from collections.abc import Callable
from dataclasses import dataclass
from typing import Any

from pandas import DataFrame

from freqtrade.enums import CandleType
from freqtrade.exceptions import OperationalException
from freqtrade.strategy.strategy_helper import merge_informative_pair


PopulateIndicators = Callable[[Any, DataFrame, dict], DataFrame]


@dataclass
class InformativeData:
    asset: str | None
    timeframe: str
    fmt: str | Callable[[Any], str] | None
    ffill: bool
    candle_type: CandleType | None


def informative(
    timeframe: str,
    asset: str = "",
    fmt: str | Callable[[Any], str] | None = None,
    *,
    candle_type: CandleType | str | None = None,
    ffill: bool = True,
) -> Callable[[PopulateIndicators], PopulateIndicators]:
    """
    A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
    define informative indicators.

    Example usage:

        @informative('1h')
        def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
            return dataframe

    :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
    :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
                  current pair. Also supports limited pair format strings (see below)
    :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
    specified, defaults to:
    * {base}_{quote}_{column}_{timeframe} if asset is specified.
    * {column}_{timeframe} if asset is not specified.
    Pair format supports these format variables:
    * {base} - base currency in lower case, for example 'eth'.
    * {BASE} - same as {base}, except in upper case.
    * {quote} - quote currency in lower case, for example 'usdt'.
    * {QUOTE} - same as {quote}, except in upper case.
    Format string additionally supports this variables.
    * {asset} - full name of the asset, for example 'BTC/USDT'.
    * {column} - name of dataframe column.
    * {timeframe} - timeframe of informative dataframe.
    :param ffill: ffill dataframe after merging informative pair.
    :param candle_type: '', mark, index, premiumIndex, or funding_rate
    """
    _asset = asset
    _timeframe = timeframe
    _fmt = fmt
    _ffill = ffill
    _candle_type = CandleType.from_string(candle_type) if candle_type else None

    def decorator(fn: PopulateIndicators):
        informative_pairs = getattr(fn, "_ft_informative", [])
        informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill, _candle_type))
        setattr(fn, "_ft_informative", informative_pairs)  # noqa: B010
        return fn

    return decorator


def __get_pair_formats(market: dict[str, Any] | None) -> dict[str, str]:
    if not market:
        return {}
    base = market["base"]
    quote = market["quote"]
    return {
        "base": base.lower(),
        "BASE": base.upper(),
        "quote": quote.lower(),
        "QUOTE": quote.upper(),
    }


def _format_pair_name(config, pair: str, market: dict[str, Any] | None = None) -> str:
    return pair.format(
        stake_currency=config["stake_currency"],
        stake=config["stake_currency"],
        **__get_pair_formats(market),
    ).upper()


def _create_and_merge_informative_pair(
    strategy,
    dataframe: DataFrame,
    metadata: dict,
    inf_data: InformativeData,
    populate_indicators_fn: PopulateIndicators,
):
    asset = inf_data.asset or ""
    timeframe = inf_data.timeframe
    timeframe1 = inf_data.timeframe
    fmt = inf_data.fmt
    candle_type = inf_data.candle_type
    if candle_type == CandleType.FUNDING_RATE:
        timeframe1 = strategy.dp.get_funding_rate_timeframe()

    config = strategy.config

    if asset:
        # Insert stake currency if needed.
        market1 = strategy.dp.market(metadata["pair"])
        asset = _format_pair_name(config, asset, market1)
    else:
        # Not specifying an asset will define informative dataframe for current pair.
        asset = metadata["pair"]

    market = strategy.dp.market(asset)
    if market is None:
        raise OperationalException(f"Market {asset} is not available.")

    # Default format. This optimizes for the common case: informative pairs using same stake
    # currency. When quote currency matches stake currency, column name will omit base currency.
    # This allows easily reconfiguring strategy to use different base currency. In a rare case
    # where it is desired to keep quote currency in column name at all times user should specify
    # fmt='{base}_{quote}_{column}_{timeframe}' format or similar.
    if not fmt:
        fmt = "{column}_{timeframe}"  # Informatives of current pair
        if inf_data.asset:
            fmt = "{base}_{quote}_" + fmt  # Informatives of other pairs

    inf_metadata = {"pair": asset, "timeframe": timeframe}
    inf_dataframe = strategy.dp.get_pair_dataframe(asset, timeframe1, candle_type)
    if inf_dataframe.empty:
        raise ValueError(
            f"Informative dataframe for ({asset}, {timeframe1}, {candle_type}) is empty. "
            "Can't populate informative indicators."
        )
    inf_dataframe = populate_indicators_fn(strategy, inf_dataframe, inf_metadata)

    formatter: Any = None
    if callable(fmt):
        formatter = fmt  # A custom user-specified formatter function.
    else:
        formatter = fmt.format  # A default string formatter.

    fmt_args = {
        **__get_pair_formats(market),
        "asset": asset,
        "timeframe": timeframe,
    }
    inf_dataframe.rename(columns=lambda column: formatter(column=column, **fmt_args), inplace=True)

    date_column = formatter(column="date", **fmt_args)
    if date_column in dataframe.columns:
        raise OperationalException(
            f"Duplicate column name {date_column} exists in "
            f"dataframe! Ensure column names are unique!"
        )
    dataframe = merge_informative_pair(
        dataframe,
        inf_dataframe,
        strategy.timeframe,
        timeframe1,
        ffill=inf_data.ffill,
        append_timeframe=False,
        date_column=date_column,
    )
    return dataframe
