Timeframe
4h
Direction
Long Only
Stoploss
-6.0%
Trailing Stop
Yes
ROI
0m: 10.0%, 240m: 5.0%, 720m: 3.0%, 1440m: 1.0%
Interface Version
3
Startup Candles
80
Indicators
1
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATEGIE : ZScoreMeanReversion
# CATEGORIE : Mean Reversion — Z-Score Extreme Low
# ══════════════════════════════════════════════════════════════
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 ZScoreMeanReversion(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "4h"
startup_candle_count = 80
minimal_roi = {"0": 0.10, "240": 0.05, "720": 0.03, "1440": 0.01}
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 ──
zscore_period = IntParameter(15, 40, default=20, space="buy")
zscore_entry = IntParameter(-30, -15, default=-20, space="buy") # /10
bb_period = IntParameter(15, 25, default=20, space="buy")
# ── Sell params ──
zscore_exit = IntParameter(0, 20, default=5, space="sell") # /10
_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="ZScoreMeanReversion")
self._notifier = TelegramNotifier()
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
for zp in range(self.zscore_period.low, self.zscore_period.high + 1):
sma = dataframe["close"].rolling(window=zp).mean()
std = dataframe["close"].rolling(window=zp).std()
dataframe[f"zscore_{zp}"] = (dataframe["close"] - sma) / std
for bb_p in range(self.bb_period.low, self.bb_period.high + 1):
dataframe = CommonIndicators.add_bollinger_bands(dataframe, period=bb_p)
dataframe = CommonIndicators.add_volume_sma(dataframe, period=20)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
zscore_col = f"zscore_{self.zscore_period.value}"
bb_lower_col = f"bb_lower_{self.bb_period.value}"
threshold = self.zscore_entry.value / 10.0
conditions = (
(dataframe[zscore_col] < threshold)
& (dataframe["close"] < dataframe[bb_lower_col])
& (dataframe["close"] > dataframe["open"])
& (dataframe["volume"] > 0)
)
dataframe.loc[conditions, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
zscore_col = f"zscore_{self.zscore_period.value}"
threshold = self.zscore_exit.value / 10.0
conditions = (
dataframe[zscore_col] > threshold
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe