# source: https://raw.githubusercontent.com/ssssi/freqtrade_strs/691886e289e8abc08e0a3c0670ada7b886424793/gateio/BinHV/BinHV45.py
# --- Do not remove these libs ---
from datetime import datetime

from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, DecimalParameter, stoploss_from_open
from freqtrade.strategy import IntParameter
from pandas import DataFrame
# --------------------------------

import freqtrade.vendor.qtpylib.indicators as qtpylib


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 rolling_mean, lower_band


class github_ssssi_freqtrade_strs__BinHV45__20220615_120734(IStrategy):

    minimal_roi = {
        "0": 100
    }

    # Hyperopt parameters
    buy_params = {
        "buy_bbdelta": 7,
        "buy_closedelta": 17,
        "buy_tail": 25,
        "leverage_num": 1
    }

    sell_params = {
        # custom stop loss params
        "pHSL": -0.99,
        "pPF_1": 0.012,
        "pPF_2": 0.05,
        "pSL_1": 0.01,
        "pSL_2": 0.04,
    }

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

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

    # default False
    use_custom_stoploss = True

    stoploss = -0.99
    timeframe = '1m'

    buy_bbdelta = IntParameter(low=1, high=15, default=30, space='buy', optimize=True)
    buy_closedelta = IntParameter(low=15, high=20, default=30, space='buy', optimize=True)
    buy_tail = IntParameter(low=20, high=30, default=30, space='buy', optimize=True)
    leverage_num = IntParameter(low=1, high=20, default=1, space='buy', optimize=True)

    # trailing stoploss
    trailing_optimize = False
    pHSL = DecimalParameter(-0.990, -0.040, default=-0.08, decimals=3, space='sell', optimize=trailing_optimize)
    pPF_1 = DecimalParameter(0.008, 0.050, default=0.016, decimals=3, space='sell', optimize=trailing_optimize)
    pSL_1 = DecimalParameter(0.008, 0.050, default=0.011, decimals=3, space='sell', optimize=trailing_optimize)
    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=trailing_optimize)
    pSL_2 = DecimalParameter(0.040, 0.100, default=0.040, decimals=3, space='sell', optimize=trailing_optimize)

    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, is_short=trade.is_short)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)

        dataframe['upper'] = bollinger['upper']
        dataframe['mid'] = bollinger['mid']
        dataframe['lower'] = bollinger['lower']
        dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs()
        dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe['lower'].shift().gt(0) &
                dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bbdelta.value / 1000) &
                dataframe['closedelta'].gt(dataframe['close'] * self.buy_closedelta.value / 1000) &
                dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_tail.value / 1000) &
                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift())
            ),
            ['entry_long', 'entry_tag']] = (1, 'long_in')
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        no sell signal
        """
        dataframe.loc[:, ['exit_long', 'exit_tag']] = (0, 'long_out')
        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:

        return self.leverage_num.value