ClucHAnixV2

from typing import Optional

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta

from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open
from pandas import DataFrame, Series
from datetime import datetime


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)


def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
    return Series(index=bars.index, data=res)


class ClucHAnixV2(IStrategy):

    """
    PASTE OUTPUT FROM HYPEROPT HERE
    Can be overridden for specific sub-strategies (stake currencies) at the bottom.
    """

    buy_params = {
        'bbdelta-close': 0.01965,
        'bbdelta-tail': 0.95089,
        'close-bblower': 0.00799,
        'closedelta-close': 0.00556,
        'rocr-1h': 0.54904
    }

    # Sell hyperspace params:
    sell_params = {
        # custom stoploss params, come from BB_RPB_TSL
        "pHSL": -0.15,
        "pPF_1": 0.02,
        "pPF_2": 0.05,
        "pSL_1": 0.02,
        "pSL_2": 0.04,

        'sell-fisher': 0.38414, 
        'sell-bbmiddle-close': 1.07634
    }

    # ROI table:
    minimal_roi = {
        "0": 100
    }

    # Stoploss:
    stoploss = -0.99   # use custom stoploss

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = False

    """
    END HYPEROPT
    """
    
    timeframe = '1m'

    # Make sure these match or are not overridden in config
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Custom stoploss
    use_custom_stoploss = True

    process_only_new_candles = True
    startup_candle_count = 168

    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'emergencysell': 'limit',
        'forcebuy': "limit",
        'forcesell': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    # hard stoploss profit
    pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True)

    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True)

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        informative_pairs += [(f"BTC/USDT", '1d')]
        return informative_pairs

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
    
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value

        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.

        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
        else:
            sl_profit = HSL

        # Only for hyperopt invalid return
        if sl_profit >= current_profit:
            return -0.99
    
        return stoploss_from_open(sl_profit, current_profit)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # # Heikin Ashi Candles
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']

        # Set Up Bollinger Bands
        mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['mid'] = mid
        
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
        dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()

        dataframe['bb_lowerband'] = dataframe['lower']
        dataframe['bb_middleband'] = dataframe['mid']

        dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3)
        dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)

        rsi = ta.RSI(dataframe)
        dataframe["rsi"] = rsi
        rsi = 0.1 * (rsi - 50)
        dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
        
        inf_tf = '1h'
        
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        
        inf_heikinashi = qtpylib.heikinashi(informative)

        informative['ha_close'] = inf_heikinashi['close']
        informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        # get btc 1d informative
        inf_tf_1d = '1d'
        informative_btc_1d = self.dp.get_pair_dataframe(pair=f"BTC/USDT", timeframe=inf_tf_1d)
        informative_btc_1d['ema90'] = ta.EMA(informative_btc_1d, timeperiod=90)

        macd = ta.MACD(informative_btc_1d, fastperiod=12, slowperiod=26, signalperiod=9)
        informative_btc_1d['macd'] = macd['macd']
        informative_btc_1d['macdsignal'] = macd['macdsignal']
        informative_btc_1d['macdhist'] = macd['macdhist']
     
        dataframe = merge_informative_pair(dataframe, informative_btc_1d, self.timeframe, inf_tf_1d, ffill=True)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.buy_params
        dataframe.loc[:, 'buy_tag'] = 'clucha '

        dataframe.loc[
            (
                    (
                        ((
                             (dataframe['macd_1d'] > dataframe['macdsignal_1d']) &
                             (dataframe['macdhist_1d'] > dataframe['macdhist_1d'].shift())
                         ) |

                         (
                             (dataframe['close_1d'] > dataframe['ema90_1d'])
                         ))
                    ) &
                    ((
                             (dataframe['rocr_1h'].gt(params['rocr-1h'])) &
                             (dataframe['lower'].shift().gt(0)) &
                             (dataframe['bbdelta'].gt(dataframe['ha_close'] * params['bbdelta-close'])) &
                             (dataframe['closedelta'].gt(dataframe['ha_close'] * params['closedelta-close'])) &
                             (dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail'])) &
                             (dataframe['ha_close'].lt(dataframe['lower'].shift())) &
                             (dataframe['ha_close'].le(dataframe['ha_close'].shift()))
                     ) |
                     (
                             (dataframe['ha_close'] < dataframe['ema_slow']) &
                             (dataframe['ha_close'] < params['close-bblower'] * dataframe['bb_lowerband'])
                     ))),
            'buy'
        ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.sell_params

        dataframe.loc[
            (dataframe['fisher'] > params['sell-fisher']) &
            (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) &
            (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) &
            (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) &
            (dataframe['ema_fast'] > dataframe['ha_close']) &
            ((dataframe['ha_close'] * params['sell-bbmiddle-close']) > dataframe['bb_middleband']) &
            (dataframe['volume'] > 0)
            ,
            'sell'
        ] = 1

        return dataframe


class ClucDCAV2(ClucHAnixV2):
    position_adjustment_enable = True

    max_rebuy_orders = 2
    max_rebuy_multiplier = 3

    # This is called when placing the initial order (opening trade)
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: Optional[str], **kwargs) -> float:

        if (self.config['position_adjustment_enable'] is True) and (self.config['stake_amount'] == 'unlimited'):
            return proposed_stake / self.max_rebuy_multiplier
        else:
            return proposed_stake

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):

        if (self.config['position_adjustment_enable'] is False) or (current_profit > -0.06):
            return None

        filled_buys = trade.select_filled_orders('buy')
        count_of_buys = len(filled_buys)

        # Maximum 2 rebuys, equal stake as the original
        if 0 < count_of_buys <= self.max_rebuy_orders:
            try:
                # This returns first order stake size
                stake_amount = filled_buys[0].cost
                # This then calculates current safety order size
                stake_amount = stake_amount
                return stake_amount
            except Exception as exception:
                return None

        return None