MT_CMO_RSI_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_CMO_RSI_1(IStrategy):
    """
    Multi-Timeframe Strategy with CMO and RSI
    """

    # 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")

    # CMO (Lower Timeframe)
    buy_cmo_ltf_period = IntParameter(5, 25, default=14, space="buy")
    buy_cmo_ltf_threshold = IntParameter(-50, 0, default=-25, 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")

    # Exit Parameters
    sell_cmo_ltf_threshold = IntParameter(0, 50, default=25, 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["cmo_ltf"] = ta.CMO(dataframe, timeperiod=self.buy_cmo_ltf_period.value)
        dataframe["rsi_ltf"] = ta.RSI(dataframe, timeperiod=self.buy_rsi_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["cmo_ltf"] < self.buy_cmo_ltf_threshold.value)
            & (dataframe["rsi_ltf"] < self.buy_rsi_ltf_threshold.value),
            "enter_long",
        ] = 1

        return dataframe

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

        return dataframe