AdvancedScalpingStrategy

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
                                IStrategy, IntParameter)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from scipy import stats
import warnings
warnings.filterwarnings('ignore')

class AdvancedScalpingStrategy(IStrategy):
    """
    Advanced Multi-Dimensional Scalping Strategy for FreqTrade
    
    Bu strateji şu gelişmiş teknikleri kullanır:
    - Adaptive Volatility Filtering
    - Multi-Timeframe Momentum Analysis  
    - Dynamic Support/Resistance Detection
    - Volume-Price Divergence Analysis
    - Neural Network-Inspired Signal Generation
    """

    # Strategy interface version
    INTERFACE_VERSION = 3

    # Optimal timeframe for the strategy
    timeframe = '5m'
    
    # Can this strategy go short?
    can_short: bool = True

    # Minimal ROI designed for the strategy
    minimal_roi = {
        "0": 0.02,      # 2% kar hedefi
        "5": 0.015,     # 5 dakika sonra 1.5%
        "10": 0.01,     # 10 dakika sonra 1%
        "20": 0.005,    # 20 dakika sonra 0.5%
        "30": 0.0       # 30 dakika sonra başabaş
    }

    # Optimal stoploss
    stoploss = -0.015  # 1.5% stop loss

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    # Hyperopt parameters
    buy_params = {
        "avf_threshold": 0.6,
        "mtmo_threshold": 0.25,
        "signal_strength_min": 0.4,
        "volume_factor": 1.2,
    }

    sell_params = {
        "sell_signal_threshold": -0.3,
        "profit_multiplier": 1.5,
        "volume_confirm": True,
    }

    # Strategy parameters
    avf_threshold = DecimalParameter(0.4, 0.8, decimals=2, default=0.6, space="buy")
    mtmo_threshold = DecimalParameter(0.15, 0.4, decimals=2, default=0.25, space="buy") 
    signal_strength_min = DecimalParameter(0.2, 0.6, decimals=2, default=0.4, space="buy")
    volume_factor = DecimalParameter(1.0, 2.0, decimals=1, default=1.2, space="buy")
    
    sell_signal_threshold = DecimalParameter(-0.5, -0.2, decimals=2, default=-0.3, space="sell")
    profit_multiplier = DecimalParameter(1.2, 2.0, decimals=1, default=1.5, space="sell")
    volume_confirm = BooleanParameter(default=True, space="sell")

    # Strategy specific parameters
    fast_period = 5
    slow_period = 21
    ultra_fast = 3
    sensitivity = 2.0

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Tüm indikatörleri hesaplar
        """
        
        # === ADAPTIVE VOLATILITY FILTER ===
        dataframe = self.adaptive_volatility_filter(dataframe)
        
        # === MULTI-TIMEFRAME MOMENTUM ===
        dataframe = self.multi_timeframe_momentum(dataframe)
        
        # === DYNAMIC SUPPORT/RESISTANCE ===
        dataframe = self.dynamic_support_resistance(dataframe)
        
        # === VOLUME-PRICE DIVERGENCE ===
        dataframe = self.volume_price_divergence(dataframe)
        
        # === NEURAL INSPIRED SIGNAL ===
        dataframe = self.neural_inspired_signal(dataframe)
        
        # === ADDITIONAL FILTERS ===
        dataframe = self.additional_filters(dataframe)
        
        return dataframe

    def adaptive_volatility_filter(self, dataframe: DataFrame) -> DataFrame:
        """Adaptif Volatilite Filtresi"""
        
        # True Range hesaplama
        dataframe['tr1'] = dataframe['high'] - dataframe['low']
        dataframe['tr2'] = abs(dataframe['high'] - dataframe['close'].shift(1))
        dataframe['tr3'] = abs(dataframe['low'] - dataframe['close'].shift(1))
        dataframe['true_range'] = dataframe[['tr1', 'tr2', 'tr3']].max(axis=1)
        
        # Adaptif ATR
        dataframe['atr_fast'] = dataframe['true_range'].rolling(self.fast_period).mean()
        dataframe['atr_slow'] = dataframe['true_range'].rolling(self.slow_period).mean()
        
        # Volatilite rejimi
        dataframe['vol_regime_raw'] = dataframe['atr_fast'] / dataframe['atr_slow']
        
        # Normalize et (0-1 arası)
        rolling_min = dataframe['vol_regime_raw'].rolling(100).min()
        rolling_max = dataframe['vol_regime_raw'].rolling(100).max()
        dataframe['vol_regime'] = (dataframe['vol_regime_raw'] - rolling_min) / (rolling_max - rolling_min)
        dataframe['vol_regime'] = dataframe['vol_regime'].fillna(0.5)
        
        return dataframe

    def multi_timeframe_momentum(self, dataframe: DataFrame) -> DataFrame:
        """Çoklu Zaman Dilimi Momentum"""
        
        # Farklı periyotlarda RSI
        dataframe['rsi_ultra'] = ta.RSI(dataframe, timeperiod=self.ultra_fast)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=self.fast_period)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=self.slow_period)
        
        # Ağırlıklı momentum skoru
        dataframe['momentum_score'] = (
            0.5 * (dataframe['rsi_ultra'] - 50) / 50 +
            0.3 * (dataframe['rsi_fast'] - 50) / 50 +
            0.2 * (dataframe['rsi_slow'] - 50) / 50
        )
        
        return dataframe

    def dynamic_support_resistance(self, dataframe: DataFrame) -> DataFrame:
        """Dinamik Destek/Direnç Detektörü"""
        
        # Pivot hesaplama
        pivot_window = 5
        
        # Son 20 barın max/min'i
        dataframe['resistance_level'] = dataframe['high'].rolling(20).max()
        dataframe['support_level'] = dataframe['low'].rolling(20).min()
        
        # Mesafe hesaplama
        dataframe['distance_to_resistance'] = (dataframe['close'] - dataframe['resistance_level']) / dataframe['close']
        dataframe['distance_to_support'] = (dataframe['support_level'] - dataframe['close']) / dataframe['close']
        
        # Birleşik sinyal
        dataframe['sr_signal'] = dataframe['distance_to_resistance'] + dataframe['distance_to_support']
        
        return dataframe

    def volume_price_divergence(self, dataframe: DataFrame) -> DataFrame:
        """Hacim-Fiyat Uyumsuzluk Analizi"""
        
        # Fiyat değişimi
        dataframe['price_change'] = dataframe['close'].pct_change()
        
        # Hacim analizi
        dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
        dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_ma']
        
        # OBV
        dataframe['obv'] = ta.OBV(dataframe)
        dataframe['obv_rsi'] = ta.RSI(dataframe['obv'], timeperiod=14)
        
        # Volume-Price Divergence skoru
        dataframe['vpd_score'] = (
            0.4 * np.sign(dataframe['price_change']) * dataframe['volume_ratio'] +
            0.6 * (dataframe['obv_rsi'] - 50) / 50
        )
        
        return dataframe

    def neural_inspired_signal(self, dataframe: DataFrame) -> DataFrame:
        """YZ-esinlenmiş Sinyal Üretimi"""
        
        # Aktivasyon fonksiyonu
        def activation(x):
            return np.tanh(x * self.sensitivity)
        
        # Ağırlıklı kombinasyon
        weights = [0.25, 0.35, 0.20, 0.20]  # AVF, MTMO, DSRD, VPDA
        
        # Ana sinyal hesaplama
        dataframe['raw_signal'] = (
            weights[0] * dataframe['vol_regime'] +
            weights[1] * dataframe['momentum_score'] +
            weights[2] * dataframe['sr_signal'] +
            weights[3] * dataframe['vpd_score']
        )
        
        # Aktivasyon uygula
        dataframe['main_signal'] = activation(dataframe['raw_signal'])
        
        # Sinyal yumuşatma
        dataframe['main_signal'] = dataframe['main_signal'].rolling(3).mean()
        
        # Sinyal gücü
        dataframe['signal_strength'] = abs(dataframe['main_signal'])
        
        return dataframe

    def additional_filters(self, dataframe: DataFrame) -> DataFrame:
        """Ek filtreler ve konfirmasyon sinyalleri"""
        
        # Trend filtresi
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=21)
        dataframe['trend_direction'] = np.where(
            dataframe['ema_fast'] > dataframe['ema_slow'], 1, -1
        )
        
        # MACD konfirmasyonu
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macd_signal'] = macd['macdsignal']
        dataframe['macd_hist'] = macd['macdhist']
        
        # ADX (Trend gücü)
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        
        # Bollinger Bands
        bollinger = ta.BBANDS(dataframe, timeperiod=20)
        dataframe['bb_upper'] = bollinger['upperband']
        dataframe['bb_middle'] = bollinger['middleband']
        dataframe['bb_lower'] = bollinger['lowerband']
        dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lower']) / (dataframe['bb_upper'] - dataframe['bb_lower'])
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Long pozisyon giriş sinyalleri
        """
        
        # Ana alım koşulları
        long_conditions = [
            # Ana sinyal pozitif
            (dataframe['main_signal'] > self.mtmo_threshold.value),
            
            # Sinyal gücü yeterli
            (dataframe['signal_strength'] > self.signal_strength_min.value),
            
            # Volatilite rejimi uygun
            (dataframe['vol_regime'] > self.avf_threshold.value),
            
            # Hacim konfirmasyonu
            (dataframe['volume_ratio'] > self.volume_factor.value),
            
            # Trend yönü uygun
            (dataframe['trend_direction'] > 0),
            
            # MACD konfirmasyonu
            (dataframe['macd'] > dataframe['macd_signal']),
            
            # ADX trend gücü
            (dataframe['adx'] > 25),
            
            # Bollinger Bands pozisyonu
            (dataframe['bb_percent'] < 0.8),
            
            # Momentum artan
            (dataframe['momentum_score'] > dataframe['momentum_score'].shift(1)),
        ]
        
        # Tüm koşulları birleştir
        dataframe.loc[
            reduce(lambda x, y: x & y, long_conditions), 'enter_long'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Long pozisyon çıkış sinyalleri
        """
        
        # Ana satış koşulları
        exit_conditions = [
            # Ana sinyal negatif
            (dataframe['main_signal'] < self.sell_signal_threshold.value),
            
            # Momentum tersine döndü
            (dataframe['momentum_score'] < -0.2),
            
            # MACD tersine döndü
            (dataframe['macd'] < dataframe['macd_signal']),
            
            # Hacim konfirmasyonu (isteğe bağlı)
            (~self.volume_confirm.value | (dataframe['volume_ratio'] > 1.0)),
        ]
        
        # Tüm koşulları birleştir
        dataframe.loc[
            reduce(lambda x, y: x & y, exit_conditions), 'exit_long'
        ] = 1

        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], 
                 side: str, **kwargs) -> float:
        """
        Volatiliteye göre kaldıraç ayarlama
        """
        # Mevcut volatilite rejimini al
        # Bu gerçek implementasyonda dataframe'den alınmalı
        # Şimdilik sabit değer döndürüyoruz
        return min(max_leverage, 5.0)  # Maksimum 5x kaldıraç

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        """
        Dinamik stop loss
        """
        # ATR bazlı stop loss
        # Bu gerçek implementasyonda son ATR değerini kullanmalı
        
        if current_profit > 0.01:  # %1 kar varsa
            # Trailing stop aktif et
            return -0.005  # %0.5 trailing stop
        
        return self.stoploss  # Normal stop loss

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
                           rate: float, time_in_force: str, current_time: datetime,
                           entry_tag: Optional[str], side: str, **kwargs) -> bool:
        """
        İşlem girişi son kontrol
        """
        # Burada son dakika kontrolleri yapılabilir
        # Örneğin: spread kontrolü, volatilite kontrolü vs.
        return True

    def bot_loop_start(self, **kwargs) -> None:
        """
        Her bot döngüsünün başında çalışır
        """
        # Dinamik parametreler burada güncellenebilir
        pass

# Utility function for condition combination
def reduce(function, iterable, initializer=None):
    """Reduce function for combining conditions"""
    it = iter(iterable)
    if initializer is None:
        value = next(it)
    else:
        value = initializer
    for element in it:
        value = function(value, element)
    return value