✨ AI Search 🔒
💬 Forum

NASOSv4

♡

uploads/NASOSv4.py · uploaded by 🐋 Ron · first seen 2026-07-18 · ⬇ 1 download

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.15 has minimal roi trailing custom stoploss process only new candles startup candle count: 200 hyperopt hyperopt params: 17
Indicators EMA HMA RSI SMA talib technical
Concepts trailing
Methods EWO confirm_trade_exit custom_stoploss informative_1h_indicators normal_tf_indicators
15 related strategies (⧉ identical code, ≈ similar name)

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

Download Raw 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
# for live trailing_stop = False and use_custom_stoploss = True
# for backtest trailing_stop = True and use_custom_stoploss = False
# --- Do not remove these libs ---
# --- Do not remove these libs ---
from logging import FATAL
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
# @pluxury
# Buy hyperspace params:
entry_params = {'base_nb_candles_entry': 8, 'ewo_high': 2.403, 'ewo_high_2': -5.585, 'ewo_low': -14.378, 'lookback_candles': 3, 'low_offset': 0.984, 'low_offset_2': 0.942, 'profit_threshold': 1.008, 'rsi_entry': 72}
# Sell hyperspace params:
exit_params = {'base_nb_candles_exit': 16, 'high_offset': 1.084, 'high_offset_2': 1.401, 'pHSL': -0.15, 'pPF_1': 0.016, 'pPF_2': 0.024, 'pSL_1': 0.014, 'pSL_2': 0.022}

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 NASOSv4(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    # "0": 0.283,
    # "40": 0.086,
    # "99": 0.036,
    minimal_roi = {'0': 10}
    # Stoploss:
    stoploss = -0.15
    # SMAOffset
    base_nb_candles_entry = IntParameter(2, 20, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True)
    base_nb_candles_exit = IntParameter(2, 25, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True)
    low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=False)
    low_offset_2 = DecimalParameter(0.9, 0.99, default=entry_params['low_offset_2'], space='entry', optimize=False)
    high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True)
    high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True)
    # Protection
    fast_ewo = 50
    slow_ewo = 200
    lookback_candles = IntParameter(1, 24, default=entry_params['lookback_candles'], space='entry', optimize=True)
    profit_threshold = DecimalParameter(1.0, 1.03, default=entry_params['profit_threshold'], space='entry', optimize=True)
    ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=False)
    ewo_high_2 = DecimalParameter(-6.0, 12.0, default=entry_params['ewo_high_2'], space='entry', optimize=False)
    rsi_entry = IntParameter(50, 100, default=entry_params['rsi_entry'], space='entry', optimize=False)
    # trailing stoploss hyperopt parameters
    # hard stoploss profit
    pHSL = DecimalParameter(-0.2, -0.04, default=-0.15, decimals=3, space='exit', optimize=False, load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='exit', optimize=False, load=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.014, decimals=3, space='exit', optimize=False, load=True)
    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.024, decimals=3, space='exit', optimize=False, load=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.022, decimals=3, space='exit', optimize=False, load=True)
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.016
    trailing_only_offset_is_reached = True
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    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_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}}
    slippage_protection = {'retries': 3, 'max_slippage': -0.02}
    # Custom Trailing Stoploss by Perkmeister

    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 current_profit < 0.001 and current_time - timedelta(minutes=600) > trade.open_date_utc:
        #     return -0.005
        return stoploss_from_open(sl_profit, current_profit)

    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 informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        # EMA
        # informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        # informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # # RSI
        # informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        # informative_1h['bb_lowerband'] = bollinger['lower']
        # informative_1h['bb_middleband'] = bollinger['mid']
        # informative_1h['bb_upperband'] = bollinger['upper']
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Calculate all ma_entry values
        for val in self.base_nb_candles_entry.range:
            dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ma_exit values
        for val in self.base_nb_candles_exit.range:
            dataframe[f'ma_exit_{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_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dont_entry_conditions = []
        # don't entry if there isn't 3% profit to be made
        dont_entry_conditions.append(dataframe['close_1h'].rolling(self.lookback_candles.value).max() < dataframe['close'] * self.profit_threshold.value)
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewo1')
        dataframe.loc[(dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.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_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewolow')
        if dont_entry_conditions:
            for condition in dont_entry_conditions:
                dataframe.loc[condition, 'enter_long'] = 0
        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_exit_{self.base_nb_candles_exit.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_exit_{self.base_nb_candles_exit.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