shaibu01/Titan-Engine

1"""2TITAN CORTEX (v42.0 - JIT PHYSICS ORCHESTRATOR)3TYPE: Global Macro & Multi-Index Orchestrator4DATA: Injected ZMQ Bar Cache (Zero API Overhead - Pure Memory)5MATH: Numba JIT Hawking Thermodynamics, Harmonic Oscillators, Kerr Metrics6UPGRADE: Eliminated Pandas Overhead. Eager JIT Compilation.7"""8 9import pandas as pd10import numpy as np11import logging12import math13from numba import njit14 15logger = logging.getLogger("TITAN")16 17# --- C++ CORE INJECTION ---18try:19    import titan_engine20    HAS_CPP_CORE = True21except ImportError:22    HAS_CPP_CORE = False23 24class FastMath:25    @staticmethod26    def get_std(array_data):27        """28        Calculates Standard Deviation.29        Prioritizes the C++ Titan Engine for microsecond execution.30        """31        if len(array_data) == 0:32             return 0.0133             34        # ⚡ C++ OFFLOAD ATTEMPT35        if HAS_CPP_CORE and hasattr(titan_engine, 'calc_stats'):36            try:37                # calc_stats returns a tuple: (mean, std_dev)38                arr = np.ascontiguousarray(array_data, dtype=np.float64)39                return float(titan_engine.calc_stats(arr)[1])40            except Exception:41                 pass42                 43        # 🐍 PYTHON FALLBACK44        return float(np.std(array_data))45 46fast_math = FastMath()47 48# ==============================================================================49# 1. PURE MACHINE-CODE PHYSICS (Bypassing Python Overhead)50# ==============================================================================51@njit(fastmath=True, nogil=True)52def jit_hawking_temperature(volatility, liquidity):53    """54    Derives 'Temperature' from Volatility (Energy) and Liquidity (Mass).55    T_H ~ E / M. High Temp = Unstable = Reduce Position Size.56    """57    M = max(1.0, liquidity / 1_000_000.0)58    E = max(0.01, volatility * 100.0)59    return E / M60 61@njit(fastmath=True, nogil=True)62def jit_harmonic_oscillator_potential(price, mean_val, std_val):63    """64    Calculates the restoring force of a mean-reverting asset.65    V(x) = 1/2 * k * x^266    """67    safe_std = max(0.001, std_val)68    displacement = price - mean_val69    k = 1.0 / safe_std70    return 0.5 * k * (displacement ** 2)71 72@njit(fastmath=True, nogil=True)73def jit_kerr_metric_drawdown(depth, recovery_speed):74    """Estimates 'Event Horizon' risk."""75    return depth / (recovery_speed + 0.01)76 77 78# ==============================================================================79# 2. GLOBAL CORTEX ENGINE80# ==============================================================================81class TitanCortex:82    def __init__(self):83        logger.info("🧠 CORTEX: ONLINE [ZMQ ZERO-LATENCY ORCHESTRATOR]")84        # These must match the MT5 REVERSE_MAP keys perfectly85        self.indexes = ["SPY", "QQQ", "IWM", "BTC/USD"]86        self.regime_map = {sym: "SCANNING" for sym in self.indexes}87 88    def _calculate_synthetic_macro(self, bar_cache):89        """Mathematically derives Macro Indicators from the injected cache."""90        syn_vix = 20.091        syn_rates = 4.092        93        # 1. Synthetic VIX (Derived from SPY realized volatility)94        if "SPY" in bar_cache:95            spy_df = bar_cache["SPY"]96            if len(spy_df) > 20:97                closes = spy_df['close'].values[-20:]98                # 🚨 DIVIDE BY ZERO PROTECTION (+ 1e-9)99                returns = np.diff(closes) / (closes[:-1] + 1e-9)100                vol = fast_math.get_std(returns)101                syn_vix = float(vol * math.sqrt(252) * 100)102                103        # 2. Synthetic Rates (Derived from Tech vs Market Beta)104        if "QQQ" in bar_cache and "SPY" in bar_cache:105            try:106                q_closes = bar_cache["QQQ"]['close'].values[-20:]107                s_closes = bar_cache["SPY"]['close'].values[-20:]108                109                # 🚨 DIVIDE BY ZERO PROTECTION110                q_trend = np.mean(np.diff(q_closes) / (q_closes[:-1] + 1e-9))111                s_trend = np.mean(np.diff(s_closes) / (s_closes[:-1] + 1e-9))112                113                if q_trend < s_trend:114                     syn_rates += 0.1115            except Exception:116                 pass117                 118        return float(syn_vix), float(syn_rates)119 120    def _determine_regime(self, df, symbol):121        """Runs the Physics logic PER INDEX."""122        if df is None or df.empty or len(df) < 50:123             return "NEUTRAL", 0.5124 125        closes = df['close'].values126        127        # 🚨 DIVIDE BY ZERO PROTECTION128        returns = np.diff(closes) / (closes[:-1] + 1e-9)129        vol = fast_math.get_std(returns) * math.sqrt(252)130        131        # 🚨 PANDAS PURGE: Using raw NumPy arrays instead of df['volume'].mean()132        if 'volume' in df.columns:133            v_arr = df['volume'].values134            avg_vol = float(np.mean(v_arr)) if np.mean(v_arr) > 0 else 1_000_000.0135        else:136            avg_vol = 1_000_000.0137        138        # 🚨 LLVM JIT PHYSICS INFERENCE139        temp = jit_hawking_temperature(vol, avg_vol)140        141        ma_50 = float(np.mean(closes[-50:]))142        current = float(closes[-1])143        std_50 = fast_math.get_std(closes[-50:])144        145        potential = jit_harmonic_oscillator_potential(current, ma_50, std_50)146        147        regime = "NEUTRAL"148        if temp > 0.0005:149             regime = "HIGH_VOL_RISK"150        elif current > ma_50 and potential < 1.0:151             regime = "STABLE_TREND"152        elif potential > 2.0:153             regime = "OVEREXTENDED_REVERT"154             155        return regime, float(temp)156 157    def get_macro_state(self, bar_cache, l2_cache):158        """159        The Main Pulse.160        🚨 API UPGRADE: Receives data directly from Core memory. No network latency.161        🚨 FLOAT FIX: All outputs cast to standard Python objects for JSON Safety.162        """163        vix, rates = self._calculate_synthetic_macro(bar_cache)164        global_fear = 0.5165        regime_report = {}166        167        for sym in self.indexes:168            if sym in bar_cache:169                df = bar_cache[sym]170                regime, temp = self._determine_regime(df, sym)171                172                # Fetch L2 OBI from cache instead of querying external APIs173                imb = float(l2_cache.get(sym, 0.0))174                175                self.regime_map[sym] = regime176                177                if sym == "SPY" and regime == "HIGH_VOL_RISK": global_fear += 0.2178                if sym == "BTC/USD" and regime == "STABLE_TREND": global_fear -= 0.1179                180                regime_report[str(sym)] = {181                    "regime": str(regime),182                    "temp": float(round(temp, 6)),183                    "l2_imbalance": float(round(imb, 2))184                }185 186        # VIX Logic Overlay187        if vix > 30.0: global_fear = 0.9188        elif vix < 15.0: global_fear = 0.2189 190        global_regime = "NEUTRAL"191        if global_fear > 0.8: global_regime = "LIQUIDITY_CRISIS"192        elif global_fear > 0.6: global_regime = "DEFENSIVE"193        elif global_fear < 0.3: global_regime = "SNIPER_AGGRESSIVE"194 195        # Strictly formatted dictionary for ZMQ JSON payload196        return {197            "fear_score": float(round(global_fear, 2)),198            "global_regime": str(global_regime),199            "vix": float(round(vix, 2)),200            "index_details": regime_report201        }202 203# ==============================================================================204# 3. EAGER COMPILATION WARM-UP205# ==============================================================================206logger.info("🔥 Warming up Cortex Physics LLVM Binaries...")207_ = jit_hawking_temperature(0.02, 1_500_000.0)208_ = jit_harmonic_oscillator_potential(105.0, 100.0, 2.0)209_ = jit_kerr_metric_drawdown(5.0, 0.2)210logger.info("✅ Cortex Binaries Locked. Zero Cold-Start Latency Guaranteed.")