Timeframe
1h
Direction
Long Only
Stoploss
-5.0%
Trailing Stop
Yes
ROI
0m: 10.0%, 240m: 5.0%, 720m: 2.0%
Interface Version
3
Startup Candles
100
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATÉGIE : HullMACross
# CATÉGORIE : Trend Following — Hull Moving Average Crossover
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# 1. Hull MA est plus rapide et moins en retard que l'EMA classique
# Formule : HMA(n) = WMA(2*WMA(n/2) - WMA(n), sqrt(n))
# 2. HMA fast croise au-dessus de HMA slow (signal haussier)
# 3. RSI entre rsi_min et rsi_max (filtre momentum)
# 4. Volume > multiplicateur * moyenne volume (confirmation)
# 5. Sortie : HMA fast < HMA slow OU RSI > seuil exit
# ══════════════════════════════════════════════════════════════
import sys
from pathlib import Path
import pandas_ta as ta
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 HullMACross(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "1h"
startup_candle_count = 100
minimal_roi = {"0": 0.10, "240": 0.05, "720": 0.02}
stoploss = -0.05
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.04
trailing_only_offset_is_reached = True
# ── Buy params ──
hma_fast = IntParameter(5, 20, default=9, space="buy")
hma_slow = IntParameter(20, 60, default=21, space="buy")
rsi_period = IntParameter(7, 21, default=14, space="buy")
rsi_min = IntParameter(30, 50, default=40, 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=1.2, 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="HullMACross")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
# Pre-calculer HMA pour TOUTES les valeurs possibles (hyperopt-safe)
all_hma_periods = set(
list(range(self.hma_fast.low, self.hma_fast.high + 1))
+ list(range(self.hma_slow.low, self.hma_slow.high + 1))
)
for p in all_hma_periods:
dataframe[f"hma_{p}"] = ta.hma(dataframe["close"], length=p)
# 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)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
hma_f = f"hma_{self.hma_fast.value}"
hma_s = f"hma_{self.hma_slow.value}"
rsi_col = f"rsi_{self.rsi_period.value}"
vol_col = f"volume_ratio_{self.volume_period.value}"
conditions = (
(dataframe[hma_f] > dataframe[hma_s])
& (dataframe[hma_f].shift(1) <= dataframe[hma_s].shift(1)) # crossover frais
& (dataframe[rsi_col] > self.rsi_min.value)
& (dataframe[rsi_col] < self.rsi_max.value)
& (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:
hma_f = f"hma_{self.hma_fast.value}"
hma_s = f"hma_{self.hma_slow.value}"
rsi_col = f"rsi_{self.rsi_period.value}"
conditions = (
(dataframe[hma_f] < dataframe[hma_s])
| (dataframe[rsi_col] > self.rsi_exit.value)
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe