Timeframe
4h
Direction
Long Only
Stoploss
-6.0%
Trailing Stop
Yes
ROI
0m: 10.0%, 240m: 5.0%, 720m: 3.0%, 1440m: 1.0%
Interface Version
3
Startup Candles
100
Indicators
4
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATEGIE : SuperTrendADX
# CATEGORIE : Trend Following Dynamique (ATR-adaptatif)
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# SuperTrend = bandes dynamiques basees sur ATR (plus reactif que EMA)
# 1. SuperTrend en mode haussier (prix au-dessus de la bande)
# 2. ADX > seuil → tendance forte confirmee
# 3. Close > EMA longue → filtre directionnel
# 4. Sortie : SuperTrend passe baissier OU ADX faiblit
# ══════════════════════════════════════════════════════════════
import sys
from pathlib import Path
import numpy as np
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter
sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
from utils.indicators import CommonIndicators
from utils.logging_utils import TradeLogger
from utils.telegram_notifier import TelegramNotifier
def _calc_supertrend(close, high, low, atr, mult):
"""Calcul SuperTrend vectorise avec boucle optimisee."""
n = len(close)
hl2 = (high + low) / 2
upper_basic = hl2 + mult * atr
lower_basic = hl2 - mult * atr
st_upper = np.full(n, np.nan)
st_lower = np.full(n, np.nan)
direction = np.ones(n, dtype=int)
ub = upper_basic
lb = lower_basic
st_lower[0] = lb[0]
st_upper[0] = ub[0]
for i in range(1, n):
st_lower[i] = lb[i] if (lb[i] > st_lower[i - 1] or close[i - 1] < st_lower[i - 1]) else st_lower[i - 1]
st_upper[i] = ub[i] if (ub[i] < st_upper[i - 1] or close[i - 1] > st_upper[i - 1]) else st_upper[i - 1]
if direction[i - 1] == 1:
direction[i] = -1 if close[i] < st_lower[i] else 1
else:
direction[i] = 1 if close[i] > st_upper[i] else -1
return direction
class SuperTrendADX(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "4h"
startup_candle_count = 100
minimal_roi = {"0": 0.10, "240": 0.05, "720": 0.03, "1440": 0.01}
stoploss = -0.06
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.03
trailing_only_offset_is_reached = True
# ── Buy params ──
atr_period = IntParameter(7, 20, default=10, space="buy")
atr_mult_x10 = IntParameter(15, 35, default=30, space="buy") # /10 → 1.5-3.5
adx_period = IntParameter(10, 25, default=14, space="buy")
adx_threshold = IntParameter(15, 35, default=25, space="buy")
ema_period = IntParameter(100, 250, default=200, space="buy")
# ── Sell params ──
adx_exit = IntParameter(10, 25, default=20, space="sell")
_logger = None
_notifier = None
def __getstate__(self):
state = self.__dict__.copy()
state["_logger"] = None
state["_notifier"] = None
return state
def __setstate__(self, state):
self.__dict__.update(state)
def _init_utils(self) -> None:
if self._logger is None:
self._logger = TradeLogger(strategy_name="SuperTrendADX")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
close = dataframe["close"].values
high = dataframe["high"].values
low = dataframe["low"].values
# Pre-calc ATR pour toutes les valeurs
for p in range(self.atr_period.low, self.atr_period.high + 1):
dataframe = CommonIndicators.add_atr(dataframe, period=p)
# Pre-calc ADX pour toutes les valeurs
for p in range(self.adx_period.low, self.adx_period.high + 1):
dataframe = CommonIndicators.add_adx(dataframe, period=p)
# Pre-calc EMA pour toutes les valeurs
for p in range(self.ema_period.low, self.ema_period.high + 1):
dataframe = CommonIndicators.add_ema(dataframe, period=p)
# Pre-calc SuperTrend pour toutes les combinaisons (atr_period, atr_mult)
for atr_p in range(self.atr_period.low, self.atr_period.high + 1):
atr_vals = dataframe[f"atr_{atr_p}"].values
for mult_x10 in range(self.atr_mult_x10.low, self.atr_mult_x10.high + 1):
mult = mult_x10 / 10.0
col = f"st_dir_{atr_p}_{mult_x10}"
dataframe[col] = _calc_supertrend(close, high, low, atr_vals, mult)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
st_col = f"st_dir_{self.atr_period.value}_{self.atr_mult_x10.value}"
adx_col = f"adx_{self.adx_period.value}"
ema_col = f"ema_{self.ema_period.value}"
conditions = (
(dataframe[st_col] == 1)
& (dataframe[adx_col] > self.adx_threshold.value)
& (dataframe["close"] > dataframe[ema_col])
& (dataframe["volume"] > 0)
)
dataframe.loc[conditions, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
st_col = f"st_dir_{self.atr_period.value}_{self.atr_mult_x10.value}"
adx_col = f"adx_{self.adx_period.value}"
conditions = (
(dataframe[st_col] == -1)
| (dataframe[adx_col] < self.adx_exit.value)
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe