# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/BBRSI4cust.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
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
# This class is a sample. Feel free to customize it.

class Github_DerSalvador_freqtrade_helm_chart__BBRSI4cust__20260115_122204(IStrategy):
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3
    # Can this strategy go short?
    can_short: bool = False
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    # "60": 0.01,
    # "30": 0.02,
    minimal_roi = {'0': 0.003}
    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.1
    # 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
    # Optimal timeframe for the strategy.
    timeframe = '15m'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False
    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Hyperoptable parameters
    # entry_rsi = IntParameter(low=25, high=35, default=35, space='entry', optimize=True, load=True)
    entry_bb = IntParameter(low=1, high=4, default=1, space='entry', optimize=True, load=True)
    entry_di = IntParameter(low=10, high=20, default=20, space='entry', optimize=True, load=True)
    exit_bb = IntParameter(low=1, high=4, default=1, space='exit', optimize=True, load=True)
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    plot_config = {'main_plot': {'bb_lowerband': {'color': 'blue'}, 'bb_middleband': {'color': 'orange'}, 'bb_upperband': {'color': 'blue'}}, 'subplots': {'DI': {'plus_di': {'color': 'green'}, 'di_overbought': {'color': 'black'}}, 'RSI': {'rsi': {'color': 'red'}}}}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Momentum Indicators
        # ------------------------------------
        # Plus Directional Indicator / Movement
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
        dataframe['di_overbought'] = 20
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=self.entry_bb.value)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=self.exit_bb.value)
        dataframe['bb_lowerband1'] = bollinger1['lower']
        dataframe['bb_middleband1'] = bollinger1['mid']
        dataframe['bb_upperband1'] = bollinger1['upper']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Signal: RSI crosses above 30
        # Make sure Volume is not 0
        dataframe.loc[(dataframe['plus_di'] > self.entry_di.value) & qtpylib.crossed_below(dataframe['low'], dataframe['bb_lowerband']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Signal: RSI crosses above 70
        # Make sure Volume is not 0
        dataframe.loc[qtpylib.crossed_above(dataframe['high'], dataframe['bb_middleband1']) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        """
        Sell only when matching some criteria other than those used to generate the exit signal
        :return: str exit_reason, if any, otherwise None
        """
        # get dataframe
        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        # get the current candle
        current_candle = dataframe.iloc[-1].squeeze()
        # if (qtpylib.crossed_above(current_candle['high'], dataframe['bb_middleband1'])) == True:
        if qtpylib.crossed_above(current_rate, current_candle['bb_middleband1']):
            return 'bb_profit_exit'
        # else, hold
        return None