
+WEIGHTS = {
+    "technical_flow": 0.35,
+    "options_sentiment": 0.20,
+    "dark_pool": 0.15,
+    "momentum": 0.20,
+    "growth": 0.10,
+}
+
+# ---------------------------
+# Utils
+# ---------------------------
+def winsorize_series(s: pd.Series, p: float = 0.01) -> pd.Series:
+    if s.dropna().empty: return s
+    lo, hi = s.quantile(p), s.quantile(1-p)
+    return s.clip(lower=lo, upper=hi)
+
+def scale_0_100(s: pd.Series) -> pd.Series:
+    if s.dropna().nunique() <= 1: 
+        return pd.Series(50.0, index=s.index)  # flat middle if no variance
+    s_w = winsorize_series(s)
+    lo, hi = s_w.min(), s_w.max()
+    return (100*(s_w - lo)/(hi - lo)).astype(float)
+
+def pct_change(df: pd.DataFrame, col="Adj Close", window=21) -> float:
+    if len(df) < window+1: return np.nan
+    return df[col].iloc[-1] / df[col].iloc[-1-window] - 1.0
+
+def safe_div(a, b):
+    try:
+        return a / b if b and not np.isnan(b) else np.nan
+    except ZeroDivisionError:
+        return np.nan
+
+# ---------------------------
+# Data classes
+# ---------------------------
+@dataclass
+class DarkPoolRecord:
+    ticker: str
+    dp_net_flow: Optional[float] = None
+    dp_score: Optional[float] = None  # if already 0-100
+
+@dataclass
+class FundamentalsRecord:
+    ticker: str
+    revenue_yoy: Optional[float] = None
+    eps_yoy: Optional[float] = None
+
+# ---------------------------
+# Fetchers
+# ---------------------------
+def fetch_price_history(ticker: str, years: int = DEFAULT_LOOKBACK_YEARS) -> Optional[pd.DataFrame]:
+    try:
+        df = yf.Ticker(ticker).history(period=f"{years}y", auto_adjust=False)
+        if df is None or df.empty or len(df) < MIN_HISTORY_DAYS//2:
+            return None
+        return df
+    except Exception:
+        return None
+
+def fetch_option_chain_put_call_ratio(ticker: str) -> Optional[float]:
+    """
+    Basic net options sentiment via Put/Call volume ratio (lower = bullish).
+    Returns PCR; we'll map to a 0-100 bullish score later.
+    """
+    try:
+        tk = yf.Ticker(ticker)
+        exps = tk.options
+        if not exps: return None
+        # choose nearest non-expired expiration
+        exp = exps[0]
+        chain = tk.option_chain(exp)
+        calls, puts = chain.calls, chain.puts
+        if calls is None or puts is None or calls.empty or puts.empty:
+            return None
+        call_vol = calls["volume"].replace(0, np.nan).sum(skipna=True)
+        put_vol  = puts["volume"].replace(0, np.nan).sum(skipna=True)
+        pcr = safe_div(put_vol, call_vol)
+        return float(pcr) if pcr is not None else None
+    except Exception:
+        return None
+
+def load_dark_pool_csv(path: str) -> Dict[str, DarkPoolRecord]:
+    if not path or not os.path.exists(path): return {}
+    df = pd.read_csv(path)
+    out = {}
+    for _, r in df.iterrows():
+        out[str(r["ticker"]).upper()] = DarkPoolRecord(
+            ticker=str(r["ticker"]).upper(),
+            dp_net_flow=(r["dp_net_flow"] if "dp_net_flow" in df.columns else None),
+            dp_score=(r["dp_score"] if "dp_score" in df.columns else None),
+        )
+    return out
+
+def load_fundamentals_csv(path: str) -> Dict[str, FundamentalsRecord]:
+    if not path or not os.path.exists(path): return {}
+    df = pd.read_csv(path)
+    out = {}
+    for _, r in df.iterrows():
+        out[str(r["ticker"]).upper()] = FundamentalsRecord(
+            ticker=str(r["ticker"]).upper(),
+            revenue_yoy=(r["revenue_yoy"] if "revenue_yoy" in df.columns else None),
+            eps_yoy=(r["eps_yoy"] if "eps_yoy" in df.columns else None),
+        )
+    return out
+
+# ---------------------------
+# Scoring pillars
+# ---------------------------
+def technical_flow_score(df: pd.DataFrame) -> float:
+    """
+    0-100 score from EMAs stack, RSI, MACD, ADX, Volume trend.
+    """
+    try:
+        close = df["Adj Close"].rename("close") if "Adj Close" in df else df["Close"].rename("close")
+        high, low, vol = df["High"], df["Low"], df["Volume"]
+
+        ema20 = close.ewm(span=TECH_EMAS[0]).mean()
+        ema50 = close.ewm(span=TECH_EMAS[1]).mean()
+        ema200= close.ewm(span=TECH_EMAS[2]).mean()
+
+        rsi = ta.momentum.RSIIndicator(close, window=14).rsi()
+        macd = ta.trend.MACD(close).macd_diff()
+        adx = ta.trend.ADXIndicator(high, low, close).adx()
+
+        # Normalize components
+        # Trend stack (0-40)
+        stack_score = (
+            (1 if ema20.iloc[-1] > ema50.iloc[-1] else 0) +
+            (1 if ema50.iloc[-1] > ema200.iloc[-1] else 0) +
+            (1 if ema20.iloc[-1] > ema200.iloc[-1] else 0)
+        ) / 3.0 * 40.0
+
+        # RSI favor 50-70 (bullish), scale to 0-30
+        rsi_now = rsi.iloc[-1]
+        rsi_component = max(0.0, min(30.0, (rsi_now - 30) / 40 * 30)) if not np.isnan(rsi_now) else 15.0
+
+        # MACD histogram latest vs distribution (0-15)
+        macd_scaled = scale_0_100(macd).iloc[-1] if macd.notna().any() else 50.0
+        macd_component = macd_scaled * 0.15
+
+        # ADX trend strength (25+ is trending) → 0-15
+        adx_now = adx.iloc[-1]
+        adx_component = max(0.0, min(15.0, (adx_now - 15) / 20 * 15)) if not np.isnan(adx_now) else 7.5
+
+        # Volume trend (20d vs 200d)
+        vol20 = vol.rolling(20).mean().iloc[-1]
+        vol200= vol.rolling(200).mean().iloc[-1]
+        vol_ratio = safe_div(vol20, vol200)
+        vol_component = 0 if vol_ratio is None or np.isnan(vol_ratio) else max(0.0, min(10.0, (vol_ratio-0.8)/0.7*10))
+
+        score = stack_score + rsi_component + macd_component + adx_component + vol_component
+        return float(max(0.0, min(100.0, score)))
+    except Exception:
+        return np.nan
+
+def options_sentiment_score(pcr: Optional[float]) -> float:
+    """
+    Map Put/Call ratio to bullish 0-100.
+    Typical PCR ranges: ~0.5-1.5. Lower => more bullish.
+    """
+    if pcr is None or np.isnan(pcr): return np.nan
+    # Clip to [0.3, 2.0] then invert
+    pcr_c = max(0.3, min(2.0, pcr))
+    # 0.3 -> ~100, 1.0 -> 50, 2.0 -> ~0
+    score = (2.0 - pcr_c) / (2.0 - 0.3) * 100.0
+    return float(max(0.0, min(100.0, score)))
+
+def momentum_scores(df: pd.DataFrame) -> Dict[str, float]:
+    out = {}
+    for label, win in MOM_WINDOW_DAYS.items():
+        out[f"mom_{label}"] = pct_change(df, window=win)
+    # Composite momentum (skip 1m if you prefer medium-term)
+    comp = 0.1*out.get("mom_1m", np.nan) + 0.3*out.get("mom_3m", np.nan) + 0.25*out.get("mom_6m", np.nan) + 0.35*out.get("mom_12m", np.nan)
+    out["mom_composite_raw"] = comp
+    return out
+
+def growth_score(revenue_yoy: Optional[float], eps_yoy: Optional[float], peers_df: pd.DataFrame) -> float:
+    """
+    Rank/percentile within peers for revenue & EPS YoY → average → 0-100.
+    If missing, returns NaN.
+    """
+    if revenue_yoy is None or eps_yoy is None or np.isnan(revenue_yoy) or np.isnan(eps_yoy):
+        return np.nan
+    # Build temp arrays with peers + this point for robust scaling
+    rev = peers_df["revenue_yoy"].astype(float)
+    eps = peers_df["eps_yoy"].astype(float)
+    rev2 = pd.concat([rev, pd.Series([revenue_yoy])], ignore_index=True)
+    eps2 = pd.concat([eps, pd.Series([eps_yoy])], ignore_index=True)
+    rev_p = rankdata(rev2, method="average")/len(rev2)*100
+    eps_p = rankdata(eps2, method="average")/len(eps2)*100
+    # last element corresponds to our ticker
+    return float(np.nanmean([rev_p[-1], eps_p[-1]]))
+
+def dark_pool_score(rec: Optional[DarkPoolRecord], peer_records: List[DarkPoolRecord]) -> float:
+    if rec is None: return np.nan
+    if rec.dp_score is not None and not np.isnan(rec.dp_score):
+        return float(max(0.0, min(100.0, rec.dp_score)))
+    # derive from dp_net_flow vs peers
+    if rec.dp_net_flow is None or np.isnan(rec.dp_net_flow): return np.nan
+    vals = pd.Series([r.dp_net_flow for r in peer_records if r.dp_net_flow is not None and not np.isnan(r.dp_net_flow)])
+    if vals.empty: 
+        return np.nan
+    vals = winsorize_series(vals)
+    lo, hi = vals.min(), vals.max()
+    sc = (rec.dp_net_flow - lo) / (hi - lo) * 100 if hi > lo else 50.0
+    return float(max(0.0, min(100.0, sc)))
+
+# ---------------------------
+# Orchestrator
+# ---------------------------
+def analyze_tickers(
+    tickers: List[str],
+    dark_pool_csv: Optional[str] = None,
+    fundamentals_csv: Optional[str] = None
+) -> pd.DataFrame:
+
+    tickers = [t.upper() for t in tickers]
+    dp_map = load_dark_pool_csv(dark_pool_csv) if dark_pool_csv else {}
+    f_map  = load_fundamentals_csv(fundamentals_csv) if fundamentals_csv else {}
+
+    # Pre-collect peer lists for scaling DP/Growth
+    dp_peers = list(dp_map.values())
+    growth_peers = pd.DataFrame(
+        [{"ticker":k, "revenue_yoy":v.revenue_yoy, "eps_yoy":v.eps_yoy}
+         for k,v in f_map.items() if v.revenue_yoy is not None and v.eps_yoy is not None]
+    )
+    if growth_peers.empty:
+        growth_peers = pd.DataFrame({"ticker":[], "revenue_yoy":[], "eps_yoy":[]})
+
+    rows = []
+    # Pass 1: compute price-based things
+    cache_price = {}
+    for t in tickers:
+        df = fetch_price_history(t)
+        cache_price[t] = df
+
+    # Pass 2: compute pillar scores
+    # Momentum raw scores for all (for cross-sectional scaling)
+    raw_moms = {}
+    for t in tickers:
+        df = cache_price.get(t)
+        raw_moms[t] = np.nan
+        if df is not None:
+            ms = momentum_scores(df)
+            raw_moms[t] = ms["mom_composite_raw"]
+
+    # Scale momentum cross-sectionally
+    mom_scaled = scale_0_100(pd.Series(raw_moms))
+    # Gather outputs
+    for t in tickers:
+        df = cache_price.get(t)
+        tech = technical_flow_score(df) if df is not None else np.nan
+        pcr = fetch_option_chain_put_call_ratio(t)
+        opt = options_sentiment_score(pcr)
+
+        dp_rec = dp_map.get(t)
+        dp_sc = dark_pool_score(dp_rec, dp_peers)
+
+        # growth
+        f_rec = f_map.get(t, FundamentalsRecord(ticker=t, revenue_yoy=np.nan, eps_yoy=np.nan))
+        gr_sc = growth_score(f_rec.revenue_yoy, f_rec.eps_yoy, growth_peers) if not growth_peers.empty else np.nan
+
+        # final composite (handle NaNs by redistributing weights over available pillars)
+        components = {
+            "technical_flow": tech,
+            "options_sentiment": opt,
+            "dark_pool": dp_sc,
+            "momentum": mom_scaled.get(t, np.nan),
+            "growth": gr_sc
+        }
+        avail = {k:v for k,v in components.items() if v is not None and not np.isnan(v)}
+        if not avail:
+            composite = np.nan
+        else:
+            w = {k:WEIGHTS[k] for k in avail.keys()}
+            w_sum = sum(w.values())
+            w_norm = {k:v/w_sum for k,v in w.items()}
+            composite = sum(avail[k]*w_norm[k] for k in avail.keys())
+
+        # add a simple signal
+        signal = (
+            "Strong Buy" if composite >= 80 else
+            "Buy"        if composite >= 65 else
+            "Watch"      if composite >= 50 else
+            "Avoid"
+        )
+
+        # momentum components for visibility
+        mom_parts = momentum_scores(df) if df is not None else {}
+        rows.append({
+            "ticker": t,
+            "technical_flow": round(tech,2) if not np.isnan(tech) else np.nan,
+            "options_pcr": round(pcr,3) if pcr is not None and not np.isnan(pcr) else np.nan,
+            "options_sentiment": round(opt,2) if not np.isnan(opt) else np.nan,
+            "dark_pool": round(dp_sc,2) if not np.isnan(dp_sc) else np.nan,
+            "mom_1m": mom_parts.get("mom_1m", np.nan),
+            "mom_3m": mom_parts.get("mom_3m", np.nan),
+            "mom_6m": mom_parts.get("mom_6m", np.nan),
+            "mom_12m": mom_parts.get("mom_12m", np.nan),
+            "momentum": round(mom_scaled.get(t, np.nan),2) if not np.isnan(mom_scaled.get(t, np.nan)) else np.nan,
+            "growth": round(gr_sc,2) if not np.isnan(gr_sc) else np.nan,
+            "composite": round(composite,2) if not np.isnan(composite) else np.nan,
+            "signal": signal
+        })
+
+    out = pd.DataFrame(rows)
+    # tidy momentum pct columns
+    for c in ["mom_1m","mom_3m","mom_6m","mom_12m"]:
+        if c in out.columns:
+            out[c] = (out[c]*100).round(2)
+    out = out.sort_values("composite", ascending=False, na_position="last").reset_index(drop=True)
+    return out
+
+# ---------------------------
+# CLI
+# ---------------------------
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="Net Options Sentiment + Dark Pool + Technical Flow + Momentum + Growth ranker")
+    parser.add_argument("--tickers", nargs="+", required=True, help="e.g., AAPL MSFT NVDA")
+    parser.add_argument("--dark_pool_csv", type=str, default=None, help="Optional path to dark pool CSV (ticker,dp_net_flow,dp_score)")
+    parser.add_argument("--fundamentals_csv", type=str, default=None, help="Optional path to fundamentals CSV (ticker,revenue_yoy,eps_yoy)")
+    parser.add_argument("--out_csv", type=str, default="trading_setup_output.csv", help="Output CSV path")
+    args = parser.parse_args()
+
+    df = analyze_tickers(args.tickers, args.dark_pool_csv, args.fundamentals_csv)
+    pd.set_option("display.max_columns", None)
+    print(df.to_string(index=False))
+    df.to_csv(args.out_csv, index=False)
+    print(f"\nSaved: {args.out_csv}")