# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/TM3ExtremumHunter.py

import sys
import os

ROOT_DIR = os.path.realpath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(ROOT_DIR)

import sdnotify
from freqtrade.enums.runmode import RunMode
from typing import Dict, List, Optional
from lib.ma import MovingAveragesCalculate, MovingAveragesCalculator2
from lib.mom import MomentumANDVolatilityCalculate
from lib.cycle import CycleCalculate
from lib.trend import TrendCalculate
from lib.oscillators import OscillatorsCalculate
from lib import helpers
from lib.sagemaster import SageMasterClient
from lib.Alpha101 import get_alpha
from scipy.special import softmax

import lib.glassnode as gn

import warnings
import json
import logging
from functools import reduce
import time
import numpy as np
from technical.pivots_points import pivots_points

import pandas as pd
from pandas import DataFrame, Series
from freqtrade.persistence.trade_model import Trade
from freqtrade.strategy import IStrategy
from freqtrade.strategy.parameters import BooleanParameter, DecimalParameter, IntParameter
from datetime import timedelta, datetime, timezone
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
import talib.abstract as ta
from sqlalchemy import desc
from sklearn.preprocessing import StandardScaler, RobustScaler
from scipy.signal import argrelextrema
from joblib import Parallel, delayed

from technical import qtpylib
import pandas_ta as pta
from freqtrade.exchange import timeframe_to_minutes

logger = logging.getLogger(__name__)

warnings.simplefilter(action='ignore', category=FutureWarning)

def get_distance(p1, p2):
    return abs((p1) - (p2))

def candle_stats(dataframe):


    dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
    dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
    dataframe['ohlc4'] = (dataframe['open'] + dataframe['high'] +
                          dataframe['low'] + dataframe['close']) / 4

    dataframe['hlc3_log'] = np.log(dataframe['hlc3'])
    dataframe['hl2_log'] = np.log(dataframe['hl2'])
    dataframe['ohlc4_log'] = np.log(dataframe['ohlc4'])

    dataframe['close_log'] = np.log(dataframe['close'])
    dataframe['high_log'] = np.log(dataframe['high'])
    dataframe['low_log'] = np.log(dataframe['low'])
    dataframe['open_log'] = np.log(dataframe['open'])
    return dataframe

def f(x):
    return x

def top_percent_change(dataframe: DataFrame, length: int) -> float:
    """
    Percentage change of the current close from the range maximum Open price
    :param dataframe: DataFrame The original OHLC dataframe
    :param length: int The length to look back
    """
    if length == 0:
        return (dataframe['open'] - dataframe['close']) / dataframe['close']
    else:
        return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']


def chaikin_mf(df, periods=20):
    close = df['close']
    low = df['low']
    high = df['high']
    volume = df['volume']
    mfv = ((close - low) - (high - close)) / (high - low)
    mfv = mfv.fillna(0.0)
    mfv *= volume
    cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()
    return Series(cmf, name='cmf')



def VWAPB(dataframe, window_size=20, num_of_std=1):
    df = dataframe.copy()
    df['vwap'] = qtpylib.rolling_vwap(df, window=window_size)
    rolling_std = df['vwap'].rolling(window=window_size).std()
    df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std)
    df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std)
    return df['vwap_low'], df['vwap'], df['vwap_high']


def EWO(dataframe, sma_length=5, sma2_length=35):
    df = dataframe.copy()
    sma1 = ta.EMA(df, timeperiod=sma_length)
    sma2 = ta.EMA(df, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / df['close'] * 100
    return smadif


def get_distance(p1, p2):
    return abs((p1) - (p2))


class Github_remiotore_freqtrade__TM3ExtremumHunter__20260111_210550(IStrategy):
    """
    Example strategy showing how the user connects their own
    IFreqaiModel to the strategy. Namely, the user uses:
    self.freqai.start(dataframe, metadata)

    to make predictions on their data. populate_any_indicators() automatically
    generates the variety of features indicated by the user in the
    canonical freqtrade configuration file under config['freqai'].
    """

    def heartbeat(self):
        sdnotify.SystemdNotifier().notify("WATCHDOG=1")

    def log(self, msg, *args, **kwargs):
        self.heartbeat()
        logger.info(msg, *args, **kwargs)

    class HyperOpt:
        def generate_estimator(dimensions: List['Dimension'], **kwargs):
            return "ET"



        def stoploss_space():
            return [SKDecimal(-0.04, -0.01, decimals=3, name='stoploss')]

        def roi_space() -> List[Dimension]:
            return [
                Integer(1, 180, name='roi_t1'),
                Integer(1, 180, name='roi_t2'),
                Integer(1, 180, name='roi_t3'),
                SKDecimal(0, 0.2, decimals=3, name='roi_p1'),
                SKDecimal(0, 0.06, decimals=3, name='roi_p2'),
                SKDecimal(0, 0.15, decimals=3, name='roi_p3'),
            ]

        def generate_roi_table(params: Dict) -> Dict[int, float]:

            roi_table = {}
            roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
            roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
            roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
            roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0

            return roi_table



        plot_config = {
        "main_plot": {},
        "subplots": {
            "real": {
            "L1": {
                "color": "#616161",
                "type": "line"
            },
            "L-1": {
                "color": "#575757",
                "type": "line"
            },
            "ohlc4_log6_exp_slope": {
                "color": "#73da2b"
            }
            },
            "trend": {
            "trend_long": {
                "color": "#49ee5c",
                "type": "line"
            },
            "trend_short": {
                "color": "#e36cc7",
                "type": "line"
            }
            },
            "extrema": {
            "maxima": {
                "color": "#b719c2",
                "type": "line"
            },
            "minima": {
                "color": "#1fe07c",
                "type": "line"
            },
            "do_predict": {
                "color": "#116417",
                "type": "bar"
            }
            }
        }
        }

    minimal_roi = { "0": 0.07 }

    TARGET_VAR = "ohlc4_log"

    process_only_new_candles = True
    use_exit_signal = True
    can_short = True
    ignore_roi_if_entry_signal = True

    stoploss = -0.012
    trailing_stop = True
    trailing_only_offset_is_reached  = False
    trailing_stop_positive_offset = 0



    startup_candle_count: int = 200














    LONG_ENTRY_SIGNAL_TRESHOLD = DecimalParameter(0.7, 0.95, decimals=2, default=0.8, space="buy", optimize=True)
    SHORT_ENTRY_SIGNAL_TRESHOLD = DecimalParameter(0.7, 0.95, decimals=2, default=0.8, space="buy", optimize=True)

    ENTRY_STRENGTH_TRESHOLD = DecimalParameter(0.4, 0.7, decimals=2, default=0.3, space="buy", optimize=True)

    LONG_TP = DecimalParameter(0.01, 0.03, decimals=3, default=0.016, space="sell", optimize=True)
    SHORT_TP = DecimalParameter(0.01, 0.03, decimals=3, default=0.016, space="sell", optimize=True)



    startup_candle_count: int = 100

    @property
    def PREDICT_TARGET(self):
        return self.config["freqai"].get("label_period_candles", 6)

    @property
    def PREDICT_STORAGE_ENABLED(self):
        return self.config["sagemaster"].get("PREDICT_STORAGE_ENABLED")

    @property
    def PREDICT_STORAGE_CONN_STRING(self):
        return self.config["sagemaster"].get("PREDICT_STORAGE_CONN_STRING")


    @property
    def TARGET_EXTREMA_KERNEL(self):
        return self.config["sagemaster"].get('TARGET_EXTREMA_KERNEL', 24)

    @property
    def TARGET_EXTREMA_WINDOW(self):
        return self.config["sagemaster"].get('TARGET_EXTREMA_WINDOW', 5)

    def bot_start(self, **kwargs) -> None:
        print("bot_start")


    def feature_engineering_trend(self, df: DataFrame, metadata, **kwargs):
        self.log(f"ENTER .feature_engineering_trend() {metadata} {df.shape}")
        start_time = time.time()

        all_cols = filter(lambda col:
            (col != 'trend')
            and col.find('pmX') == -1
            and col.find('date') == -1
            and col.find('_signal') == -1
            and col.find('_trend') == -1
            and col.find('_rising') == -1
            and col.find('_std') == -1
            and col.find('_change_') == -1
            and col.find('_lower_band') == -1
            and col.find('_upper_band') == -1
            and col.find('_upper_envelope') == -1
            and col.find('_lower_envelope') == -1
            and col.find('%-dist_to_') == -1
            and col.find('%-s1') == -1
            and col.find('%-s2') == -1
            and col.find('%-s3') == -1
            and col.find('%-r1') == -1
            and col.find('%-r2') == -1
            and col.find('%-r3') == -1
            and col.find('_divergence') == -1, df.columns)

        results = []
        result_cols = []


        result_cols = Parallel(n_jobs=self.config["freqai"].get("data_kitchen_thread_count", 4))(
            delayed(helpers.create_col_trend)(col, self.PREDICT_TARGET, df, "polyfit") for col in all_cols
        )

        result_df = pd.concat(result_cols, axis=1)
        result_df.columns = ["%-"+x for x in result_df.columns]

        df = pd.concat([df, result_df], axis=1)

        self.log(f"EXIT .feature_engineering_trend() {metadata} {df.shape}, execution time: {time.time() - start_time:.2f} seconds")

        return df


    def feature_engineering_expand_basic(self, df, metadata, **kwargs):
        self.log(f"ENTER .feature_engineering_expand_basic() {metadata} {df.shape}")
        start_time = time.time()

        df = candle_stats(df)




        mac = MovingAveragesCalculator2(col_prefix="%-mac-", col_target='ohlc4_log', config = {
                "SMA": [24, 48, 96, 192],
                "EMA": [12, 24, 48, 96],
                "HMA": [12, 24, 48, 96],

                "KAMA": [12, 24, 48, 96],
                "ZLSMA": [12, 24, 48, 96],
            })

        df = mac.calculate_moving_averages(df)

        mvc = MomentumANDVolatilityCalculate(
            df, open_col = 'open_log', close_col='close_log', high_col='high_log', low_col='low_log')
        df = mvc.calculate_all().copy()

        cc = CycleCalculate(df, calc_col='close_log')
        df = cc.calculate_all().copy()

        tc = TrendCalculate(df, close_col='close_log', high_col='high_log', low_col='low_log',
                            open_col='open_log')
        df = tc.calculate_all().copy()

        oc = OscillatorsCalculate(df, close_col='close_log')
        df = oc.calculate_all().copy()

        pp = pivots_points(df, timeperiod=100)
        df['r1'] = pp['r1']
        df['s1'] = pp['s1']
        df['r2'] = pp['r2']
        df['s2'] = pp['s2']
        df['r3'] = pp['r3']
        df['s3'] = pp['s3']

        df['%-dist_to_r1'] = get_distance(df['close'], df['r1'])
        df['%-dist_to_r2'] = get_distance(df['close'], df['r2'])
        df['%-dist_to_r3'] = get_distance(df['close'], df['r3'])
        df['%-dist_to_s1'] = get_distance(df['close'], df['s1'])
        df['%-dist_to_s2'] = get_distance(df['close'], df['s2'])
        df['%-dist_to_s3'] = get_distance(df['close'], df['s3'])

        cat_col = [x for x in df if x.find('pmX_10_3_12_1') != -1]

        for col in cat_col:
            df[col] = df[col].map({'down': 0, 'up': 1})

        for col in df.columns:
            if col.startswith('%-') or col in ['date', 'volume', 'hl2_log', 'hl2', 'hlc3', 'hlc3_log', 'ohlc4', 'ohlc4_log', 'open', 'high', 'low', 'close', 'low_log', 'open_log', 'high_log', 'low_log', 'close_log']:
                continue
            else:
                df.rename(columns={col: "%-" + col}, inplace=True)

        df = df.copy()

        df = self.feature_engineering_trend(df, metadata, **kwargs).copy()



        self.log(f"EXIT .feature_engineering_expand_basic() {metadata} {df.shape}, execution time: {time.time() - start_time:.2f} seconds")

        return df

    def feature_engineering_standard(self, df: DataFrame, metadata, **kwargs):
        self.log(f"ENTER .feature_engineering_standard() {metadata} {df.shape}")
        start_time = time.time()

        df = helpers.create_lag(df, 6)

        df["%-pct-change"] = df["close"].pct_change()
        df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
        df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25

        df["%-volume"] = df["volume"].copy()
        df = gn.extract_feature_metrics(df, "%-volume")
        df = df.copy()

        df = df.replace([np.inf, -np.inf], np.nan)
        df = df.fillna(method='ffill')

        df = df.fillna(0).copy()

        self.log(f"EXIT .feature_engineering_standard() {metadata} {df.shape}, execution time: {time.time() - start_time:.2f} seconds")
        return df


    def set_freqai_targets(self, df: DataFrame, metadata, **kwargs):
        self.log(f"ENTER .set_freqai_targets() {metadata} {df.shape}")
        start_time = time.time()

        df = candle_stats(df)




















        df['extrema'] = 0
        min_peaks = argrelextrema(
            df["low_log"].values, np.less,
            order=self.TARGET_EXTREMA_KERNEL
        )
        max_peaks = argrelextrema(
            df["high_log"].values, np.greater,
            order=self.TARGET_EXTREMA_KERNEL
        )

        print(f"min_peaks: {len(min_peaks[0])}, max_peaks: {len(max_peaks[0])}")

        for mp in min_peaks[0]:
            df.at[mp, "extrema"] = -1
        for mp in max_peaks[0]:
            df.at[mp, "extrema"] = 1

        df['extrema'] = df['extrema'].rolling(
            window=self.TARGET_EXTREMA_WINDOW, win_type='gaussian', center=True).mean(std=0.5)


        df['&-extrema_maxima'] = np.where(df['extrema'] > 0, 'maxima', 'not_maxima')
        df['&-extrema_minima'] = np.where(df['extrema'] < 0, 'minima', 'not_minima')

        print(df['&-extrema_maxima'].value_counts())
        print(df['&-extrema_minima'].value_counts())

        df = df.loc[:, ~df.columns.duplicated(keep='first')]






        df.drop(columns=['open_log', 'low_log', 'high_log', 'close_log', 'hl2_log', 'hlc3_log', 'ohlc4_log', 'extrema'], inplace=True)
        self.log(f"EXIT .set_freqai_targets() {df.shape}, execution time: {time.time() - start_time:.2f} seconds")

        return df

    def add_slope_indicator(self, df: DataFrame, target_var = "ohlc4_log", predict_target = 6) -> DataFrame:
        df = df.set_index(df['date'])

        target = helpers.create_target(df, predict_target, method='polyfit', polyfit_var=target_var)
        target = target[['trend', 'slope', 'start_windows']].set_index('start_windows')
        target.fillna(0)

        target['slope'] = RobustScaler().fit_transform(target['slope'].values.reshape(-1, 1)).reshape(-1)

        target.rename(columns={'slope': f'{target_var}{predict_target}_exp_slope', 'trend': f'{target_var}{predict_target}_exp_trend'}, inplace=True)

        df = df.join(target[[f'{target_var}{predict_target}_exp_slope', f'{target_var}{predict_target}_exp_trend']], how='left')
        df = df.reset_index(drop=True)

        return df

    def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame:
        self.log(f"ENTER .populate_indicators() {metadata} {df.shape}")
        start_time = time.time()

        df = self.freqai.start(df, metadata, self)

        df = candle_stats(df)




        df = self.add_slope_indicator(df, 'ohlc4_log', self.PREDICT_TARGET)

        df['L1'] = 1.0
        df['L0'] = 0
        df['L-1'] = -1.0



        last_candle = df.iloc[-1].squeeze()

        self.dp.send_msg(f"{metadata['pair']} predictions: \n  minima={last_candle['minima']:.2f}, \n  maxima={last_candle['maxima']:.2f}")

        self.log(f"EXIT populate_indicators {df.shape}, execution time: {time.time() - start_time:.2f} seconds")
        return df

    def protection_di(self, df: DataFrame):
        return (df["DI_values"] < df["DI_cutoff"])

    def signal_entry_long(self, df: DataFrame):
        minima_condition1 = qtpylib.crossed_below(df['minima'], 0.8) & (df['maxima'] < 0.6)
        minima_condition2 = qtpylib.crossed_above(df['minima'], 0.9) & (df['maxima'] < 0.6)


        return minima_condition1 | minima_condition2

    def signal_exit_long(self, df: DataFrame):
        maxima_condition = df['maxima'] >= 0.8


        return maxima_condition


    def signal_entry_short(self, df: DataFrame):
        maxima_condition1 = qtpylib.crossed_below(df['maxima'], 0.8) & (df['minima'] < 0.6)
        maxima_condition2 = qtpylib.crossed_above(df['maxima'], 0.9) & (df['minima'] < 0.6)


        return maxima_condition1 | maxima_condition2

    def signal_exit_short(self, df: DataFrame):
        maxima_condition = df['minima'] >= 0.8


        return maxima_condition


    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        df.loc[self.signal_entry_long(df), 'enter_long'] = 1

        df.loc[self.signal_entry_short(df),'enter_short'] = 1

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        df.loc[self.signal_exit_long(df), 'exit_long'] = 1

        df.loc[self.signal_exit_short(df), 'exit_short'] = 1

        return df


    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                    current_profit: float, **kwargs):
        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)

        if df.empty:

            return None  # Or other appropriate handling

        last_candle = df.iloc[-1].squeeze()

        trade_duration = (current_time - trade.open_date_utc).seconds / 60
        is_short = trade.is_short == True
        is_long = trade.is_short == False
        is_profitable = current_profit > 0

        roi_result = self.check_roi(pair, current_time, trade.open_date_utc, current_profit)
        if roi_result:
            return roi_result

        if trade.is_open and is_long and last_candle['maxima'] >= 0.6 and is_profitable:
            return "almost_maxima"

        if trade.is_open and is_short and last_candle['minima'] >= 0.6 and is_profitable:
            return "almost_minima"


    cached_roi_tables = {}

    def get_or_create_roi_table(self, pair, kernel=6):

        if pair in self.cached_roi_tables:
            return self.cached_roi_tables[pair]

        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if df.empty:
            return None

        min_peaks = argrelextrema(df["low"].values, np.less, order=kernel)[0]
        max_peaks = argrelextrema(df["high"].values, np.greater, order=kernel)[0]

        distances = []
        candles_between_peaks = []

        for low_peak in min_peaks:
            next_high_peaks = max_peaks[max_peaks > low_peak]
            if next_high_peaks.size > 0:
                high_peak = next_high_peaks[0]
                low_price = df.at[low_peak, 'close']
                high_price = df.at[high_peak, 'close']
                distance_percentage = ((high_price - low_price) / low_price)
                distances.append(distance_percentage)
                num_candles = high_peak - low_peak
                candles_between_peaks.append(num_candles)

        for high_peak in max_peaks:
            next_low_peaks = min_peaks[min_peaks > high_peak]
            if next_low_peaks.size > 0:
                low_peak = next_low_peaks[0]
                high_price = df.at[high_peak, 'close']
                low_price = df.at[low_peak, 'close']
                distance_percentage = -((low_price - high_price) / high_price)
                distances.append(distance_percentage)
                num_candles = low_peak - high_peak
                candles_between_peaks.append(num_candles)

        if not distances or not candles_between_peaks:
            return None

        distances_description = pd.Series(distances).describe()
        candles_between_peaks_description = pd.Series(candles_between_peaks).describe()

        minutes = timeframe_to_minutes(self.timeframe)
        dynamic_roi = {
            "0": distances_description['75%'],
            str(int(candles_between_peaks_description['25%'] * minutes)): distances_description['50%'],
            str(int(candles_between_peaks_description['50%'] * minutes)): distances_description['25%'],
            str(int(candles_between_peaks_description['75%'] * minutes)): 0.00  # Using 75th percentile for the last tier
        }

        self.cached_roi_tables[pair] = dynamic_roi
        return dynamic_roi


    def check_roi(self, pair, current_time, trade_open_date_utc, current_profit):
        dynamic_roi = self.get_or_create_roi_table(pair, kernel=self.TARGET_EXTREMA_KERNEL)
        if not dynamic_roi:
            return None



        trade_duration = (current_time - trade_open_date_utc).seconds / 60
        for roi_time, roi_value in dynamic_roi.items():
            if trade_duration >= int(roi_time) and current_profit >= roi_value:

                return "dynamic_roi"

        return None