Timeframe
4h
Direction
Long Only
Stoploss
-6.0%
Trailing Stop
Yes
ROI
0m: 15.0%, 720m: 8.0%, 1440m: 4.0%, 2880m: 2.0%
Interface Version
3
Startup Candles
100
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATEGIE : FibonacciPullback
# CATEGORIE : Retracement / Fibonacci
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# Retracement de Fibonacci — acheter quand le prix retrace aux
# niveaux cles 38.2% ou 61.8% d'un swing recent.
# Ces niveaux agissent comme support naturel dans une tendance
# haussiere et offrent des entrees a probabilite elevee.
#
# ENTREE :
# 1. Close proche du niveau fib 38.2% ou 61.8% (tolerance configurable)
# 2. Close > fib_level (rebond confirme, pas juste un touch)
# 3. RSI < seuil (pas en surachat)
#
# SORTIE :
# Close >= swing_high * 0.98 (retour au sommet) OU RSI > seuil
# ══════════════════════════════════════════════════════════════
import sys
from pathlib import Path
import numpy as np
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
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 FibonacciPullback(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "4h"
startup_candle_count = 100
minimal_roi = {"0": 0.15, "720": 0.08, "1440": 0.04, "2880": 0.02}
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 ──
swing_period = IntParameter(10, 40, default=20, space="buy")
fib_tolerance = DecimalParameter(0.005, 0.02, default=0.01, decimals=3, space="buy")
rsi_period = IntParameter(7, 21, default=14, space="buy")
rsi_entry = IntParameter(40, 65, default=55, space="buy")
volume_period = IntParameter(10, 50, default=20, space="buy")
# ── Sell params ──
rsi_exit = IntParameter(60, 80, default=70, space="sell")
# ── ADX trend filter ──
adx_period = IntParameter(10, 20, default=14, space="buy")
adx_threshold = IntParameter(15, 30, default=20, space="buy")
_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="FibonacciPullback")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
# Pre-calculer swing high/low pour TOUTES les valeurs de swing_period
for sp in range(self.swing_period.low, self.swing_period.high + 1):
dataframe[f"swing_high_{sp}"] = dataframe["high"].rolling(window=sp).max()
dataframe[f"swing_low_{sp}"] = dataframe["low"].rolling(window=sp).min()
swing_range = dataframe[f"swing_high_{sp}"] - dataframe[f"swing_low_{sp}"]
dataframe[f"fib_382_{sp}"] = dataframe[f"swing_low_{sp}"] + 0.382 * swing_range
dataframe[f"fib_618_{sp}"] = dataframe[f"swing_low_{sp}"] + 0.618 * swing_range
# Pre-calculer RSI pour TOUTES les valeurs possibles
for rsi_p in range(self.rsi_period.low, self.rsi_period.high + 1):
dataframe = CommonIndicators.add_rsi(dataframe, period=rsi_p)
# Pre-calculer volume SMA pour TOUTES les valeurs possibles
for vol_p in range(self.volume_period.low, self.volume_period.high + 1):
dataframe = CommonIndicators.add_volume_sma(dataframe, period=vol_p)
# Pre-calculer ADX pour filtre tendance
for p in range(self.adx_period.low, self.adx_period.high + 1):
dataframe = CommonIndicators.add_adx(dataframe, period=p)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
sp = self.swing_period.value
rsi_col = f"rsi_{self.rsi_period.value}"
fib_382 = dataframe[f"fib_382_{sp}"]
fib_618 = dataframe[f"fib_618_{sp}"]
tol = self.fib_tolerance.value
# Proche du niveau fib 38.2%
near_382 = (
(np.abs(dataframe["close"] - fib_382) / fib_382 < tol)
& (dataframe["close"] > fib_382)
)
# Proche du niveau fib 61.8%
near_618 = (
(np.abs(dataframe["close"] - fib_618) / fib_618 < tol)
& (dataframe["close"] > fib_618)
)
adx_col = f"adx_{self.adx_period.value}"
conditions = (
(near_382 | near_618)
& (dataframe[rsi_col] < self.rsi_entry.value)
& (dataframe[adx_col] > self.adx_threshold.value)
& (dataframe["volume"] > 0)
)
dataframe.loc[conditions, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
sp = self.swing_period.value
rsi_col = f"rsi_{self.rsi_period.value}"
conditions = (
# Prix revient au swing high
(dataframe["close"] >= dataframe[f"swing_high_{sp}"] * 0.98)
# OU RSI en surachat
| (dataframe[rsi_col] > self.rsi_exit.value)
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe