# source: https://raw.githubusercontent.com/ShabbirHasan1/MultiMA_TSL/832377c981e0cf673eaf46a9b2c9ed5202b6cefa/user_data/strategies/Cenderawasih/Cenderawasih_2.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import IStrategy, informative
from freqtrade.strategy import (merge_informative_pair,
                                DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open)
from pandas import DataFrame, Series
from typing import Dict, List, Optional, Tuple
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime, timedelta, timezone
from freqtrade.exchange import timeframe_to_prev_date
from technical.indicators import zema, ichimoku
import talib.abstract as ta
import math
import pandas_ta as pta
from finta import TA as fta
import logging
from logging import FATAL
import time
import requests

logger = logging.getLogger(__name__)

###########################################################################################################
##                                                                                                       ##
##    Strategy for Freqtrade https://github.com/freqtrade/freqtrade                                      ##
##    This strategy is available on https://patreon.com/stash86                                          ##
##                                                                                                       ##
##                                                                                                       ##
###########################################################################################################

def tv_wma(df, length = 9) -> DataFrame:
    """
    Source: Tradingview "Moving Average Weighted"
    Pinescript Author: Unknown
    Args :
        dataframe : Pandas Dataframe
        length : WMA length
        field : Field to use for the calculation
    Returns :
        dataframe : Pandas DataFrame with new columns 'tv_wma'
    """

    norm = 0
    sum = 0

    for i in range(1, length - 1):
        weight = (length - i) * length
        norm = norm + weight
        sum = sum + df.shift(i) * weight

    tv_wma = (sum / norm) if norm > 0 else 0
    return tv_wma

def tv_hma(dataframe, length = 9) -> DataFrame:
    """
    Source: Tradingview "Hull Moving Average"
    Pinescript Author: Unknown
    Args :
        dataframe : Pandas Dataframe
        length : HMA length
        field : Field to use for the calculation
    Returns :
        dataframe : Pandas DataFrame with new columns 'tv_hma'
    """

    h = 2 * tv_wma(dataframe['close'], math.floor(length / 2)) - tv_wma(dataframe['close'], length)

    tv_hma = tv_wma(h, math.floor(math.sqrt(length)))
    # dataframe.drop("h", inplace=True, axis=1)

    return tv_hma

def rvol(dataframe, window=24):
    av = ta.SMA(dataframe['volume'], timeperiod=int(window))
    rvol = dataframe['volume'] / av
    return rvol

class github_ShabbirHasan1_MultiMA_TSL__Cenderawasih_2__20220901_015620 (IStrategy):

    def version(self) -> str:
        return "v2"

    INTERFACE_VERSION = 3

    # ROI table:
    minimal_roi = {
        "0": 100.0
    }

    # Buy hyperspace params:
    buy_params = {
        "base_nb_candles_buy_hma": 37,
        "low_offset_hma": 0.915,

        "base_nb_candles_buy_ema": 32,
        "low_offset_ema": 1.01,

        "buy_length_volatility": 10,
        "buy_max_volatility": 1.62,

        "base_nb_candles_buy_ema2": 36,
        "low_offset_ema2": 0.999,

        "base_nb_candles_buy_vwma": 54,
        "low_offset_vwma": 0.988,

        "buy_rsi_1": 65,
        "buy_rsi_fast_1": 39,
        "rsi_buy_ema": 56,

        "buy_length_volume": 26,
        "buy_volume_volatility": 2.73,

    }

    # Sell hyperspace params:
    sell_params = {
        "base_nb_candles_sell_hma": 87,
        "high_offset_hma": 0.933,

        "base_nb_candles_sell_ema": 16,
        "high_offset_ema": 0.97,

        "base_nb_candles_sell_ema2": 70,
        "high_offset_ema2": 0.989,

        "base_nb_candles_sell_ema3": 82,
        "high_offset_ema3": 0.927,

    }
   
    # Protection hyperspace params:
    protection_params = {
        "cooldown_lookback": 2,  # value loaded from strategy
    }

    cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=False)

    @property
    def protections(self):
        prot = []

        prot.append({
            "method": "CooldownPeriod",
            "stop_duration_candles": self.cooldown_lookback.value
        })
        return prot
    
    dummy = IntParameter(20, 70, default=61, space='buy', optimize=False)

    rsi_buy_ema = IntParameter(20, 70, default=61, space='buy', optimize=False)
    buy_rsi_1 = IntParameter(0, 70, default=50, space='buy', optimize=False)
    buy_rsi_fast_1 = IntParameter(0, 70, default=50, space='buy', optimize=False)

    optimize_buy_hma = False
    base_nb_candles_buy_hma = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_hma)
    low_offset_hma = DecimalParameter(0.9, 0.99, default=0.95, space='buy', optimize=optimize_buy_hma)

    optimize_buy_ema = False
    base_nb_candles_buy_ema = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_ema)
    low_offset_ema = DecimalParameter(0.9, 1.1, default=1, space='buy', optimize=optimize_buy_ema)

    optimize_buy_vwma = False
    base_nb_candles_buy_vwma = IntParameter(5, 80, default=6, space='buy', optimize=optimize_buy_vwma)
    low_offset_vwma = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma)


    optimize_buy_volatility = False
    buy_length_volatility = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility)
    buy_min_volatility = DecimalParameter(0, 0.5, default=0, decimals = 2, space='buy', optimize=False)
    buy_max_volatility = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility)

    optimize_buy_volume = False
    buy_length_volume = IntParameter(5, 100, default=6, optimize=optimize_buy_volume)
    buy_volume_volatility = DecimalParameter(0.5, 3, default=1, decimals=2, optimize=optimize_buy_volume)

    # Sell
    optimize_sell_hma = False
    base_nb_candles_sell_hma = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_hma)
    high_offset_hma = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_hma)

    optimize_sell_ema = False
    base_nb_candles_sell_ema = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema)
    high_offset_ema = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema)

    optimize_sell_ema2 = False
    base_nb_candles_sell_ema2 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema2)
    high_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema2)

    optimize_sell_ema3 = False
    base_nb_candles_sell_ema3 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema3)
    high_offset_ema3 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema3)

    # Stoploss:
    stoploss = -0.098

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 200

    age_filter = 30

    @informative('1d')
    def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=self.age_filter, min_periods=self.age_filter).min() > 0)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)

        dataframe['sqzmi'] = fta.SQZMI(dataframe)

        dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0)

        if not self.optimize_buy_hma:
            dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value

        if not self.optimize_buy_ema:
            dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value

        if not self.optimize_buy_vwma:
            dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value

        if not self.optimize_buy_volatility:
            df_std = dataframe['close'].rolling(int(self.buy_length_volatility.value)).std()
            dataframe["volatility"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility.value)

        if not self.optimize_buy_volume:
            df_rvol = rvol(dataframe, int(self.buy_length_volume.value))
            dataframe['volume_volatility'] = (df_rvol < self.buy_volume_volatility.value)

        if not self.optimize_sell_hma:
            dataframe['hma_offset_sell'] = tv_hma(dataframe, int(self.base_nb_candles_sell_hma.value)) *self.high_offset_hma.value

        if not self.optimize_sell_ema:
            dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema.value)) *self.high_offset_ema.value

        if not self.optimize_sell_ema2:
            dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema2.value)) *self.high_offset_ema2.value

        if not self.optimize_sell_ema3:
            dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema3.value)) *self.high_offset_ema3.value

        return dataframe
    
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        conditions = []

        if self.optimize_buy_hma:
            dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value

        if self.optimize_buy_ema:
            dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value

        if self.optimize_buy_vwma:
            dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value

        if self.optimize_buy_volatility:
            df_std = dataframe['close'].rolling(int(self.buy_length_volatility.value)).std()
            dataframe["volatility"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility.value)
        
        if self.optimize_buy_volume:
            df_rvol = rvol(dataframe, int(self.buy_length_volume.value))
            dataframe['volume_volatility'] = (df_rvol < self.buy_volume_volatility.value)

        dataframe.loc[:, 'enter_tag'] = ''
        dataframe.loc[:, 'buy'] = 0

        add_check = (
            dataframe['live_data_ok']
            &
            dataframe['age_filter_ok_1d']
            &
            dataframe["volatility"]
            &
            dataframe['volume_volatility']
            &
            (dataframe['close'] < dataframe['ema_offset_buy'])
            &
            (dataframe['volume'] > 0)
            &
            (dataframe['sqzmi'] == False)
            &
            (dataframe['rsi_fast'] < self.buy_rsi_fast_1.value)
            &
            (dataframe['rsi'] < self.buy_rsi_1.value)
        )

        buy_offset_hma = (
            ((dataframe['close'] < dataframe['hma_offset_buy']))
        )
        dataframe.loc[buy_offset_hma, 'enter_tag'] += 'hma '
        conditions.append(buy_offset_hma)

        buy_offset_vwma = (
            ((dataframe['close'] < dataframe['vwma_offset_buy']))
        )
        dataframe.loc[buy_offset_vwma, 'enter_tag'] += 'vwma '
        conditions.append(buy_offset_vwma)

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions)
                &
                add_check,
                'buy',
            ]= 1


        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        if self.optimize_sell_hma:
            dataframe['hma_offset_sell'] = tv_hma(dataframe, int(self.base_nb_candles_sell_hma.value)) *self.high_offset_hma.value

        if self.optimize_sell_ema:
            dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema.value)) *self.high_offset_ema.value

        if self.optimize_sell_ema2:
            dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema2.value)) *self.high_offset_ema2.value

        if self.optimize_sell_ema3:
            dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema3.value)) *self.high_offset_ema3.value

        dataframe.loc[:, 'exit_tag'] = ''
        conditions = []
        
        sell_cond_1 = (                   
            (dataframe['close'] > dataframe['hma_offset_sell'])
        )
        conditions.append(sell_cond_1)
        dataframe.loc[sell_cond_1, 'exit_tag'] += 'HMA_1 '

        sell_cond_3 = (                   
            ((dataframe['close'] < dataframe['ema_offset_sell3']).rolling(2).sum() == 2)
        )
        conditions.append(sell_cond_3)
        dataframe.loc[sell_cond_3, 'exit_tag'] += 'EMA_3 '

        sell_cond_2 = (                   
            (dataframe['close'] > dataframe['ema_offset_sell'])
        )
        conditions.append(sell_cond_2)
        dataframe.loc[sell_cond_2, 'exit_tag'] += 'EMA_1 '

        sell_cond_4 = (                   
            (dataframe['close'] < dataframe['ema_offset_sell2'])
        )
        conditions.append(sell_cond_4)
        dataframe.loc[sell_cond_4, 'exit_tag'] += 'EMA_2 '

        add_check = (
            (dataframe['volume'] > 0)
        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions) & add_check,
                'sell'
            ] = 1

        return dataframe