SeeYouLaterTSL

# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
import numpy as np
from functools import reduce
from freqtrade.strategy import stoploss_from_open
from freqtrade.strategy.hyper import DecimalParameter
from freqtrade.persistence import Trade
from datetime import datetime

def alligator(
    
    dataframe,
    period_jaw=13,
    period_teeth=8,
    period_lips=5,
    shift_jaw=8,
    shift_teeth=5,
    shift_lips=3,
):
    """Construct Williams Alligator."""
    data = dataframe.copy()
    
    df_tmp = data[["high", "low"]]
    col_tmp = "col_tmp"
    df_tmp = df_tmp.assign(
        col_tmp=lambda x: (x["high"] + x["low"])
        / 2
    )
    df_j = calculate_smma(df_tmp, period_jaw, 'jaw', col_tmp)
    df_t = calculate_smma(df_tmp, period_teeth, 'teeth', col_tmp)
    df_l = calculate_smma(df_tmp, period_lips, 'lips', col_tmp)

    # Shift SMMAs
    df_j['jaw'] = df_j['jaw'].shift(shift_jaw)
    df_t['teeth'] = df_t['teeth'].shift(shift_teeth)
    df_l['lips'] = df_l['lips'].shift(shift_lips)

    data = data.merge(df_j, left_index=True, right_index=True)
    data = data.merge(df_t, left_index=True, right_index=True)
    data = data.merge(df_l, left_index=True, right_index=True)

    return data


def calculate_smma(df, period, column_name, apply_to):
    """Calculate Smoothed Moving Average."""
    df_tmp = df[[apply_to]]
    first_val = df_tmp[apply_to].iloc[:period].mean()
    df_tmp = df_tmp.assign(column_name=None)
    df_tmp.at[period, column_name] = first_val
    for index, row in df_tmp.iterrows():
        if index > period:
            smma_val = (df_tmp.at[index - 1, column_name] *
                        (period - 1) + row[apply_to]) / period
            df_tmp.at[index, column_name] = smma_val
    df_tmp = df_tmp[[column_name]]

    return df_tmp


class SeeYouLaterTSL(IStrategy):
    """
        My first humble strategy using Williams Alligator Indicator and Fractals
        Changelog:
            0.9 Inital version, some improvements needed
            1.0 Code optimizations
            1.1 Added custom stoplosse

        https://github.com/cyberjunky/freqtrade-strategies
    """

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.99

    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.015

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = True
    ignore_roi_if_buy_signal = False

    # Custom stoploss
    use_custom_stoploss = True

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optimal timeframe for the strategy.
    timeframe = '5m'

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 0.08,
        "36": 0.031,
        "50": 0.021,
        "60": 0.01,
        "70": 0
    }

    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                'lips': {'color': 'green'},
                'teeth': {'color': 'red'},
                'jaw': {'color': 'blue'},
            },
            'subplots': {
                # Subplots - each dict defines one additional plot
                "sell": {
                    'bullish': {'color': 'orange'},
                },
                "buy": {
                    'bearish': {'color': 'lightgreen'},
                }
            }
        }

    # hard stoploss profit
    pHSL = DecimalParameter(-0.500, -0.040, default=-0.99, decimals=3, space='sell', load=True)

    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.009, decimals=3, space='sell', load=True)

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

    # Custom stoploss
    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:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

        # Williams Alligator
        gator_df = alligator(dataframe)
        dataframe['lips'] = gator_df['lips']
        dataframe['jaw'] = gator_df['jaw']
        dataframe['teeth'] = gator_df['teeth']

        # Fractals
        dataframe['bearish'] = (
                        dataframe['high'].shift(4).lt(dataframe['high'].shift(2)) &
                        dataframe['high'].shift(3).lt(dataframe['high'].shift(2)) &
                        dataframe['high'].shift(1).lt(dataframe['high'].shift(2)) &
                        dataframe['high'].lt(dataframe['high'].shift(2))
                )

        dataframe['bullish'] = (
                dataframe['low'].shift(4).gt(dataframe['low'].shift(2)) &
                dataframe['low'].shift(3).gt(dataframe['low'].shift(2)) &
                dataframe['low'].shift(1).gt(dataframe['low'].shift(2)) &
                dataframe['low'].gt(dataframe['high'].shift(2))
        )

        return dataframe


    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        If the bullish fractal is active and below the teeth of the gator -> buy
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        conditions = []
        conditions.append(
            (
                (dataframe['bearish']) &
                (dataframe['close'] < dataframe['teeth'] ) &
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe


    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        If the bearish fractal is active and above the teeth of the gator -> sell
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        conditions = []
        conditions.append(
            (
                (dataframe['bullish']) &
                (dataframe['close'] > dataframe['teeth'] ) &
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe