import logging
from functools import cached_property, reduce
from typing import Any

# import talib.abstract as ta
from freqtrade.strategy import IStrategy
from pandas import DataFrame

logger = logging.getLogger(__name__)

ACTION_COLUMN = "&-action"


class RLAgentStrategy(IStrategy):
    """
    RLAgentStrategy
    """

    INTERFACE_VERSION = 3

    @cached_property
    def can_short(self) -> bool:
        return self.is_short_allowed()

    # def feature_engineering_expand_all(
    #     self, dataframe: DataFrame, period: int, metadata: dict[str, Any], **kwargs
    # ) -> DataFrame:
    #     dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)

    #     return dataframe

    def feature_engineering_expand_basic(
        self, dataframe: DataFrame, metadata: dict[str, Any], **kwargs
    ) -> DataFrame:
        dataframe["%-close_pct_change"] = dataframe.get("close").pct_change()
        dataframe["%-raw_volume"] = dataframe.get("volume")

        return dataframe

    def feature_engineering_standard(
        self, dataframe: DataFrame, metadata: dict[str, Any], **kwargs
    ) -> DataFrame:
        dates = dataframe.get("date")
        dataframe["%-day_of_week"] = (dates.dt.dayofweek + 1) / 7
        dataframe["%-hour_of_day"] = (dates.dt.hour + 1) / 25

        dataframe["%-raw_close"] = dataframe.get("close")
        dataframe["%-raw_open"] = dataframe.get("open")
        dataframe["%-raw_high"] = dataframe.get("high")
        dataframe["%-raw_low"] = dataframe.get("low")

        return dataframe

    def set_freqai_targets(
        self, dataframe: DataFrame, metadata: dict[str, Any], **kwargs
    ) -> DataFrame:
        dataframe[ACTION_COLUMN] = 0

        return dataframe

    def populate_indicators(
        self, dataframe: DataFrame, metadata: dict[str, Any]
    ) -> DataFrame:
        dataframe = self.freqai.start(dataframe, metadata, self)

        return dataframe

    def populate_entry_trend(
        self, dataframe: DataFrame, metadata: dict[str, Any]
    ) -> DataFrame:
        enter_long_conditions = [
            dataframe.get("do_predict") == 1,
            dataframe.get(ACTION_COLUMN) == 1,
        ]
        dataframe.loc[
            reduce(lambda x, y: x & y, enter_long_conditions),
            ["enter_long", "enter_tag"],
        ] = (1, "long")

        enter_short_conditions = [
            dataframe.get("do_predict") == 1,
            dataframe.get(ACTION_COLUMN) == 3,
        ]
        dataframe.loc[
            reduce(lambda x, y: x & y, enter_short_conditions),
            ["enter_short", "enter_tag"],
        ] = (1, "short")

        return dataframe

    def populate_exit_trend(
        self, dataframe: DataFrame, metadata: dict[str, Any]
    ) -> DataFrame:
        exit_long_conditions = [
            dataframe.get("do_predict") == 1,
            dataframe.get(ACTION_COLUMN) == 2,
        ]
        dataframe.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1

        exit_short_conditions = [
            dataframe.get("do_predict") == 1,
            dataframe.get(ACTION_COLUMN) == 4,
        ]
        dataframe.loc[
            reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"
        ] = 1

        return dataframe

    def is_short_allowed(self) -> bool:
        trading_mode = self.config.get("trading_mode")
        if trading_mode in {"margin", "futures"}:
            return True
        elif trading_mode == "spot":
            return False
        else:
            raise ValueError(f"Invalid trading_mode: {trading_mode}")