# source: https://raw.githubusercontent.com/Danson77/Freqtrade_AIO_UI/f0bf29851d0577f244c17c0aa7d593ba3a7f3551/user_data/backtest_reports/Registerd_with_100USDT/CombinedBinHAndCluc1/CombinedBinHAndCluc1.py
# directory_url: https://github.com/Danson77/Freqtrade_AIO_UI/blob/main/user_data/backtest_reports/Registerd_with_100USDT/CombinedBinHAndCluc1/
# User: Danson77
# Repository: Freqtrade_AIO_UI
# --------------------# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
# --------------------------------
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)


class Github_Danson77_Freqtrade_AIO_UI__CombinedBinHAndCluc1__20260520_191933(IStrategy):
    # Based on a backtesting:
    # - the best perfomance is reached with "max_open_trades" = 2 (in average for any market),
    #   so it is better to increase "stake_amount" value rather then "max_open_trades" to get more profit
    # - if the market is constantly green(like in JAN 2018) the best performance is reached with
    #   "max_open_trades" = 2 and minimal_roi = 0.01
########################################################################################################################################################
# Main
########################################################################################################################################################
    can_short = False

    minimal_roi = {
        
    }
    ignore_roi_if_entry_signal = False

    stoploss = -0.25
    use_custom_stoploss = False

    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.01
    trailing_only_offset_is_reached = True

    use_entry_signal = True
    use_custom_entry = False

    use_exit_signal = True
    use_custom_exit = True

    exit_profit_only = False
    exit_profit_offset = 0.03
########################################################################################################################################################
# Main
########################################################################################################################################################
    timeframe = '5m'
    informative = '1h'
    process_only_new_candles = False
    startup_candle_count = 400

    position_adjustment_enable = True

    # Define the settings for the Adjust trade.
    initial_safety_order_trigger = -0.018  # Initial trigger for the first safety order.
    max_safety_orders = 8  # Maximum number of safety orders to prevent overexposure.
    safety_order_step_scale = 1.2  # How much to increase the trigger for each additional safety order.
    safety_order_volume_scale = 1.4  # How much to increase the volume of each safety order.

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'trailing_stop_loss': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }
    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},  # Color for the buy moving average.
            'ma_sell': {'color': 'orange'},  # Color for the sell moving average.
        },
    }
########################################################################################################################################################
# Trade Protections
########################################################################################################################################################
    @property
    def protections(self):
        return [
            # Cooldown any signal for 5 candles (25 m) after a trade
            { "method": "CooldownPeriod", "stop_duration_candles": 5 },

            # Allow up to 3% drawdown over the last 9 h before pausing
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": 72,     # 6 h → 9 h
                "trade_limit": 20,
                "stop_duration_candles": 6,        # longer pause
                "max_allowed_drawdown": 0.03       # 3% drawdown allowed
            },

            # Only guard if you’ve lost >3% over a rolling 4 h period
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 48,     # 4 h
                "trade_limit": 4,
                "stop_duration_candles": 4,
                "only_per_pair": False
            },

            # Prevent pairs that only net <2% profit over 2 h, block for 1 h
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,     # 2 h
                "trade_limit": 2,
                "stop_duration_candles": 12,       # 1 h
                "required_profit": 0.02            # 2%
            },

            # Prevent pairs that only net <4% profit over 12 h, block for 2 h
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 144,    # 12 h
                "trade_limit": 4,
                "stop_duration_candles": 24,       # 2 h
                "required_profit": 0.04            # 4%
            }
        ]
########################################################################################################################################################
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # strategy BinHV45
        mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        # strategy ClucMay72018
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (  # strategy BinHV45
                    dataframe['lower'].shift().gt(0) &
                    dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
                    dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
                    dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
                    dataframe['close'].lt(dataframe['lower'].shift()) &
                    dataframe['close'].le(dataframe['close'].shift())
            ) |
            (  # strategy ClucMay72018
                    (dataframe['close'] < dataframe['ema_slow']) &
                    (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
                    (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20))
            ),
            'buy'
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        """
        dataframe.loc[
            (dataframe['close'] > dataframe['bb_middleband']),
            'sell'
        ] = 1
        return dataframe