# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

from datetime import datetime

import talib.abstract as ta
from pandas import DataFrame

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import (
    BooleanParameter,
    DecimalParameter,
    IntParameter,
    IStrategy,
    RealParameter,
)


class StrategyTestV3(IStrategy):
    """
    Strategy used by tests freqtrade bot.
    Please do not modify this strategy, it's  intended for internal use only.
    Please look at the SampleStrategy in the user_data/strategy directory
    or strategy repository https://github.com/freqtrade/freqtrade-strategies
    for samples and inspiration.
    """

    INTERFACE_VERSION = 3

    # Minimal ROI designed for the strategy
    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}

    # Optimal max_open_trades for the strategy
    max_open_trades = -1

    # Optimal stoploss designed for the strategy
    stoploss = -0.10

    # Optimal timeframe for the strategy
    timeframe = "5m"

    # Optional order type mapping
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "limit",
        "stoploss_on_exchange": False,
    }

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20

    # Optional time in force for orders
    order_time_in_force = {
        "entry": "gtc",
        "exit": "gtc",
    }

    buy_params = {
        "buy_rsi": 35,
        # Intentionally not specified, so "default" is tested
        # 'buy_plusdi': 0.4
    }

    sell_params = {"sell_rsi": 74, "sell_minusdi": 0.4}

    buy_rsi = IntParameter([0, 50], default=30, space="buy")
    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
    sell_minusdi = DecimalParameter(
        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
    )
    protection_enabled = BooleanParameter(default=True)
    protection_cooldown_lookback = IntParameter([0, 50], default=30)

    # TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
    @property
    def protections(self):
        prot = []
        if self.protection_enabled.value:
            # Workaround to simplify tests. This will not work in real scenarios.
            prot = self.config.get("_strategy_protections", {})
        return prot

    bot_started = False

    def bot_start(self):
        self.bot_started = True

    def informative_pairs(self):
        return []

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

        # ADX
        dataframe["adx"] = ta.ADX(dataframe)

        # MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        # Minus Directional Indicator / Movement
        dataframe["minus_di"] = ta.MINUS_DI(dataframe)

        # Plus Directional Indicator / Movement
        dataframe["plus_di"] = ta.PLUS_DI(dataframe)

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe)

        # Stoch fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

        # Bollinger bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]

        # EMA - Exponential Moving Average
        dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["rsi"] < self.buy_rsi.value)
                & (dataframe["fastd"] < 35)
                & (dataframe["adx"] > 30)
                & (dataframe["plus_di"] > self.buy_plusdi.value)
            )
            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > self.buy_plusdi.value)),
            "enter_long",
        ] = 1
        dataframe.loc[
            (qtpylib.crossed_below(dataframe["rsi"], self.sell_rsi.value)),
            ("enter_short", "enter_tag"),
        ] = (1, "short_Tag")

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
                )
                & (dataframe["adx"] > 10)
                & (dataframe["minus_di"] > 0)
            )
            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > self.sell_minusdi.value)),
            "exit_long",
        ] = 1

        dataframe.loc[
            (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)),
            ("exit_short", "exit_tag"),
        ] = (1, "short_Tag")

        return dataframe

    def leverage(
        self,
        pair: str,
        current_time: datetime,
        current_rate: float,
        proposed_leverage: float,
        max_leverage: float,
        entry_tag: str | None,
        side: str,
        **kwargs,
    ) -> float:
        # Return 3.0 in all cases.
        # Bot-logic must make sure it's an allowed leverage and eventually adjust accordingly.

        return 3.0

    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:
        if current_profit < -0.0075:
            orders = trade.select_filled_orders(trade.entry_side)
            return round(orders[0].stake_amount, 0)

        return None


class StrategyTestV3Futures(StrategyTestV3):
    can_short = True