💬 Forum

KivancSupertrendedMovingAverages1D

🏆 League #419 / 1941
♡

EmreUludasdemir/Buy-Sell-Algorithm-for-all-exchange-/freqtrade/user_data/strategies/KivancSupertrendedMovingAverages1D.py · ★1 · first seen 2026-07-16 · repo updated 2026-07-09 · ⬇ 1 download

Basics mode: spot timeframe: 1d interface version: 3
Settings stoploss: -0.2 has minimal roi process only new candles startup candle count: 400 hyperopt hyperopt params: 12
Indicators ADX ATR EMA Linear_Regression SMA WMA talib
Methods _get_ma _supertrended_ma _t3 _tsf _var_ma _wma _wwma _zlema
1 related strategy (⧉ 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

🔒 Log in to copy 🔒 Log in to 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
286
287
288
import math
from typing import Tuple

import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame, Series

from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter, IStrategy


class KivancSupertrendedMovingAverages1D(IStrategy):
    """
    Single production strategy for this repository.

    The system ports Kivanc Ozbilgic's SuperTrended Moving Averages logic into
    a long-horizon Freqtrade strategy for daily crypto data.

    The class keeps the canonical entry logic and the production 1D defaults.
    Alternate validated risk packs live under user_data/profiles and are
    applied temporarily by the research scripts during 4H validation runs.
    Short-side signals are calculated for research, but spot production remains
    long-only until a futures configuration is introduced.
    """

    INTERFACE_VERSION = 3
    timeframe = "1d"
    can_short = False

    # Risk layer defaults for the production 1D profile.
    minimal_roi = {"0": 1.0}
    stoploss = -0.20
    trailing_stop = False

    use_exit_signal = True
    exit_profit_only = False
    process_only_new_candles = True
    startup_candle_count = 400
    plot_config = {
        "main_plot": {
            "st_ma": {"color": "orange"},
            "st_up": {"color": "green"},
            "st_dn": {"color": "red"},
            "regime_ema": {"color": "blue"},
        },
        "subplots": {
            "Signals": {
                "buy_signal": {"color": "green"},
                "sell_signal": {"color": "red"},
                "st_trend": {"color": "blue"},
                "research_short_signal": {"color": "purple"},
            },
            "Trend": {
                "adx": {"color": "black"},
                "regime_slope_pct": {"color": "brown"},
            }
        },
    }

    ma_type = CategoricalParameter(
        ["SMA", "EMA", "WMA", "DEMA", "TMA", "VAR", "WWMA", "ZLEMA", "TSF", "HULL", "TILL"],
        default="EMA",
        space="buy",
        optimize=True,
    )
    ma_length = IntParameter(20, 300, default=100, space="buy", optimize=True)
    atr_period = IntParameter(7, 30, default=10, space="buy", optimize=True)
    atr_multiplier = DecimalParameter(0.1, 5.0, default=0.5, decimals=1, space="buy", optimize=True)
    use_builtin_atr = CategoricalParameter([True, False], default=True, space="buy", optimize=True)
    t3_volume_factor = DecimalParameter(0.1, 1.0, default=0.7, decimals=1, space="buy", optimize=True)
    use_regime_filter = CategoricalParameter([True, False], default=False, space="buy", optimize=True)
    regime_ema_length = IntParameter(100, 300, default=200, space="buy", optimize=True)
    regime_slope_lookback = IntParameter(3, 30, default=10, space="buy", optimize=True)
    regime_slope_min = DecimalParameter(0.0, 0.1, default=0.0, decimals=3, space="buy", optimize=True)
    use_adx_filter = CategoricalParameter([True, False], default=False, space="buy", optimize=True)
    adx_threshold = IntParameter(10, 40, default=20, space="buy", optimize=True)

    @staticmethod
    def _wma(src: Series, length: int) -> Series:
        period = max(1, int(length))
        return pd.Series(ta.WMA(src, timeperiod=period), index=src.index)

    @staticmethod
    def _wwma(src: Series, length: int) -> Series:
        period = max(1, int(length))
        alpha = 1.0 / period
        out = np.zeros(len(src), dtype=float)
        out[0] = float(src.iloc[0])
        for i in range(1, len(src)):
            value = float(src.iloc[i])
            out[i] = alpha * value + (1 - alpha) * out[i - 1]
        return pd.Series(out, index=src.index)

    @staticmethod
    def _var_ma(src: Series, length: int) -> Series:
        period = max(1, int(length))
        valpha = 2.0 / (period + 1.0)

        diff = src.diff()
        vud1 = diff.where(diff > 0, 0.0)
        vdd1 = (-diff).where(diff < 0, 0.0)
        vud = vud1.rolling(window=9, min_periods=1).sum()
        vdd = vdd1.rolling(window=9, min_periods=1).sum()

        denom = (vud + vdd).replace(0, np.nan)
        vcmo = ((vud - vdd) / denom).fillna(0.0)

        out = np.zeros(len(src), dtype=float)
        out[0] = float(src.iloc[0])
        for i in range(1, len(src)):
            k = valpha * abs(float(vcmo.iloc[i]))
            out[i] = k * float(src.iloc[i]) + (1.0 - k) * out[i - 1]
        return pd.Series(out, index=src.index)

    @staticmethod
    def _zlema(src: Series, length: int) -> Series:
        period = max(1, int(length))
        lag = period // 2 if period % 2 == 0 else (period - 1) // 2
        ema_data = src + src - src.shift(lag)
        return pd.Series(ta.EMA(ema_data, timeperiod=period), index=src.index)

    @staticmethod
    def _tsf(src: Series, length: int) -> Series:
        period = max(2, int(length))
        lrc = pd.Series(ta.LINEARREG(src, timeperiod=period), index=src.index)
        lrc1 = lrc.shift(1)
        lrs = lrc - lrc1
        return lrc + lrs

    @staticmethod
    def _t3(src: Series, length: int, volume_factor: float) -> Series:
        period = max(1, int(length))
        b = float(max(0.0, min(1.0, volume_factor)))
        b2 = b * b
        b3 = b2 * b

        e1 = pd.Series(ta.EMA(src, timeperiod=period), index=src.index)
        e2 = pd.Series(ta.EMA(e1, timeperiod=period), index=src.index)
        e3 = pd.Series(ta.EMA(e2, timeperiod=period), index=src.index)
        e4 = pd.Series(ta.EMA(e3, timeperiod=period), index=src.index)
        e5 = pd.Series(ta.EMA(e4, timeperiod=period), index=src.index)
        e6 = pd.Series(ta.EMA(e5, timeperiod=period), index=src.index)

        c1 = -b3
        c2 = 3 * b2 + 3 * b3
        c3 = -6 * b2 - 3 * b - 3 * b3
        c4 = 1 + 3 * b + b3 + 3 * b2
        return c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3

    def _get_ma(self, src: Series) -> Series:
        length = int(self.ma_length.value)
        ma_type = str(self.ma_type.value)

        ema = pd.Series(ta.EMA(src, timeperiod=length), index=src.index)
        dema = 2 * ema - pd.Series(ta.EMA(ema, timeperiod=length), index=src.index)

        if ma_type == "SMA":
            return pd.Series(ta.SMA(src, timeperiod=length), index=src.index)
        if ma_type == "EMA":
            return ema
        if ma_type == "WMA":
            return self._wma(src, length)
        if ma_type == "DEMA":
            return dema
        if ma_type == "TMA":
            return pd.Series(
                ta.SMA(
                    pd.Series(ta.SMA(src, timeperiod=math.ceil(length / 2.0)), index=src.index),
                    timeperiod=math.floor(length / 2.0) + 1,
                ),
                index=src.index,
            )
        if ma_type == "VAR":
            return self._var_ma(src, length)
        if ma_type == "WWMA":
            return self._wwma(src, length)
        if ma_type == "ZLEMA":
            return self._zlema(src, length)
        if ma_type == "TSF":
            return self._tsf(src, length)
        if ma_type == "HULL":
            half_len = max(1, int(length / 2))
            sqrt_len = max(1, int(round(math.sqrt(length))))
            hma_input = 2 * self._wma(src, half_len) - self._wma(src, length)
            return self._wma(hma_input, sqrt_len)
        if ma_type == "TILL":
            return self._t3(src, length, float(self.t3_volume_factor.value))
        return ema

    def _supertrended_ma(self, dataframe: DataFrame, ma: Series) -> Tuple[Series, Series, Series, Series, Series]:
        close = dataframe["close"]
        period = int(self.atr_period.value)
        multiplier = float(self.atr_multiplier.value)

        tr = pd.Series(ta.TRANGE(dataframe), index=dataframe.index)
        atr_sma = tr.rolling(window=period, min_periods=1).mean()
        atr_builtin = pd.Series(ta.ATR(dataframe, timeperiod=period), index=dataframe.index)
        atr = atr_builtin if bool(self.use_builtin_atr.value) else atr_sma
        atr = atr.fillna(atr_sma).bfill()

        up = (ma - multiplier * atr).astype(float).copy()
        dn = (ma + multiplier * atr).astype(float).copy()

        trend = np.ones(len(dataframe), dtype=int)

        for i in range(1, len(dataframe)):
            prev_up = float(up.iloc[i - 1])
            prev_dn = float(dn.iloc[i - 1])
            prev_close = float(close.iloc[i - 1])

            if prev_close > prev_up:
                up.iloc[i] = max(float(up.iloc[i]), prev_up)
            if prev_close < prev_dn:
                dn.iloc[i] = min(float(dn.iloc[i]), prev_dn)

            if trend[i - 1] == -1 and float(close.iloc[i]) > prev_dn:
                trend[i] = 1
            elif trend[i - 1] == 1 and float(close.iloc[i]) < prev_up:
                trend[i] = -1
            else:
                trend[i] = trend[i - 1]

        trend_s = pd.Series(trend, index=dataframe.index)
        buy_signal = (trend_s == 1) & (trend_s.shift(1) == -1)
        sell_signal = (trend_s == -1) & (trend_s.shift(1) == 1)
        return trend_s, up, dn, buy_signal, sell_signal

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ma = self._get_ma(dataframe["close"])
        trend, up, dn, buy_signal, sell_signal = self._supertrended_ma(dataframe, ma)
        regime_ema = pd.Series(
            ta.EMA(dataframe["close"], timeperiod=int(self.regime_ema_length.value)),
            index=dataframe.index,
        )
        regime_slope = regime_ema.pct_change(int(self.regime_slope_lookback.value)).fillna(0.0)
        adx = pd.Series(ta.ADX(dataframe, timeperiod=14), index=dataframe.index).fillna(0.0)
        slope_min = float(self.regime_slope_min.value)
        bull_regime = (dataframe["close"] > regime_ema) & (regime_slope > slope_min)
        bear_regime = (dataframe["close"] < regime_ema) & (regime_slope < -slope_min)
        trend_strength_ok = adx > int(self.adx_threshold.value)

        dataframe["st_ma"] = ma
        dataframe["st_up"] = up
        dataframe["st_dn"] = dn
        dataframe["st_trend"] = trend
        dataframe["regime_ema"] = regime_ema
        dataframe["regime_slope_pct"] = regime_slope * 100.0
        dataframe["adx"] = adx
        dataframe["bull_regime"] = bull_regime.astype(int)
        dataframe["bear_regime"] = bear_regime.astype(int)
        dataframe["trend_strength_ok"] = trend_strength_ok.astype(int)
        dataframe["buy_signal"] = buy_signal.astype(int)
        dataframe["sell_signal"] = sell_signal.astype(int)
        dataframe["research_short_signal"] = 0
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        condition = (dataframe["buy_signal"] == 1) & (dataframe["volume"] > 0)
        if bool(self.use_regime_filter.value):
            condition &= dataframe["bull_regime"] == 1
        if bool(self.use_adx_filter.value):
            condition &= dataframe["trend_strength_ok"] == 1

        dataframe.loc[condition, "enter_long"] = 1
        dataframe.loc[condition, "enter_tag"] = "ST_MA_BUY"

        short_condition = (dataframe["sell_signal"] == 1) & (dataframe["volume"] > 0)
        if bool(self.use_regime_filter.value):
            short_condition &= dataframe["bear_regime"] == 1
        if bool(self.use_adx_filter.value):
            short_condition &= dataframe["trend_strength_ok"] == 1
        dataframe.loc[short_condition, "research_short_signal"] = 1

        if self.can_short:
            dataframe.loc[short_condition, "enter_short"] = 1
            dataframe.loc[short_condition, "enter_tag"] = "ST_MA_SHORT"
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        condition = (dataframe["sell_signal"] == 1) & (dataframe["volume"] > 0)
        dataframe.loc[condition, "exit_long"] = 1
        dataframe.loc[condition, "exit_tag"] = "ST_MA_SELL"

        if self.can_short:
            short_exit = (dataframe["buy_signal"] == 1) & (dataframe["volume"] > 0)
            dataframe.loc[short_exit, "exit_short"] = 1
            dataframe.loc[short_exit, "exit_tag"] = "ST_MA_COVER"
        return dataframe