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DS_BNC_EV1E_5m

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Danson77/Freqtrade_AIO_UI/user_data/backtest_reports/Registerd_with_1000USDT/DS_BNC_EV1E_5m/DS_BNC_EV1E_5m.py · first seen 2026-07-16 · repo updated 2026-05-20 · ⬇ 1 download

Basics mode: spot timeframe: 5m
Settings stoploss: -0.99 has minimal roi trailing custom stoploss protections process only new candles startup candle count: 200 hyperopt hyperopt params: 14
Indicators Bollinger_Bands EMA RSI SMA Stochastic pandas_ta talib
Concepts risk_management trailing
Methods custom_stoploss ewo protections
7 related strategies (⧉ identical code, ≈ similar name)

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from datetime import datetime, timedelta
from typing import Optional, Union, Tuple
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce
########################################################################################################################################################
# Custom indicators and helper functions
########################################################################################################################################################
def ewo(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif
########################################################################################################################################################
class DS_BNC_EV1E_5m(IStrategy):
########################################################################################################################################################
# Hyperopt
########################################################################################################################################################
    buy_params = {
        # Ewo
        "buy_rsi_fast": 50,
        "buy_rsi": 30,
        "buy_ewo": -1.238,
        "buy_ema_low": 0.956,
        "buy_ema_high": 0.986,
        # Rsi Fast cti
        "buy_rsi_fast_32": 63,
        "buy_rsi_32": 16,
        "buy_sma15_32": 0.932,
        "buy_cti_32": 2,
    }
    sell_params = {
        # Sell Deadfish
        "sell_deadfish_bb_width": 0.05,
        "sell_deadfish_profit": 0.05,
        "sell_deadfish_bb_factor": 1.0,
        "sell_deadfish_volume_factor": 1.0,
        # Custom Stoplose 
        "sell_fastx": 75,
    }
    minimal_roi = {
        "0": 10
    }
    stoploss = -0.99
    trailing_stop = False
    trailing_stop_positive = 0.01  # Positive offset for trailing stop.
    trailing_stop_positive_offset = 0.0135  # Offset for triggering the trailing stop.
    trailing_only_offset_is_reached = True  # Only trigger trailing stop if the offset is reached.
    use_custom_stoploss = True
########################################################################################################################################################
# Main
    can_short = False
########################################################################################################################################################
    timeframe = '5m'  # The primary timeframe for analysis.
    process_only_new_candles = True # Consider candles upon startup.
    startup_candle_count = 200 # Number of past candles to consider upon startup.

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01  # Offset added to exit signal (profitable threshold).
    ignore_roi_if_entry_signal = False  # If True, ignore ROI when the buy signal is still present.

    # Configuration of order types.
    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 defines how long an order will remain active before it is executed or expired.
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }

    # Plotting configuration for visualizing indicators in backtesting.
    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 [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 5
            },
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": 48,
                "trade_limit": 20,
                "stop_duration_candles": 4,
                "max_allowed_drawdown": 0.2
            },
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "only_per_pair": False
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 6,
                "trade_limit": 2,
                "stop_duration_candles": 60,
                "required_profit": 0.02
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "required_profit": 0.01
            }
        ]
########################################################################################################################################################
# Paramiters
########################################################################################################################################################
    # Ewo
    is_optimize_ewo = True
    buy_rsi_fast = IntParameter(35, 50, default=45, space='buy', optimize=False) # Hard to optimize
    buy_rsi = IntParameter(15, 35, default=35, space='buy', optimize=False) # Hard to optimize
    buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, space='buy', optimize=is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, space='buy', optimize=False)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, space='buy', optimize=False)
    # Ewo 2
    is_optimize_32 = False
    buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32)
    # Sell Deadfish
    is_optimize_deadfish = False
    sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish)
    # Custom Stoploss 
    is_optimize_stoploss = False
    sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=is_optimize_stoploss)
########################################################################################################################################################
# Indicators
########################################################################################################################################################
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # buy_1 indicators
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        # ewo indicators
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
        dataframe['EWO'] = ewo(dataframe, 50, 200)
        # profit sell indicators
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        # loss sell indicators
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']

        dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])

        dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
        dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)

        return dataframe
########################################################################################################################################################
# Custom Stoploss
########################################################################################################################################################
    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        if current_time - timedelta(minutes=60) > trade.open_date_utc:
            if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.01):
                return -0.001

        if current_time - timedelta(days=1) > trade.open_date_utc:
            if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.05):
                return -0.001

        enter_tag = ''
        if hasattr(trade, 'enter_tag') and trade.enter_tag is not None:
            enter_tag = trade.enter_tag
        enter_tags = enter_tag.split()

        if "ewo" in enter_tags:
            if current_profit >= 0.05:
                return -0.005

        if current_profit > 0:
            if current_candle["fastk"] > self.sell_fastx.value:
                return -0.001

        return self.stoploss
########################################################################################################################################################
# Buy
########################################################################################################################################################
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        is_ewo = (
                (dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
                (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) &
                (dataframe['EWO'] > self.buy_ewo.value) &
                (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) &
                (dataframe['rsi'] < self.buy_rsi.value)
        )

        buy_1 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
                (dataframe['rsi'] > self.buy_rsi_32.value) &
                (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
                (dataframe['cti'] < self.buy_cti_32.value)
        )

        conditions.append(is_ewo)
        dataframe.loc[is_ewo, 'enter_tag'] += 'ewo'

        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'enter_long'] = 1

        return dataframe
########################################################################################################################################################
# Exit Sell
########################################################################################################################################################
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        # Apply conditions similar to those in `custom_exit`
        exit_condition = (
            (dataframe['bb_width'] < self.sell_deadfish_bb_width.value) &
            (dataframe['close'] > dataframe['bb_middleband2'] * self.sell_deadfish_bb_factor.value) &
            (dataframe['volume_mean_12'] < dataframe['volume_mean_24'] * self.sell_deadfish_volume_factor.value)
        )
        
        # Mark the rows where the exit condition is met
        dataframe.loc[exit_condition, 'exit_long'] = 1
        dataframe.loc[exit_condition, 'exit_tag'] = 'sell_stoploss_deadfish'

        return dataframe