AJ_SMC_FVG_OB_V44_SWEEP_BREAKDOWN_VARIANT_B

# pragma pylint: disable=missing-docstring, invalid-name
# flake8: noqa: F401
from pandas import DataFrame

from freqtrade.strategy import IStrategy

import talib.abstract as ta


class AJ_SMC_FVG_OB_V44_SWEEP_BREAKDOWN_VARIANT_B(IStrategy):
    """
    Research-only V44 diagnostic strategy.

    Tests only the V44 Variant B timing model selected by the sweep/rejection
    timing diagnostic. This is not recovered V41, not V42 tuning, not live, and
    must not be used for dry-run without a separate adoption decision.
    """

    INTERFACE_VERSION = 3

    timeframe = "5m"
    can_short = True

    minimal_roi = {
        "0": 0.022,
        "45": 0.012,
        "120": 0.006,
        "240": 0.002,
    }

    stoploss = -0.020

    trailing_stop = True
    trailing_stop_positive = 0.006
    trailing_stop_positive_offset = 0.014
    trailing_only_offset_is_reached = True

    process_only_new_candles = True
    use_exit_signal = False
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    startup_candle_count = 260

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC",
    }

    protections = [
        {
            "method": "CooldownPeriod",
            "stop_duration_candles": 4,
        },
        {
            "method": "StoplossGuard",
            "lookback_period_candles": 60,
            "trade_limit": 2,
            "stop_duration_candles": 24,
            "only_per_pair": False,
        },
    ]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_21"] = ta.EMA(dataframe, timeperiod=21)
        dataframe["ema_55"] = ta.EMA(dataframe, timeperiod=55)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)
        dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)

        dataframe["volume_mean_30"] = dataframe["volume"].rolling(30).mean()
        dataframe["candle_range"] = dataframe["high"] - dataframe["low"]
        dataframe["body"] = (dataframe["close"] - dataframe["open"]).abs()
        dataframe["upper_wick"] = dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
        dataframe["lower_wick"] = dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
        dataframe["close_position"] = (
            (dataframe["close"] - dataframe["low"]) / dataframe["candle_range"].replace(0, None)
        )

        dataframe["prior_high_72"] = dataframe["high"].rolling(72).max().shift(1)
        dataframe["prior_low_8"] = dataframe["low"].rolling(8).min().shift(1)

        dataframe["liquidity_sweep"] = (
            (dataframe["high"] > dataframe["prior_high_72"] * 1.001)
            & (dataframe["close"] < dataframe["prior_high_72"])
            & (dataframe["upper_wick"] > dataframe["body"] * 1.4)
            & (dataframe["close_position"] <= 0.45)
        )

        dataframe["sweep_recent_3"] = (
            dataframe["liquidity_sweep"].rolling(3).max().shift(1).fillna(0).astype(bool)
        )

        dataframe["breakdown_confirmation"] = (
            (dataframe["close"] < dataframe["prior_low_8"])
            & (dataframe["close"] < dataframe["ema_21"])
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["enter_short"] = 0
        dataframe["enter_tag"] = None

        short_entry = (
            dataframe["sweep_recent_3"]
            & dataframe["breakdown_confirmation"]
            & (dataframe["volume"] > 0)
        )

        dataframe.loc[short_entry, "enter_short"] = 1
        dataframe.loc[short_entry, "enter_tag"] = "V44_SWEEP_BREAKDOWN_B"

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["exit_short"] = 0
        return dataframe