FSupertrendStrategy

"""
Supertrend strategy:
* Description: Generate a 3 supertrend indicators for 'buy' strategies & 3 supertrend indicators for 'sell' strategies
               Buys if the 3 'buy' indicators are 'up'
               Sells if the 3 'sell' indicators are 'down'
* Author: @juankysoriano (Juan Carlos Soriano)
* github: https://github.com/juankysoriano/
*** NOTE: This Supertrend strategy is just one of many possible strategies using `Supertrend` as indicator. It should on any case used at your own risk.
          It comes with at least a couple of caveats:
            1. The implementation for the `supertrend` indicator is based on the following discussion: https://github.com/freqtrade/freqtrade-strategies/issues/30 . Concretelly https://github.com/freqtrade/freqtrade-strategies/issues/30#issuecomment-853042401
            2. The implementation for `supertrend` on this strategy is not validated; meaning this that is not proven to match the results by the paper where it was originally introduced or any other trusted academic resources
"""

import datetime
import logging
from numpy.lib import math
from freqtrade.strategy import IStrategy, IntParameter
from pandas import DataFrame
import talib.abstract as ta
import numpy as np


class FSupertrendStrategy(IStrategy):
    # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
    # It's encourage you find the values that better suites your needs and risk management strategies

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_m1": 4,
        "buy_m2": 7,
        "buy_m3": 1,
        "buy_p1": 8,
        "buy_p2": 9,
        "buy_p3": 8,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_m1": 1,
        "sell_m2": 3,
        "sell_m3": 6,
        "sell_p1": 16,
        "sell_p2": 18,
        "sell_p3": 18,
    }

    # ROI table:
    # minimal_roi = {"0": 0.15, "30": 0.75, "60": 0.05, "120": 0.025}
    minimal_roi = {"0": 100}
    
    

    # Stoploss:
    stoploss = -0.70

    profit_max_enabled = True

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.05
    trailing_stop_positive_offset = 0.1
    trailing_only_offset_is_reached = False

    timeframe = "1m"
    position_adjustment_enable = True
    can_short: bool = True

    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True

    startup_candle_count = 18

    buy_m1 = IntParameter(1, 7, default=1)
    buy_m2 = IntParameter(1, 7, default=3)
    buy_m3 = IntParameter(1, 7, default=4)
    buy_p1 = IntParameter(7, 21, default=14)
    buy_p2 = IntParameter(7, 21, default=10)
    buy_p3 = IntParameter(7, 21, default=10)

    sell_m1 = IntParameter(1, 7, default=1)
    sell_m2 = IntParameter(1, 7, default=3)
    sell_m3 = IntParameter(1, 7, default=4)
    sell_p1 = IntParameter(7, 21, default=14)
    sell_p2 = IntParameter(7, 21, default=10)
    sell_p3 = IntParameter(7, 21, default=10)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for multiplier in self.buy_m1.range:
            for period in self.buy_p1.range:
                dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.buy_m2.range:
            for period in self.buy_p2.range:
                dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.buy_m3.range:
            for period in self.buy_p3.range:
                dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.sell_m1.range:
            for period in self.sell_p1.range:
                dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.sell_m2.range:
            for period in self.sell_p2.range:
                dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.sell_m3.range:
            for period in self.sell_p3.range:
                dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        return dataframe

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

        dataframe.loc[
            (
                dataframe[f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"]
                == "up"
            )
            & (
                dataframe[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"]
                == "up"
            )
            & (
                dataframe[f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"]
                == "up"
            )
            & (  # The three indicators are 'up' for the current candle
                dataframe["volume"] > 0
            ),
            "enter_long",
        ] = 1

        dataframe.loc[
            (
                dataframe[
                    f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}"
                ]
                == "down"
            )
            & (
                dataframe[
                    f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"
                ]
                == "down"
            )
            & (
                dataframe[
                    f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}"
                ]
                == "down"
            )
            & (  # The three indicators are 'down' for the current candle
                dataframe["volume"] > 0
            ),
            "enter_short",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe[
                    f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"
                ]
                == "down"
            ),
            "exit_long",
        ] = 1

        dataframe.loc[
            (
                dataframe[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"]
                == "up"
            ),
            "exit_short",
        ] = 1

        return dataframe

    """
        Supertrend Indicator; adapted for freqtrade
        from: https://github.com/freqtrade/freqtrade-strategies/issues/30
    """

    def supertrend(self, dataframe: DataFrame, multiplier, period):
        df = dataframe.copy()

        df["TR"] = ta.TRANGE(df)
        df["ATR"] = ta.SMA(df["TR"], period)

        st = "ST_" + str(period) + "_" + str(multiplier)
        stx = "STX_" + str(period) + "_" + str(multiplier)

        # Compute basic upper and lower bands
        df["basic_ub"] = (df["high"] + df["low"]) / 2 + multiplier * df["ATR"]
        df["basic_lb"] = (df["high"] + df["low"]) / 2 - multiplier * df["ATR"]

        # Compute final upper and lower bands
        df["final_ub"] = 0.00
        df["final_lb"] = 0.00
        for i in range(period, len(df)):
            df["final_ub"].iat[i] = (
                df["basic_ub"].iat[i]
                if df["basic_ub"].iat[i] < df["final_ub"].iat[i - 1]
                or df["close"].iat[i - 1] > df["final_ub"].iat[i - 1]
                else df["final_ub"].iat[i - 1]
            )
            df["final_lb"].iat[i] = (
                df["basic_lb"].iat[i]
                if df["basic_lb"].iat[i] > df["final_lb"].iat[i - 1]
                or df["close"].iat[i - 1] < df["final_lb"].iat[i - 1]
                else df["final_lb"].iat[i - 1]
            )

        # Set the Supertrend value
        df[st] = 0.00
        for i in range(period, len(df)):
            df[st].iat[i] = (
                df["final_ub"].iat[i]
                if df[st].iat[i - 1] == df["final_ub"].iat[i - 1]
                and df["close"].iat[i] <= df["final_ub"].iat[i]
                else df["final_lb"].iat[i]
                if df[st].iat[i - 1] == df["final_ub"].iat[i - 1]
                and df["close"].iat[i] > df["final_ub"].iat[i]
                else df["final_lb"].iat[i]
                if df[st].iat[i - 1] == df["final_lb"].iat[i - 1]
                and df["close"].iat[i] >= df["final_lb"].iat[i]
                else df["final_ub"].iat[i]
                if df[st].iat[i - 1] == df["final_lb"].iat[i - 1]
                and df["close"].iat[i] < df["final_lb"].iat[i]
                else 0.00
            )
        # Mark the trend direction up/down
        df[stx] = np.where(
            (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), np.NaN
        )

        # Remove basic and final bands from the columns
        df.drop(["basic_ub", "basic_lb", "final_ub", "final_lb"], inplace=True, axis=1)

        df.fillna(0, inplace=True)

        return DataFrame(index=df.index, data={"ST": df[st], "STX": df[stx]})

    
    def leverage(self, pair: str, current_time: 'datetime', current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:
        max_leverage = 25
        return max_leverage