E0V1E20231004085308

from datetime import datetime
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce

class E0V1E20231004085308(IStrategy):
    minimal_roi = {
        "0": 0.02,
        "120": 0.01
    }

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 120
    position_adjustment_enable = True

    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_market_ratio': 0.99
    }

    stoploss = -0.13

    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32)

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            leverage: float, entry_tag: str, side: str,
                            **kwargs) -> float:
        return proposed_stake * 0.7

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake: float, max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs) -> float:
        # Take half of profit
        count_of_exits = trade.nr_of_successful_exits
        if current_profit >= 0.02 and count_of_exits == 0:
            return -0.5 * trade.stake_amount
        # DCA
        count_of_entries = trade.nr_of_successful_entries
        filled_entries = trade.select_filled_orders(trade.entry_side)
        initial_stake = filled_entries[0].cost

        if current_profit < -0.05 and count_of_entries == 1:
            return initial_stake / 0.7 * 0.3
        return None

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

        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastk'] = stoch_fast['fastk']

        # 🛠 Correction : Cast vers float pour éviter erreurs Pandas (LossySetitemError)
        for col in ['sma_15', 'cti', 'rsi', 'rsi_fast', 'rsi_slow', 'fastk']:
            dataframe[col] = dataframe[col].astype(float)

        return dataframe

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

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy_1 = (
            (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
            (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
            (dataframe['rsi'] > self.buy_rsi_32.value) &
            (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
            (dataframe['cti'] < self.buy_cti_32.value)
        )

        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

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

        return dataframe

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