TEST Simple

Simple.py

@@ -0,0 +1,265 @@
+# Zeus Strategy: First Generation of GodStra Strategy with maximum
+# AVG/MID profit in USDT
+# Author: @Mablue (Masoud Azizi)
+# github: https://github.com/mablue/
+# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
+# --- Do not remove these libs ---
+from datetime import timedelta, datetime
+from freqtrade.persistence import Trade
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
+                                IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
+import pandas as pd
+import numpy as np
+from pandas import DataFrame
+from typing import Optional, Union, Tuple
+from typing import List
+
+import logging
+import configparser
+from technical import pivots_points
+# --------------------------------
+
+# Add your lib to import here test git
+import ta
+import talib.abstract as talib
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import requests
+from datetime import timezone, timedelta
+from scipy.signal import savgol_filter
+from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
+from collections import Counter
+
+logger = logging.getLogger(__name__)
+
+from tabulate import tabulate
+
+# Couleurs ANSI de base
+RED = "\033[31m"
+GREEN = "\033[32m"
+YELLOW = "\033[33m"
+BLUE = "\033[34m"
+MAGENTA = "\033[35m"
+CYAN = "\033[36m"
+RESET = "\033[0m"
+
+
+def pprint_df(dframe):
+    print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False))
+
+
+def normalize(df):
+    df = (df - df.min()) / (df.max() - df.min())
+    return df
+
+"""
+SMA z-score derivative strategy with trailing exit (large).
+- timeframe: 1h
+- sma5, relative derivatives, z-score normalization (rolling z over z_window)
+- smoothing: ewm(span=5) on z-scores
+- entry: z_d1_smooth > entry_z1 AND z_d2_smooth > entry_z2
+- exit: inversion (z_d1_smooth < 0) AND retrace from highest since entry >= trailing_stop
+"""
+class Simple(IStrategy):
+    levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
+    startup_candle_count = 12 * 24 * 2
+
+    # ROI table:
+    minimal_roi = {
+        "0": 10
+    }
+    stakes = 40
+
+    # Stoploss:
+    stoploss = -1  # 0.256
+    # Custom stoploss
+    use_custom_stoploss = True
+
+    # Buy hypers
+    timeframe = '1h'
+
+    max_open_trades = 5
+    max_amount = 40
+
+    # DCA config
+    position_adjustment_enable = True
+
+    # Parameters (tweakable)
+    z_window = 50  # window for rolling mean/std to compute zscore
+    entry_z1 = 0.1  # threshold on z-score of first derivative
+    entry_z2 = 0.1  # threshold on z-score of second derivative
+    min_volume = 0.0  # minimal volume to accept an entry
+    min_relative_d1 = 1e-6  # clip tiny d1 relative values to reduce noise
+
+    # Trailing parameters for "large" trailing requested
+    trailing_stop_pct = 0.05  # 5% retracement from highest since entry
+
+    # Smoothing for z-scores
+    ewm_span = 5
+
+    # Plot config: price + sma5 + markers + slope/accel subplots
+    plot_config = {
+        "main_plot": {
+            "close": {"color": "blue"},
+            "sma5": {"color": "orange"},
+        },
+        "subplots": {
+            "slope_and_accel": {
+                "z_d1": {"color": "green"},
+                "z_d2": {"color": "red"},
+            }
+        },
+        # Markers (Freqtrade charting supports markers via these keys)
+        "markers": [
+            # buy marker: '^' green when enter_long==1
+            {"type": "buy", "column": "enter_long", "marker": "^", "color": "green", "markersize": 10},
+            # sell marker: 'v' red when exit_long==1
+            {"type": "sell", "column": "exit_long", "marker": "v", "color": "red", "markersize": 10},
+        ],
+    }
+
+    def informative_pairs(self):
+        return []
+
+    def _zscore(self, series: pd.Series, window: int) -> pd.Series:
+        mean = series.rolling(window=window, min_periods=3).mean()
+        std = series.rolling(window=window, min_periods=3).std(ddof=0)
+        z = (series - mean) / std
+        z = z.replace([np.inf, -np.inf], np.nan)
+        return z
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        df = dataframe
+
+        # SMA(5)
+        df['sma5'] = df['close'].rolling(5, min_periods=1).mean()
+
+        # Absolute derivatives
+        df['sma5_d1'] = df['sma5'].diff().rolling(5).mean()
+        df['sma5_d2'] = df['sma5_d1'].diff().rolling(5).mean()
+
+        # Relative derivatives (percentage-like)
+        eps = 1e-9
+        df['sma5_d1_rel'] = df['sma5_d1'] / (df['sma5'].shift(1).replace(0, np.nan) + eps)
+        df['sma5_d2_rel'] = df['sma5_d2'] / (df['sma5'].shift(2).replace(0, np.nan) + eps)
+
+        # Clip micro-noise
+        df.loc[df['sma5_d1_rel'].abs() < self.min_relative_d1, 'sma5_d1_rel'] = 0.0
+        df.loc[df['sma5_d2_rel'].abs() < self.min_relative_d1, 'sma5_d2_rel'] = 0.0
+
+        # Z-scores on relative derivatives
+        df['z_d1'] = self._zscore(df['sma5_d1_rel'], self.z_window)
+        df['z_d2'] = self._zscore(df['sma5_d2_rel'], self.z_window)
+
+        # Smoothing z-scores with EWM to reduce jitter
+        df['z_d1_smooth'] = df['z_d1'].ewm(span=self.ewm_span, adjust=False).mean()
+        df['z_d2_smooth'] = df['z_d2'].ewm(span=self.ewm_span, adjust=False).mean()
+
+        # Prepare marker columns (for plots). They will be filled in populate_entry_trend/populate_exit_trend
+        df['enter_long'] = 0
+        df['exit_long'] = 0
+
+        return df
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        df = dataframe.copy()
+
+        # Use last closed candle for signals -> shift(1)
+        cond_entry = (
+                (df['z_d1'] > self.entry_z1) &
+                (df['z_d2'] > self.entry_z2) &
+                (df['volume'].shift(1) > self.min_volume)
+        )
+
+        df.loc[cond_entry, 'enter_long'] = 1
+        # Ensure others are explicitly zero (for clean plotting)
+        df.loc[~cond_entry, 'enter_long'] = 0
+
+        return df
+
+    def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> \
+    Optional[str]:
+        """
+        Exit policy (mode C - trailing large):
+        - Must detect inversion: z_d1_smooth < 0 on last closed candle
+        - Compute highest close since trade entry (inclusive)
+        - If price has retraced >= trailing_stop_pct from that highest point and we're in inversion -> exit
+        """
+
+        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last = df.iloc[-1].squeeze()
+
+        z1 = last.get('z_d1_smooth', None)
+        if z1 is None:
+            return None
+
+        # Only consider exits when inversion detected (z1 < 0)
+        inversion = (z1 < 0)
+
+        if not inversion:
+            return None
+
+        # If we don't have profit info, be conservative
+        if current_profit is None:
+            return None
+
+        # Determine highest close since entry
+        highest_since_entry = None
+        try:
+            # trade.open_date_utc is available: find rows after that timestamp
+            # df.index is expected to be pd.DatetimeIndex in UTC or local; convert safely
+            if hasattr(trade, 'open_date_utc') and trade.open_date_utc:
+                # pandas comparison: ensure same tz awareness
+                entry_time = pd.to_datetime(trade.open_date_utc)
+                # select rows with index >= entry_time
+                mask = df.index >= entry_time
+                if mask.any():
+                    highest_since_entry = df.loc[mask, 'close'].max()
+            # fallback: use trade.open_rate or the max over full df
+        except Exception as e:
+            logger.debug(f"Couldn't compute highest_since_entry from open_date_utc: {e}")
+
+        if highest_since_entry is None:
+            # fallback: use the maximum close in the entire provided dataframe slice
+            highest_since_entry = df['close'].max() if not df['close'].empty else current_rate
+
+        # Calculate retracement ratio from the highest
+        if highest_since_entry and highest_since_entry > 0:
+            retrace = 1.0 - (current_rate / highest_since_entry)
+        else:
+            retrace = 0.0
+
+        # Exit if:
+        # - currently in profit AND
+        # - retracement >= trailing_stop_pct (i.e. price has fallen enough from top since entry) AND
+        # - inversion detected (z1 < 0)
+        if (current_profit > 0) and (retrace >= self.trailing_stop_pct):
+            # Mark the dataframe for plotting (if possible)
+            # Note: freqtrade expects strategies to set exit flags in populate_exit_trend,
+            # but we set exit via custom_exit return reason; plotting will read exit_long if populated.
+            return "zscore"
+
+        # Otherwise, do not exit yet
+        return None
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        For plotting only: mark exit points where our logic would trigger.
+        This is approximate: we mark an exit when z_d1_smooth < 0 and the price has retraced >= trailing_stop_pct
+        based on the available dataframe window (best-effort).
+        """
+        df = dataframe
+        # df['exit_long'] = 0
+        #
+        # # compute highest close since each possible entry (best-effort: use rolling max up to current index)
+        # rolling_max = df['close'].cummax()
+        #
+        # # retracement relative to rolling max
+        # retrace = 1.0 - (df['close'] / rolling_max.replace(0, np.nan))
+        #
+        # # mark exits where inversion and retrace >= threshold
+        # cond_exit = (df['z_d1_smooth'] < 0) & (retrace >= self.trailing_stop_pct)
+        # # shift by 0: we mark the candle where the exit condition appears
+        # df.loc[cond_exit, 'exit_long'] = 1
+
+        return df

Simple_01.py

@@ -0,0 +1,633 @@
+# sma_zscore_trailing_dca.py
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.persistence import Trade
+from pandas import DataFrame
+import pandas as pd
+import numpy as np
+from typing import Optional
+import logging
+from datetime import timedelta, datetime
+import talib.abstract as talib
+
+logger = logging.getLogger(__name__)
+
+class Simple_01(IStrategy):
+    """
+    SMA z-score derivative strategy with trailing exit and intelligent DCA (large pullback).
+    - timeframe: 1h
+    - sma5, relative derivatives, z-score normalization (rolling z over z_window)
+    - smoothing: ewm(span=5) on z-scores
+    - entry: z_d1_smooth > entry_z1 AND z_d2_smooth > entry_z2
+    - exit: inversion (z_d1_smooth < 0) AND retrace from highest since entry >= trailing_stop_pct
+    - adjust_trade_position: add to position on controlled pullback (large = 4-6%)
+    """
+
+    timeframe = "1h"
+    startup_candle_count = 24
+    # Risk mgmt (we handle exit in custom_exit)
+    minimal_roi = {"0": 0.99}
+    stoploss = -0.99
+    use_custom_exit = True
+    position_adjustment_enable = True
+
+    columns_logged = False
+
+    # Parameters
+    z_window = 10              # window for rolling mean/std to compute zscore
+    entry_z1 = 0.1             # threshold on z-score of first derivative
+    entry_z2 = 0               # threshold on z-score of second derivative
+    min_volume = 0.0           # minimal volume to accept an entry
+    min_relative_d1 = 1e-6     # clip tiny d1 relative values to reduce noise
+
+    # Trailing parameters for "large" trailing (exit)
+    trailing_stop_pct = 0.01   # 5% retracement from highest since entry
+
+    # Smoothing for z-scores
+    ewm_span = 5
+
+    # DCA intelligent (adjust_trade_position) parameters for "large" pullback
+    dca_enabled = True
+    # Pullback bounds (large): allow adding when retrace is between 4% and 6%
+    dca_pullback_min = 0.01
+    dca_pullback_max = 0.02
+    # Maximum number of adds per trade (to avoid infinite pyramiding)
+    dca_max_adds = 8
+    # Percentage of base position to add on each reinforcement (50% of original size by default)
+    dca_add_ratio = 0.5
+    # Require momentum still positive to add
+    dca_require_z1_positive = True
+    dca_require_z2_positive = True
+    # Do not add if current_profit < min_profit_to_add (avoid averaging down when deep in loss)
+    min_profit_to_add = -0.02  # allow small loss but not big drawdown
+
+    pairs = {
+        pair: {
+            "first_buy": 0,
+            "last_buy": 0.0,
+            "first_amount": 0.0,
+            "last_min": 999999999999999.5,
+            "last_max": 0,
+            "trade_info": {},
+            "max_touch": 0.0,
+            "last_sell": 0.0,
+            'count_of_buys': 0,
+            'current_profit': 0,
+            'expected_profit': 0,
+            "last_candle": {},
+            "last_trade": None,
+            "last_count_of_buys": 0,
+            'base_stake_amount': 0,
+            'stop_buy': False,
+            'last_date': 0,
+            'stop': False,
+            'max_profit': 0,
+            'last_palier_index': -1,
+            'total_amount': 0,
+            'has_gain': 0,
+            'force_sell': False,
+            'force_buy': False
+        }
+        for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
+                     "BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
+    }
+
+    # Plot config
+    plot_config = {
+        "main_plot": {
+            "close": {"color": "blue"},
+            "sma5": {"color": "orange"},
+        },
+        "subplots": {
+            "slope_and_accel": {
+                "z_d1_smooth": {"color": "green"},
+                "z_d2_smooth": {"color": "red"},
+            },
+            "sma_deriv": {
+                "sma5_deriv1_rel": {"color": "green"},
+                "sma5_deriv2_rel": {"color": "red"},
+            },
+            "rsi": {
+                "rsi": {"color": "blue"}
+            }
+
+        },
+        "markers": [
+            {"type": "buy", "column": "enter_long", "marker": "^", "color": "green", "markersize": 10},
+            {"type": "sell", "column": "exit_long", "marker": "v", "color": "red", "markersize": 10},
+        ],
+    }
+
+    def informative_pairs(self):
+        return []
+
+    def _zscore(self, series: pd.Series, window: int) -> pd.Series:
+        mean = series.rolling(window=window, min_periods=3).mean()
+        std = series.rolling(window=window, min_periods=3).std(ddof=0)
+        z = (series - mean) / std
+        z = z.replace([np.inf, -np.inf], np.nan)
+        return z
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        df = dataframe
+
+        # SMA(5)
+        df['sma5'] = df['close'].rolling(5, min_periods=1).mean()
+
+        # Absolute derivatives
+        df['sma5_deriv1'] = df['sma5'].diff()
+        df['sma5_deriv2'] = df['sma5_deriv1'].diff()
+
+        # Relative derivatives (percentage-like)
+        eps = 1e-9
+        df['sma5_deriv1_rel'] = df['sma5_deriv1'] / (df['sma5'].shift(1).replace(0, np.nan) + eps)
+        df['sma5_deriv2_rel'] = df['sma5_deriv2'] / (df['sma5'].shift(2).replace(0, np.nan) + eps)
+
+        # Clip micro-noise
+        df.loc[df['sma5_deriv1_rel'].abs() < self.min_relative_d1, 'sma5_deriv1_rel'] = 0.0
+        df.loc[df['sma5_deriv2_rel'].abs() < self.min_relative_d1, 'sma5_deriv2_rel'] = 0.0
+
+        # Z-scores on relative derivatives
+        df['z_d1'] = self._zscore(df['sma5_deriv1_rel'], self.z_window)
+        df['z_d2'] = self._zscore(df['sma5_deriv2_rel'], self.z_window)
+
+        # Smoothing z-scores with EWM to reduce jitter
+        df['z_d1_smooth'] = df['z_d1'].ewm(span=self.ewm_span, adjust=False).mean()
+        df['z_d2_smooth'] = df['z_d2'].ewm(span=self.ewm_span, adjust=False).mean()
+
+        # Prepare marker columns (for plots)
+        df['enter_long'] = 0
+        df['exit_long'] = 0
+        df['rsi'] = talib.RSI(df['close'], timeperiod=14)
+        df['max_rsi_12'] = talib.MAX(df['rsi'], timeperiod=12)
+        df['min_rsi_12'] = talib.MIN(df['rsi'], timeperiod=12)
+        # self.calculeDerivees(df, 'rsi', horizon=12)
+
+        return df
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        df = dataframe
+
+        # Use last closed candle for signals -> shift(1)
+        cond_entry = (
+            (df['z_d1_smooth'] > self.entry_z1) &
+            (df['z_d2_smooth'] > self.entry_z2) &
+            (df['max_rsi_12'] < 70) &
+            (df['volume'] > self.min_volume)
+        )
+
+        df.loc[cond_entry, 'enter_long'] = 1
+        df.loc[~cond_entry, 'enter_long'] = 0
+
+        return df
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        df = dataframe
+        # df['exit_long'] = 0
+        #
+        # # compute rolling max (best-effort for plotting)
+        # rolling_max = df['close'].cummax()
+        # retrace = 1.0 - (df['close'] / rolling_max.replace(0, np.nan))
+        #
+        # cond_exit = (df['z_d1_smooth'] < 0) & (retrace >= self.trailing_stop_pct)
+        # df.loc[cond_exit, 'exit_long'] = 1
+
+        return df
+
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
+                           time_in_force: str,
+                           exit_reason: str, current_time, **kwargs, ) -> bool:
+        # allow_to_sell = (minutes > 30)
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        force = self.pairs[pair]['force_sell']
+        allow_to_sell = True #(last_candle['percent'] < 0) #or force
+
+        minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
+
+        if allow_to_sell:
+            self.trades = list()
+            self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries  # self.pairs[pair]['count_of_buys']
+            self.pairs[pair]['last_sell'] = rate
+            self.pairs[pair]['last_trade'] = trade
+            self.pairs[pair]['last_candle'] = last_candle
+            self.trades = list()
+            dispo = round(self.wallets.get_available_stake_amount())
+            print(f"Sell {pair} {current_time} {exit_reason}  dispo={dispo}  amount={amount} rate={rate} open_rate={trade.open_rate}")
+            self.log_trade(
+                last_candle=last_candle,
+                date=current_time,
+                action="🟥Sell " + str(minutes),
+                pair=pair,
+                trade_type=exit_reason,
+                rate=last_candle['close'],
+                dispo=dispo,
+                profit=round(trade.calc_profit(rate, amount), 2)
+            )
+            self.pairs[pair]['max_profit'] = 0
+            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['has_gain'] = 0
+            self.pairs[pair]['current_profit'] = 0
+            self.pairs[pair]['total_amount'] = 0
+            self.pairs[pair]['count_of_buys'] = 0
+            self.pairs[pair]['max_touch'] = 0
+            self.pairs[pair]['last_buy'] = 0
+            self.pairs[pair]['last_date'] = current_time
+            self.pairs[pair]['last_palier_index'] = -1
+            self.pairs[pair]['last_trade'] = trade
+            self.pairs[pair]['current_trade'] = None
+
+        return (allow_to_sell) | (exit_reason == 'force_exit')
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
+                            current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
+
+        minutes = 0
+        if self.pairs[pair]['last_date'] != 0:
+            minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        last_candle_2 = dataframe.iloc[-2].squeeze()
+        last_candle_3 = dataframe.iloc[-3].squeeze()
+        # val = self.getProbaHausse144(last_candle)
+
+        # allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
+        allow_to_buy = not self.pairs[pair]['stop']  # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--')  # (rate <= float(limit)) | (entry_tag == 'force_entry')
+
+        # force = self.pairs[pair]['force_buy']
+        # if self.pairs[pair]['force_buy']:
+        #     self.pairs[pair]['force_buy'] = False
+        #     allow_to_buy = True
+        # else:
+        #     if not self.should_enter_trade(pair, last_candle, current_time):
+        #         allow_to_buy = False
+
+        if allow_to_buy:
+            self.trades = list()
+            self.pairs[pair]['first_buy'] = rate
+            self.pairs[pair]['last_buy'] = rate
+            self.pairs[pair]['max_touch'] = last_candle['close']
+            self.pairs[pair]['last_candle'] = last_candle
+            self.pairs[pair]['count_of_buys'] = 1
+            self.pairs[pair]['current_profit'] = 0
+            self.pairs[pair]['last_palier_index'] = -1
+            self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
+            self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
+
+            dispo = round(self.wallets.get_available_stake_amount())
+            self.printLineLog()
+
+            stake_amount = self.adjust_stake_amount(pair, last_candle)
+
+            self.pairs[pair]['total_amount'] = stake_amount
+
+            self.log_trade(
+                last_candle=last_candle,
+                date=current_time,
+                action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
+                pair=pair,
+                rate=rate,
+                dispo=dispo,
+                profit=0,
+                trade_type=entry_tag,
+                buys=1,
+                stake=round(stake_amount, 2)
+            )
+
+        return allow_to_buy
+
+    def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> Optional[str]:
+        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = df.iloc[-1].squeeze()
+
+        self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
+        self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
+        self.pairs[pair]['current_trade'] = trade
+        count_of_buys = trade.nr_of_successful_entries
+
+        profit = round(current_profit * trade.stake_amount, 1)
+        self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
+        max_profit = self.pairs[pair]['max_profit']
+        baisse = 0
+        if profit > 0:
+            baisse = 100 * abs(max_profit - profit) / max_profit
+        mx = max_profit / 5
+        self.pairs[pair]['count_of_buys'] = count_of_buys
+        self.pairs[pair]['current_profit'] = profit
+
+        dispo = round(self.wallets.get_available_stake_amount())
+        hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
+        days_since_first_buy = (current_time - trade.open_date_utc).days
+        hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
+
+        if hours % 4 == 0:
+            self.log_trade(
+                last_candle=last_candle,
+                date=current_time,
+                action="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT",
+                dispo=dispo,
+                pair=pair,
+                rate=last_candle['close'],
+                trade_type='',
+                profit=profit,
+                buys='',
+                stake=0
+            )
+
+        z1 = last_candle.get('z_d1_smooth', None)
+        if z1 is None:
+            return None
+
+        if z1 >= 0:
+            return None
+
+        if current_profit is None:
+            return None
+
+        # highest close since entry
+        highest_since_entry = self.pairs[trade.pair]['max_touch']
+        # try:
+        #     if hasattr(trade, 'open_date_utc') and trade.open_date_utc:
+        #         entry_time = pd.to_datetime(trade.open_date_utc)
+        #         mask = df.index >= entry_time
+        #         if mask.any():
+        #             highest_since_entry = df.loc[mask, 'close'].max()
+        # except Exception as e:
+        #     logger.debug(f"Couldn't compute highest_since_entry: {e}")
+        #
+        # if highest_since_entry is None:
+        #     highest_since_entry = df['close'].max() if not df['close'].empty else current_rate
+
+        if highest_since_entry and highest_since_entry > 0:
+            retrace = 1.0 - (current_rate / highest_since_entry)
+        else:
+            retrace = 0.0
+        z1 = last_candle.get('z_d1_smooth', None)
+        z2 = last_candle.get('z_d2_smooth', None)
+
+        if (current_profit > 0) and (current_profit >= self.trailing_stop_pct) and last_candle['sma5_deriv1'] < -0.002:
+            return str(count_of_buys) + '_' + "zscore"
+
+        self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
+
+        return None
+
+    def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
+        # Calculer le minimum des 14 derniers jours
+        return self.config.get('stake_amount')
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs):
+        """
+        DCA intelligent (mode C - 'large'):
+        - Only add if:
+            * DCA is enabled
+            * number of adds done so far < dca_max_adds
+            * retracement since highest_since_entry is between dca_pullback_min and dca_pullback_max
+            * momentum still positive (z_d1_smooth, z_d2_smooth) if required
+            * current_profit >= min_profit_to_add (avoid averaging down into large loss)
+        - Returns a dict describing the desired order for Freqtrade to place (common format).
+          Example returned dict: {'type': 'market', 'amount': 0.01}
+        """
+        if not self.dca_enabled:
+            return None
+        pair = trade.pair
+
+        # Basic guards
+        if current_profit is None:
+            current_profit = 0.0
+
+        # Do not add if we're already deeply in loss
+        if current_profit < self.min_profit_to_add:
+            return None
+
+        df, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        last_candle = df.iloc[-1].squeeze()
+
+        # Compute highest close since entry
+        highest_since_entry = self.pairs[trade.pair]['last_buy']
+
+        last = df.iloc[-1]
+        z1 = last.get('z_d1_smooth', None)
+        z2 = last.get('z_d2_smooth', None)
+
+        # Count how many adds have been done for this trade.
+        # Trade.extra might contain meta; otherwise try trade.tags or use trade.open_rate/amount heuristics.
+        adds_done = trade.nr_of_successful_entries
+        # Calculate retracement since the highest
+        retrace = 0.0
+        if highest_since_entry and highest_since_entry > 0:
+            retrace = (last['close'] - highest_since_entry) / highest_since_entry
+
+        # logger.info(f"{pair} {current_rate} {current_time} {highest_since_entry} add: retrace={retrace:.4f}, adds_done={adds_done} z1={z1} z2={z2}")
+
+        # Enforce momentum requirements if requested
+        if self.dca_require_z1_positive and (z1 is None or z1 <= 0):
+            return None
+        if self.dca_require_z2_positive and (z2 is None or z2 <= 0):
+            return None
+
+        #
+        # try:
+        #     meta = getattr(trade, 'meta', None) or {}
+        #     adds_done = int(meta.get('adds_done', 0))
+        # except Exception:
+        #     adds_done = 0
+
+        if adds_done >= self.dca_max_adds:
+            return None
+
+        # try:
+        #     if hasattr(trade, 'open_date_utc') and trade.open_date_utc:
+        #         entry_time = pd.to_datetime(trade.open_date_utc)
+        #         mask = df.index >= entry_time
+        #         if mask.any():
+        #             highest_since_entry = df.loc[mask, 'close'].max()
+        # except Exception as e:
+        #     logger.debug(f"adjust_trade_position: couldn't compute highest_since_entry: {e}")
+        #
+        # if highest_since_entry is None:
+        #     highest_since_entry = df['close'].max() if not df['close'].empty else current_rate
+
+        # Check if retrace is inside the allowed DCA window (large)
+        if ((retrace >= self.dca_pullback_min) and (retrace <= self.dca_pullback_max)):
+            # Determine amount to add: a fraction of the original trade amount
+            # Try to get trade.amount (base asset amount). If not available, fall back to stake percentage
+            add_amount = None
+            try:
+                base_amount = self.config.get('stake_amount')
+                if base_amount:
+                    add_amount = base_amount * self.dca_add_ratio
+                else:
+                    # Fallback: attempt to compute amount from trade.open_rate and desired quote stake
+                    # We'll propose to use a fraction of current rate worth (this is best-effort)
+                    add_amount = None
+            except Exception:
+                add_amount = None
+
+            # If we couldn't compute an absolute amount, propose a relative size via a suggested stake (user must map)
+            if add_amount is None:
+                # Return a suggested instruction; adapt according to your freqtrade version.
+                suggested = {
+                    'type': 'market',
+                    'amount': None,  # caller should compute actual amount from stake management
+                    'note': f'suggest_add_ratio={self.dca_add_ratio}'
+                }
+                # logger.info(f"{pair} {current_rate} DCA suggestion (no absolute amount): retrace={retrace:.4f}, adds_done={adds_done}")
+                return None
+
+            dispo = round(self.wallets.get_available_stake_amount())
+            trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
+            self.pairs[trade.pair]['count_of_buys'] += 1
+            self.pairs[pair]['total_amount'] += add_amount
+            self.log_trade(
+                last_candle=last_candle,
+                date=current_time,
+                action="🟧 Loss -",
+                dispo=dispo,
+                pair=trade.pair,
+                rate=current_rate,
+                trade_type=trade_type,
+                profit=round(current_profit * trade.stake_amount, 1),
+                buys=trade.nr_of_successful_entries + 1,
+                stake=round(add_amount, 2)
+            )
+
+            self.pairs[trade.pair]['last_buy'] = current_rate
+            self.pairs[trade.pair]['max_touch'] = last_candle['close']
+            self.pairs[trade.pair]['last_candle'] = last_candle
+
+            # All checks passed -> create market order instruction
+            # logger.info(f"{pair} {current_rate} {current_time} {highest_since_entry} add: retrace={retrace:.4f}, adds_done={adds_done}, add_amount={add_amount:.8f}")
+            return add_amount
+
+        # Not in allowed retrace window -> no action
+        return None
+
+    def getPctFirstBuy(self, pair, last_candle):
+        return round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
+
+    def getPctLastBuy(self, pair, last_candle):
+        return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
+
+    def getPct60D(self, pair, last_candle):
+        return round((last_candle['max60'] - last_candle['min60']) / last_candle['max60'], 4)
+
+    def getPctClose60D(self, pair, last_candle):
+        if last_candle['close'] > last_candle['max12']:
+            return 1
+        if last_candle['close'] < last_candle['min12']:
+            return 0
+        return round(
+            (last_candle['close'] - last_candle['min12']) / (last_candle['max12'] - last_candle['min12']), 4)
+
+
+    def printLineLog(self):
+        self.printLog(
+            f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
+            f"{'-' * 3}"
+            # "+{'-' * 3}+{'-' * 3}
+            f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
+        )
+
+    def printLog(self, str):
+        if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
+            return
+        if not self.dp.runmode.value in ('backtest', 'hyperopt', 'lookahead-analysis'):
+            logger.info(str)
+        else:
+            if not self.dp.runmode.value in ('hyperopt'):
+                print(str)
+
+    def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None,
+                  stake=None,
+                  last_candle=None):
+        # Couleurs ANSI de base
+        RED = "\033[31m"
+        GREEN = "\033[32m"
+        YELLOW = "\033[33m"
+        BLUE = "\033[34m"
+        MAGENTA = "\033[35m"
+        CYAN = "\033[36m"
+        RESET = "\033[0m"
+
+        # Afficher les colonnes une seule fois
+        if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
+            return
+        if self.columns_logged % 10 == 0:
+            self.printLog(
+                f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
+                f"Tdc|{'val':>6}|  RSI  |s201d|s5_1d|s5_2d|s51h|s52h"
+            )
+            self.printLineLog()
+            df = pd.DataFrame.from_dict(self.pairs, orient='index')
+            colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index',
+                                  'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys',
+                                  'base_stake_amount', 'stop_buy']
+            df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
+            # df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
+
+            print(df_filtered)
+
+        self.columns_logged += 1
+        date = str(date)[:16] if date else "-"
+        limit = None
+        # if buys is not None:
+        #     limit = round(last_rate * (1 - self.fibo[buys] / 100), 4)
+
+        rsi = ''
+        rsi_pct = ''
+        sma5_1d = ''
+        sma5_1h = ''
+
+        sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
+
+        last_lost = '' #self.getLastLost(last_candle, pair)
+
+        if buys is None:
+            buys = ''
+
+        max_touch = ''  # round(last_candle['max12'], 1) #round(self.pairs[pair]['max_touch'], 1)
+        pct_max = '' #self.getPctFirstBuy(pair, last_candle)
+
+        total_counts = str(buys) + '/' + str(
+            sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
+
+        dist_max = '' #self.getDistMax(last_candle, pair)
+        val = 0 #self.getProbaHausseSma5d(last_candle)
+
+        pct60 = 0 #round(100 * self.getPct60D(pair, last_candle), 2)
+
+        color = GREEN if profit > 0 else RED
+        # color_sma20 = GREEN if last_candle['sma20_deriv1'] > 0 else RED
+        # color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1'] > 0 else RED
+        # color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2'] > 0 else RED
+        # color_sma5_1h = GREEN if last_candle['sma5_deriveriv1'] > 0 else RED
+        # color_sma5_2h = GREEN if last_candle['sma5_deriveriv2'] > 0 else RED
+
+        last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(
+            self.pairs[pair]['last_max'], 3)
+        last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(
+            self.pairs[pair]['last_min'], 3)
+
+        profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
+
+        #     🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
+        #     🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
+        #     🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
+        #     🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
+
+        # tdc last_candle['tendency_12']
+        self.printLog(
+            f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
+            f"|{color}{profit or '-':>10}{RESET}| {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
+            f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
+            f"|{'-':>3}|"
+            f"{round(val, 1) or '-' :>6}|"
+            # f"{round(last_candle['rsi'], 0):>7}|{color_sma20}{round(last_candle['sma20_deriv1'], 2):>5}{RESET}"
+            # f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2'], 2):>5}{RESET}"
+            # f"|{color_sma5_1h}{round(last_candle['sma5_deriveriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma5_deriveriv2'], 2):>5}{RESET}"
+            # f"|{last_candle['min60']}|{last_candle['max60']}"
+        )

Zeus_8_1d.py

@@ -99,7 +99,7 @@ class Zeus_8_1d(IStrategy):
         },
         "subplots": {
             "Rsi": {
-                "max_rsi_12": {
+                "max_rsi_24": {
                     "color": "blue"
                 },
             },
@@ -145,6 +145,7 @@ class Zeus_8_1d(IStrategy):
         pair: {
             "first_buy": 0,
             "last_buy": 0.0,
+            "first_amount": 0.0,
             "last_min": 999999999999999.5,
             "last_max": 0,
             "trade_info": {},
@@ -405,16 +406,19 @@ class Zeus_8_1d(IStrategy):
         #     self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
         #     return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
 
-        if last_candle['mid_smooth_5_deriv1'] <= -0.1 and profit > expected_profit and last_candle['rsi'] > 65:
+        if last_candle['mid_smooth_5_deriv1'] <= -0.12 and profit > expected_profit and last_candle['max_rsi_24'] > 75 \
-            self.pairs[pair]['force_sell'] = False
+               and last_candle['mid_smooth_5_deriv2'] <= -0.15 :
-            self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
+            return str(count_of_buys) + '_' + 'RSI80_' + pair_name + '_'  + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
-            return 'RSI_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
 
-        if last_candle['mid_smooth_24_deriv1'] <= -0.1 \
+        # if last_candle['mid_smooth_5_deriv1'] <= -0.1 and profit > expected_profit and last_candle['max_rsi_24'] > 65:
-                and profit > expected_profit:
+        #     self.pairs[pair]['force_sell'] = False
+        #     self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
+        #     return str(count_of_buys) + '_' + 'RSI65_' + pair_name + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
+
+        if last_candle['mid_smooth_24_deriv1'] <= -0.1 and last_candle['mid_smooth_5_deriv1'] <= -0 and profit > expected_profit:
             self.pairs[pair]['force_sell'] = False
-            self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
+            # self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
-            return 'Drv3_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
+            return str(count_of_buys) + '_' + 'Drv3_' + pair_name + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
 
         self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
 
@@ -591,8 +595,9 @@ class Zeus_8_1d(IStrategy):
 
         # print(metadata['pair'])
         dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
-        dataframe['max_rsi_12'] = talib.MAX(dataframe['rsi'], timeperiod=12)
+        dataframe['max_rsi_24'] = talib.MAX(dataframe['rsi'], timeperiod=24)
-        self.calculeDerivees(dataframe, 'rsi', horizon=12)
+        dataframe['min_rsi_24'] = talib.MIN(dataframe['rsi'], timeperiod=24)
+        self.calculeDerivees(dataframe, 'rsi', horizon=24)
 
         dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
         dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
@@ -663,6 +668,7 @@ class Zeus_8_1d(IStrategy):
                         if count == 0:
                             dataframe['first_price'] = buy.price
                             self.pairs[pair]['first_buy'] = buy.price
+                            self.pairs[pair]['first_amount'] = buy.price * buy.filled
                             # dataframe['close01'] = buy.price * 1.01
 
                         # Order(id=2396, trade=1019, order_id=29870026652, side=buy, filled=0.00078, price=63921.01,
@@ -674,12 +680,10 @@ class Zeus_8_1d(IStrategy):
                     # dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
                     count_buys = count
                     # dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
-                    # dataframe['amount'] = amount
                     self.pairs[pair]['total_amount'] = amount
 
         # Compter les baisses / hausses consécutives
-        self.calculateDownAndUp(dataframe, limit=0.0001)
+        # self.calculateDownAndUp(dataframe, limit=0.0001)
-
         horizon_h = 12
 
         dataframe['close_smooth'] = self.conditional_smoothing(dataframe['mid'].rolling(3).mean().dropna(),
@@ -701,6 +705,32 @@ class Zeus_8_1d(IStrategy):
 
         self.calculeDerivees(dataframe, 'ema_volume', factor_1=10, factor_2=1, horizon=14)
 
+        dataframe['futur_percent_3'] = 100 * ((dataframe['sma5'].shift(-3) - dataframe['sma5']) / dataframe['sma5'])
+        futur_cols = ['futur_percent_3']
+        indic_1 = 'mid_smooth_5_deriv1'
+        indic_2 = 'mid_smooth_5_deriv2'
+
+        self.calculateProbabilite2Index(dataframe, futur_cols, indic_1, indic_2)
+
+        ################### INFORMATIVE 1d
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
+        heikinashi = qtpylib.heikinashi(informative)
+        informative['haopen'] = heikinashi['open']
+        informative['haclose'] = heikinashi['close']
+        informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
+        informative = self.calculateDerivation(informative, window=5, suffixe="_5")
+        informative['sma5'] = talib.SMA(informative, timeperiod=5)
+        informative['sma20'] = talib.SMA(informative, timeperiod=20)
+        informative['max60'] = talib.MAX(informative['close'], timeperiod=60)
+        informative['min60'] = talib.MIN(informative['close'], timeperiod=60)
+
+        self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1, horizon=5)
+        self.calculeDerivees(informative, 'sma20', factor_1=10, factor_2=1, horizon=20)
+        if self.dp.runmode.value in ('backtest'):
+            informative['futur_percent_3'] = 100 * (informative['close'].shift(-3) - informative['close']) / informative['close']
+            self.calculateProbabilite2Index(informative, futur_cols, indic_1, indic_2)
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
+
         return dataframe
 
     def calculeDerivees(self, dataframe, indic, factor_1=100, factor_2=10, horizon=5):
@@ -801,13 +831,20 @@ class Zeus_8_1d(IStrategy):
                 #     (dataframe['mid_smooth_24_deriv1'].shift(1) <= 0)
                     (dataframe['mid_smooth_24_deriv1'] >= 0.05)
                     & (dataframe['mid_smooth_24_deriv2'] > 0)
+                    & (dataframe['mid_smooth_5_deriv1'] > 0.001)
                     & (dataframe['mid_smooth_5_deriv2'] > 0)
                     # & (dataframe['hapercent'] > 0)
-                    #& (dataframe['max_rsi_12'] < 50)
+                    #& (dataframe['max_rsi_24'] < 50)
                     # & (dataframe['open'] <= dataframe['bb_middleband'])
 
             ), ['enter_long', 'enter_tag']] = (1, 'smth_12')
 
+        dataframe.loc[
+            (
+                    (dataframe['min_rsi_24'] < 20)
+                    & (dataframe['hapercent'] > 0)
+            ), ['enter_long', 'enter_tag']] = (1, 'min_rsi_24')
+
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
 
         return dataframe
@@ -990,12 +1027,12 @@ class Zeus_8_1d(IStrategy):
                     return stake_amount
                 return None
             except Exception as exception:
-                # print(exception)
+                print(exception)
                 return None
 
         last_lost = self.getLastLost(last_candle, pair)
 
-        if (False and hours > 6 and last_candle['mid_smooth_5_deriv1'] > 0 and last_candle['mid_smooth_5_deriv2'] > 0):
+        if (hours > 6 and last_candle['mid_smooth_24_deriv1'] > 0.03 and last_candle['mid_smooth_5_deriv2'] > 0):
             try:
                 stake_amount = self.pairs[pair]['first_amount'] / 4
                 if self.wallets.get_available_stake_amount() > stake_amount:
@@ -1074,6 +1111,7 @@ class Zeus_8_1d(IStrategy):
         # pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2)
 
         if True:  # not pair in ('BTC/USDT', 'BTC/USDC'):
+            # factors = [1, 1.2, 1.3, 1.4]
             # factors = [1, 1.2, 1.3, 1.4]
             if self.pairs[pair]['count_of_buys'] == 0:
                 # pctClose60 = self.getPctClose60D(pair, last_candle)
@@ -1230,6 +1268,13 @@ class Zeus_8_1d(IStrategy):
                 return self.labels[i]
         return self.labels[-1]  # cas limite pour la borne max
 
+    def informative_pairs(self):
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        informative_pairs = [(pair, '1d') for pair in pairs]
+
+        return informative_pairs
+
     def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
         """
         Renvoie une approximation de la valeur à partir des labels binaires (e.g. B5, H1)
@@ -1401,7 +1446,6 @@ class Zeus_8_1d(IStrategy):
         # print(pivot_mean.round(2))
 
     def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
-
         return True
         limit = 3
 
@@ -1413,7 +1457,8 @@ class Zeus_8_1d(IStrategy):
         # if not pair.startswith('BTC'):
         dispo = round(self.wallets.get_available_stake_amount())
 
-        if self.pairs[pair]['stop'] and last_candle['mid_smooth_5_deriv1'] > -0.9 and last_candle['sma5_deriv1'] > 0 and last_candle['sma5_deriv2'] > 0:
+        if self.pairs[pair]['stop'] and last_candle['mid_smooth_5_deriv1_1d'] > -0.9 \
+                and last_candle['sma5_deriv1_1d'] > 0 and last_candle['sma5_deriv2_1d'] > 0:
             self.pairs[pair]['stop'] = False
             self.log_trade(
                 last_candle=last_candle,
@@ -1428,7 +1473,7 @@ class Zeus_8_1d(IStrategy):
                 stake=0
             )
         else:
-            if self.pairs[pair]['stop'] == False and (last_candle['sma5_deriv1'] < -0.2 or last_candle['sma5_deriv2'] < -3):
+            if self.pairs[pair]['stop'] == False and (last_candle['sma5_deriv1_1d'] < -0.2 or last_candle['sma5_deriv2_1d'] < -3):
                 self.pairs[pair]['stop'] = True
                 # if self.pairs[pair]['current_profit'] > 0:
                 #     self.pairs[pair]['force_sell'] = True