def __init__(self):
    # Add default parameter initialization
    self.rsi_period = 14  # Default RSI period
    self.ema_fast = 12    # Default fast EMA
    self.ema_slow = 26    # Default slow EMA 
    self.bollinger_period = 20
    self.bollinger_std = 2.0  add this to    import numpy as np
import pandas as pd
import torch
import ta
from typing import Dict, Tuple, Optional
import logging
from datetime import datetime


class QuantumIndicators:
    def __init__(self):
        """Initialize Quantum Indicators with CUDA acceleration"""
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.current_price = 0.0
        self.current_volume = 0.0
        self.current_volatility = 0.0
        self.time_series = []
        self.price_series = []
        self.volume_series = []
        self.rsi_series = []
        self.macd_series = []
        self.signal_series = []
        self.histogram_series = []
        if torch.cuda.is_available():
            self.streams = {
                "momentum": torch.cuda.Stream(),
                "trend": torch.cuda.Stream(),
                "volatility": torch.cuda.Stream(),
            }
            torch.backends.cuda.matmul.allow_tf32 = True
            torch.backends.cudnn.benchmark = True
        logging.info(f"🧠 Quantum Indicators initialized on {self.device}")

    def get_market_data_sync(self) -> Dict:
        """Get current market data and indicators synchronously"""
        try:
            logging.debug("Fetching market data synchronously.")
            return {
                "price": self.current_price,
                "volume": self.current_volume,
                "price_data": {"time": self.time_series, "close": self.price_series},
                "volume_data": {"time": self.time_series, "volume": self.volume_series},
                "rsi": self.rsi_series,
                "macd": self.macd_series,
                "signal": self.signal_series,
                "histogram": self.histogram_series,
                "volatility": self.current_volatility,
            }
        except Exception as e:
            logging.error(f"Error getting market data: {e}")
            return {
                "price": 0,
                "volume": 0,
                "price_data": {"time": [], "close": []},
                "volume_data": {"time": [], "volume": []},
                "rsi": [],
                "macd": [],
                "signal": [],
                "histogram": [],
                "volatility": 0,
            }

    async def get_market_data(self):
        """Get current market data and indicators asynchronously"""
        try:
            logging.debug("Fetching market data asynchronously.")
            market_data = self.get_market_data_sync()
            analysis = self.analyze_market_state()
            market_data.update(analysis)
            return market_data

        except Exception as e:
            logging.error(f"Error getting async market data: {e}")
            return self.get_market_data_sync()  # Fallback to sync data

    def update_market_data(
        self, price: float, volume: float, timestamp: datetime = None
    ):
        """Update current market data"""
        try:
            logging.debug(
                f"Updating market data with price: {price}, volume: {volume}."
            )
            self.current_price = price
            self.current_volume = volume
            if timestamp is None:
                timestamp = datetime.now()
            self.time_series.append(timestamp)
            self.price_series.append(price)
            self.volume_series.append(volume)

            max_points = 1000
            if len(self.time_series) > max_points:
                logging.info(f"Trimming time series to the last {max_points} points.")
                self.time_series = self.time_series[-max_points:]
                self.price_series = self.price_series[-max_points:]
                self.volume_series = self.volume_series[-max_points:]

            if len(self.price_series) >= 2:
                self._update_indicators()
        except Exception as e:
            logging.error(f"Error updating market data: {e}")

    def _update_indicators(self):
        """Update technical indicators"""
        try:
            logging.debug("Updating indicators.")
            price_tensor = torch.tensor(self.price_series, device=self.device)
            indicators = self.process_market_data(price_tensor)

            self.rsi_series = indicators["rsi"].cpu().numpy().tolist()
            self.macd_series = indicators["macd"].cpu().numpy().tolist()
            self.signal_series = indicators["macd_signal"].cpu().numpy().tolist()
            self.histogram_series = [
                m - s for m, s in zip(self.macd_series, self.signal_series)
            ]
            self.current_volatility = float(
                indicators["bb_upper"][-1] - indicators["bb_lower"][-1]
            ) / float(indicators["bb_middle"][-1])
            logging.info("Indicators updated successfully.")
        except Exception as e:
            logging.error(f"Error updating indicators: {e}")

    def setup_indicators(self, rsi_config, ema_config, bollinger_config):
        """Configure technical indicators with parameters from config"""
        try:
            self.rsi_period = rsi_config.get("period", 14)
            self.ema_fast = ema_config.get("fast", 12)
            self.ema_slow = ema_config.get("slow", 26)
            self.bollinger_period = bollinger_config.get("period", 20)
            self.bollinger_std = bollinger_config.get("std_dev", 2.0)
            logging.info(
                f"🎯 Indicators configured: RSI({self.rsi_period}), EMA({self.ema_fast}/{self.ema_slow})"
            )
        except Exception as e:
            logging.error(f"Error configuring indicators: {e}")
            self.rsi_period = 14
            self.ema_fast = 12
            self.ema_slow = 26
            self.bollinger_period = 20
            self.bollinger_std = 2.0

    @torch.amp.autocast("cuda")
    def process_market_data(self, price_data):
        """Process market data with GPU-accelerated indicators"""
        try:
            logging.debug("Processing market data.")
            with torch.no_grad():
                price_np = (
                    price_data.cpu().numpy()
                    if torch.is_tensor(price_data)
                    else np.array(price_data)
                )
                df = pd.DataFrame({"close": price_np})
                indicators = {}

                with torch.cuda.stream(self.streams["momentum"]):
                    rsi = ta.momentum.RSIIndicator(
                        df["close"], window=self.rsi_period, fillna=True
                    )
                    stoch = ta.momentum.StochasticOscillator(
                        df["close"], df["close"], df["close"], fillna=True
                    )
                    indicators.update(
                        {
                            "rsi": torch.tensor(
                                rsi.rsi(), device=self.device, dtype=torch.float16
                            ),
                            "stoch_k": torch.tensor(
                                stoch.stoch(), device=self.device, dtype=torch.float16
                            ),
                            "stoch_d": torch.tensor(
                                stoch.stoch_signal(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                        }
                    )
                with torch.cuda.stream(self.streams["trend"]):
                    macd = ta.trend.MACD(
                        df["close"],
                        window_slow=self.ema_slow,
                        window_fast=self.ema_fast,
                        fillna=True,
                    )
                    ema_short = ta.trend.EMAIndicator(
                        df["close"], window=7, fillna=True
                    )
                    ema_long = ta.trend.EMAIndicator(
                        df["close"], window=21, fillna=True
                    )
                    indicators.update(
                        {
                            "macd": torch.tensor(
                                macd.macd(), device=self.device, dtype=torch.float16
                            ),
                            "macd_signal": torch.tensor(
                                macd.macd_signal(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                            "ema_short": torch.tensor(
                                ema_short.ema_indicator(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                            "ema_long": torch.tensor(
                                ema_long.ema_indicator(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                        }
                    )
                with torch.cuda.stream(self.streams["volatility"]):
                    bb = ta.volatility.BollingerBands(
                        df["close"],
                        window=self.bollinger_period,
                        window_dev=self.bollinger_std,
                        fillna=True,
                    )
                    indicators.update(
                        {
                            "bb_upper": torch.tensor(
                                bb.bollinger_hband(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                            "bb_lower": torch.tensor(
                                bb.bollinger_lband(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                            "bb_middle": torch.tensor(
                                bb.bollinger_mavg(),
                                device=self.device,
                                dtype=torch.float16,
                            ),
                        }
                    )
                torch.cuda.synchronize()
                logging.info("Market data processed successfully.")
                return indicators
        except Exception as e:
            logging.error(f"Error processing market data: {e}")
            return {}

    @torch.amp.autocast("cuda")
    def get_quantum_signals(
        self, indicators: Dict[str, torch.Tensor]
    ) -> Dict[str, float]:
        """Calculate quantum trading signals using GPU acceleration"""
        try:
            logging.debug("Calculating quantum signals.")
            with torch.no_grad():
                momentum_signal = (
                    indicators["rsi"][-1] * 0.4
                    + indicators["stoch_k"][-1] * 0.3
                    + indicators["stoch_d"][-1] * 0.3
                )
                trend_signal = torch.tanh(
                    (indicators["ema_short"][-1] / indicators["ema_long"][-1] - 1) * 10
                )
                bb_width = indicators["bb_upper"][-1] - indicators["bb_lower"][-1]
                volatility_signal = torch.clamp(
                    bb_width / indicators["bb_middle"][-1], 0, 1
                )
                pressure = self.calculate_quantum_pressure(indicators)
                wave_analysis = self.quantum_wave_analysis(indicators["ema_short"])
                return {
                    "momentum": float(momentum_signal.cpu()),
                    "trend": float(trend_signal.cpu()),
                    "volatility": float(volatility_signal.cpu()),
                    "pressure": pressure,
                    "wave_strength": wave_analysis["wave_strength"],
                    "wave_phase": wave_analysis["wave_phase"],
                    "cycle_length": wave_analysis["cycle_length"],
                }
        except Exception as e:
            logging.error(f"Error calculating quantum signals: {e}")
            return {
                "momentum": 0.0,
                "trend": 0.0,
                "volatility": 0.0,
                "pressure": 0.0,
                "wave_strength": 0.0,
                "wave_phase": 0.0,
                "cycle_length": 0.0,
            }

    @torch.amp.autocast("cuda")
    def calculate_quantum_pressure(self, indicators: Dict[str, torch.Tensor]) -> float:
        """Calculate quantum buying/selling pressure"""
        try:
            with torch.no_grad():
                pressure = (
                    (indicators["rsi"][-1] - 50) / 50 * 0.3
                    + (indicators["macd"][-1] / indicators["macd"].std()) * 0.3
                    + (
                        (indicators["ema_short"][-1] / indicators["ema_long"][-1] - 1)
                        * 100
                    )
                    * 0.4
                )
                logging.debug(f"Calculated quantum pressure: {pressure.item()}")
                return float(pressure.cpu())
        except Exception as e:
            logging.error(f"Error calculating quantum pressure: {e}")
            return 0.0

    @torch.amp.autocast("cuda")
    def quantum_wave_analysis(self, price_data: torch.Tensor) -> Dict[str, float]:
        """Advanced quantum wave pattern detection"""
        try:
            with torch.no_grad():
                wave_patterns = torch.fft.fft(price_data.float())
                dominant_freq = torch.argmax(torch.abs(wave_patterns))
                wave_strength = float(torch.abs(wave_patterns[dominant_freq]))
                wave_phase = float(torch.angle(wave_patterns[dominant_freq]))
                cycle_length = len(price_data) / (dominant_freq + 1)
                logging.debug(
                    f"Wave analysis results - Wave Strength: {wave_strength}, Wave Phase: {wave_phase}, Cycle Length: {cycle_length}"
                )
                return {
                    "wave_strength": wave_strength,
                    "wave_phase": wave_phase,
                    "cycle_length": cycle_length,
                }
        except Exception as e:
            logging.error(f"Error in wave analysis: {e}")
            return {"wave_strength": 0.0, "wave_phase": 0.0, "cycle_length": 0.0}

    def calculate_confidence(self, signals: Dict[str, float]) -> float:
        """Calculate trade confidence score with quantum enhancement"""
        try:
            momentum_norm = (signals["momentum"] - 50) / 50  # Normalize to -1 to 1
            confidence = (
                abs(momentum_norm) * 0.3
                + abs(signals["trend"]) * 0.3
                + signals["volatility"] * 0.2
                + abs(signals["pressure"]) * 0.1
                + min(signals["wave_strength"] / 100, 0.1)
            ) * 100
            confidence = min(max(confidence, 0.0), 100.0)
            logging.debug(f"Calculated confidence score: {confidence}")
            return confidence
        except Exception as e:
            logging.error(f"Error calculating confidence: {e}")
            return 0.0

    def analyze_market_state(self) -> Dict[str, any]:
        """Comprehensive market state analysis"""
        try:
            if len(self.price_series) < 2:
                logging.warning("Not enough data points to analyze market state.")
                return {}
            logging.debug("Analyzing market state.")
            price_tensor = torch.tensor(self.price_series, device=self.device)
            indicators = self.process_market_data(price_tensor)
            if not indicators:
                logging.warning("No indicators returned for market analysis.")
                return {}
            signals = self.get_quantum_signals(indicators)
            confidence = self.calculate_confidence(signals)
            entry_points = self.get_quantum_entry_points(indicators)
            risk_score = self.calculate_quantum_risk(indicators)
            return {
                "signals": signals,
                "confidence": confidence,
                "entry_points": entry_points,
                "risk_score": risk_score,
                "current_price": self.current_price,
                "current_volume": self.current_volume,
            }
        except Exception as e:
            logging.error(f"Error analyzing market state: {e}")
            return {}

    def cleanup(self):
        """Cleanup resources"""
        try:
            logging.info("Cleaning up resources.")
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
                for stream in self.streams.values():
                    stream.synchronize()
            logging.info("Cleanup completed successfully.")
        except Exception as e:
            logging.error(f"Error during cleanup: {e}")