ContraRatingsStrategy

"""
ContraRatingsStrategy — Kontra (Contrarian) Teknik Puan Stratejisi

Mantik:
  4H timeframe'de her coinin teknik durumunu 6 indikator ile puanlar.
  Puan -1 (Strong Sell) ile +1 (Strong Buy) aras�ndad�r.

  Kontra yaklasim:
  4H puani "Strong Sell" (≤ -0.5) oldugunda piyasa asiri satimdadir.
  1 dakikalikta alim yapilir.

  DCA (3 seviye): %0.4, %0.8, %1.2 d�s�ste ek alim.
  Cikis: Toplam pozisyon %1.2 kara gectiginde kapat.
"""

from pandas import DataFrame, Series
from freqtrade.strategy import IStrategy, informative
from freqtrade.persistence import Trade
from datetime import datetime
import talib.abstract as ta


class ContraRatingsStrategy(IStrategy):

    timeframe = '1m'
    startup_candle_count = 200

    minimal_roi = {}
    stoploss = -0.10

    process_only_new_candles = True

    position_adjustment_enable = True
    max_entry_position_adjustment = 3

    dca_steps = [-0.004, -0.008, -0.012]

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float | None, max_stake: float,
                            leverage: float, entry_tag: str | None, side: str,
                            **kwargs) -> float:
        return proposed_stake / (self.max_entry_position_adjustment + 1)

    @informative('4h')
    def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['stoch_k'] = ta.STOCH(dataframe, 14, 3, 3)['slowk']
        dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
        dataframe['willr'] = ta.WILLR(dataframe, timeperiod=14)
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macd_signal'] = macd['macdsignal']
        dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)

        bearish = Series(0, index=dataframe.index)
        bearish += (dataframe['rsi'] < 40).astype(int)
        bearish += (dataframe['stoch_k'] < 20).astype(int)
        bearish += (dataframe['cci'] < -100).astype(int)
        bearish += (dataframe['willr'] < -80).astype(int)
        bearish += (dataframe['macd'] < dataframe['macd_signal']).astype(int)
        bearish += (dataframe['close'] < dataframe['sma50']).astype(int)

        dataframe['rating'] = -bearish / 6 * 2 + 1
        dataframe['strong_sell'] = bearish >= 4

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['signal'] = dataframe['strong_sell_4h']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['signal'] == True) &
                (dataframe['signal'].shift(1) == False) &
                (dataframe['volume'] > 0)
            ),
            ['enter_long', 'enter_tag']] = (1, 'contra_entry')

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> str | None:
        if current_profit >= 0.012:
            return 'tp_1pct'
        return None

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake: float | None, max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs) -> float | None | tuple[float | None, str | None]:
        if trade.nr_of_successful_entries - 1 >= self.max_entry_position_adjustment:
            return None

        entry_price = trade.open_rate
        price_drop = (entry_price - current_rate) / entry_price
        dca_index = trade.nr_of_successful_entries - 1

        if dca_index < len(self.dca_steps) and price_drop >= abs(self.dca_steps[dca_index]):
            return trade.stake_amount, f'dca_{dca_index + 1}'

        return None