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Bins

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uploads/Bins.py · uploaded by 🐋 Ron · first seen 2026-07-17

Basics mode: futures timeframe: 5m interface version: 3 1h
Settings stoploss: -0.99 has minimal roi custom stoploss process only new candles startup candle count: 240 hyperopt hyperopt params: 23
Indicators ADX EMA RSI SMA talib
Methods custom_exit custom_stoploss leverage populate_indicators_btc_1h
15 related strategies (⧉ identical code, ≈ similar name)

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from datetime import datetime
from functools import reduce
from freqtrade.persistence import Trade
from freqtrade.strategy import IntParameter, DecimalParameter, stoploss_from_open, CategoricalParameter, informative
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
# noqa
import numpy

class Bins(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n        strategy sponsored by user BinH from slack\n\n    '
    minimal_roi = {'0': 1}
    buy_params = {'buy_adx1': 62, 'buy_adx2': 29, 'buy_adx3': 33, 'buy_adx4': 88, 'buy_emarsi1': 29, 'buy_emarsi2': 30, 'buy_emarsi3': 22, 'buy_emarsi4': 57}
    sell_params = {'adx2': 21, 'emarsi1': 30, 'emarsi2': 71, 'emarsi3': 72, 'leverage_num': 3, 'sell_1': False, 'sell_2': True, 'sell_3': True, 'sell_4': True, 'sell_5': False, 'pHSL': -0.75, 'pPF_1': 0.03, 'pPF_2': 0.09, 'pSL_1': 0.028, 'pSL_2': 0.08}
    stoploss = -0.99
    timeframe = '5m'
    process_only_new_candles = True
    startup_candle_count = 240
    # default False
    use_custom_stoploss = True
    can_short = True
    order_types = {'entry': 'market', 'exit': '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}
    # buy params
    buy_optimize = True
    buy_adx1 = IntParameter(low=10, high=100, default=25, space='buy', optimize=buy_optimize)
    buy_emarsi1 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx2 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
    buy_emarsi2 = IntParameter(low=20, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx3 = IntParameter(low=10, high=100, default=35, space='buy', optimize=buy_optimize)
    buy_emarsi3 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
    buy_adx4 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
    buy_emarsi4 = IntParameter(low=20, high=100, default=25, space='buy', optimize=buy_optimize)
    # trailing stoploss
    trailing_optimize = False
    pHSL = DecimalParameter(-0.99, -0.04, default=-0.08, decimals=3, space='sell', optimize=trailing_optimize)
    pPF_1 = DecimalParameter(0.008, 0.1, default=0.016, decimals=3, space='sell', optimize=trailing_optimize)
    pSL_1 = DecimalParameter(0.008, 0.1, default=0.011, decimals=3, space='sell', optimize=trailing_optimize)
    pPF_2 = DecimalParameter(0.04, 0.2, default=0.08, decimals=3, space='sell', optimize=trailing_optimize)
    pSL_2 = DecimalParameter(0.04, 0.2, default=0.04, decimals=3, space='sell', optimize=trailing_optimize)
    # sell params
    sell_optimize = True
    adx2 = IntParameter(low=10, high=100, default=30, space='sell', optimize=sell_optimize)
    emarsi1 = IntParameter(low=10, high=100, default=75, space='sell', optimize=sell_optimize)
    emarsi2 = IntParameter(low=20, high=100, default=80, space='sell', optimize=sell_optimize)
    emarsi3 = IntParameter(low=20, high=100, default=75, space='sell', optimize=sell_optimize)
    sell2_optimize = True
    sell_1 = CategoricalParameter([True, False], default=True, space='sell', optimize=sell2_optimize)
    sell_2 = CategoricalParameter([True, False], default=True, space='sell', optimize=sell2_optimize)
    sell_3 = CategoricalParameter([True, False], default=True, space='sell', optimize=sell2_optimize)
    sell_4 = CategoricalParameter([True, False], default=True, space='sell', optimize=sell2_optimize)
    sell_5 = CategoricalParameter([True, False], default=True, space='sell', optimize=sell2_optimize)
    leverage_optimize = False
    leverage_num = IntParameter(low=1, high=20, default=1, space='sell', optimize=leverage_optimize)

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        if self.can_short:
            if -1 + (1 - sl_profit) / (1 - current_profit) <= 0:
                return 1
        elif 1 - (1 + sl_profit) / (1 + current_profit) <= 0:
            return 1
        return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short)

    @informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')
    def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=25)
        dataframe['uptrend'] = (dataframe['ema_fast'] > dataframe['ema_slow']).astype('int')
        dataframe['downtrend'] = (dataframe['ema_fast'] < dataframe['ema_slow']).astype('int')
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
        rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
        dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
        dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
        dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
        minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
        dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
        dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
        plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
        dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
        dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
        dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
        dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
        dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
        dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & (dataframe['fastsma'] - dataframe['slowsma'] > dataframe['close'] / 300)
        dataframe['bigdown'] = ~dataframe['bigup']
        dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
        dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
        dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(dataframe['trend'].shift(2))
        dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(dataframe['slowsma'].shift(2))
        dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
        dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
        dataframe['short'] = dataframe['btc_downtrend_1h'] == 1
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        buy_1 = dataframe['slowsma'].gt(0) & dataframe['short'] & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['preparechangetrend'] & ~dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx1.value) & dataframe['bigdown'] & dataframe['emarsi'].le(self.buy_emarsi1.value)
        buy_2 = dataframe['slowsma'].gt(0) & dataframe['short'] & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['preparechangetrend'] & dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx2.value) & dataframe['bigdown'] & dataframe['emarsi'].le(self.buy_emarsi2.value)
        buy_3 = dataframe['slowsma'].gt(0) & dataframe['short'] & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx3.value) & dataframe['bigup'] & dataframe['emarsi'].le(self.buy_emarsi3.value)
        buy_4 = dataframe['slowsma'].gt(0) & dataframe['short'] & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx4.value) & dataframe['bigup'] & dataframe['emarsi'].le(self.buy_emarsi4.value)
        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'
        conditions.append(buy_2)
        dataframe.loc[buy_2, 'enter_tag'] += 'buy_2'
        conditions.append(buy_3)
        dataframe.loc[buy_3, 'enter_tag'] += 'buy_3'
        conditions.append(buy_4)
        dataframe.loc[buy_4, 'enter_tag'] += 'buy_4'
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_short'] = 1
        dataframe.loc[(), ['enter_long', 'enter_tag']] = (0, 'long_in')
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'short_out')
        dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
        return dataframe

    def custom_exit(self, pair: str, trade: Trade, current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        (dataframe, _) = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        if current_profit >= self.pPF_1.value:
            return None
        if self.sell_1.value:
            if ~last_candle['preparechangetrendconfirm'] and ~last_candle['continueup'] and (last_candle['close'] > last_candle['lowsma'] or last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and last_candle['bigdown']:
                return 'sell_1'
        if self.sell_2.value:
            if ~last_candle['preparechangetrendconfirm'] and ~last_candle['continueup'] and (last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['emarsi'] > self.emarsi1.value or last_candle['close'] > last_candle['slowsma']) and last_candle['bigdown']:
                return 'sell_2'
        if self.sell_3.value:
            if ~last_candle['preparechangetrendconfirm'] and last_candle['close'] > last_candle['highsma'] and (last_candle['highsma'] > 0) and (last_candle['adx'] > self.adx2.value) and (last_candle['emarsi'] >= self.emarsi2.value) and last_candle['bigup']:
                return 'sell_3'
        if self.sell_4.value:
            if last_candle['preparechangetrendconfirm'] and ~last_candle['continueup'] and last_candle['slowingdown'] and (last_candle['emarsi'] >= self.emarsi3.value) and (last_candle['slowsma'] > 0):
                return 'sell_4'
        if self.sell_5.value:
            if last_candle['preparechangetrendconfirm'] and last_candle['minusdi'] < last_candle['plusdi'] and (last_candle['close'] > last_candle['lowsma']) and (last_candle['slowsma'] > 0):
                return 'sell_5'

    def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float:
        return self.leverage_num.value