# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/bbrsi1_strategy.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

class Github_DerSalvador_freqtrade_helm_chart__bbrsi1_strategy__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
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {'60': 0.01, '30': 0.02, '0': 0.04}
    # 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 = '5m'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # 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': {'tema': {}, 'sar': {'color': 'white'}}, 'subplots': {'MACD': {'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}}, 'RSI': {'rsi': {'color': 'red'}}}}

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Bollinger bands stds 2
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband_2'] = bollinger['lower']
        dataframe['bb_middleband_2'] = bollinger['mid']
        dataframe['bb_upperband_2'] = bollinger['upper']
        # Bollinger bands stds 3
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband_3'] = bollinger['lower']
        dataframe['bb_middleband_3'] = bollinger['mid']
        dataframe['bb_upperband_3'] = bollinger['upper']
        # Bollinger bands stds 4
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
        dataframe['bb_lowerband_4'] = bollinger['lower']
        dataframe['bb_middleband_4'] = bollinger['mid']
        dataframe['bb_upperband_4'] = bollinger['upper']
        return dataframe
    # Hyperoptable parameters
    entry_rsi = (IntParameter(low=1, high=50, default=30, space='entry', optimize=True, load=True),)
    entry_rsi_enabled = (CategoricalParameter([True, False], default=True, space='entry', optimize=True),)
    entry_trigger = (CategoricalParameter(['bb_lowerband_2', 'bb_lowerband_3', 'bb_lowerband_4'], default='bb_lowerband_2', space='entry', optimize=True),)
    exit_rsi = (IntParameter(low=50, high=100, default=70, space='exit', optimize=True, load=True),)
    exit_rsi_enabled = (CategoricalParameter([True, False], default=True, space='exit'),)
    exit_trigger = CategoricalParameter(['bb_middleband_2', 'bb_middleband_3', 'bb_middleband_4', 'bb_upperband_2', 'bb_upperband_3', 'bb_upperband_4'], default='bb_upperband_2', space='exit')

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['rsi'] < 30) & (dataframe['close'] < dataframe['bb_lowerband_2']) & (dataframe['volume'] > 0), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['rsi'] > 70) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe