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DS_Liot_SMA_5m

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Danson77/Freqtrade_AIO_UI/user_data/strategies/Old strategies/DS_Liot_SMA_5m_converted.py · first seen 2026-07-28 · repo updated 2026-05-20

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.99 has minimal roi trailing process only new candles startup candle count: 300 hyperopt hyperopt params: 7
Indicators ADX Aroon Bollinger_Bands CCI EMA Heikin_Ashi Keltner MACD MFI ROC RSI SAR SMA Stoch_RSI Stochastic TEMA UO talib technical
Concepts trailing
Methods EWO get_informative_indicators
9 related strategies (⧉ identical code, ≈ similar name)

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from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
########################################################################################################################################################

def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.SMA(df, timeperiod=ema_length)
    ema2 = ta.SMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['close'] * 100
    return emadif
########################################################################################################################################################

class DS_Liot_SMA_5m(IStrategy):
    INTERFACE_VERSION = 3
    ########################################################################################################################################################
    # Hyperopt
    ########################################################################################################################################################
    #22
    #3.71
    #0.975, # 0.918,
    buy_params = {'base_nb_candles_buy': 6, 'ewo_high': 9.513, 'ewo_low': -8.077, 'low_offset': 0.93, 'rsi_buy': 58}
    sell_params = {'base_nb_candles_sell': 43, 'high_offset': 1.022}
    minimal_roi = {'0': 0.215, '40': 0.132, '87': 0.086, '201': 0.03}
    stoploss = -0.99  #-0.322
    trailing_stop = True
    trailing_stop_positive = 0.005  # Offest from profit for trailing stop.
    trailing_stop_positive_offset = 0.03  # Profit necessary to have trailing_stop_positive apply.
    trailing_only_offset_is_reached = True  # Keep stoploss static UNTIL the offset is reached then trigger trailing stop.
    use_custom_stoploss = False
    ########################################################################################################################################################
    # Main
    ########################################################################################################################################################
    timeframe = '5m'  # The primary timeframe for analysis.
    informative_timeframe = '1h'
    process_only_new_candles = True
    startup_candle_count = 300
    # Sell signal configuration.
    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 = True  # 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': True, '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.
    # Color for the buy moving average.
    # Color for the sell moving average.
    plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}}}
    ########################################################################################################################################################
    # Parameters
    ########################################################################################################################################################
    fast_ewo = 50
    slow_ewo = 200
    # Buy_params
    is_optimize_buy = False
    base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=is_optimize_buy)
    ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=is_optimize_buy)
    ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=is_optimize_buy)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=is_optimize_buy)
    low_offset = DecimalParameter(0.8, 1.0, default=buy_params['low_offset'], space='buy', optimize=True)
    # Sell_params
    is_optimize_sell = True
    base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=is_optimize_sell)
    high_offset = DecimalParameter(0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=is_optimize_sell)
    ########################################################################################################################################################

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
        aroon = ta.AROON(dataframe)
        dataframe['aroonup'] = aroon['aroonup']
        dataframe['aroondown'] = aroon['aroondown']
        dataframe['aroonosc'] = ta.AROONOSC(dataframe)
        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
        keltner = qtpylib.keltner_channel(dataframe)
        dataframe['kc_upperband'] = keltner['upper']
        dataframe['kc_lowerband'] = keltner['lower']
        dataframe['kc_middleband'] = keltner['mid']
        dataframe['kc_percent'] = (dataframe['close'] - dataframe['kc_lowerband']) / (dataframe['kc_upperband'] - dataframe['kc_lowerband'])
        dataframe['kc_width'] = (dataframe['kc_upperband'] - dataframe['kc_lowerband']) / dataframe['kc_middleband']
        dataframe['uo'] = ta.ULTOSC(dataframe)
        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
        stoch = ta.STOCH(dataframe)
        dataframe['slowd'] = stoch['slowd']
        dataframe['slowk'] = stoch['slowk']
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        stoch_rsi = ta.STOCHRSI(dataframe)
        dataframe['fastd_rsi'] = stoch_rsi['fastd']
        dataframe['fastk_rsi'] = stoch_rsi['fastk']
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['roc'] = ta.ROC(dataframe)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband'])
        dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
        dataframe['sar'] = ta.SAR(dataframe)
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
        hilbert = ta.HT_SINE(dataframe)
        dataframe['htsine'] = hilbert['sine']
        dataframe['htleadsine'] = hilbert['leadsine']
        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe)  # values [0, 100]
        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe)  # values [0, 100]
        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)  # values [0, 100]
        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe)  # values [0, -100, 100]
        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)  # values [0, -100, 100]
        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe)  # values [0, -100, 100]
        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)  # values [0, -100, 100]
        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)  # values [0, -100, 100]
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']
        return dataframe
    ############################################################################################################################################################################
    # Informative
    ############################################################################################################################################################################

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def get_informative_indicators(self, metadata: dict):
        dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        return dataframe
    ########################################################################################################################################################
    # Indicators
    ########################################################################################################################################################

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe
    ########################################################################################################################################################
    # Enter Trade
    ########################################################################################################################################################

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Condition 1: EWO above high threshold
        condition_1 = (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0)
        # Condition 2: EWO below low threshold
        condition_2 = (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0)
        # Assign signals for Condition 1
        dataframe.loc[condition_1, 'enter_long'] = 1
        dataframe.loc[condition_1, 'enter_tag'] = 'buy_condition_1'
        # Assign signals for Condition 2
        dataframe.loc[condition_2, 'enter_long'] = 1
        dataframe.loc[condition_2, 'enter_tag'] = 'buy_condition_2'
        return dataframe
    ########################################################################################################################################################
    # Exit Trade
    ########################################################################################################################################################

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0))
        if conditions:
            condition_combined = reduce(lambda x, y: x | y, conditions)
            dataframe.loc[condition_combined, 'exit_long'] = 1
            dataframe.loc[condition_combined, 'exit_tag'] = 'sell_1'
        return dataframe