Timeframe
1h
Direction
Long Only
Stoploss
-5.0%
Trailing Stop
Yes
ROI
0m: 10.0%, 180m: 5.0%, 480m: 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 : ChaikinMoneyFlow
# CATÉGORIE : Volume-Weighted Momentum — Accumulation/Distribution
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# 1. CMF > seuil (accumulation — smart money buying)
# 2. CMF vient de croiser au-dessus du seuil (signal frais)
# 3. Close > EMA fast > EMA slow (tendance haussière)
# 4. RSI entre rsi_min et rsi_max (ni survendu ni suracheté)
# 5. Volume > 0
# Sortie : CMF < -seuil (distribution) OU close < EMA fast OU RSI > rsi_exit
#
# INDICATEUR CLÉ :
# CMF = SUM(Money Flow Volume, period) / SUM(Volume, period)
# Money Flow Volume = ((close - low) - (high - close)) / (high - low) * volume
# Valeurs positives = accumulation, négatives = distribution
# ══════════════════════════════════════════════════════════════
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 ChaikinMoneyFlow(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "1h"
startup_candle_count = 100
minimal_roi = {"0": 0.10, "180": 0.05, "480": 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 ──
cmf_period = IntParameter(10, 30, default=20, space="buy")
cmf_threshold = DecimalParameter(0.0, 0.3, default=0.05, decimals=2, space="buy")
ema_fast = IntParameter(10, 30, default=20, 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")
# ── 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="ChaikinMoneyFlow")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
# Pre-calculer CMF pour TOUTES les valeurs possibles (hyperopt-safe)
for p in range(self.cmf_period.low, self.cmf_period.high + 1):
dataframe[f"cmf_{p}"] = ta.cmf(
dataframe["high"], dataframe["low"],
dataframe["close"], dataframe["volume"],
length=p,
)
# Pre-calculer EMA pour TOUTES les valeurs possibles (hyperopt-safe)
all_ema_periods = set(
list(range(self.ema_fast.low, self.ema_fast.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)
# Pre-calculer RSI pour TOUTES les valeurs possibles (hyperopt-safe)
for rsi_p in range(self.rsi_period.low, self.rsi_period.high + 1):
dataframe = CommonIndicators.add_rsi(dataframe, period=rsi_p)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
cmf_col = f"cmf_{self.cmf_period.value}"
ema_f = f"ema_{self.ema_fast.value}"
ema_s = f"ema_{self.ema_slow.value}"
rsi_col = f"rsi_{self.rsi_period.value}"
threshold = self.cmf_threshold.value
conditions = (
(dataframe[cmf_col] > threshold) # accumulation
& (dataframe[cmf_col].shift(1) <= threshold) # crossover frais
& (dataframe["close"] > dataframe[ema_f]) # prix > EMA fast
& (dataframe[ema_f] > dataframe[ema_s]) # EMA fast > EMA slow
& (dataframe[rsi_col] > self.rsi_min.value) # RSI pas survendu
& (dataframe[rsi_col] < self.rsi_max.value) # RSI pas suracheté
& (dataframe["volume"] > 0)
)
dataframe.loc[conditions, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
cmf_col = f"cmf_{self.cmf_period.value}"
ema_f = f"ema_{self.ema_fast.value}"
rsi_col = f"rsi_{self.rsi_period.value}"
threshold = self.cmf_threshold.value
conditions = (
(dataframe[cmf_col] < -threshold) # distribution
| (dataframe["close"] < dataframe[ema_f]) # perte du support EMA
| (dataframe[rsi_col] > self.rsi_exit.value) # RSI suracheté
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe