Delete governance_engine.py

governance_engine.py

@@ -1,773 +0,0 @@
-# ============================================================
-# 🏛️ governance_engine.py (V1.3 - Stability Fix)
-# ============================================================
-# Description: 
-#   Evaluates trade quality using 156 INDICATORS.
-#   Fixes: Solved "The truth value of a Series is ambiguous" error.
-#   Update: Enhanced error logging to show real causes.
-# ============================================================
-
-import numpy as np
-import pandas as pd
-import pandas_ta as ta
-from typing import Dict, Any, List
-
-class GovernanceEngine:
-    def __init__(self):
-        # ⚖️ Strategic Weights
-        self.WEIGHTS = {
-            "order_book": 0.25,       # 25%
-            "market_structure": 0.20, # 20%
-            "trend": 0.15,            # 15%
-            "momentum": 0.15,         # 15%
-            "volume": 0.10,           # 10%
-            "volatility": 0.05,       # 5%
-            "cycle_math": 0.10        # 10%
-        }
-        print("🏛️ [Governance Engine V1.3] Stability Patch Applied. Ready.")
-
-    async def evaluate_trade(self, symbol: str, ohlcv_data: Dict[str, Any], order_book: Dict[str, Any], verbose: bool = True) -> Dict[str, Any]:
-        """
-        Main Execution Entry.
-        """
-        try:
-            # 1. Data Prep
-            if '15m' not in ohlcv_data:
-                return self._create_rejection("No 15m Data")
-            
-            df = self._prepare_dataframe(ohlcv_data['15m'])
-            if len(df) < 60: return self._create_rejection("Insufficient Data Length (<60)")
-
-            if verbose:
-                print(f"\n📝 [Gov Audit] Opening Session for {symbol}...")
-                print("-" * 80)
-
-            # 2. Calculate Domains
-            s_trend = self._calc_trend_domain(df, verbose)
-            s_mom = self._calc_momentum_domain(df, verbose)
-            s_vol = self._calc_volatility_domain(df, verbose)
-            s_volu = self._calc_volume_domain(df, verbose)
-            s_cycle = self._calc_cycle_math_domain(df, verbose)
-            s_struct = self._calc_structure_domain(df, verbose)
-            s_ob = self._calc_orderbook_domain(order_book, verbose)
-
-            if verbose: print("-" * 80)
-
-            # 3. Weighted Aggregation
-            raw_weighted_score = (
-                (s_trend * self.WEIGHTS['trend']) +
-                (s_mom * self.WEIGHTS['momentum']) +
-                (s_vol * self.WEIGHTS['volatility']) +
-                (s_volu * self.WEIGHTS['volume']) +
-                (s_cycle * self.WEIGHTS['cycle_math']) +
-                (s_struct * self.WEIGHTS['market_structure']) +
-                (s_ob * self.WEIGHTS['order_book'])
-            )
-
-            # 4. Final Scoring & Grading
-            final_score = max(0.0, min(100.0, ((raw_weighted_score + 1) / 2) * 100))
-            grade = self._get_grade(final_score)
-
-            return {
-                "governance_score": round(final_score, 2),
-                "grade": grade,
-                "components": {
-                    "trend": round(s_trend, 2),
-                    "momentum": round(s_mom, 2),
-                    "volatility": round(s_vol, 2),
-                    "volume": round(s_volu, 2),
-                    "cycle_math": round(s_cycle, 2),
-                    "structure": round(s_struct, 2),
-                    "order_book": round(s_ob, 2)
-                },
-                "status": "APPROVED" if grade != "REJECT" else "REJECTED"
-            }
-
-        except Exception as e:
-            print(f"❌ [Governance Critical Error] {e}")
-            return self._create_rejection(f"Exception: {str(e)}")
-
-    # ==============================================================================
-    # 📈 DOMAIN 1: TREND (Fixed)
-    # ==============================================================================
-    def _calc_trend_domain(self, df: pd.DataFrame, verbose: bool) -> float:
-        points = 0.0
-        details = []
-        try:
-            c = df['close']
-            
-            # 1. EMA 9 > 21
-            ema9 = ta.ema(c, 9); ema21 = ta.ema(c, 21)
-            if self._valid(ema9) and self._valid(ema21) and ema9.iloc[-1] > ema21.iloc[-1]: 
-                points += 1; details.append("EMA9>21")
-            
-            # 2. EMA 21 > 50
-            ema50 = ta.ema(c, 50)
-            if self._valid(ema21) and self._valid(ema50) and ema21.iloc[-1] > ema50.iloc[-1]: 
-                points += 1; details.append("EMA21>50")
-            
-            # 3. Price > EMA 200
-            ema200 = ta.ema(c, 200)
-            if self._valid(ema200):
-                if c.iloc[-1] > ema200.iloc[-1]: points += 2; details.append("Price>EMA200")
-                else: points -= 2; details.append("Price<EMA200")
-
-            # 4. Supertrend
-            st = ta.supertrend(df['high'], df['low'], c, length=10, multiplier=3)
-            if self._valid(st):
-                # Supertrend returns [trend, direction, long, short], usually col 0 is trend line
-                st_line = st.iloc[:, 0] 
-                if c.iloc[-1] > st_line.iloc[-1]: points += 1; details.append("ST:Bull")
-                else: points -= 1
-
-            # 5. Parabolic SAR
-            psar = ta.psar(df['high'], df['low'], c)
-            if self._valid(psar):
-                # Handle both single series or dataframe return
-                val = psar.iloc[-1]
-                if isinstance(val, pd.Series): val = val.dropna().iloc[0] if not val.dropna().empty else 0
-                
-                if val != 0:
-                    if val < c.iloc[-1]: points += 1; details.append("PSAR:Bull")
-                    else: points -= 1
-
-            # 6. ADX
-            adx = ta.adx(df['high'], df['low'], c, length=14)
-            if self._valid(adx):
-                val = adx[adx.columns[0]].iloc[-1]
-                dmp = adx[adx.columns[1]].iloc[-1]
-                dmn = adx[adx.columns[2]].iloc[-1]
-                if val > 25:
-                    if dmp > dmn: points += 1.5; details.append("ADX:StrongBull")
-                    else: points -= 1.5; details.append("ADX:StrongBear")
-                else: details.append("ADX:Weak")
-
-            # 7. Ichimoku
-            ichi = ta.ichimoku(df['high'], df['low'], c)
-            # Ichimoku returns a tuple of (DataFrame, DataFrame)
-            if ichi is not None and isinstance(ichi, tuple) and self._valid(ichi[0]):
-                span_a = ichi[0][ichi[0].columns[0]].iloc[-1]
-                span_b = ichi[0][ichi[0].columns[1]].iloc[-1]
-                if c.iloc[-1] > span_a and c.iloc[-1] > span_b: points += 1; details.append("Ichi:AboveCloud")
-
-            # 8. Vortex
-            vortex = ta.vortex(df['high'], df['low'], c)
-            if self._valid(vortex):
-                if vortex[vortex.columns[0]].iloc[-1] > vortex[vortex.columns[1]].iloc[-1]: 
-                    points += 1; details.append("Vortex:Bull")
-
-            # 9. Aroon
-            aroon = ta.aroon(df['high'], df['low'])
-            if self._valid(aroon):
-                if aroon[aroon.columns[0]].iloc[-1] > 70: points += 1; details.append("Aroon:Up")
-                elif aroon[aroon.columns[1]].iloc[-1] > 70: points -= 1; details.append("Aroon:Down")
-
-            # 10. Slope
-            slope = ta.slope(c, length=14)
-            if self._valid(slope) and slope.iloc[-1] > 0: points += 1; details.append("Slope:Pos")
-            
-            # 11. KAMA
-            kama = ta.kama(c, length=10)
-            if self._valid(kama) and c.iloc[-1] > kama.iloc[-1]: points += 1; details.append("KAMA:Bull")
-
-            # 12. TRIX
-            trix = ta.trix(c, length=30)
-            if self._valid(trix) and trix.iloc[-1] > 0: points += 1; details.append("TRIX:Bull")
-
-            # 13. DPO
-            dpo = ta.dpo(c, length=20)
-            if self._valid(dpo) and dpo.iloc[-1] > 0: points += 1; details.append("DPO:Bull")
-
-            # 14. SMA Cluster
-            sma20 = ta.sma(c, 20); sma50 = ta.sma(c, 50)
-            if self._valid(sma20) and self._valid(sma50) and sma20.iloc[-1] > sma50.iloc[-1]: 
-                points += 1; details.append("SMA20>50")
-
-            # 15. ZigZag
-            if df['high'].iloc[-1] > df['high'].iloc[-5]: points += 1; details.append("ZigZag:Up")
-
-            # 16. MACD Slope
-            macd = ta.macd(c)
-            if self._valid(macd):
-                ml = macd[macd.columns[0]]
-                if ml.iloc[-1] > ml.iloc[-2]: points += 1; details.append("MACD_Slope:Up")
-
-            # 17. Coppock
-            coppock = ta.coppock(c)
-            if self._valid(coppock) and coppock.iloc[-1] > 0: points += 0.5; details.append("Coppock:Bull")
-
-            # 18. HMA
-            hma = ta.hma(c, length=9)
-            if self._valid(hma) and c.iloc[-1] > hma.iloc[-1]: points += 1; details.append("HMA:Bull")
-
-            # 19. Donchian
-            dc = ta.donchian(df['high'], df['low'])
-            if self._valid(dc) and c.iloc[-1] > dc[dc.columns[1]].iloc[-1]: 
-                points += 1; details.append("Donchian:Upper")
-
-            # 20. Keltner
-            kc = ta.kc(df['high'], df['low'], c)
-            if self._valid(kc) and c.iloc[-1] > kc[kc.columns[0]].iloc[-1]: 
-                points += 0.5; details.append("Keltner:Safe")
-
-        except Exception as e: details.append(f"TrendErr:{str(e)[:15]}")
-        
-        norm_score = self._normalize(points, max_possible=22.0)
-        if verbose: print(f"   📈 [TREND] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # 🚀 DOMAIN 2: MOMENTUM (Fixed)
-    # ==============================================================================
-    def _calc_momentum_domain(self, df: pd.DataFrame, verbose: bool) -> float:
-        points = 0.0
-        details = []
-        try:
-            c = df['close']
-            
-            # 1. RSI
-            rsi = ta.rsi(c, length=14)
-            if self._valid(rsi):
-                val = rsi.iloc[-1]
-                if 50 < val < 70: points += 2; details.append(f"RSI:{val:.0f}")
-                elif val > 70: points -= 1; details.append("RSI:OB")
-                elif val < 30: points += 1; details.append("RSI:OS")
-
-            # 2. MACD
-            macd = ta.macd(c)
-            if self._valid(macd):
-                if macd[macd.columns[0]].iloc[-1] > macd[macd.columns[2]].iloc[-1]: 
-                    points += 1.5; details.append("MACD:X_Bull")
-                if macd[macd.columns[1]].iloc[-1] > 0: 
-                    points += 1; details.append("MACD_Hist:Pos")
-
-            # 4. Stochastic
-            stoch = ta.stoch(df['high'], df['low'], c)
-            if self._valid(stoch):
-                k = stoch[stoch.columns[0]].iloc[-1]
-                d = stoch[stoch.columns[1]].iloc[-1]
-                if 20 < k < 80 and k > d: points += 1; details.append("Stoch:Bull")
-
-            # 5. AO
-            ao = ta.ao(df['high'], df['low'])
-            if self._valid(ao) and ao.iloc[-1] > 0 and ao.iloc[-1] > ao.iloc[-2]: 
-                points += 1; details.append("AO:Rising")
-
-            # 6. CCI
-            cci = ta.cci(df['high'], df['low'], c)
-            if self._valid(cci):
-                val = cci.iloc[-1]
-                if val > 100: points += 1; details.append("CCI:>100")
-                elif val < -100: points -= 1
-
-            # 7. Williams %R
-            willr = ta.willr(df['high'], df['low'], c)
-            if self._valid(willr) and willr.iloc[-1] < -80: 
-                points += 1; details.append("WillR:OS")
-            
-            # 8. ROC
-            roc = ta.roc(c, length=10)
-            if self._valid(roc) and roc.iloc[-1] > 0: 
-                points += 1; details.append(f"ROC:{roc.iloc[-1]:.2f}")
-
-            # 9. MOM
-            mom = ta.mom(c, length=10)
-            if self._valid(mom) and mom.iloc[-1] > 0: 
-                points += 1; details.append("MOM:Pos")
-
-            # 10. PPO
-            ppo = ta.ppo(c)
-            if self._valid(ppo) and ppo[ppo.columns[0]].iloc[-1] > 0: 
-                points += 1; details.append("PPO:Pos")
-
-            # 11. TSI
-            tsi = ta.tsi(c)
-            if self._valid(tsi) and tsi[tsi.columns[0]].iloc[-1] > tsi[tsi.columns[1]].iloc[-1]:
-                points += 1; details.append("TSI:Bull")
-
-            # 12. Fisher
-            fish = ta.fisher(df['high'], df['low'])
-            if self._valid(fish) and fish[fish.columns[0]].iloc[-1] > fish[fish.columns[1]].iloc[-1]: 
-                points += 1; details.append("Fisher:Bull")
-
-            # 13. CMO
-            cmo = ta.cmo(c, length=14)
-            if self._valid(cmo) and cmo.iloc[-1] > 0: 
-                points += 1; details.append("CMO:Pos")
-
-            # 14. Squeeze
-            bb = ta.bbands(c, length=20)
-            kc = ta.kc(df['high'], df['low'], c)
-            if self._valid(bb) and self._valid(kc):
-                if bb[bb.columns[0]].iloc[-1] < kc[kc.columns[0]].iloc[-1]:
-                    points += 1; details.append("SQZ:Active")
-
-            # 15. UO
-            uo = ta.uo(df['high'], df['low'], c)
-            if self._valid(uo) and uo.iloc[-1] > 50: 
-                points += 0.5; details.append("UO:>50")
-
-            # 16. KDJ (kdj returns df)
-            kdj = ta.kdj(df['high'], df['low'], c)
-            if self._valid(kdj) and kdj[kdj.columns[0]].iloc[-1] > kdj[kdj.columns[1]].iloc[-1]: 
-                points += 0.5; details.append("KDJ:Bull")
-
-            # 17. StochRSI
-            stochrsi = ta.stochrsi(c)
-            if self._valid(stochrsi) and stochrsi[stochrsi.columns[0]].iloc[-1] < 20: 
-                points += 1; details.append("StochRSI:OS")
-
-            # 18. Elder Ray
-            ema13 = ta.ema(c, 13)
-            if self._valid(ema13):
-                bull_power = df['high'] - ema13
-                if bull_power.iloc[-1] > 0 and bull_power.iloc[-1] > bull_power.iloc[-2]: 
-                    points += 1; details.append("BullPower:Rising")
-
-            # 19. Streak
-            if c.iloc[-1] > c.iloc[-2] and c.iloc[-2] > c.iloc[-3]: 
-                points += 0.5; details.append("Streak:Up")
-
-            # 20. Bias
-            ema20 = ta.ema(c, 20)
-            if self._valid(ema20):
-                bias = (c.iloc[-1] - ema20.iloc[-1]) / ema20.iloc[-1]
-                if 0 < bias < 0.05: points += 1; details.append("Bias:Healthy")
-
-        except Exception as e: details.append(f"MomErr:{str(e)[:10]}")
-        
-        norm_score = self._normalize(points, max_possible=20.0)
-        if verbose: print(f"   🚀 [MOMENTUM] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # 🌊 DOMAIN 3: VOLATILITY (Fixed)
-    # ==============================================================================
-    def _calc_volatility_domain(self, df: pd.DataFrame, verbose: bool) -> float:
-        points = 0.0
-        details = []
-        try:
-            # 1. BB Width
-            bb = ta.bbands(df['close'], length=20)
-            if self._valid(bb):
-                width = bb[bb.columns[2]].iloc[-1] # BBP/Bandwidth usually at index 2 or named specifically
-                if width < 0.10: points -= 1; details.append("BBW:Tight")
-                elif width > 1.0: points += 1; details.append("BBW:Wide")
-                
-                # 2. %B
-                pct_b = bb[bb.columns[2]].iloc[-1] # Assuming BBP
-                if pct_b > 0.8: points += 1; details.append("BB%B:High")
-
-            # 3. ATR
-            atr = ta.atr(df['high'], df['low'], df['close'], length=14)
-            if self._valid(atr) and atr.iloc[-1] > atr.iloc[-5]: 
-                points += 1; details.append("ATR:Rising")
-
-            # 4. KC Break
-            kc = ta.kc(df['high'], df['low'], df['close'])
-            if self._valid(kc) and df['close'].iloc[-1] > kc[kc.columns[0]].iloc[-1]: 
-                points += 2; details.append("KC:Breakout")
-
-            # 5. Donchian
-            dc = ta.donchian(df['high'], df['low'])
-            if self._valid(dc) and df['high'].iloc[-1] >= dc[dc.columns[1]].iloc[-2]: 
-                points += 1; details.append("DC:High")
-
-            # 6. Mass Index
-            mass = ta.massi(df['high'], df['low'])
-            if self._valid(mass) and mass.iloc[-1] > 25: 
-                points -= 1; details.append("Mass:Risk")
-
-            # 7. Chaikin Vol
-            c_vol = ta.stdev(df['close'], 20)
-            if self._valid(c_vol) and c_vol.iloc[-1] > c_vol.iloc[-10]: 
-                points += 1; details.append("Vol:Exp")
-
-            # 8. Ulcer
-            ui = ta.ui(df['close'])
-            if self._valid(ui):
-                val = ui.iloc[-1]
-                if val < 2: points += 1; details.append("UI:Safe")
-                else: points -= 1
-
-            # 9. NATR
-            natr = ta.natr(df['high'], df['low'], df['close'])
-            if self._valid(natr) and natr.iloc[-1] > 1.0: 
-                points += 1; details.append(f"NATR:{natr.iloc[-1]:.1f}")
-
-            # 10. Gap
-            if self._valid(atr):
-                gap = abs(df['open'].iloc[-1] - df['close'].iloc[-2])
-                if gap > atr.iloc[-1] * 0.5: points += 1; details.append("Gap")
-
-            # 11. Vol Ratio
-            if self._valid(atr):
-                vr = atr.iloc[-1] / atr.iloc[-20]
-                if vr > 1.2: points += 1; details.append("VolRatio:High")
-
-            # 12. RVI (Proxy)
-            if self._valid(c_vol):
-                std_rsi = ta.rsi(c_vol, length=14)
-                if self._valid(std_rsi) and std_rsi.iloc[-1] > 50: points += 0.5
-
-            # 13. StdDev Channel
-            mean = df['close'].rolling(20).mean()
-            std = df['close'].rolling(20).std()
-            z = (df['close'].iloc[-1] - mean.iloc[-1]) / std.iloc[-1]
-            if abs(z) < 2: points += 0.5
-
-            # 14. ATS
-            if self._valid(atr):
-                ats = df['close'].iloc[-1] - (atr.iloc[-1] * 2)
-                if df['close'].iloc[-1] > ats: points += 1
-
-            # 15. Chop
-            chop = ta.chop(df['high'], df['low'], df['close'])
-            if self._valid(chop):
-                val = chop.iloc[-1]
-                if val < 38.2: points += 1; details.append("Chop:Trend")
-                elif val > 61.8: points -= 1; details.append("Chop:Range")
-
-            # 16. KC Width
-            if self._valid(kc):
-                kw = kc[kc.columns[0]].iloc[-1] - kc[kc.columns[2]].iloc[-1]
-                if kw > kw * 1.1: points += 0.5
-
-            # 17. Accel
-            if df['close'].diff().iloc[-1] > df['close'].diff().iloc[-2]: points += 0.5
-
-            # 18. Efficiency
-            denom = (df['high'].rolling(10).max() - df['low'].rolling(10).min()).iloc[-1]
-            if denom > 0:
-                eff = abs(df['close'].iloc[-1] - df['close'].iloc[-10]) / denom
-                if eff > 0.5: points += 1; details.append("Eff:High")
-
-            # 19. Gator
-            if ta.ema(df['close'], 5).iloc[-1] > ta.ema(df['close'], 13).iloc[-1]: points += 0.5
-
-            # 20. Range
-            if self._valid(atr):
-                rng = df['high'].iloc[-1] - df['low'].iloc[-1]
-                if rng > atr.iloc[-1]: points += 1
-
-        except Exception as e: details.append(f"VolErr:{str(e)[:10]}")
-        norm_score = self._normalize(points, max_possible=18.0)
-        if verbose: print(f"   🌊 [VOLATILITY] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # ⛽ DOMAIN 4: VOLUME (Fixed)
-    # ==============================================================================
-    def _calc_volume_domain(self, df: pd.DataFrame, verbose: bool) -> float:
-        points = 0.0
-        details = []
-        try:
-            c = df['close']; v = df['volume']
-            # 1. OBV
-            obv = ta.obv(c, v)
-            if self._valid(obv) and obv.iloc[-1] > obv.iloc[-5]: 
-                points += 1.5; details.append("OBV:Up")
-
-            # 2. CMF
-            cmf = ta.cmf(df['high'], df['low'], c, v, length=20)
-            if self._valid(cmf):
-                val = cmf.iloc[-1]
-                if val > 0.05: points += 2; details.append(f"CMF:{val:.2f}")
-                elif val < -0.05: points -= 2
-
-            # 3. MFI
-            mfi = ta.mfi(df['high'], df['low'], c, v, length=14)
-            if self._valid(mfi):
-                val = mfi.iloc[-1]
-                if 50 < val < 80: points += 1; details.append(f"MFI:{val:.0f}")
-
-            # 4. Vol > Avg
-            vol_ma = v.rolling(20).mean().iloc[-1]
-            if v.iloc[-1] > vol_ma: points += 1
-
-            # 5. Vol Spike
-            if v.iloc[-1] > vol_ma * 1.5: points += 1; details.append("Vol:Spike")
-
-            # 6. EOM
-            eom = ta.eom(df['high'], df['low'], c, v)
-            if self._valid(eom) and eom.iloc[-1] > 0: points += 1; details.append("EOM:Pos")
-
-            # 7. VWAP
-            vwap = ta.vwap(df['high'], df['low'], c, v)
-            if self._valid(vwap) and c.iloc[-1] > vwap.iloc[-1]: points += 1; details.append("Price>VWAP")
-
-            # 8. NVI
-            nvi = ta.nvi(c, v)
-            if self._valid(nvi) and nvi.iloc[-1] > nvi.iloc[-5]: points += 1; details.append("NVI:Smart")
-
-            # 9. PVI
-            pvi = ta.pvi(c, v)
-            if self._valid(pvi) and pvi.iloc[-1] > pvi.iloc[-5]: points += 0.5
-
-            # 10. ADL
-            adl = ta.ad(df['high'], df['low'], c, v)
-            if self._valid(adl) and adl.iloc[-1] > adl.iloc[-2]: points += 1; details.append("ADL:Up")
-
-            # 11. PVT
-            pvt = ta.pvt(c, v)
-            if self._valid(pvt) and pvt.iloc[-1] > pvt.iloc[-2]: points += 1
-
-            # 12. Vol Osc
-            if v.rolling(5).mean().iloc[-1] > v.rolling(10).mean().iloc[-1]: points += 1
-
-            # 13. KVO
-            kvo = ta.kvo(df['high'], df['low'], c, v)
-            if self._valid(kvo) and kvo[kvo.columns[0]].iloc[-1] > 0: points += 1; details.append("KVO:Bull")
-
-            # 14. Force
-            fi = (c.diff() * v).rolling(13).mean()
-            if fi.iloc[-1] > 0: points += 1
-
-            # 15. MFI (Bill Williams)
-            if v.iloc[-1] > 0:
-                my_mfi = (df['high'] - df['low']) / v
-                if my_mfi.iloc[-1] > my_mfi.iloc[-2] and v.iloc[-1] > v.iloc[-2]: points += 1
-
-            # 16. Buying Climax
-            if v.iloc[-1] > vol_ma * 3 and c.iloc[-1] > df['high'].iloc[-2]: points -= 1
-
-            # 17. RVOL
-            if vol_ma > 0:
-                rvol = v.iloc[-1] / vol_ma
-                if rvol > 1.2: points += 1; details.append(f"RVOL:{rvol:.1f}")
-
-            # 18. Delta
-            delta = (c.iloc[-1] - df['open'].iloc[-1]) * v.iloc[-1]
-            if delta > 0: points += 1
-
-            # 20. Low Vol Gap
-            if self._valid(ta.atr(df['high'], df['low'], c)):
-                if v.iloc[-1] < vol_ma * 0.5 and abs(c.diff().iloc[-1]) > ta.atr(df['high'], df['low'], c).iloc[-1]:
-                    points -= 1
-
-        except Exception as e: details.append(f"VolErr:{str(e)[:10]}")
-        norm_score = self._normalize(points, max_possible=18.0)
-        if verbose: print(f"   ⛽ [VOLUME] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # 🔢 DOMAIN 5: CYCLE & MATH (Fixed)
-    # ==============================================================================
-    def _calc_cycle_math_domain(self, df: pd.DataFrame, verbose: bool) -> float:
-        points = 0.0
-        details = []
-        try:
-            c = df['close']; h = df['high']; l = df['low']
-            
-            # 1. Pivot
-            pp = (h.iloc[-2] + l.iloc[-2] + c.iloc[-2]) / 3
-            if c.iloc[-1] > pp: points += 1; details.append("AbovePP")
-
-            # 2. R1
-            r1 = (2 * pp) - l.iloc[-2]
-            if c.iloc[-1] > r1: points += 1; details.append("AboveR1")
-
-            # 3. Fib 618
-            range_h = h.rolling(100).max().iloc[-1]
-            range_l = l.rolling(100).min().iloc[-1]
-            fib_618 = range_l + (range_h - range_l) * 0.618
-            if c.iloc[-1] > fib_618: points += 1; details.append("AboveFib")
-
-            # 4. Z-Score
-            zscore = ta.zscore(c, length=30)
-            if self._valid(zscore):
-                z = zscore.iloc[-1]
-                if z < -2: points += 2; details.append("Z:OS")
-                elif -1 < z < 1: points += 0.5; details.append("Z:Norm")
-
-            # 5. Entropy
-            entropy = ta.entropy(c, length=10)
-            if self._valid(entropy) and entropy.iloc[-1] < 0.5: 
-                points += 1; details.append(f"Ent:{entropy.iloc[-1]:.2f}")
-
-            # 6. Kurtosis
-            kurt = c.rolling(30).kurt().iloc[-1]
-            if kurt > 3: points -= 0.5
-
-            # 7. Skew
-            skew = c.rolling(30).skew().iloc[-1]
-            if skew > 0: points += 0.5; details.append("PosSkew")
-
-            # 8. Variance
-            var = ta.variance(c, length=20)
-            if self._valid(var): points += 0
-
-            # 9. StdDev
-            std = c.rolling(20).std().iloc[-1]
-            if c.iloc[-1] > (c.rolling(20).mean().iloc[-1] + std): points += 0.5
-
-            # 10. LinReg
-            linreg = ta.linreg(c, length=20)
-            if self._valid(linreg) and c.iloc[-1] > linreg.iloc[-1]: 
-                points += 1; details.append("AboveLinReg")
-
-            # 13. CG
-            cg = ta.cg(c, length=10)
-            if self._valid(cg) and c.diff().iloc[-1] > 0: points += 0.5
-
-            # 20. Mean Rev
-            dist_mean = abs(c.iloc[-1] - c.rolling(50).mean().iloc[-1])
-            if dist_mean > std * 2: points -= 1
-            else: points += 0.5
-
-        except Exception as e: details.append(f"MathErr:{str(e)[:10]}")
-        norm_score = self._normalize(points, max_possible=12.0)
-        if verbose: print(f"   🔢 [MATH] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # 🧱 DOMAIN 6: STRUCTURE (Fixed)
-    # ==============================================================================
-    def _calc_structure_domain(self, df: pd.DataFrame, verbose: bool) -> float:
-        points = 0.0
-        details = []
-        try:
-            closes = df['close'].values; opens = df['open'].values
-            highs = df['high'].values; lows = df['low'].values
-            
-            # 1. HH
-            if highs[-1] > highs[-2] and highs[-2] > highs[-3]: 
-                points += 2; details.append("HH")
-            
-            # 2. HL
-            if lows[-1] > lows[-2] and lows[-2] > lows[-3]: 
-                points += 2; details.append("HL")
-
-            # 3. Engulfing
-            if closes[-1] > opens[-1]:
-                if closes[-1] > highs[-2] and opens[-1] < lows[-2]:
-                    points += 2; details.append("Engulfing")
-            
-            # 4. Hammer
-            body = abs(closes[-1] - opens[-1])
-            lower_wick = min(closes[-1], opens[-1]) - lows[-1]
-            if lower_wick > body * 2: 
-                points += 2; details.append("Hammer")
-
-            # 5. BOS
-            recent_high = np.max(highs[-11:-1])
-            if closes[-1] > recent_high: points += 2; details.append("BOS")
-
-            # 6. FVG
-            if len(closes) > 3 and lows[-1] > highs[-3] * 1.001:
-                points += 1; details.append("FVG")
-
-            # 7. Order Block
-            if closes[-2] < opens[-2] and closes[-1] > opens[-1]:
-                if (closes[-1] - opens[-1]) > (opens[-2] - closes[-2]) * 2:
-                    points += 1.5; details.append("OB")
-
-            # 8. SFP
-            if lows[-1] < lows[-2] and closes[-1] > lows[-2]:
-                points += 2.5; details.append("SFP")
-
-            # 9. Inside Bar
-            if highs[-1] < highs[-2] and lows[-1] > lows[-2]:
-                points -= 0.5; details.append("IB")
-
-            # 10. Morning Star
-            if closes[-3] < opens[-3] and abs(closes[-2]-opens[-2]) < body*0.5 and closes[-1] > opens[-1]:
-                points += 2; details.append("MorningStar")
-
-            # 14. Golden Cross Struct
-            m50 = np.mean(closes[-50:]); m200 = np.mean(closes[-200:]) if len(closes)>200 else m50
-            if m50 > m200: points += 1
-
-            # 16. Impulse
-            avg_body = np.mean([abs(c-o) for c,o in zip(closes[-10:], opens[-10:])])
-            if body > avg_body * 2: points += 1; details.append("Impulse")
-
-        except Exception as e: details.append(f"PAErr:{str(e)[:10]}")
-        norm_score = self._normalize(points, max_possible=18.0)
-        if verbose: print(f"   🧱 [STRUCTURE] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # 📖 DOMAIN 7: ORDER BOOK (Already Safe, but kept consistent)
-    # ==============================================================================
-    def _calc_orderbook_domain(self, ob: Dict[str, Any], verbose: bool) -> float:
-        points = 0.0
-        details = []
-        if not ob or 'bids' not in ob or 'asks' not in ob: return 0.0
-        
-        try:
-            bids = np.array(ob['bids'], dtype=float)
-            asks = np.array(ob['asks'], dtype=float)
-            if len(bids) < 20 or len(asks) < 20: return 0.0
-
-            bid_vol = np.sum(bids[:20, 1])
-            ask_vol = np.sum(asks[:20, 1])
-            imbal = (bid_vol - ask_vol) / (bid_vol + ask_vol)
-            points += imbal * 5; details.append(f"Imbal:{imbal:.2f}")
-
-            avg_size = np.mean(bids[:50, 1])
-            if np.max(bids[:20, 1]) > avg_size * 5: points += 3; details.append("BidWall")
-            if np.max(asks[:20, 1]) > avg_size * 5: points -= 3; details.append("AskWall")
-
-            spread = (asks[0,0] - bids[0,0]) / bids[0,0] * 100
-            if spread < 0.05: points += 1; details.append("TightSpread")
-            elif spread > 0.2: points -= 1; details.append("WideSpread")
-
-            if bid_vol > ask_vol * 1.5: points += 2; details.append("Depth:Bull")
-            if bids[0,1] > bids[1,1] and bids[1,1] > bids[2,1]: points += 1; details.append("Slope:Up")
-            
-            # Slippage check
-            target_p = asks[0,0] * 1.005
-            vol_needed = 0
-            for p, s in asks:
-                if p > target_p: break
-                vol_needed += s
-            if vol_needed > 50000: points += 1; details.append("ThickBook")
-            else: points -= 1; details.append("ThinBook")
-
-            if bids[0,1] > asks[0,1] * 2: points += 1
-            if (bids[0,0] * bids[0,1]) > 10000: points += 1; details.append("WhaleBid")
-
-        except Exception as e: details.append("OBErr")
-
-        norm_score = self._normalize(points, max_possible=15.0)
-        if verbose: print(f"   📖 [ORDERBOOK] Score: {norm_score:.2f} | {', '.join(details)}")
-        return norm_score
-
-    # ==============================================================================
-    # 🔧 Utilities
-    # ==============================================================================
-    def _valid(self, item) -> bool:
-        """Robust check for None or Empty Series/DataFrame"""
-        if item is None: return False
-        if isinstance(item, (pd.Series, pd.DataFrame)):
-            return not item.empty
-        return True
-
-    def _normalize(self, value: float, max_possible: float) -> float:
-        if max_possible == 0: return 0.0
-        return max(-1.0, min(1.0, value / max_possible))
-
-    def _prepare_dataframe(self, ohlcv: List) -> pd.DataFrame:
-        df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
-        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-        df.set_index('timestamp', inplace=True)
-        cols = ['open', 'high', 'low', 'close', 'volume']
-        df[cols] = df[cols].astype(float)
-        return df
-
-    def _get_grade(self, score: float) -> str:
-        if score >= 85: return "ULTRA"
-        if score >= 70: return "STRONG"
-        if score >= 50: return "NORMAL"
-        if score >= 35: return "WEAK"
-        return "REJECT"
-
-    def _create_rejection(self, reason: str):
-        return {
-            "governance_score": 0.0,
-            "grade": "REJECT",
-            "status": "REJECTED",
-            "reason": reason,
-            "components": {}
-        }