Timeframe
4h
Direction
Long Only
Stoploss
-6.0%
Trailing Stop
Yes
ROI
0m: 10.0%, 480m: 5.0%, 1440m: 2.0%
Interface Version
3
Startup Candles
100
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATÉGIE : ElderRayTrend
# CATÉGORIE : Trend Following — Elder Ray Index
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# Elder Ray Index mesure la force des bulls et bears par rapport
# à une EMA de référence.
# Bull Power = High - EMA(close, period)
# Bear Power = Low - EMA(close, period)
#
# CONDITIONS D'ENTRÉE (toutes requises) :
# 1. Close > EMA slow (tendance haussière confirmée)
# 2. EMA period en hausse (ema > ema.shift(1))
# 3. Bear Power < 0 mais en hausse (bears faiblissent)
# 4. Bull Power > 0 (bulls en contrôle)
# 5. RSI entre rsi_min et rsi_max
# 6. Volume > volume_mult * volume SMA
#
# CONDITIONS DE SORTIE :
# - Bull Power < 0 (bulls perdent le contrôle)
# - OU Bear Power en baisse (bears se renforcent)
# - OU RSI > rsi_exit
# ══════════════════════════════════════════════════════════════
import sys
from pathlib import Path
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 ElderRayTrend(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "4h"
startup_candle_count = 100
minimal_roi = {"0": 0.10, "480": 0.05, "1440": 0.02}
stoploss = -0.06
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.04
trailing_only_offset_is_reached = True
# ── Buy params ──
ema_period = IntParameter(10, 30, default=13, space="buy")
ema_slow = IntParameter(30, 80, default=50, space="buy")
rsi_period = IntParameter(7, 21, default=14, space="buy")
rsi_min = IntParameter(30, 50, default=35, space="buy")
rsi_max = IntParameter(60, 80, default=70, space="buy")
volume_period = IntParameter(10, 50, default=20, space="buy")
volume_mult = DecimalParameter(0.8, 3.0, default=1.0, decimals=1, space="buy")
# ── Sell params ──
rsi_exit = IntParameter(65, 85, default=75, space="sell")
_logger = None
_notifier = None
def _init_utils(self) -> None:
if self._logger is None:
self._logger = TradeLogger(strategy_name="ElderRayTrend")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
# Pre-calculer EMA pour TOUTES les valeurs possibles (hyperopt-safe)
all_ema_periods = set(
list(range(self.ema_period.low, self.ema_period.high + 1))
+ list(range(self.ema_slow.low, self.ema_slow.high + 1))
)
for p in all_ema_periods:
dataframe = CommonIndicators.add_ema(dataframe, period=p)
# Bull Power et Bear Power pour chaque ema_period possible
for p in range(self.ema_period.low, self.ema_period.high + 1):
dataframe[f"bull_power_{p}"] = dataframe["high"] - dataframe[f"ema_{p}"]
dataframe[f"bear_power_{p}"] = dataframe["low"] - dataframe[f"ema_{p}"]
# 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)
# 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)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
ema_p = self.ema_period.value
ema_col = f"ema_{ema_p}"
ema_s = f"ema_{self.ema_slow.value}"
bull_col = f"bull_power_{ema_p}"
bear_col = f"bear_power_{ema_p}"
rsi_col = f"rsi_{self.rsi_period.value}"
vol_col = f"volume_ratio_{self.volume_period.value}"
conditions = (
# 1. Uptrend : close > EMA slow
(dataframe["close"] > dataframe[ema_s])
# 2. EMA period en hausse
& (dataframe[ema_col] > dataframe[ema_col].shift(1))
# 3. Bear Power < 0 mais en hausse (bears faiblissent)
& (dataframe[bear_col] < 0)
& (dataframe[bear_col] > dataframe[bear_col].shift(1))
# 4. Bull Power > 0 (bulls en contrôle)
& (dataframe[bull_col] > 0)
# 5. RSI dans la zone
& (dataframe[rsi_col] > self.rsi_min.value)
& (dataframe[rsi_col] < self.rsi_max.value)
# 6. Volume suffisant
& (dataframe[vol_col] > self.volume_mult.value)
& (dataframe["volume"] > 0)
)
dataframe.loc[conditions, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
ema_p = self.ema_period.value
bull_col = f"bull_power_{ema_p}"
bear_col = f"bear_power_{ema_p}"
rsi_col = f"rsi_{self.rsi_period.value}"
conditions = (
# Bull Power < 0 (bulls perdent le contrôle)
(dataframe[bull_col] < 0)
# OU Bear Power en baisse (bears se renforcent)
| (dataframe[bear_col] < dataframe[bear_col].shift(1))
# OU RSI > seuil exit
| (dataframe[rsi_col] > self.rsi_exit.value)
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe