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NotAnotherSMAOffsetStrategyHOv3_b

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webclinic017/test-1/NotAnotherSMAOffsetStrategyHOv3_b_converted.py · ★3 · ⑂6 · first seen 2026-07-28 · repo updated 2021-09-05

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.04 has minimal roi trailing process only new candles startup candle count: 200 hyperopt hyperopt params: 10
Indicators EMA HMA RSI SMA talib technical
Concepts trailing
Methods EWO confirm_trade_exit
15 related strategies (⧉ identical code, ≈ similar name)

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# --- Do not remove these libs ---
# --- Do not remove these libs ---
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
# @Rallipanos
# # Buy hyperspace params:
# buy_params = {
#     "base_nb_candles_buy": 14,
#     "ewo_high": 2.327,
#     "ewo_high_2": -2.327,
#     "ewo_low": -20.988,
#     "low_offset": 0.975,
#     "low_offset_2": 0.955,
#     "rsi_buy": 69
# }
# # Buy hyperspace params:
# buy_params = {
#     "base_nb_candles_buy": 18,
#     "ewo_high": 3.422,
#     "ewo_high_2": -3.436,
#     "ewo_low": -8.562,
#     "low_offset": 0.966,
#     "low_offset_2": 0.959,
#     "rsi_buy": 66,
# }
# # # Sell hyperspace params:
# # sell_params = {
# #     "base_nb_candles_sell": 17,
# #     "high_offset": 0.997,
# #     "high_offset_2": 1.01,
# # }
# # Sell hyperspace params:
# sell_params = {
#     "base_nb_candles_sell": 7,
#     "high_offset": 1.014,
#     "high_offset_2": 0.995,
# }
# # Buy hyperspace params:
# buy_params = {
#     "ewo_high_2": -5.642,
#     "low_offset_2": 0.951,
#     "rsi_buy": 54,
#     "base_nb_candles_buy": 16,  # value loaded from strategy
#     "ewo_high": 3.422,  # value loaded from strategy
#     "ewo_low": -8.562,  # value loaded from strategy
#     "low_offset": 0.966,  # value loaded from strategy
# }
# # Sell hyperspace params:
# sell_params = {
#     "base_nb_candles_sell": 8,
#     "high_offset_2": 1.002,
#     "high_offset": 1.014,  # value loaded from strategy
# }
# Buy hyperspace params:
# value loaded from strategy
# value loaded from strategy
# value loaded from strategy
# value loaded from strategy
buy_params = {'base_nb_candles_buy': 8, 'ewo_high': 4.179, 'ewo_low': -16.917, 'ewo_high_2': -2.609, 'low_offset': 0.986, 'low_offset_2': 0.944, 'rsi_buy': 58}
# Sell hyperspace params:
# value loaded from strategy
sell_params = {'base_nb_candles_sell': 16, 'high_offset': 1.054, 'high_offset_2': 1.018}

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 NotAnotherSMAOffsetStrategyHOv3_b(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    # "0": 0.283,
    # "40": 0.086,
    # "99": 0.036,
    minimal_roi = {'0': 10}
    # Stoploss:
    stoploss = -0.04
    # SMAOffset
    base_nb_candles_buy = IntParameter(2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(10, 40, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False)
    low_offset_2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False)
    high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False)
    high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False)
    # Protection
    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False)
    ewo_high_2 = DecimalParameter(-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.018
    trailing_only_offset_is_reached = True
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.001
    ignore_roi_if_entry_signal = False
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'ioc'}
    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'
    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = False
    plot_config = {'main_plot': {'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}}}
    slippage_protection = {'retries': 3, 'max_slippage': -0.02}
    buy_signals = {}

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        if last_candle is not None:
            if exit_reason in ['exit_signal']:
                if last_candle['hma_50'] * 1.149 > last_candle['ema_100'] and last_candle['close'] < last_candle['ema_100'] * 0.951:  # *1.2
                    return False
        # slippage
        try:
            state = self.slippage_protection['__pair_retries']
        except KeyError:
            state = self.slippage_protection['__pair_retries'] = {}
        candle = dataframe.iloc[-1].squeeze()
        slippage = rate / candle['close'] - 1
        if slippage < self.slippage_protection['max_slippage']:
            pair_retries = state.get(pair, 0)
            if pair_retries < self.slippage_protection['retries']:
                state[pair] = pair_retries + 1
                return False
        state[pair] = 0
        return True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (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) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewo1')
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['rsi'] < 25), ['enter_long', 'enter_tag']] = (1, 'ewo2')
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (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) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewolow')
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe