MT_ICH_ADX_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
import technical.indicators as ftt


class MT_ICH_ADX_1(IStrategy):
    """
    Multi-Timeframe Strategy with Ichimoku Cloud and ADX
    """

    # 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 = 100

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

    # Ichimoku Cloud (Lower Timeframe)
    buy_ichimoku_conversion_period = IntParameter(5, 25, default=9, space="buy")
    buy_ichimoku_base_period = IntParameter(20, 60, default=26, space="buy")
    buy_ichimoku_span_b_period = IntParameter(40, 100, default=52, space="buy")

    # ADX (Lower Timeframe)
    buy_adx_ltf_period = IntParameter(10, 50, default=14, space="buy")
    buy_adx_ltf_threshold = IntParameter(15, 50, default=25, space="buy")

    # Exit Parameters
    sell_adx_ltf_threshold = IntParameter(40, 80, default=60, 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
        ichimoku = ftt.ichimoku(
            dataframe,
            conversion_line_period=self.buy_ichimoku_conversion_period.value,
            base_line_periods=self.buy_ichimoku_base_period.value,
            laggin_span=self.buy_ichimoku_span_b_period.value,
        )
        dataframe["tenkan_sen"] = ichimoku["tenkan_sen"]
        dataframe["kijun_sen"] = ichimoku["kijun_sen"]
        dataframe["senkou_span_a"] = ichimoku["senkou_span_a"]
        dataframe["senkou_span_b"] = ichimoku["senkou_span_b"]
        dataframe["adx_ltf"] = ta.ADX(dataframe, timeperiod=self.buy_adx_ltf_period.value)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["close"] > dataframe[f"ema_htf_{self.informative_timeframe}"])
            & (dataframe["tenkan_sen"] > dataframe["kijun_sen"])
            & (dataframe["close"] > dataframe["senkou_span_a"])
            & (dataframe["close"] > dataframe["senkou_span_b"])
            & (dataframe["adx_ltf"] > self.buy_adx_ltf_threshold.value),
            "enter_long",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe["adx_ltf"] > self.sell_adx_ltf_threshold.value), "exit_long"] = 1

        return dataframe