Timeframe
4h
Direction
Long Only
Stoploss
-6.0%
Trailing Stop
Yes
ROI
0m: 10.0%, 120m: 5.0%, 360m: 3.0%, 720m: 1.0%
Interface Version
3
Startup Candles
210
Indicators
2
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# ══════════════════════════════════════════════════════════════
# anis solidscale - Elite Spot Trading Suite
# STRATEGIE : ConnorsRSI (CRSI)
# CATEGORIE : Mean-Reversion — Triple composant RSI
# ══════════════════════════════════════════════════════════════
#
# LOGIQUE :
# CRSI = (RSI(3) + StreakRSI + RankPercentile) / 3
# - RSI(3) : momentum court terme
# - StreakRSI : RSI applique aux streaks up/down consecutifs
# - RankPercentile : rang du rendement actuel sur N periodes
# CRSI < 10 → long, CRSI > 90 → exit
# SOURCE : Larry Connors — version amelioree du RSI2
# ══════════════════════════════════════════════════════════════
import sys
from pathlib import Path
import numpy as np
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 ConnorsRSI(IStrategy):
INTERFACE_VERSION = 3
can_short = False
timeframe = "4h"
startup_candle_count = 210
minimal_roi = {"0": 0.10, "120": 0.05, "360": 0.03, "720": 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 ──
rsi_period = IntParameter(2, 5, default=3, space="buy")
streak_rsi_period = IntParameter(2, 5, default=2, space="buy")
rank_period = IntParameter(50, 150, default=100, space="buy")
crsi_entry = IntParameter(5, 20, default=10, space="buy")
sma_trend = IntParameter(150, 250, default=200, space="buy")
# ── Sell params ──
crsi_exit = IntParameter(80, 95, default=90, space="sell")
_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="ConnorsRSI")
self._notifier = TelegramNotifier()
@staticmethod
def _calc_streak(series):
"""Calcule la serie de jours consecutifs up/down."""
streak = np.zeros(len(series))
for i in range(1, len(series)):
if series.iloc[i] > series.iloc[i - 1]:
streak[i] = streak[i - 1] + 1 if streak[i - 1] > 0 else 1
elif series.iloc[i] < series.iloc[i - 1]:
streak[i] = streak[i - 1] - 1 if streak[i - 1] < 0 else -1
else:
streak[i] = 0
return streak
@staticmethod
def _calc_percent_rank(series, period: int):
"""Rang percentile du rendement actuel sur N periodes."""
pct_change = series.pct_change()
result = np.full(len(series), np.nan)
for i in range(period, len(series)):
window = pct_change.iloc[i - period:i]
current = pct_change.iloc[i]
if not np.isnan(current):
result[i] = (window < current).sum() / period * 100
return result
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self._init_utils()
# RSI pour toutes les valeurs
for rsi_p in range(self.rsi_period.low, self.rsi_period.high + 1):
dataframe = CommonIndicators.add_rsi(dataframe, period=rsi_p)
# SMA trend
for sma_p in range(self.sma_trend.low, self.sma_trend.high + 1):
dataframe = CommonIndicators.add_sma(dataframe, period=sma_p)
# Streak
streak = self._calc_streak(dataframe["close"])
dataframe["streak"] = streak
# Streak RSI pour toutes les valeurs
for srsi_p in range(self.streak_rsi_period.low, self.streak_rsi_period.high + 1):
dataframe = CommonIndicators.add_rsi(dataframe, period=srsi_p, column="streak")
# Percent Rank pour toutes les valeurs
for rank_p in range(self.rank_period.low, self.rank_period.high + 1):
dataframe[f"pct_rank_{rank_p}"] = self._calc_percent_rank(dataframe["close"], rank_p)
# Pre-calc CRSI pour toutes les combinaisons
for rsi_p in range(self.rsi_period.low, self.rsi_period.high + 1):
for srsi_p in range(self.streak_rsi_period.low, self.streak_rsi_period.high + 1):
for rank_p in range(self.rank_period.low, self.rank_period.high + 1):
rsi_col = f"rsi_{rsi_p}"
# Streak RSI uses same add_rsi but on streak column
streak_rsi_col = f"rsi_{srsi_p}"
rank_col = f"pct_rank_{rank_p}"
crsi_col = f"crsi_{rsi_p}_{srsi_p}_{rank_p}"
dataframe[crsi_col] = (dataframe[rsi_col] + dataframe[streak_rsi_col] + dataframe[rank_col]) / 3
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
crsi_col = f"crsi_{self.rsi_period.value}_{self.streak_rsi_period.value}_{self.rank_period.value}"
sma_col = f"sma_{self.sma_trend.value}"
conditions = (
(dataframe[crsi_col] < self.crsi_entry.value)
& (dataframe["close"] > dataframe[sma_col])
& (dataframe["volume"] > 0)
)
dataframe.loc[conditions, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
crsi_col = f"crsi_{self.rsi_period.value}_{self.streak_rsi_period.value}_{self.rank_period.value}"
conditions = (
dataframe[crsi_col] > self.crsi_exit.value
)
dataframe.loc[conditions, "exit_long"] = 1
return dataframe