import logging

import numpy as np  # noqa
import pandas as pd  # noqa
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib

from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair  # noqa


logger = logging.getLogger(__name__)


class FreqaiExampleHybridStrategy(IStrategy):
    """
    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
    FreqAI to bolster a typical Freqtrade strategy.

    Launching this strategy would be:

    freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
    --freqaimodel XGBoostClassifier --config config_examples/config_freqai.example.json

    or the user simply adds this to their config:

    "freqai": {
        "enabled": true,
        "purge_old_models": 2,
        "train_period_days": 15,
        "identifier": "unique-id",
        "feature_parameters": {
            "include_timeframes": [
                "3m",
                "15m",
                "1h"
            ],
            "include_corr_pairlist": [
                "BTC/USDT",
                "ETH/USDT"
            ],
            "label_period_candles": 20,
            "include_shifted_candles": 2,
            "DI_threshold": 0.9,
            "weight_factor": 0.9,
            "principal_component_analysis": false,
            "use_SVM_to_remove_outliers": true,
            "indicator_periods_candles": [10, 20]
        },
        "data_split_parameters": {
            "test_size": 0,
            "random_state": 1
        },
        "model_training_parameters": {
            "n_estimators": 800
        }
    },

    Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
    """

    minimal_roi = {
        # "120": 0.0,  # exit after 120 minutes at break even
        "60": 0.01,
        "30": 0.02,
        "0": 0.04,
    }

    plot_config = {
        "main_plot": {
            "tema": {},
        },
        "subplots": {
            "MACD": {
                "macd": {"color": "blue"},
                "macdsignal": {"color": "orange"},
            },
            "RSI": {
                "rsi": {"color": "red"},
            },
            "Up_or_down": {
                "&s-up_or_down": {"color": "green"},
            },
        },
    }

    process_only_new_candles = True
    stoploss = -0.05
    use_exit_signal = True
    startup_candle_count: int = 30
    can_short = True

    # Hyperoptable parameters
    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)

    def feature_engineering_expand_all(
        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
    ) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
        `include_corr_pairs`. In other words, a single feature defined in this function
        will automatically expand to a total of
        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
        `include_corr_pairs` numbers of features added to the model.

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/stable/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/stable/freqai-feature-engineering/#defining-the-features

        :param dataframe: strategy dataframe which will receive the features
        :param period: period of the indicator - usage example:
        :param metadata: metadata of current pair
        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
        """

        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=period, stds=2.2
        )
        dataframe["bb_lowerband-period"] = bollinger["lower"]
        dataframe["bb_middleband-period"] = bollinger["mid"]
        dataframe["bb_upperband-period"] = bollinger["upper"]

        dataframe["%-bb_width-period"] = (
            dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
        ) / dataframe["bb_middleband-period"]
        dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]

        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)

        dataframe["%-relative_volume-period"] = (
            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
        )

        return dataframe

    def feature_engineering_expand_basic(
        self, dataframe: DataFrame, metadata: dict, **kwargs
    ) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
        In other words, a single feature defined in this function
        will automatically expand to a total of
        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
        numbers of features added to the model.

        Features defined here will *not* be automatically duplicated on user defined
        `indicator_periods_candles`

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/stable/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/stable/freqai-feature-engineering/#defining-the-features

        :param dataframe: strategy dataframe which will receive the features
        :param metadata: metadata of current pair
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
        """
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-raw_volume"] = dataframe["volume"]
        dataframe["%-raw_price"] = dataframe["close"]
        return dataframe

    def feature_engineering_standard(
        self, dataframe: DataFrame, metadata: dict, **kwargs
    ) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This optional function will be called once with the dataframe of the base timeframe.
        This is the final function to be called, which means that the dataframe entering this
        freqai_feature_engineering_* functions.

        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
        This function is a good place for any feature that should not be auto-expanded upon
        (e.g. day of the week).

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/stable/freqai-feature-engineering

        :param dataframe: strategy dataframe which will receive the features
        :param metadata: metadata of current pair
        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
        """
        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
        return dataframe

    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        Required function to set the targets for the model.
        All targets must be prepended with `&` to be recognized by the FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/stable/freqai-feature-engineering

        :param dataframe: strategy dataframe which will receive the targets
        :param metadata: metadata of current pair
        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
        """
        self.freqai.class_names = ["down", "up"]
        dataframe["&s-up_or_down"] = np.where(
            dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
        )

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # noqa: C901
        # User creates their own custom strat here. Present example is a supertrend
        # based strategy.

        dataframe = self.freqai.start(dataframe, metadata, self)

        # TA indicators to combine with the Freqai targets
        # RSI
        dataframe["rsi"] = ta.RSI(dataframe)

        # 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"]
        dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
        )
        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
            "bb_middleband"
        ]

        # TEMA - Triple Exponential Moving Average
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df.loc[
            (
                # Signal: RSI crosses above 30
                (qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
                & (df["tema"] <= df["bb_middleband"])  # Guard: tema below BB middle
                & (df["tema"] > df["tema"].shift(1))  # Guard: tema is raising
                & (df["volume"] > 0)  # Make sure Volume is not 0
                & (df["do_predict"] == 1)  # Make sure Freqai is confident in the prediction
                &
                # Only enter trade if Freqai thinks the trend is in this direction
                (df["&s-up_or_down"] == "up")
            ),
            "enter_long",
        ] = 1

        df.loc[
            (
                # Signal: RSI crosses above 70
                (qtpylib.crossed_above(df["rsi"], self.short_rsi.value))
                & (df["tema"] > df["bb_middleband"])  # Guard: tema above BB middle
                & (df["tema"] < df["tema"].shift(1))  # Guard: tema is falling
                & (df["volume"] > 0)  # Make sure Volume is not 0
                & (df["do_predict"] == 1)  # Make sure Freqai is confident in the prediction
                &
                # Only enter trade if Freqai thinks the trend is in this direction
                (df["&s-up_or_down"] == "down")
            ),
            "enter_short",
        ] = 1

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df.loc[
            (
                # Signal: RSI crosses above 70
                (qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
                & (df["tema"] > df["bb_middleband"])  # Guard: tema above BB middle
                & (df["tema"] < df["tema"].shift(1))  # Guard: tema is falling
                & (df["volume"] > 0)  # Make sure Volume is not 0
            ),
            "exit_long",
        ] = 1

        df.loc[
            (
                # Signal: RSI crosses above 30
                (qtpylib.crossed_above(df["rsi"], self.exit_short_rsi.value))
                &
                # Guard: tema below BB middle
                (df["tema"] <= df["bb_middleband"])
                & (df["tema"] > df["tema"].shift(1))  # Guard: tema is raising
                & (df["volume"] > 0)  # Make sure Volume is not 0
            ),
            "exit_short",
        ] = 1

        return df