# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/FSupertrendStrategy.py
"""
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 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 Github_rmallarapu_bc_brahma__FSupertrendStrategy__20240229_213751(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,
    }

    minimal_roi = {
        "0": 0.1
    }
    stoploss = -0.3

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.003
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True


    timeframe = "1h"

    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]})

    from freqtrade.persistence import Trade
    from datetime import timedelta, datetime
    from typing import Optional

    # DCA options
    position_adjustment_enable = True
    max_entry_position_adjustment = 10
    initial_order_size = 0.2  # 20% of total capital
    incremental_order_size = 0.4  # increase capital by 40% of initial order for every {initial_profit} loss
    initial_profit = 0.01
    recurring_loss = -0.01
    loss_upper_limit = -0.10

    # leverage
    leverage_num = 1

    # 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: Optional[float], max_stake: float,
                            leverage: float, entry_tag: Optional[str], side: str,
                            **kwargs) -> float:

        # We need to leave most of the funds for possible further DCA orders
        # This also applies to fixed stakes
        return proposed_stake * self.initial_order_size

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake: Optional[float], max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs) -> Optional[float]:
        """
        Custom trade adjustment logic, returning the stake amount that a trade should be
        increased or decreased.
        This means extra buy or sell orders with additional fees.
        Only called when `position_adjustment_enable` is set to True.

        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

        When not implemented by a strategy, returns None

        :param trade: trade object.
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Current buy rate.
        :param current_profit: Current profit (as ratio), calculated based on current_rate.
        :param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
        :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
        :param current_entry_rate: Current rate using entry pricing.
        :param current_exit_rate: Current rate using exit pricing.
        :param current_entry_profit: Current profit using entry pricing.
        :param current_exit_profit: Current profit using exit pricing.
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return float: Stake amount to adjust your trade,
                       Positive values to increase position, Negative values to decrease position.
                       Return None for no action.
        """

        filled_entries = trade.select_filled_orders(trade.entry_side)
        count_of_entries = trade.nr_of_successful_entries
        count_of_exits = trade.nr_of_successful_exits
        stake_amount = filled_entries[0].cost

        if current_profit > self.initial_profit and count_of_exits == 0:
            return -(trade.stake_amount / 2)

        # if current_profit < self.loss_upper_limit: return None  # if the losses are greater than 10%, stop

        # Only buy when prices are not actively falling.
        try:
            dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
            last_candle = dataframe.iloc[-1].squeeze()
            previous_candle = dataframe.iloc[-2].squeeze()
            if last_candle['close'] < previous_candle['close']:
                return None
        except:
            pass

        # determine the stake amount now
        if current_profit < self.recurring_loss and count_of_entries <= self.max_entry_position_adjustment:
            return stake_amount * self.incremental_order_size
        else:
            return None

    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