# source: https://raw.githubusercontent.com/qninhdt/lmao/51422ef7e20abcaf301cbf81c4fff13188d92b3b/user_data/strategies/E0V1E.py
from datetime import datetime, timedelta
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
import warnings

warnings.simplefilter(action="ignore", category=RuntimeWarning)


class Github_qninhdt_lmao__E0V1E__20250915_072043(IStrategy):
    minimal_roi = {"0": 1}
    timeframe = "5m"
    process_only_new_candles = True
    startup_candle_count = 20
    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 = -0.25
    trailing_stop = True
    trailing_stop_positive = 0.002
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    use_custom_stoploss = False

    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(
        20, 70, default=40, space="buy", optimize=is_optimize_32
    )
    buy_rsi_32 = IntParameter(15, 50, default=42, space="buy", optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(
        0.900, 1, default=0.973, decimals=3, space="buy", optimize=is_optimize_32
    )
    buy_cti_32 = DecimalParameter(
        -1, 1, default=0.69, decimals=2, space="buy", optimize=is_optimize_32
    )

    sell_fastx = IntParameter(50, 100, default=84, space="sell", optimize=True)

    @property
    def protections(self):
        return [{"method": "CooldownPeriod", "stop_duration_candles": 96}]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # buy_1 indicators
        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)

        # profit sell indicators
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe["fastk"] = stoch_fast["fastk"]

        dataframe["cci"] = ta.CCI(dataframe, timeperiod=20)

        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)
        )

        buy_new = (
            (dataframe["rsi_slow"] < dataframe["rsi_slow"].shift(1))
            & (dataframe["rsi_fast"] < 34)
            & (dataframe["rsi"] > 28)
            & (dataframe["close"] < dataframe["sma_15"] * 0.96)
            & (dataframe["cti"] < self.buy_cti_32.value)
        )

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

        conditions.append(buy_new)
        dataframe.loc[buy_new, "enter_tag"] += "buy_new"

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

    def custom_exit(
        self,
        pair: str,
        trade: "Trade",
        current_time: "datetime",
        current_rate: float,
        current_profit: float,
        **kwargs
    ):
        dataframe, _ = self.dp.get_analyzed_dataframe(
            pair=pair, timeframe=self.timeframe
        )
        current_candle = dataframe.iloc[-1].squeeze()

        min_profit = trade.calc_profit_ratio(trade.min_rate)

        if current_profit > 0:
            if current_candle["fastk"] > self.sell_fastx.value:
                return "fastk_profit_sell"

        if current_profit > -0.03:
            if current_candle["cci"] > 80:
                return "cci_loss_sell"

        return None

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