# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Trend_Strength_Directional.py
import talib.abstract as ta
import numpy as np  # noqa
import pandas as pd
from functools import reduce
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter, RealParameter
__author__ = 'Robert Roman'
__copyright__ = 'Free For Use'
__license__ = 'MIT'
__version__ = '1.0'
__maintainer__ = 'Robert Roman'
__email__ = 'robertroman7@gmail.com'
__BTC_donation__ = '3FgFaG15yntZYSUzfEpxr5mDt1RArvcQrK'
# Optimized With Sharpe Ratio and 1 years data
# 12520 trades. 6438/5337/745 Wins/Draws/Losses. Avg profit   1.55%. Median profit   0.17%. Total profit  194026.95473822 USDT ( 194.03%). Avg duration 1 day, 9:13:00 min. Objective: -63.61104

class Github_DerSalvador_freqtrade_helm_chart__Trend_Strength_Directional__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '15m'
    # ROI table:
    minimal_roi = {'0': 0.383, '120': 0.082, '283': 0.045, '495': 0}
    # Stoploss:
    stoploss = -0.314
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.307
    trailing_stop_positive_offset = 0.364
    trailing_only_offset_is_reached = False
    # Hyperopt Buy Parameters
    entry_plusdi_enabled = CategoricalParameter([True, False], space='entry', optimize=True, default=False)
    entry_adx = IntParameter(low=1, high=100, default=12, space='entry', optimize=True, load=True)
    entry_adx_timeframe = IntParameter(low=1, high=50, default=9, space='entry', optimize=True, load=True)
    entry_plusdi = IntParameter(low=1, high=100, default=44, space='entry', optimize=True, load=True)
    entry_minusdi = IntParameter(low=1, high=100, default=74, space='entry', optimize=True, load=True)
    # Hyperopt Sell Parameters
    exit_plusdi_enabled = CategoricalParameter([True, False], space='exit', optimize=True, default=True)
    exit_adx = IntParameter(low=1, high=100, default=3, space='exit', optimize=True, load=True)
    exit_adx_timeframe = IntParameter(low=1, high=50, default=41, space='exit', optimize=True, load=True)
    exit_plusdi = IntParameter(low=1, high=100, default=49, space='exit', optimize=True, load=True)
    exit_minusdi = IntParameter(low=1, high=100, default=11, space='exit', optimize=True, load=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        # GUARDS
        if self.entry_plusdi_enabled.value:
            conditions.append(ta.PLUS_DI(dataframe, timeperiod=int(self.entry_plusdi.value)) > ta.MINUS_DI(dataframe, timeperiod=int(self.entry_minusdi.value)))
        # TRIGGERS
        try:
            conditions.append(ta.ADX(dataframe, timeperiod=int(self.entry_adx_timeframe.value)) > self.entry_adx.value)
        except Exception:
            pass
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        # GUARDS
        if self.exit_plusdi_enabled.value:
            conditions.append(ta.PLUS_DI(dataframe, timeperiod=int(self.exit_plusdi.value)) < ta.MINUS_DI(dataframe, timeperiod=int(self.exit_minusdi.value)))
        # TRIGGERS
        try:
            conditions.append(ta.ADX(dataframe, timeperiod=int(self.exit_adx_timeframe.value)) < self.exit_adx.value)
        except Exception:
            pass
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit'] = 1
        return dataframe