MT_ERMB_1

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from typing import Optional, Union

from freqtrade.strategy import (
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IStrategy,
    IntParameter,
    merge_informative_pair,
)

# --------------------------------
from datetime import datetime
from freqtrade.persistence import Trade
import talib.abstract as ta


class MT_ERMB_1(IStrategy):
    """
    Multi-Timeframe Strategy with EMA, RSI, MACD, and Bollinger Bands
    """

    # Strategy interface version
    INTERFACE_VERSION = 3

    timeframe = "15m"
    informative_timeframe = "1h"
    can_short = False

    # Minimal ROI designed for the strategy
    minimal_roi = {"0": 0.1}

    # Optimal stoploss
    stoploss = -0.1

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    # Run "populate_indicators()" only for new candle
    process_only_new_candles = True

    # These values can be overridden in the config
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # --- Hyperparameters ---
    # EMA (Higher Timeframe)
    buy_ema_htf_period = IntParameter(10, 50, default=20, space="buy")

    # RSI (Lower Timeframe)
    buy_rsi_ltf_period = IntParameter(10, 50, default=14, space="buy")
    buy_rsi_ltf_threshold = IntParameter(20, 50, default=30, space="buy")

    # MACD (Lower Timeframe)
    buy_macd_ltf_fast = IntParameter(6, 24, default=12, space="buy")
    buy_macd_ltf_slow = IntParameter(13, 52, default=26, space="buy")
    buy_macd_ltf_signal = IntParameter(5, 18, default=9, space="buy")

    # Bollinger Bands (Lower Timeframe)
    buy_bb_ltf_period = IntParameter(10, 50, default=20, space="buy")
    buy_bb_ltf_stddev = DecimalParameter(1.5, 3.0, default=2.0, space="buy")

    # Exit Parameters
    sell_rsi_ltf_threshold = IntParameter(50, 80, default=70, space="sell")


    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Higher Timeframe Indicators
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        informative['ema_htf'] = ta.EMA(informative, timeperiod=self.buy_ema_htf_period.value)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)


        # Lower Timeframe Indicators
        dataframe['rsi_ltf'] = ta.RSI(dataframe, timeperiod=self.buy_rsi_ltf_period.value)
        macd = ta.MACD(dataframe, fastperiod=self.buy_macd_ltf_fast.value, slowperiod=self.buy_macd_ltf_slow.value, signalperiod=self.buy_macd_ltf_signal.value)
        dataframe['macd_ltf'] = macd['macd']
        dataframe['macdsignal_ltf'] = macd['macdsignal']
        bb = ta.BBANDS(dataframe, timeperiod=self.buy_bb_ltf_period.value, nbdevup=self.buy_bb_ltf_stddev.value, nbdevdn=self.buy_bb_ltf_stddev.value)
        dataframe['bb_lower_ltf'] = bb['lowerband']
        dataframe['bb_upper_ltf'] = bb['upperband']

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['close'] > dataframe[f'ema_htf_{self.informative_timeframe}']) &
            (dataframe['rsi_ltf'] < self.buy_rsi_ltf_threshold.value) &
            (dataframe['macd_ltf'] > dataframe['macdsignal_ltf']) &
            (dataframe['close'] < dataframe['bb_lower_ltf']),
            'enter_long'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['rsi_ltf'] > self.sell_rsi_ltf_threshold.value),
            'exit_long'
        ] = 1

        return dataframe