Timeframe
4h
Direction
Long Only
Stoploss
-8.0%
Trailing Stop
Yes
ROI
0m: 15.0%, 240m: 8.0%, 720m: 4.0%, 1440m: 2.0%
Interface Version
3
Startup Candles
80
Indicators
2
freqtrade/freqtrade-strategies
This strategy uses custom_stoploss() to enforce a fixed risk/reward ratio by first calculating a dynamic initial stoploss via ATR - last negative peak
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATEGIE : ADOSCTrailing
# CATEGORIE : Trend+Volume — ADOSC + ATR Trailing Stop
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# L'ADOSC (Accumulation/Distribution Oscillator) mesure la pression
# acheteuse/vendeuse via le flux de volume. Quand il est positif ET
# croissant, les institutionnels accumulent.
# 1. ADOSC positif ET croissant + prix > EMA(50) → long
# 2. Sortie : ATR trailing stop (multiplicateur configurable)
# SOURCE : PyQuantLab — +65.8% annualise sur ETH
# ══════════════════════════════════════════════════════════════
import sys
from pathlib import Path
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
class ADOSCTrailing(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "4h"
startup_candle_count = 80
minimal_roi = {"0": 0.15, "240": 0.08, "720": 0.04, "1440": 0.02}
stoploss = -0.08
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.04
trailing_only_offset_is_reached = True
# ── Buy params ──
adosc_fast = IntParameter(2, 5, default=3, space="buy")
adosc_slow = IntParameter(8, 15, default=10, space="buy")
ema_period = IntParameter(30, 80, default=50, space="buy")
adosc_lookback = IntParameter(1, 5, default=1, space="buy")
# ── Sell params ──
atr_period = IntParameter(10, 25, default=14, space="sell")
atr_mult = IntParameter(25, 50, default=35, space="sell") # /10 → 2.5 a 5.0
_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="ADOSCTrailing")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
# EMA pour toutes les valeurs
for ema_p in range(self.ema_period.low, self.ema_period.high + 1):
dataframe = CommonIndicators.add_ema(dataframe, period=ema_p)
# ATR pour toutes les valeurs
for atr_p in range(self.atr_period.low, self.atr_period.high + 1):
dataframe = CommonIndicators.add_atr(dataframe, period=atr_p)
# ADOSC pour toutes les combinaisons fast/slow
for fast in range(self.adosc_fast.low, self.adosc_fast.high + 1):
for slow in range(self.adosc_slow.low, self.adosc_slow.high + 1):
dataframe = CommonIndicators.add_adosc(dataframe, fast=fast, slow=slow)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
adosc_col = f"adosc_{self.adosc_fast.value}_{self.adosc_slow.value}"
ema_col = f"ema_{self.ema_period.value}"
lb = self.adosc_lookback.value
conditions = (
(dataframe[adosc_col] > 0)
& (dataframe[adosc_col] > dataframe[adosc_col].shift(lb))
& (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:
adosc_col = f"adosc_{self.adosc_fast.value}_{self.adosc_slow.value}"
# Sortie quand ADOSC devient negatif
conditions = (
dataframe[adosc_col] < 0
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe