✨ AI Search 🔒
💬 Forum

NASOSv7LongShort

🏆 League #44 / 1911
♡

remiotore/ccxt-freqtrade/strategies/NASOSv7_Futures.py · ★3 · ⑂2 · first seen 2026-07-16 · repo updated 2026-01-11 · ⬇ 1 download

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.15 has minimal roi trailing dca custom stoploss process only new candles startup candle count: 200 hyperopt hyperopt params: 17
Indicators EMA HMA RSI SMA talib technical
Concepts dca 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
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
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


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 NASOSv7LongShort(IStrategy):
    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 10
    }

    # Adjust stoploss for both long and short
    stoploss = -0.15

    # Parameters for both long and short entries
    base_nb_candles_buy = IntParameter(2, 20, default=8, space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(2, 25, default=16, space='sell', optimize=True)
    
    # Long buy parameters
    long_low_offset = DecimalParameter(0.9, 0.99, default=0.984, space='buy', optimize=False)
    long_low_offset_2 = DecimalParameter(0.9, 0.99, default=0.942, space='buy', optimize=False)
    
    # Short sell parameters
    short_high_offset = DecimalParameter(0.95, 1.1, default=1.084, space='sell', optimize=True)
    short_high_offset_2 = DecimalParameter(0.99, 1.5, default=1.401, space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200

    lookback_candles = IntParameter(1, 24, default=3, space='buy', optimize=True)

    profit_threshold = DecimalParameter(1.0, 1.03, default=1.008, space='buy', optimize=True)

    # EWO parameters for both long and short
    ewo_low = DecimalParameter(-20.0, -8.0, default=-14.378, space='buy', optimize=False)
    ewo_high = DecimalParameter(2.0, 12.0, default=2.403, space='buy', optimize=False)
    ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-5.585, space='buy', optimize=False)

    rsi_buy = IntParameter(50, 100, default=72, space='buy', optimize=False)

    # Custom stoploss parameters
    pHSL = DecimalParameter(-0.200, -0.040, default=-0.15, decimals=3,
                            space='sell', optimize=False, load=True)

    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3,
                             space='sell', optimize=False, load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.014, decimals=3,
                             space='sell', optimize=False, load=True)

    pPF_2 = DecimalParameter(0.040, 0.100, default=0.024, decimals=3,
                             space='sell', optimize=False, load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.022, decimals=3,
                             space='sell', optimize=False, load=True)

    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.016
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    timeframe = '5m'
    inf_1h = '1h'

    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = False

    # Enable both long and short trading
    position_adjustment_enable = True
    max_entry_position_adjustment = 0
    max_dist_to_main_market_price_pct = 0.002

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    slippage_protection = {
        'retries': 3,
        'max_slippage': -0.02
    }

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        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

        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

        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, sell_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):
            # Additional exit confirmation for both longs and shorts
            if (sell_reason in ['sell_signal']):
                if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951):
                    return False

        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."
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        return informative_1h

    def normal_tf_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['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)

        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        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)

        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dont_buy_conditions = []

        dont_buy_conditions.append(
            (
                (dataframe['close_1h'].rolling(self.lookback_candles.value).max()
                 < (dataframe['close'] * self.profit_threshold.value))
            )
        )

        # Long Entry Conditions (unchanged from original)
        dataframe.loc[
            (
                (dataframe['rsi_fast'] < 35) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_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.short_high_offset.value))
            ),
            ['enter_long', 'buy_tag']] = (1, 'ewo1')

        # Short Entry Conditions (new)
        dataframe.loc[
            (
                (dataframe['rsi_fast'] > 65) &
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_high_offset.value)) &
                (dataframe['EWO'] < self.ewo_low.value) &
                (dataframe['rsi'] > (100 - self.rsi_buy.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['close'] > (
                    dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset.value))
            ),
            ['enter_short', 'sell_tag']] = (1, 'short_ewo')

        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'enter_long'] = 0
                dataframe.loc[condition, 'enter_short'] = 0

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Long Exit Conditions
        long_conditions = [
            ((dataframe['close'] > dataframe['sma_9']) &
             (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.short_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.short_high_offset.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['rsi_fast'] > dataframe['rsi_slow'])
            )
        ]

        # Short Exit Conditions
        short_conditions = [
            ((dataframe['close'] < dataframe['sma_9']) &
             (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset.value)) &
             (dataframe['rsi'] < 50) &
             (dataframe['volume'] > 0) &
             (dataframe['rsi_fast'] < dataframe['rsi_slow'])
            ) |
            (
                (dataframe['close'] > dataframe['hma_50']) &
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.long_low_offset_2.value)) &
                (dataframe['volume'] > 0) &
                (dataframe['rsi_fast'] < dataframe['rsi_slow'])
            )
        ]

        # Set exit signals
        if long_conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, long_conditions),
                'exit_long'
            ] = 1

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

        return dataframe