# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/FReinforcedStrategy.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
from functools import reduce
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import timeframe_to_minutes
from technical.util import resample_to_interval, resampled_merge
# This class is a sample. Feel free to customize it.

class Github_DerSalvador_freqtrade_helm_chart__FReinforcedStrategy__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '5m'
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {'60': 0.075, '30': 0.1, '0': 0.05}
    # minimal_roi = {"0": 1}
    stoploss = -0.05
    can_short = True
    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 14
    # Hyperoptable parameters
    # Define the guards spaces
    pos_entry_adx = DecimalParameter(15, 40, decimals=1, default=30.0, space='entry')
    pos_exit_adx = DecimalParameter(15, 40, decimals=1, default=30.0, space='exit')
    # Define the parameter spaces
    adx_period = IntParameter(4, 24, default=14)
    ema_short_period = IntParameter(4, 24, default=8)
    ema_long_period = IntParameter(12, 175, default=21)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Calculate all adx values
        for val in self.adx_period.range:
            dataframe[f'adx_{val}'] = ta.ADX(dataframe, timeperiod=val)
        # Calculate all ema_short values
        for val in self.ema_short_period.range:
            dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ema_long values
        for val in self.ema_long_period.range:
            dataframe[f'ema_long_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']
        self.resample_interval = timeframe_to_minutes(self.timeframe) * 12
        dataframe_long = resample_to_interval(dataframe, self.resample_interval)
        dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
        dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions_long = []
        conditions_short = []
        # GUARDS AND TRIGGERS
        conditions_long.append(dataframe['close'] > dataframe[f'resample_{self.resample_interval}_sma'])
        conditions_short.append(dataframe['close'] < dataframe[f'resample_{self.resample_interval}_sma'])
        conditions_long.append(qtpylib.crossed_above(dataframe[f'ema_short_{self.ema_short_period.value}'], dataframe[f'ema_long_{self.ema_long_period.value}']))
        conditions_short.append(qtpylib.crossed_below(dataframe[f'ema_short_{self.ema_short_period.value}'], dataframe[f'ema_long_{self.ema_long_period.value}']))
        dataframe.loc[reduce(lambda x, y: x & y, conditions_long), 'enter_long'] = 1
        dataframe.loc[reduce(lambda x, y: x & y, conditions_short), 'enter_short'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions_close = []
        conditions_close.append(dataframe[f'adx_{self.adx_period.value}'] < self.pos_entry_adx.value)
        dataframe.loc[reduce(lambda x, y: x & y, conditions_close), 'exit_long'] = 1
        dataframe.loc[reduce(lambda x, y: x & y, conditions_close), 'exit_short'] = 1
        return dataframe