From dfe04a3f0cef7a362b76a228a88ba64353c860e1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Delacotte?= <jerome.delacotte@bappli.com>
Date: Sun, 16 Nov 2025 20:47:09 +0100
Subject: [PATCH] TensorFlow

---
 Zeus_TensorFlow.py | 3165 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 3165 insertions(+)
 create mode 100644 Zeus_TensorFlow.py

diff --git a/Zeus_TensorFlow.py b/Zeus_TensorFlow.py
new file mode 100644
index 0000000..8be7767
--- /dev/null
+++ b/Zeus_TensorFlow.py
@@ -0,0 +1,3165 @@
+# 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
+import os
+import json
+from pandas import DataFrame
+from typing import Optional, Union, Tuple
+import math
+import logging
+from pathlib import Path
+
+# --------------------------------
+
+# Add your lib to import here test git
+import ta
+import talib.abstract as talib
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from datetime import timezone, timedelta
+
+logger = logging.getLogger(__name__)
+
+# Machine Learning
+from sklearn.model_selection import train_test_split
+import joblib
+import matplotlib.pyplot as plt
+from sklearn.metrics import (
+    classification_report,
+    confusion_matrix,
+    accuracy_score,
+    roc_auc_score,
+    roc_curve,
+    precision_score, recall_score, precision_recall_curve,
+    f1_score, mean_squared_error, r2_score
+)
+from sklearn.tree import export_text
+import inspect
+from sklearn.feature_selection import SelectFromModel
+from tabulate import tabulate
+from sklearn.feature_selection import VarianceThreshold
+import seaborn as sns
+import lightgbm as lgb
+from sklearn.model_selection import cross_val_score
+import optuna.visualization as vis
+import optuna
+from lightgbm import LGBMRegressor
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+
+# Tensorflow
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers, models
+from tensorflow.keras.models import load_model
+from keras.utils import plot_model
+from keras.models import Sequential
+from keras.layers import LSTM, Dense
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from tensorflow.keras.optimizers import Adam
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"    # désactive complètement le GPU
+os.environ["TF_XLA_FLAGS"] = "--tf_xla_enable_xla_devices=false"
+
+
+# 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"
+
+import warnings
+warnings.filterwarnings(
+    "ignore",
+    message=r".*No further splits with positive gain.*"
+)
+
+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
+
+
+class Zeus_LGBMRegressor(IStrategy):
+    startup_candle_count = 24 * 12
+
+    # Machine Learning
+    model_indicators = []
+    indicator_target = 'percent12'
+
+    model = None
+    # Tensorflow
+    lookback = 60
+    future_steps = 12
+
+    path = f"user_data/plots/"
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.564,
+        "567": 0.273,
+        "2814": 0.12,
+        "7675": 0
+    }
+
+    # Stoploss:
+    stoploss = -1  # 0.256
+    # Custom stoploss
+    use_custom_stoploss = False
+
+    trailing_stop = True
+    trailing_stop_positive = 0.15
+    trailing_stop_positive_offset = 0.20
+    trailing_only_offset_is_reached = True
+
+    # Buy hypers
+    timeframe = '5m'
+
+    max_open_trades = 5
+    max_amount = 40
+
+    parameters = {}
+    # DCA config
+    position_adjustment_enable = True
+
+    plot_config = {
+        "main_plot": {
+            "sma24_1h": {
+                "color": "pink"
+            },
+            "sma5_1d": {
+                "color": "blue"
+            },
+            # "sma24": {
+            #     "color": "yellow"
+            # },
+            "sma60": {
+                "color": "green"
+            },
+            "bb_lowerband": {
+                "color": "#da59a6"},
+            "bb_upperband": {
+                "color": "#da59a6",
+            },
+            # "sma12": {
+            #     "color": "blue"
+            # },
+            "mid_smooth_3_1h": {
+                "color": "blue"
+            }
+        },
+        "subplots": {
+            "Rsi": {
+                "max_rsi_24": {
+                    "color": "blue"
+                },
+                "max_rsi_24_1h": {
+                    "color": "pink"
+                },
+                # "rsi_1h": {
+                #     "color": "red"
+                # },
+                # "rsi_1d": {
+                #     "color": "blue"
+                # }
+            },
+            "Rsi_deriv1": {
+                "sma24_deriv1_1h": {
+                    "color": "pink"
+                },
+                "sma24_deriv1": {
+                    "color": "yellow"
+                },
+                "sma5_deriv1_1d": {
+                    "color": "blue"
+                },
+                "sma60_deriv1": {
+                    "color": "green"
+                }
+            },
+            "Rsi_deriv2": {
+                "sma24_deriv2_1h": {
+                    "color": "pink"
+                },
+                "sma24_deriv2": {
+                    "color": "yellow"
+                },
+                "sma5_deriv2_1d": {
+                    "color": "blue"
+                },
+                "sma60_deriv2": {
+                    "color": "green"
+                }
+            },
+            'Macd': {
+                "macd_rel_1d": {
+                    "color": "cyan"
+                },
+                "macdsignal_rel_1d": {
+                    "color": "pink"
+                },
+                "macdhist_rel_1d": {
+                    "color": "yellow"
+                }
+            }
+        }
+    }
+    columns_logged = False
+    pairs = {
+        pair: {
+            "first_buy": 0,
+            "last_buy": 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,
+            'previous_profit': 0,
+            "last_candle": {},
+            "last_count_of_buys": 0,
+            'base_stake_amount': 0,
+            'stop_buy': False,
+            'last_date': 0,
+            'stop': False,
+            'max_profit': 0,
+            '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"]
+    }
+    # 20 20 40   60 100 160 260 420
+    # 50 50 100 300 500
+    # fibo = [1, 1, 2, 3, 5, 8, 13, 21]
+    # my fibo
+    # 50 50 50 100 100 150 200 250 350 450 600 1050
+    fibo = [1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21]
+    baisse = [1, 2, 3, 5, 7, 10, 14, 19, 26, 35, 47, 63, 84]
+    # Ma suite 1  1  1  2  2  3  4  5  7  9  12 16 21
+    # Mise 50 50 50 100    100    150    200    250    350    450    600    800    1050
+    # Somme Mises  50 100    150    250    350    500    700    950    1300   1750   2350   3150   4200
+    # baisse   1  2  3  5  7  10 14 19 26 35 47 63 84
+
+    # factors = [1, 1.1, 1.25, 1.5, 2.0, 3]
+    # thresholds = [2, 5, 10, 20, 30, 50]
+
+    factors = [0.5, 0.75, 1, 1.25, 1.5, 2]
+    thresholds = [0, 2, 5, 10, 30, 45]
+
+    trades = list()
+    max_profit_pairs = {}
+
+    mise_factor_buy = DecimalParameter(0.01, 0.1, default=0.05, decimals=2, space='buy', optimize=True, load=True)
+
+    indicators = {'sma5', 'sma12', 'sma24', 'sma60'}
+    indicators_percent = {'percent', 'percent3', 'percent12', 'percent24', 'percent_1h', 'percent3_1h', 'percent12_1h', 'percent24_1h'}
+
+    mises = IntParameter(1, 50, default=5, space='buy', optimize=True, load=True)
+
+    ml_prob_buy = DecimalParameter(-0.5, 0.5, default=0.0, decimals=2, space='buy', optimize=True, load=True)
+    ml_prob_sell = DecimalParameter(-0.5, 0.5, default=0.0, decimals=2, space='sell', optimize=True, load=True)
+
+    pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=True, load=True)
+    pct_inc = DecimalParameter(0.0001, 0.003, default=0.0022, decimals=4, space='buy', optimize=True, load=True)
+
+    rsi_deb_protect = IntParameter(50, 90, default=70, space='protection', optimize=True, load=True)
+    rsi_end_protect = IntParameter(20, 60, default=55, space='protection', optimize=True, load=True)
+
+    sma24_deriv1_deb_protect = DecimalParameter(-4, 4, default=-2, decimals=1, space='protection', optimize=True, load=True)
+    sma24_deriv1_end_protect = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
+
+    # =========================================================================
+    should_enter_trade_count = 0
+
+    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()
+
+        condition = True #(last_candle[f"{indic_5m}_deriv1"] >= indic_deriv1_5m) and (last_candle[f"{indic_5m}_deriv2"] >= indic_deriv2_5m)
+
+        allow_to_buy = True #(condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
+
+        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_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 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()
+
+        minutes = int(round((current_time - trade.open_date_utc).seconds / 60, 0))
+        profit =trade.calc_profit(rate)
+        force = self.pairs[pair]['force_sell']
+        allow_to_sell = minutes > 30 and (last_candle['hapercent'] < 0 ) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
+
+        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_candle'] = last_candle
+            self.pairs[pair]['max_profit'] = 0
+            self.pairs[pair]['previous_profit'] = 0
+            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(profit, 2)
+            )
+            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]['current_trade'] = None
+        # else:
+            # self.printLog(f"{current_time} SELL triggered for {pair} ({exit_reason} profit={profit} minutes={minutes} percent={last_candle['hapercent']}) but condition blocked")
+        return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss')
+
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+        current_candle = dataframe.iloc[-1].squeeze()
+        adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle)
+
+        # print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
+
+        # Use default stake amount.
+        return adjusted_stake_amount
+
+    def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        last_candle_1h = dataframe.iloc[-13].squeeze()
+        before_last_candle = dataframe.iloc[-2].squeeze()
+        before_last_candle_2 = dataframe.iloc[-3].squeeze()
+        before_last_candle_12 = dataframe.iloc[-13].squeeze()
+
+        expected_profit = self.expectedProfit(pair, last_candle)
+        # print(f"current_time={current_time} current_profit={current_profit} expected_profit={expected_profit}")
+
+        max_touch_before = self.pairs[pair]['max_touch']
+        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 = trade.calc_profit(current_rate) #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 = 1 - (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'] or last_candle['stop_buying_1h'] else "🟢 CURRENT",
+                dispo=dispo,
+                pair=pair,
+                rate=last_candle['close'],
+                trade_type='',
+                profit=round(profit, 2),
+                buys=count_of_buys,
+                stake=0
+            )
+
+        pair_name = self.getShortName(pair)
+        if last_candle['max_rsi_24'] > 85 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0.05:
+            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_buy'] = False #(self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+            return str(count_of_buys) + '_' + 'Rsi85_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
+
+        if self.pairs[pair]['force_sell']:
+            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+            return str(count_of_buys) + '_' +  'Frc_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
+
+        if profit > 0 and baisse > 0.30:
+            self.pairs[pair]['force_sell'] = False
+            self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+            return str(count_of_buys) + '_' + 'B30_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
+
+        # if max_profit > 0.5 * count_of_buys and baisse > 0.15:
+        #     self.pairs[pair]['force_sell'] = False
+        #     self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+        #     return str(count_of_buys) + '_' + 'B15_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
+
+        if (last_candle['sma5_1h'] - before_last_candle_12['sma5_1h']) / last_candle['sma5_1h'] > 0.0002:
+            return None
+
+        factor = 1
+        if (self.getShortName(pair) == 'BTC'):
+            factor = 0.5
+        # if baisse > 2 and baisse > factor * self.pairs[pair]['total_amount'] / 100:
+        #     self.pairs[pair]['force_sell'] = False
+        #     self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
+        #     return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
+        #
+        # if 1 <= count_of_buys <= 3:
+        if last_candle['max_rsi_24'] > 75 and profit > expected_profit and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0:
+            self.pairs[pair]['force_sell'] = False
+            return str(count_of_buys) + '_' + 'Rsi75_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
+
+
+        self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
+
+    def getShortName(self, pair):
+        return pair.replace("/USDT", '').replace("/USDC", '').replace("_USDC", '').replace("_USDT", '')
+
+    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]
+        informative_pairs += [(pair, '1h') for pair in pairs]
+
+        return informative_pairs
+
+    from typing import List
+
+    def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float:
+        if pct <= thresholds[0]:
+            return factors[0]
+        if pct >= thresholds[-1]:
+            return factors[-1]
+
+        for i in range(1, len(thresholds)):
+            if pct <= thresholds[i]:
+                # interpolation linéaire entre thresholds[i-1] et thresholds[i]
+                return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / (
+                        thresholds[i] - thresholds[i - 1])
+
+        # Juste au cas où (devrait jamais arriver)
+        return factors[-1]
+
+    # def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
+    #                        start_factor: float = 1.0, end_factor: float = 2.0) -> float:
+    #     if pct <= start_pct:
+    #         return start_factor
+    #     if pct >= end_pct:
+    #         return end_factor
+    #     # interpolation linéaire
+    #     return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
+
+    def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
+                  last_candle=None):
+        # 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} "
+                f"| {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
+                f"{'rsi':>6}|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|tdc1d|tdc1h"
+            )
+            self.printLineLog()
+            df = pd.DataFrame.from_dict(self.pairs, orient='index')
+            colonnes_a_exclure = ['last_candle',
+                                  'trade_info', 'last_date', '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 = ''
+        # if last_candle is not None:
+        #     if (not np.isnan(last_candle['rsi_1d'])) and (not np.isnan(last_candle['rsi_1h'])):
+        #         rsi = str(int(last_candle['rsi_1d'])) + " " + str(int(last_candle['rsi_1h']))
+        #     if (not np.isnan(last_candle['rsi_pct_1d'])) and (not np.isnan(last_candle['rsi_pct_1h'])):
+        #         rsi_pct = str(int(10000 * last_candle['bb_mid_pct_1d'])) + " " + str(
+        #             int(last_candle['rsi_pct_1d'])) + " " + str(int(last_candle['rsi_pct_1h']))
+
+        # first_rate = self.percent_threshold.value
+        # last_rate = self.threshold.value
+        # action = self.color_line(action, action)
+        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 = ''
+        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 = ''
+
+        color = GREEN if profit > 0 else RED
+        color_sma24 = GREEN if last_candle['sma24_deriv1_1h'] > 0 else RED
+        color_sma24_2 = GREEN if last_candle['sma24_deriv2_1h'] > 0 else RED
+        color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1_1h'] > 0 else RED
+        color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2_1h'] > 0 else RED
+        color_sma5_1h = GREEN if last_candle['sma60_deriv1'] > 0 else RED
+        color_sma5_2h = GREEN if last_candle['sma60_deriv2'] > 0 else RED
+        color_smooth_1h = GREEN if last_candle['mid_smooth_1h_deriv1'] > 0 else RED
+        color_smooth2_1h = GREEN if last_candle['mid_smooth_1h_deriv2'] > 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.
+        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"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
+            # f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
+            f"{round(last_candle['max_rsi_24'], 1) or '-' :>6}|"
+            f"{dist_max:>7}|{color_sma24}{round(last_candle['sma24_deriv1_1h'], 2):>5}{RESET}"
+            f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1h'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1h'], 2):>5}{RESET}"
+            f"|{color_sma5_1h}{round(last_candle['sma60_deriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma60_deriv2'], 2):>5}{RESET}"
+            f"|{color_smooth_1h}{round(last_candle['mid_smooth_1h_deriv1'], 2):>5}{RESET}|{color_smooth2_1h}{round(last_candle['mid_smooth_1h_deriv2'], 2):>5}{RESET}"
+        )
+
+    def getLastLost(self, last_candle, pair):
+        last_lost = round((last_candle['close'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
+        return last_lost
+
+    def printLineLog(self):
+        # f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
+        self.printLog(
+            f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
+            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 add_tendency_column(self, dataframe: pd.DataFrame, name: str, suffixe: str = '', eps: float = 1e-3,
+                            d1_lim_inf: float = -0.01, d1_lim_sup: float = 0.01) -> pd.DataFrame:
+        """
+        Ajoute une colonne 'tendency' basée sur les dérivées 1 et 2 lissées et normalisées.
+        eps permet de définir un seuil proche de zéro.
+        suffixe permet de gérer plusieurs indicateurs.
+        """
+
+        def tag_by_derivatives(row):
+            d1 = row[f"{name}{suffixe}_deriv1"]
+            d2 = row[f"{name}{suffixe}_deriv2"]
+
+            # On considère les petites valeurs comme zéro
+            if abs(d1) < eps:
+                return 0  # Palier / neutre
+            if d1 > d1_lim_sup:
+                return 2 if d2 > eps else 1  # Acceleration Hausse / Ralentissement Hausse
+            if d1 < d1_lim_inf:
+                return -2 if d2 < -eps else -1  # Acceleration Baisse / Ralentissement Baisse
+            if abs(d1) < eps:
+                return 'DH' if d2 > eps else 'DB'  # Depart Hausse / Depart Baisse
+            return 'Mid'
+
+        print(f"{name}_tdc{suffixe}")
+        dataframe[f"{name}_tdc{suffixe}"] = dataframe.apply(tag_by_derivatives, axis=1)
+        return dataframe
+
+    # def add_tendency_column(self, dataframe: pd.DataFrame, name, suffixe='') -> pd.DataFrame:
+    #     def tag_by_derivatives(row):
+    #         d1 = row[f"{name}{suffixe}_deriv1"]
+    #         d2 = row[f"{name}{suffixe}_deriv2"]
+    #         d1_lim_inf = -0.01
+    #         d1_lim_sup = 0.01
+    #         if d1 >= d1_lim_inf and d1 <= d1_lim_sup:  # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
+    #             return 0  # Palier
+    #         if d1 == 0.0:
+    #             return 'DH' if d2 > 0 else 'DB'  # Depart Hausse / Départ Baisse
+    #         if d1 > d1_lim_sup:
+    #             return 2 if d2 > 0 else 1  # Acceleration Hausse / Ralentissement Hausse
+    #         if d1 < d1_lim_inf:
+    #             return -2 if d2 < 0 else -1  # Accéleration Baisse / Ralentissement Baisse
+    #         return 'Mid'
+    #
+    #     dataframe[f"tendency{suffixe}"] = dataframe.apply(tag_by_derivatives, axis=1)
+    #     return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Add all ta features
+        pair = metadata['pair']
+        short_pair = self.getShortName(pair)
+        self.path = f"user_data/plots/{short_pair}/"
+
+        dataframe = self.populateDataframe(dataframe, timeframe='5m')
+
+        ################### INFORMATIVE 1h
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
+        informative = self.populateDataframe(informative, timeframe='1h')
+        informative = self.calculateRegression(informative, 'mid', lookback=5)
+
+        # # TENSOR FLOW
+        # self.model_indicators = self.listUsableColumns(informative)
+        # if self.dp.runmode.value in ('backtest'):
+        #     self.trainTensorFlow(informative, future_steps = self.future_steps)
+        #
+        # self.predictTensorFlow(informative)
+        #
+        # if self.dp.runmode.value in ('backtest'):
+        #     self.kerasGenerateGraphs(informative)
+
+        informative['stop_buying_deb'] = ((informative['max_rsi_24'] > self.rsi_deb_protect.value)
+                                          & (informative['sma24_deriv1'] < self.sma24_deriv1_deb_protect.value)
+                                          )
+        informative['stop_buying_end'] = ((informative['max_rsi_24'] < self.rsi_end_protect.value)
+                                          & (informative['sma24_deriv1'] > self.sma24_deriv1_end_protect.value)
+                                          )
+
+        latched = np.zeros(len(informative), dtype=bool)
+
+        for i in range(1, len(informative)):
+            if informative['stop_buying_deb'].iloc[i]:
+                latched[i] = True
+            elif informative['stop_buying_end'].iloc[i]:
+                latched[i] = False
+            else:
+                latched[i] = latched[i - 1]
+
+        informative['stop_buying'] = latched
+
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
+
+        # ################### INFORMATIVE 1d
+        # informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
+        # informative = self.populateDataframe(informative, timeframe='1d')
+        # # informative = self.calculateRegression(informative, 'mid', lookback=15)
+        # dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
+
+        dataframe['last_price'] = dataframe['close']
+        dataframe['first_price'] = dataframe['close']
+        if self.dp:
+            if self.dp.runmode.value in ('live', 'dry_run'):
+                self.getOpenTrades()
+
+                for trade in self.trades:
+                    if trade.pair != pair:
+                        continue
+                    filled_buys = trade.select_filled_orders('buy')
+                    count = 0
+                    amount = 0
+                    for buy in filled_buys:
+                        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,
+                        # status=closed, date=2024-08-26 02:20:11)
+                        dataframe['last_price'] = buy.price
+                        self.pairs[pair]['last_buy'] = buy.price
+                        count = count + 1
+                        amount += buy.price * buy.filled
+                    # dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
+                    count_buys = count
+                    # dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
+                    self.pairs[pair]['total_amount'] = amount
+
+        # dataframe['mid_smooth_tag'] = qtpylib.crossed_below(dataframe['mid_smooth_24_deriv1'], dataframe['mid_smooth_deriv2_24'])
+
+        # ===============================
+        # lissage des valeurs horaires
+        dataframe['mid_smooth_1h'] = dataframe['mid'].rolling(window=6).mean()
+        dataframe["mid_smooth_1h_deriv1"] = 100 * dataframe["mid_smooth_1h"].diff().rolling(window=6).mean() / \
+                                            dataframe['mid_smooth_1h']
+        dataframe["mid_smooth_1h_deriv2"] = 100 * dataframe["mid_smooth_1h_deriv1"].diff().rolling(window=6).mean()
+
+        dataframe['mid_smooth_5h'] = talib.EMA(dataframe, timeperiod=60)  # dataframe['mid'].rolling(window=60).mean()
+        dataframe["mid_smooth_5h_deriv1"] = 100 * dataframe["mid_smooth_5h"].diff().rolling(window=60).mean() / \
+                                            dataframe['mid_smooth_5h']
+        dataframe["mid_smooth_5h_deriv2"] = 100 * dataframe["mid_smooth_5h_deriv1"].diff().rolling(window=60).mean()
+
+        dataframe = self.calculateRegression(dataframe, 'mid', lookback=10, future_steps=10, model_type="poly")
+        dataframe = self.calculateRegression(dataframe, 'sma24', lookback=12, future_steps=12)
+
+        # dataframe["ms-10"] = dataframe[self.indicator_target].shift(10)
+        # dataframe["ms-5"] = dataframe[self.indicator_target].shift(5)
+        # dataframe["ms-4"] = dataframe[self.indicator_target].shift(4)
+        # dataframe["ms-3"] = dataframe[self.indicator_target].shift(3)
+        # dataframe["ms-2"] = dataframe[self.indicator_target].shift(2)
+        # dataframe["ms-1"] = dataframe[self.indicator_target].shift(1)
+        # dataframe["ms-0"] = dataframe[self.indicator_target]
+        # dataframe["ms+10"] = dataframe["mid_smooth_24"].shift(-11)
+
+        self.model_indicators = self.listUsableColumns(dataframe)
+
+        # # Quantile
+        # self.add_future_quantiles(
+        #     dataframe,
+        #     indic="mid",
+        #     lookback=40,
+        #     future_steps=5
+        # )
+
+        # TENSOR FLOW
+        if self.dp.runmode.value in ('backtest'):
+            self.trainTensorFlow(dataframe, future_steps = self.future_steps)
+
+        self.predictTensorFlow(dataframe)
+
+        if self.dp.runmode.value in ('backtest'):
+            self.kerasGenerateGraphs(dataframe)
+
+        # SKLEARN
+        # if self.dp.runmode.value in ('backtest'):
+            # self.trainModel(dataframe, metadata)
+
+        # short_pair = self.getShortName(pair)
+        # self.model = joblib.load(f"{short_pair}_rf_model.pkl")
+        #
+        # # Préparer les features pour la prédiction
+        # features = dataframe[self.model_indicators].fillna(0)
+        #
+        # # Prédiction : probabilité que le prix monte
+        # # probs = self.model.predict_proba(features)[:, 1]
+        # probs = self.model.predict(features)
+        #
+        # # Sauvegarder la probabilité pour l’analyse
+        # dataframe['ml_prob'] = probs
+        #
+        # self.inspect_model(self.model)
+
+        return dataframe
+
+    def trainModel(self, dataframe: DataFrame, metadata: dict):
+        pair = self.getShortName(metadata['pair'])
+        pd.set_option('display.max_rows', None)
+        pd.set_option('display.max_columns', None)
+        pd.set_option("display.width", 200)
+
+        os.makedirs(self.path, exist_ok=True)
+
+        df = dataframe[self.model_indicators].copy()
+
+        # Corrélations des colonnes
+        corr = df.corr(numeric_only=True)
+        print("Corrélation des colonnes")
+        print(corr)
+
+        # 3️⃣ Créer la cible : 1 si le prix monte dans les prochaines bougies
+        # df['target'] = (df['sma24'].shift(-24) > df['sma24']).astype(int)
+        df['target'] = dataframe[self.indicator_target].shift(-24) # > df['sma24'] * 1.003).astype(int)
+        df['target'] = df['target'].fillna(0) #.astype(int)
+
+        # Corrélations triées par importance avec une colonne cible
+        target_corr = df.corr(numeric_only=True)["target"].sort_values(ascending=False)
+        print("Corrélations triées par importance avec une colonne cible")
+        print(target_corr)
+
+        # Corrélations triées par importance avec une colonne cible
+        corr = df.corr(numeric_only=True)
+        corr_unstacked = (
+            corr.unstack()
+                .reset_index()
+                .rename(columns={"level_0": "col1", "level_1": "col2", 0: "corr"})
+        )
+        # Supprimer les doublons col1/col2 inversés et soi-même
+        corr_unstacked = corr_unstacked[corr_unstacked["col1"] < corr_unstacked["col2"]]
+
+        # Trier par valeur absolue de corrélation
+        corr_sorted = corr_unstacked.reindex(corr_unstacked["corr"].abs().sort_values(ascending=False).index)
+        print("Trier par valeur absolue de corrélation")
+        print(corr_sorted.head(20))
+
+        # --- Calcul de la corrélation ---
+        corr = df.corr(numeric_only=True)  # évite les colonnes non numériques
+        corr = corr * 100  # passage en pourcentage
+
+        # --- Masque pour n’afficher que le triangle supérieur (optionnel) ---
+        mask = np.triu(np.ones_like(corr, dtype=bool))
+
+        # --- Création de la figure ---
+        fig, ax = plt.subplots(figsize=(96, 36))
+
+        # --- Heatmap avec un effet “température” ---
+        sns.heatmap(
+            corr,
+            mask=mask,
+            cmap="coolwarm",  # palette bleu → rouge
+            center=0,  # 0 au centre
+            annot=True,  # affiche les valeurs dans chaque case
+            fmt=".0f",  # format entier (pas de décimale)
+            cbar_kws={"label": "Corrélation (%)"},  # légende à droite
+            linewidths=0.5,  # petites lignes entre les cases
+            ax=ax
+        )
+
+        # --- Personnalisation ---
+        ax.set_title("Matrice de corrélation (en %)", fontsize=20, pad=20)
+        plt.xticks(rotation=45, ha="right")
+        plt.yticks(rotation=0)
+
+        # --- Sauvegarde ---
+        output_path = f"{self.path}/Matrice_de_correlation_temperature.png"
+        plt.savefig(output_path, bbox_inches="tight", dpi=150)
+        plt.close(fig)
+
+        print(f"✅ Matrice enregistrée : {output_path}")
+
+        # Nettoyage
+        df = df.dropna()
+
+        X = df[self.model_indicators]
+        y = df['target']  # ta colonne cible binaire ou numérique
+        print(self.feature_auc_scores(X, y))
+
+        # 4️⃣ Split train/test
+        X = df[self.model_indicators]
+        y = df['target']
+        # Séparation temporelle (train = 80 %, valid = 20 %)
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
+
+        print("NaN per column:")
+        print(X_train.isna().sum().sort_values(ascending=False).head(20))
+
+        # Nettoyage des valeurs invalides
+
+        selector = VarianceThreshold(threshold=0.0001)
+        selector.fit(X_train)
+        selected = X_train.columns[selector.get_support()]
+        print("Colonnes conservées :", list(selected))
+
+        # 1️⃣ Entraîne ton modèle LGBM normal
+
+        # train_model = LGBMRegressor(
+        #     objective='regression',
+        #     metric='rmse',  # tu peux aussi tester 'mae'
+        #     n_estimators=300,
+        #     learning_rate=0.05,
+        #     max_depth=7,
+        #     subsample=0.8,
+        #     colsample_bytree=0.8,
+        #     random_state=42
+        # )
+        # train_model.fit(X_train, y_train)
+
+        train_model, selected_features = self.optuna(self.path, X_train, X_test, y_train, y_test)
+        print("Features retenues :", list(selected_features))
+
+        # # 2️⃣ Sélection des features AVANT calibration
+        # sfm = SelectFromModel(train_model, threshold="median", prefit=True)
+        # selected_features = X_train.columns[sfm.get_support()]
+        # print(selected_features)
+
+        train_model.fit(X_train, y_train)
+
+        # Importances
+        importances = pd.DataFrame({
+            "feature": train_model.feature_name_,
+            "importance": train_model.feature_importances_
+        }).sort_values("importance", ascending=False)
+        print("\n===== 🔍 IMPORTANCE DES FEATURES =====")
+
+        print(importances)
+
+        # 6️⃣ Évaluer la précision (facultatif)
+        preds = train_model.predict(X_test)
+
+        mse = mean_squared_error(y_test, preds)
+        rmse = np.sqrt(mse)
+        r2 = r2_score(y_test, preds)
+
+        print(f"RMSE: {rmse:.5f} | R²: {r2:.3f}")
+
+        # acc = accuracy_score(y_test, preds)
+        # print(f"Accuracy: {acc:.3f}")
+
+        # 7️⃣ Sauvegarde du modèle
+        joblib.dump(train_model, f"{pair}_rf_model.pkl")
+        print(f"✅ Modèle sauvegardé sous {pair}_rf_model.pkl")
+
+        # # Quantile
+        # dataframe = self.add_future_quantiles(
+        #     df,
+        #     indic="mid",
+        #     lookback=40,
+        #     future_steps=5
+        # )
+
+        self.analyze_model(pair, train_model, X_train, X_test, y_train, y_test)
+
+    def listUsableColumns(self, dataframe):
+        # Étape 1 : sélectionner numériques
+        numeric_cols = dataframe.select_dtypes(include=['int64', 'float64']).columns
+        # Étape 2 : enlever constantes
+        usable_cols = [c for c in numeric_cols if dataframe[c].nunique() > 1
+                       and not c.endswith("_state") and not c.endswith("_1d")
+                            # and not c.endswith("_1h")
+                            and not c.endswith("_count")
+                            # and not c.startswith("open") and not c.startswith("close")
+                            # and not c.startswith("low") and not c.startswith("high")
+                            # and not c.startswith("haopen") and not c.startswith("haclose")
+                            # and not c.startswith("bb_lower") and not c.startswith("bb_upper")
+                            # and not c.startswith("bb_middle")
+                            and not c.endswith("_class") and not c.endswith("_price")
+                            and not c.startswith('stop_buying')]
+        # Étape 3 : remplacer inf et NaN par 0
+        dataframe[usable_cols] = dataframe[usable_cols].replace([np.inf, -np.inf], 0).fillna(0)
+        print("Colonnes utilisables pour le modèle :")
+        print(usable_cols)
+        self.model_indicators = usable_cols
+        # self.model_indicators = [
+        #                          'volume', 'hapercent', 'mid', 'percent', 'percent3', 'percent12',
+        #                          'percent24',
+        #                          'sma5', 'sma5_dist', 'sma5_deriv1', 'sma5_deriv2', 'sma12', 'sma12_dist',
+        #                          'sma12_deriv1', 'sma12_deriv2', 'sma24', 'sma24_dist', 'sma24_deriv1', 'sma24_deriv2',
+        #                          # 'sma48', 'sma48_dist', 'sma48_deriv1', 'sma48_deriv2', 'sma60', 'sma60_dist',
+        #                          # 'sma60_deriv1', 'sma60_deriv2', 'mid_smooth_3', 'mid_smooth_3_dist',
+        #                          # 'mid_smooth_3_deriv1', 'mid_smooth_3_deriv2', 'mid_smooth_5', 'mid_smooth_5_dist',
+        #                          # 'mid_smooth_5_deriv1', 'mid_smooth_5_deriv2', 'mid_smooth_12', 'mid_smooth_12_dist',
+        #                          # 'mid_smooth_12_deriv1', 'mid_smooth_12_deriv2', 'mid_smooth_24', 'mid_smooth_24_dist',
+        #                          # 'mid_smooth_24_deriv1', 'mid_smooth_24_deriv2', 'rsi', 'max_rsi_12', 'max_rsi_24',
+        #                          'rsi_dist', 'rsi_deriv1', 'rsi_deriv2', 'max12', 'min12', 'max60', 'min60',
+        #                          'min_max_60', 'bb_percent', 'bb_width', 'macd', 'macdsignal', 'macdhist', 'slope',
+        #                          'slope_smooth', 'atr', 'atr_norm', 'adx', 'obv', 'vol_24',
+        #                          # 'down_count', 'up_count',
+        #                          # 'down_pct', 'up_pct', 'rsi_slope', 'adx_change', 'volatility_ratio', 'rsi_diff',
+        #                          # 'slope_ratio', 'volume_sma_deriv', 'volume_dist', 'volume_deriv1', 'volume_deriv2',
+        #                          # 'slope_norm', 'mid_smooth_1h_deriv1', 'mid_smooth_1h_deriv2', 'mid_smooth_5h',
+        #                          # 'mid_smooth_5h_deriv1', 'mid_smooth_5h_deriv2', 'mid_future_pred_cons',
+        #                          # 'sma24_future_pred_cons'
+        #                         ]
+        return self.model_indicators
+
+    def inspect_model(self, model):
+        """
+        Affiche les informations d'un modèle ML déjà entraîné.
+        Compatible avec scikit-learn, xgboost, lightgbm, catboost...
+        """
+
+        print("===== 🔍 INFORMATIONS DU MODÈLE =====")
+
+        # Type de modèle
+        print(f"Type : {type(model).__name__}")
+        print(f"Module : {model.__class__.__module__}")
+
+        # Hyperparamètres
+        if hasattr(model, "get_params"):
+            params = model.get_params()
+            print(f"\n===== ⚙️ HYPERPARAMÈTRES ({len(params)}) =====")
+            for k, v in params.items():
+                print(f"{k}: {v}")
+
+        # Nombre d’estimateurs
+        if hasattr(model, "n_estimators"):
+            print(f"\nNombre d’estimateurs : {model.n_estimators}")
+
+        # Importance des features
+        if hasattr(model, "feature_importances_"):
+            print("\n===== 📊 IMPORTANCE DES FEATURES =====")
+
+            # Correction ici :
+            feature_names = getattr(model, "feature_names_in_", None)
+            if isinstance(feature_names, np.ndarray):
+                feature_names = feature_names.tolist()
+            elif feature_names is None:
+                feature_names = [f"feature_{i}" for i in range(len(model.feature_importances_))]
+
+            fi = pd.DataFrame({
+                "feature": feature_names,
+                "importance": model.feature_importances_
+            }).sort_values(by="importance", ascending=False)
+
+            print(fi)
+
+        # Coefficients (modèles linéaires)
+        if hasattr(model, "coef_"):
+            print("\n===== ➗ COEFFICIENTS =====")
+            coef = np.array(model.coef_)
+            if coef.ndim == 1:
+                for i, c in enumerate(coef):
+                    print(f"Feature {i}: {c:.6f}")
+            else:
+                print(coef)
+
+        # Intercept
+        if hasattr(model, "intercept_"):
+            print("\nIntercept :", model.intercept_)
+
+        # Classes connues
+        if hasattr(model, "classes_"):
+            print("\n===== 🎯 CLASSES =====")
+            print(model.classes_)
+
+        # Scores internes
+        for attr in ["best_score_", "best_iteration_", "best_ntree_limit", "score_"]:
+            if hasattr(model, attr):
+                print(f"\n{attr} = {getattr(model, attr)}")
+
+        # Méthodes disponibles
+        print("\n===== 🧩 MÉTHODES DISPONIBLES =====")
+        methods = [m for m, _ in inspect.getmembers(model, predicate=inspect.ismethod)]
+        print(", ".join(methods[:15]) + ("..." if len(methods) > 15 else ""))
+
+        print("\n===== ✅ FIN DE L’INSPECTION =====")
+
+    def analyze_model(self, pair, model, X_train, X_test, y_train, y_test):
+        """
+        Analyse complète d'un modèle ML supervisé (classification binaire).
+        Affiche performances, importance des features, matrices, seuils, etc.
+        """
+        output_dir = f"user_data/plots/{pair}/"
+        os.makedirs(output_dir, exist_ok=True)
+
+        # ---- Importance des features ----
+        if hasattr(model, "feature_importances_"):
+            print("\n===== 🔍 IMPORTANCE DES FEATURES =====")
+            importance = pd.DataFrame({
+                "feature": X_train.columns,
+                "importance": model.feature_importances_
+            }).sort_values(by="importance", ascending=False)
+            print(importance)
+            top_n = 20
+            importance = importance.head(top_n)
+
+            # Crée une figure plus grande
+            fig, ax = plt.subplots(figsize=(24, 8))  # largeur=24 pouces, hauteur=8 pouces
+
+            # Trace le bar plot sur cet axe
+            importance.plot.bar(x="feature", y="importance", legend=False, ax=ax)
+
+            # Tourner les labels pour plus de lisibilité
+            ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
+
+            plt.title("Importance des features")
+            # plt.show()
+            plt.savefig(os.path.join(output_dir, "Importance des features.png"), bbox_inches="tight")
+            plt.close()
+
+        # ---- Arbre de décision (extrait) ----
+        if hasattr(model, "estimators_"):
+            print("\n===== 🌳 EXTRAIT D’UN ARBRE =====")
+            print(export_text(model.estimators_[0], feature_names=list(X_train.columns))[:800])
+
+        # --- Après l'entraînement du modèle ---
+        preds = model.predict(X_test)
+
+        # --- Évaluation ---
+        mse = mean_squared_error(y_test, preds)
+        rmse = np.sqrt(mse)
+        r2 = r2_score(y_test, preds)
+
+        print(f"RMSE: {rmse:.5f} | R²: {r2:.3f}")
+
+        # --- Création du dossier de sortie ---
+        os.makedirs(output_dir, exist_ok=True)
+
+        # --- Graphique prédiction vs réel ---
+        plt.figure(figsize=(8, 8))
+        plt.scatter(y_test, preds, alpha=0.4, s=15)
+        plt.xlabel("Valeurs réelles", fontsize=12)
+        plt.ylabel("Valeurs prédites", fontsize=12)
+        plt.title(f"LightGBM Régression — Prédiction vs Réel\nRMSE={rmse:.5f} | R²={r2:.3f}", fontsize=14)
+        plt.plot(
+            [y_test.min(), y_test.max()],
+            [y_test.min(), y_test.max()],
+            'r--',
+            linewidth=1,
+            label="Ligne idéale"
+        )
+        plt.legend()
+
+        # --- Sauvegarde ---
+        plot_path = os.path.join(output_dir, "LightGBM_regression_pred_vs_real.png")
+        plt.savefig(plot_path, bbox_inches="tight", dpi=200)
+        plt.close()
+
+        self.plot_pred_vs_real_filtered(model, X_test, y_test, preds, output_dir)
+
+
+        print(f"✅ Graphique sauvegardé : {plot_path}")
+
+        # ax = lgb.plot_tree(model, tree_index=0, figsize=(30, 20), show_info=["split_gain", "internal_value", "internal_count"])
+        # plt.title("Arbre de décision n°0")
+        # plt.savefig(os.path.join(output_dir, "lgbm_tree_0.png"), bbox_inches="tight")
+        # plt.close()
+
+        for i in range(5):
+            ax = lgb.plot_tree(model, tree_index=i, figsize=(20, 12))
+            plt.title(f"Arbre {i}")
+            plt.savefig(os.path.join(output_dir, f"lgbm_tree_{i}.png"), bbox_inches="tight")
+            plt.close()
+
+        ax = lgb.plot_tree(model, figsize=(40, 20))
+        plt.title("Vue globale du modèle LGBM")
+        plt.savefig(os.path.join(output_dir, "lgbm_all_trees.png"), bbox_inches="tight")
+        plt.close()
+        # X_test = np.linspace(0, 10, 1000).reshape(-1, 1)
+        y_pred = model.predict(X_test)
+
+        self.graphFonctionApprise(output_dir, X_test, y_test, y_pred)
+        self.graphFonctionAppriseFeature(output_dir, X_test, y_test, y_pred)
+
+        # ==============================================================================
+
+        ax = lgb.plot_importance(model, max_num_features=30, figsize=(12, 6))
+        plt.title("Importance des features - LGBM")
+        plt.savefig(os.path.join(output_dir, "lgbm_feature_importance.png"), bbox_inches="tight")
+        plt.close()
+
+        corr = X_train.corr() * 100  # en pourcentage
+
+        plt.figure(figsize=(20, 16))
+        sns.heatmap(corr, cmap="coolwarm", center=0, annot=False, fmt=".1f", cbar_kws={'label': 'Corrélation (%)'})
+        plt.title("Matrice de corrélation (%)")
+        plt.savefig(os.path.join(output_dir, "correlation_matrix.png"), bbox_inches="tight")
+        plt.close()
+
+        plt.figure(figsize=(10, 6))
+        plt.scatter(y_test, model.predict(X_test), alpha=0.5)
+        plt.xlabel("Valeurs réelles")
+        plt.ylabel("Prédictions du modèle")
+        plt.title("Comparaison y_test vs y_pred")
+        plt.savefig(os.path.join(output_dir, "ytest_vs_ypred.png"), bbox_inches="tight")
+        plt.close()
+
+        print("\n===== ✅ FIN DE L’ANALYSE =====")
+
+    def plot_pred_vs_real_filtered(self, model, X_test, y_test, preds, output_dir, top_n=5):
+        """
+        Affiche le graphique prédiction vs réel pour les N features les plus importantes.
+        """
+        # --- 1️⃣ Extraire les features les plus importantes ---
+        importance_df = pd.DataFrame({
+            "feature": X_test.columns,
+            "importance": model.feature_importances_
+        }).sort_values(by="importance", ascending=False)
+
+        top_features = importance_df.head(top_n)["feature"].tolist()
+        print(f"Top {top_n} features: {top_features}")
+
+        # --- 2️⃣ Créer un masque pour ne garder que les lignes où au moins une des top features varie fortement ---
+        X_top = X_test[top_features]
+
+        # Optionnel : filtrer les points atypiques pour lisser le nuage
+        mask = np.all(np.abs((X_top - X_top.mean()) / X_top.std()) < 3, axis=1)
+        X_filtered = X_top[mask]
+        y_filtered = y_test[mask]
+        preds_filtered = preds[mask]
+
+        # --- 3️⃣ Tracer ---
+        plt.figure(figsize=(8, 8))
+        plt.scatter(y_filtered, preds_filtered, alpha=0.4, s=15, c='blue', label=f"Top {top_n} features")
+        plt.xlabel("Valeurs réelles", fontsize=12)
+        plt.ylabel("Valeurs prédites", fontsize=12)
+        plt.title(f"LightGBM Régression — Prédiction vs Réel (filtré sur top {top_n} features)", fontsize=14)
+        plt.plot(
+            [y_filtered.min(), y_filtered.max()],
+            [y_filtered.min(), y_filtered.max()],
+            'r--',
+            linewidth=1,
+            label="Ligne idéale"
+        )
+        plt.legend()
+        plt.grid(True)
+
+        out_path = f"{output_dir}/lgbm_pred_vs_real_top{top_n}.png"
+        plt.savefig(out_path, bbox_inches="tight")
+        plt.close()
+
+    def plot_threshold_analysis(self, y_true, y_proba, step=0.05, save_path=None):
+        """
+        Affiche la précision, le rappel et le F1-score selon le seuil de décision.
+        y_true : labels réels (0 ou 1)
+        y_proba : probabilités prédites (P(hausse))
+        step : pas entre les seuils testés
+        save_path : si renseigné, enregistre l'image au lieu d'afficher
+        """
+
+        # Le graphique généré affichera trois courbes :
+        #
+        # 🔵 Precision — la fiabilité de tes signaux haussiers.
+        # 🟢 Recall — la proportion de hausses que ton modèle détecte.
+        # 🟣 F1-score — le compromis optimal entre les deux.
+
+        thresholds = np.arange(0, 1.01, step)
+        precisions, recalls, f1s = [], [], []
+
+        for thr in thresholds:
+            preds = (y_proba >= thr).astype(int)
+            precisions.append(precision_score(y_true, preds))
+            recalls.append(recall_score(y_true, preds))
+            f1s.append(f1_score(y_true, preds))
+
+        plt.figure(figsize=(10, 6))
+        plt.plot(thresholds, precisions, label="Precision", linewidth=2)
+        plt.plot(thresholds, recalls, label="Recall", linewidth=2)
+        plt.plot(thresholds, f1s, label="F1-score", linewidth=2, linestyle="--")
+        plt.axvline(0.5, color='gray', linestyle=':', label="Seuil 0.5")
+        plt.title("📊 Performance selon le seuil de probabilité", fontsize=14)
+        plt.xlabel("Seuil de décision (threshold)")
+        plt.ylabel("Score")
+        plt.legend()
+        plt.grid(True, alpha=0.3)
+
+        if save_path:
+            plt.savefig(save_path, bbox_inches='tight')
+            print(f"✅ Graphique enregistré : {save_path}")
+        else:
+            plt.show()
+
+    # # =============================
+    # # Exemple d’utilisation :
+    # # =============================
+    # if __name__ == "__main__":
+    #     # Exemple : chargement d’un modèle et test
+    #     import joblib
+    #
+    #     model = joblib.load("/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/model.pkl")
+    #     data = np.load("/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/test_data.npz")
+    #     X_test, y_test = data["X"], data["y"]
+    #
+    #     y_proba = model.predict_proba(X_test)[:, 1]
+    #
+    #     # Trace ou enregistre le graphique
+    #     plot_threshold_analysis(y_test, y_proba, step=0.05,
+    #                             save_path="/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/threshold_analysis.png")
+
+
+    def populateDataframe(self, dataframe, timeframe='5m'):
+        dataframe = dataframe.copy()
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['haopen'] = heikinashi['open']
+        dataframe['haclose'] = heikinashi['close']
+        dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
+        dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
+
+        dataframe["percent"] = dataframe['close'].pct_change()
+        dataframe["percent3"] = dataframe['close'].pct_change(3).rolling(3).mean()
+        dataframe["percent12"] = dataframe['close'].pct_change(12).rolling(12).mean()
+        dataframe["percent24"] = dataframe['close'].pct_change(24).rolling(24).mean()
+
+        # if self.dp.runmode.value in ('backtest'):
+        #     dataframe['futur_percent'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
+
+        dataframe['sma5'] = dataframe['mid'].ewm(span=5, adjust=False).mean() #dataframe["mid"].rolling(window=5).mean()
+        self.calculeDerivees(dataframe, 'sma5', timeframe=timeframe, ema_period=5)
+        dataframe['sma12'] = dataframe['mid'].ewm(span=12, adjust=False).mean() #dataframe["mid"].rolling(window=12).mean()
+        self.calculeDerivees(dataframe, 'sma12', timeframe=timeframe, ema_period=12)
+        dataframe['sma24'] = dataframe['mid'].ewm(span=24, adjust=False).mean() #dataframe["mid"].rolling(window=24).mean()
+        self.calculeDerivees(dataframe, 'sma24', timeframe=timeframe, ema_period=24)
+        dataframe['sma48'] = dataframe['mid'].ewm(span=48, adjust=False).mean() #dataframe["mid"].rolling(window=48).mean()
+        self.calculeDerivees(dataframe, 'sma48', timeframe=timeframe, ema_period=48)
+        dataframe['sma60'] = dataframe['mid'].ewm(span=60, adjust=False).mean() #dataframe["mid"].rolling(window=60).mean()
+        self.calculeDerivees(dataframe, 'sma60', timeframe=timeframe, ema_period=60)
+
+        dataframe = self.calculateDerivation(dataframe, window=3, suffixe="_3",timeframe=timeframe)
+        dataframe = self.calculateDerivation(dataframe, window=5, suffixe="_5",timeframe=timeframe)
+        dataframe = self.calculateDerivation(dataframe, window=12, suffixe="_12",timeframe=timeframe)
+        dataframe = self.calculateDerivation(dataframe, window=24, suffixe="_24", timeframe=timeframe)
+        # 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', timeframe=timeframe, ema_period=12)
+        dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
+        dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
+        dataframe['max60'] = talib.MAX(dataframe['close'], timeperiod=60)
+        dataframe['min60'] = talib.MIN(dataframe['close'], timeperiod=60)
+        dataframe['min_max_60'] = ((dataframe['max60'] - dataframe['close']) / dataframe['min60'])
+        # dataframe['min36'] = talib.MIN(dataframe['close'], timeperiod=36)
+        # dataframe['max36'] = talib.MAX(dataframe['close'], timeperiod=36)
+        # dataframe['pct36'] = 100 * (dataframe['max36'] - dataframe['min36']) / dataframe['min36']
+        # dataframe['maxpct36'] = talib.MAX(dataframe['pct36'], timeperiod=36)
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["sma5"]
+
+        # dataframe["bb_width"] = (
+        #     (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        # )
+
+        # Calcul MACD
+        macd, macdsignal, macdhist = talib.MACD(
+            dataframe['close'],
+            fastperiod=12,
+            slowperiod=26,
+            signalperiod=9
+        )
+
+        # | Nom                          | Formule / définition                        | Signification                                                                                                                                                                                                                                                                                         |
+        # | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+        # | **MACD** (`macd`)            | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue. <br> - Positive → tendance haussière <br> - Négative → tendance baissière                                                                                                                                                                |
+        # | **Signal** (`macdsignal`)    | `EMA_9(MACD)`                               | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente                                                                                                                                             |
+        # | **Histogramme** (`macdhist`) | `MACD - Signal`                             | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - Négatif mais croissant → ralentissement de la baisse |
+
+        # Ajouter dans le dataframe
+        dataframe['macd'] = macd
+        dataframe['macdsignal'] = macdsignal
+        dataframe['macdhist'] = macdhist
+
+        # Regarde dans le futur
+        # # --- Rendre relatif sur chaque série (-1 → 1) ---
+        # for col in ['macd', 'macdsignal', 'macdhist']:
+        #     series = dataframe[col]
+        #     valid = series[~np.isnan(series)]  # ignorer NaN
+        #     min_val = valid.min()
+        #     max_val = valid.max()
+        #     span = max_val - min_val if max_val != min_val else 1
+        #     dataframe[f'{col}_rel'] = 2 * ((series - min_val) / span) - 1
+        #
+        # dataframe['tdc_macd'] = self.macd_tendance_int(
+        #     dataframe,
+        #     macd_col='macd_rel',
+        #     signal_col='macdsignal_rel',
+        #     hist_col='macdhist_rel'
+        # )
+
+        # --- pente brute ---
+        dataframe['slope'] = dataframe['sma24'].diff()
+
+        # --- lissage EMA ---
+        dataframe['slope_smooth'] = dataframe['slope'].ewm(span=10, adjust=False).mean()
+
+        # --- Volatilité normalisée ---
+        dataframe['atr'] = ta.volatility.AverageTrueRange(
+            high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
+        ).average_true_range()
+        dataframe['atr_norm'] = dataframe['atr'] / dataframe['close']
+
+        # --- Force de tendance ---
+        dataframe['adx'] = ta.trend.ADXIndicator(
+            high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
+        ).adx()
+
+        # --- Volume directionnel (On Balance Volume) ---
+        dataframe['obv'] = ta.volume.OnBalanceVolumeIndicator(
+            close=dataframe['close'], volume=dataframe['volume']
+        ).on_balance_volume()
+
+        # --- Volatilité récente (écart-type des rendements) ---
+        dataframe['vol_24'] = dataframe['percent'].rolling(24).std()
+
+        # Compter les baisses / hausses consécutives
+        self.calculateDownAndUp(dataframe, limit=0.0001)
+
+        # df : ton dataframe OHLCV + indicateurs existants
+        # Assurez-vous que les colonnes suivantes existent :
+        # 'max_rsi_12', 'roc_24', 'bb_percent_1h'
+
+        # --- Filtrage des NaN initiaux ---
+        # dataframe = dataframe.dropna()
+
+        dataframe['rsi_slope'] = dataframe['rsi'].diff(3) / 3  # vitesse moyenne du RSI
+        dataframe['adx_change'] = dataframe['adx'] - dataframe['adx'].shift(12)  # évolution de la tendance
+        dataframe['volatility_ratio'] = dataframe['atr_norm'] / dataframe['bb_width']
+
+        dataframe["rsi_diff"] = dataframe["rsi"] - dataframe["rsi"].shift(3)
+        dataframe["slope_ratio"] = dataframe["sma5_deriv1"] / (dataframe["sma60_deriv1"] + 1e-9)
+        dataframe["divergence"] = (dataframe["rsi_deriv1"] * dataframe["sma5_deriv1"]) < 0
+
+        ###########################
+
+        dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
+        self.calculeDerivees(dataframe, 'volume', timeframe=timeframe, ema_period=12)
+
+        self.setTrends(dataframe)
+
+        return dataframe
+
+    def feature_auc_scores(self, X, y):
+        aucs = {}
+        for col in X.columns:
+            try:
+                aucs[col] = roc_auc_score(y, X[col].ffill().fillna(0))
+            except Exception:
+                aucs[col] = np.nan
+        return pd.Series(aucs).sort_values(ascending=False)
+
+    def macd_tendance_int(self, dataframe: pd.DataFrame,
+                          macd_col='macd',
+                          signal_col='macdsignal',
+                          hist_col='macdhist',
+                          eps=0.0) -> pd.Series:
+        """
+        Renvoie la tendance MACD sous forme d'entiers.
+        2    : Haussier
+        1    : Ralentissement hausse
+        0    : Neutre
+        -1   : Ralentissement baisse
+        -2   : Baissier
+        """
+        
+        # | Nom                          | Formule / définition                        | Signification                                                                                                                                                                                                                                                                                         |
+        # | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+        # | **MACD** (`macd`)            | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue. <br> - Positive → tendance haussière <br> - Négative → tendance baissière                                                                                                                                                                |
+        # | **Signal** (`macdsignal`)    | `EMA_9(MACD)`                               | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente                                                                                                                                             |
+        # | **Histogramme** (`macdhist`) | `MACD - Signal`                             | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - Négatif mais croissant → ralentissement de la baisse |
+
+        # | Situation                  | MACD       | Signal    | Hist     | Interprétation                             |
+        # | -------------------------- | ---------- | --------- | -------- | ------------------------------------------ |
+        # | MACD > 0, Hist croissant   | au-dessus  | croissant | Haussier | Momentum fort → tendance haussière         |
+        # | MACD > 0, Hist décroissant | au-dessus  | en baisse | Momentum | La hausse ralentit, prudence               |
+        # | MACD < 0, Hist décroissant | en dessous | en baisse | Baissier | Momentum fort → tendance baissière         |
+        # | MACD < 0, Hist croissant   | en dessous | en hausse | Rebond ? | La baisse ralentit → possible retournement |
+
+        # Créer une série de 0 par défaut
+        tendance = pd.Series(0, index=dataframe.index)
+
+        # Cas MACD > signal
+        mask_up = dataframe[macd_col] > dataframe[signal_col] + eps
+        mask_up_hist_pos = mask_up & (dataframe[hist_col] > 0)
+        mask_up_hist_neg = mask_up & (dataframe[hist_col] <= 0)
+
+        tendance[mask_up_hist_pos] = 2  # Haussier
+        tendance[mask_up_hist_neg] = 1  # Ralentissement hausse
+
+        # Cas MACD < signal
+        mask_down = dataframe[macd_col] < dataframe[signal_col] - eps
+        mask_down_hist_neg = mask_down & (dataframe[hist_col] < 0)
+        mask_down_hist_pos = mask_down & (dataframe[hist_col] >= 0)
+
+        tendance[mask_down_hist_neg] = -2  # Baissier
+        tendance[mask_down_hist_pos] = -1  # Ralentissement baisse
+
+        # Les NaN deviennent neutre
+        tendance[dataframe[[macd_col, signal_col, hist_col]].isna().any(axis=1)] = 0
+
+        return tendance
+
+    def calculateDownAndUp(self, dataframe, limit=0.0001):
+        dataframe['down'] = dataframe['hapercent'] <= limit
+        dataframe['up'] = dataframe['hapercent'] >= limit
+        dataframe['down_count'] = - dataframe['down'].astype(int) * (
+                dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
+        dataframe['up_count'] = dataframe['up'].astype(int) * (
+                dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
+        # Créer une colonne vide
+        dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
+        dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
+
+    def calculateDerivation(self, dataframe, window=12, suffixe='', timeframe='5m'):
+        dataframe[f"mid_smooth{suffixe}"] = dataframe['mid'].rolling(window).mean()
+        dataframe = self.calculeDerivees(dataframe, f"mid_smooth{suffixe}", timeframe=timeframe, ema_period=window)
+        return dataframe
+
+    def calculeDerivees(
+            self,
+            dataframe: pd.DataFrame,
+            name: str,
+            suffixe: str = '',
+            window: int = 100,
+            coef: float = 0.15,
+            ema_period: int = 10,
+            verbose: bool = True,
+            timeframe: str = '5m'
+    ) -> pd.DataFrame:
+        """
+        Calcule deriv1/deriv2 (relative simple), applique EMA, calcule tendency
+        avec epsilon adaptatif basé sur rolling percentiles.
+        """
+
+        d1_col = f"{name}{suffixe}_deriv1"
+        d2_col = f"{name}{suffixe}_deriv2"
+        factor1 = 100 * (ema_period / 5)
+        factor2 = 10 * (ema_period / 5)
+
+        dataframe[f"{name}{suffixe}_inv"] = (dataframe[f"{name}{suffixe}"].shift(2) >= dataframe[f"{name}{suffixe}"].shift(1)) \
+                                 & (dataframe[f"{name}{suffixe}"].shift(1) <= dataframe[f"{name}{suffixe}"])
+        # --- Distance à la moyenne mobile ---
+        dataframe[f"{name}{suffixe}_dist"] = (dataframe['close'] - dataframe[f"{name}{suffixe}"]) / dataframe[f"{name}{suffixe}"]
+
+        # dérivée relative simple
+        dataframe[d1_col] = 1000 * (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
+        dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
+        return dataframe
+
+    def getOpenTrades(self):
+        # if len(self.trades) == 0:
+        self.trades = Trade.get_open_trades()
+        return self.trades
+
+    def calculateProbabilite2Index(self, df, futur_cols, indic_1, indic_2):
+        # # Définition des tranches pour les dérivées
+        # bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
+        # labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
+        #
+        # # Ajout des colonnes bin (catégorisation)
+        # df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_1h_deriv1'], bins=bins_deriv, labels=labels)
+        # df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_deriv1_1d'], bins=bins_deriv, labels=labels)
+        #
+        # # Colonnes de prix futur à analyser
+        # futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
+        #
+        # # Calcul des moyennes et des effectifs
+        # grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
+        #
+        # pd.set_option('display.width', 200)  # largeur max affichage
+        # pd.set_option('display.max_columns', None)
+        pd.set_option('display.max_columns', None)
+        pd.set_option('display.width', 300)  # largeur max affichage
+
+        # nettoyage
+        # series = df[f"{indic_2}"].dropna()
+        # unique_vals = df[f"{indic_2}"].nunique()
+        # print(unique_vals)
+        # print(df[f"{indic_2}"])
+        n = len(self.labels)
+
+        df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=n, labels=self.labels, retbins=True,
+                                                duplicates='drop')
+        df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
+                                                duplicates='drop')
+        # Affichage formaté pour code Python
+        print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
+        print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
+        # Agrégation
+        grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
+        # Affichage
+        with pd.option_context('display.max_rows', None, 'display.max_columns', None):
+            print(grouped.round(4))
+        # Ajout des probabilités de hausse
+        for col in futur_cols:
+            df[f"{col}_is_up"] = df[col] > 0
+
+            # Calcul de la proba de hausse
+            proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
+
+            print(f"\nProbabilité de hausse pour {col} (en %):")
+            with pd.option_context('display.max_rows', None, 'display.max_columns', None):
+                print((proba_up * 100).round(1))
+
+            # Affichage formaté des valeurs comme tableau Python
+            with pd.option_context('display.max_rows', None, 'display.max_columns', None):
+                df_formatted = (proba_up * 100).round(1)
+
+                print("data = {")
+                for index, row in df_formatted.iterrows():
+                    row_values = ", ".join([f"{val:.1f}" for val in row])
+                    print(f"'{index}': [{row_values}], ")
+                print("}")
+
+        data = {}
+        for index, row in df_formatted.iterrows():
+            # on convertit proprement avec arrondi comme dans ton print, mais en données réelles
+            data[index] = [
+                None if (isinstance(val, float) and math.isnan(val)) else val
+                for val in row
+            ]
+
+        # Niveaux unicode pour les barres verticales (style sparkline)
+        # spark_chars = "▁▂▃▄▅▆▇█"
+
+        # print(data.values())
+        # # Collecte globale min/max
+        # all_values = []
+        # for vals in data.values():
+        #     all_values.extend(v for v in vals if not (isinstance(v, float) and math.isnan(v)))
+        #
+        # global_min = min(all_values) if all_values else 0
+        # global_max = max(all_values) if all_values else 1
+        # global_span = (global_max - global_min) if global_max != global_min else 1
+        #
+        # def sparkline_global(values):
+        #     if all(isinstance(v, float) and math.isnan(v) for v in values):
+        #         return "(no data)"
+        #     out = ""
+        #     for v in values:
+        #         if isinstance(v, float) and math.isnan(v):
+        #             out += " "
+        #         else:
+        #             idx = int((v - global_min) / global_span * (len(spark_chars) - 1))
+        #             out += spark_chars[idx]
+        #     return out
+        #
+        # for key, values in data.items():
+        #     print(f"{key:>3} : {sparkline_global(values)}")
+
+        # Palette ANSI 256 couleurs pour heatmap
+        def get_ansi_color(val):
+            """
+            Échelle fixe 0→100 :
+            0-20   : bleu (21)
+            20-40  : cyan (51)
+            40-60  : vert/jaune (46 / 226)
+            60-80  : orange (208)
+            80-100 : rouge (196)
+            """
+            if val is None:
+                return ""
+            if val < 0:
+                val = 0
+            elif val > 100:
+                val = 100
+
+            if val <= 20:
+                code = 21
+            elif val <= 40:
+                code = 51
+            elif val <= 60:
+                code = 226
+            elif val <= 80:
+                code = 208
+            else:
+                code = 196
+            return f"\033[38;5;{code}m"
+
+        RESET = "\033[0m"
+
+        # Affichage
+        columns = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
+        header = "     " + " ".join([f"{col:>6}" for col in columns])
+        print(header)
+        print("-" * len(header))
+
+        for key, values in data.items():
+            line = f"{key:>3} |"
+            for v in values:
+                if v is None:
+                    line += f" {'   '} "  # vide pour NaN / None
+                else:
+                    color = get_ansi_color(v)
+                    line += f" {color}{v:5.1f}{RESET} "
+            print(line)
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (
+                    (
+                        (dataframe['mid_future_pred_cons'].shift(2) > dataframe['mid_future_pred_cons'].shift(1))
+                        & (dataframe['mid_future_pred_cons'].shift(1) < dataframe['mid_future_pred_cons'])
+                        & (dataframe['percent12'] < -0.0005)
+                     )
+                    | (
+                            (dataframe['mid_future_pred_cons'] < dataframe['min12'])
+                    )
+                )
+                &
+                (
+                    ((dataframe['mid_smooth_12_deriv1'] > 0) | (dataframe['mid_smooth_5_deriv1'] > 0))
+                )
+
+            ), ['enter_long', 'enter_tag']] = (1, f"future")
+
+        dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
+
+        if self.dp.runmode.value in ('backtest'):
+            dataframe.to_feather(f"user_data/backtest_results/{metadata['pair'].replace('/', '_')}_df.feather")
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # dataframe.loc[
+        #     (
+        #         (
+        #             (
+        #                     (dataframe['ml_prob'].shift(2) < dataframe['ml_prob'].shift(1))
+        #                     & (dataframe['ml_prob'].shift(1) > dataframe['ml_prob'])
+        #             )
+        #             | (dataframe['ml_prob'] < 0)
+        #         )
+        #         & (dataframe['hapercent'] < 0)
+        #     ), ['exit_long', 'exit_tag']] = (1, f"sma60_future")
+
+        # dataframe.loc[
+        #     (
+        #         (
+        #             (
+        #                     (dataframe['mid_future_pred_cons'].shift(2) < dataframe['mid_future_pred_cons'].shift(1))
+        #                     & (dataframe['mid_future_pred_cons'].shift(1) > dataframe['mid_future_pred_cons'])
+        #             )
+        #             # | (dataframe['mid_smooth_12_deriv1'] < 0)
+        #         )
+        #         & (dataframe['sma60_future_pred_cons'] < dataframe['sma60_future_pred_cons'].shift(1))
+        #         & (dataframe['hapercent'] < 0)
+        #     ), ['exit_long', 'exit_tag']] = (1, f"sma60_future")
+
+        #
+        # dataframe.loc[
+        #     (
+        #         (
+        #             (dataframe['mid_future_pred_cons'].shift(2) < dataframe['mid_future_pred_cons'].shift(1))
+        #             & (dataframe['mid_future_pred_cons'].shift(1) > dataframe['mid_future_pred_cons'])
+        #
+        #         )
+        #         # & (dataframe['mid_future_pred_cons'] > dataframe['max12'])
+        #         & (dataframe['hapercent'] < 0)
+        #
+        #     ), ['exit_long', 'exit_tag']] = (1, f"max12")
+
+        return dataframe
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs):
+        # ne rien faire si ordre deja en cours
+        if trade.has_open_orders:
+            # print("skip open orders")
+            return None
+        if (self.wallets.get_available_stake_amount() < 10):  # or trade.stake_amount >= max_stake:
+            return 0
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        before_last_candle = dataframe.iloc[-2].squeeze()
+        # prépare les données
+        current_time = current_time.astimezone(timezone.utc)
+        open_date = trade.open_date.astimezone(timezone.utc)
+        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
+        count_of_buys = trade.nr_of_successful_entries
+        current_time_utc = current_time.astimezone(timezone.utc)
+        open_date = trade.open_date.astimezone(timezone.utc)
+        days_since_open = (current_time_utc - open_date).days
+        pair = trade.pair
+        profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
+        last_lost = self.getLastLost(last_candle, pair)
+        pct_first = 0
+
+        total_counts = sum(
+            pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
+
+        if self.pairs[pair]['first_buy']:
+            pct_first = self.getPctFirstBuy(pair, last_candle)
+
+        pct = self.pct.value
+        if count_of_buys == 1:
+            pct_max = current_profit
+        else:
+            if self.pairs[trade.pair]['last_buy']:
+                pct_max = self.getPctLastBuy(pair, last_candle)
+            else:
+                pct_max = - pct
+
+        if (self.getShortName(pair) == 'BTC') or count_of_buys <= 2:
+            lim = - pct - (count_of_buys * self.pct_inc.value)
+        else:
+            pct = 0.05
+            lim = - pct - (count_of_buys * 0.0025)
+
+        if (len(dataframe) < 1):
+            # print("skip dataframe")
+            return None
+
+        if not self.should_enter_trade(pair, last_candle, current_time):
+            return None
+
+        condition = (last_candle['enter_long'] and last_candle['stop_buying_1h'] == False and last_candle['hapercent'] > 0)
+                    # and last_candle['sma60_deriv1'] > 0
+                    # or last_candle['enter_tag'] == 'pct3' \
+                    # or last_candle['enter_tag'] == 'pct3_1h'
+
+        # if (self.getShortName(pair) != 'BTC' and count_of_buys > 3):
+        #     condition = before_last_candle_24['mid_smooth_3_1h'] > before_last_candle_12['mid_smooth_3_1h'] and before_last_candle_12['mid_smooth_3_1h'] < last_candle['mid_smooth_3_1h'] #and last_candle['mid_smooth_3_deriv1_1h'] < -1.5
+
+        limit_buy = 40
+        if (count_of_buys < limit_buy) and condition and (pct_max < lim):
+            try:
+
+                if self.pairs[pair]['has_gain'] and profit > 0:
+                    self.pairs[pair]['force_sell'] = True
+                    return None
+
+                max_amount = self.config.get('stake_amount') * 2.5
+                stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
+                                   self.adjust_stake_amount(pair, last_candle) * abs(last_lost / self.mise_factor_buy.value))
+
+                if stake_amount > 0:
+                    trade_type = "Loss " + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
+                    self.pairs[trade.pair]['count_of_buys'] += 1
+                    self.pairs[pair]['total_amount'] += stake_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(profit, 1),
+                        buys=trade.nr_of_successful_entries + 1,
+                        stake=round(stake_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
+
+                    # df = pd.DataFrame.from_dict(self.pairs, orient='index')
+                    # colonnes_a_exclure = ['last_candle', 'stop',
+                    #                       '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)
+
+                    return stake_amount
+                return None
+            except Exception as exception:
+                print(exception)
+                return None
+
+        if (profit > self.pairs[pair]['previous_profit'] and profit > self.pairs[pair]['expected_profit']  and hours > 6
+                # and last_candle['sma60_deriv1'] > 0
+                and last_candle['max_rsi_12_1h'] < 75
+                # and last_candle['rsi_1d'] < 58
+                # and last_candle['stop_buying'] == False
+                # and last_candle['mid_smooth_5_deriv1_1d'] > 0
+                and self.wallets.get_available_stake_amount() > 0
+        ):
+            try:
+                self.pairs[pair]['previous_profit'] = profit
+                stake_amount = min(self.wallets.get_available_stake_amount(), self.pairs[pair]['first_amount'])
+                if stake_amount > 0:
+                    self.pairs[pair]['has_gain'] += 1
+
+                    trade_type = 'Gain +' + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
+                    self.pairs[trade.pair]['count_of_buys'] += 1
+                    self.pairs[pair]['total_amount'] += stake_amount
+                    self.log_trade(
+                        last_candle=last_candle,
+                        date=current_time,
+                        action="🟡 Gain +",
+                        dispo=dispo,
+                        pair=trade.pair,
+                        rate=current_rate,
+                        trade_type=str(round(pct_max, 4)),
+                        profit=round(profit, 1),
+                        buys=trade.nr_of_successful_entries + 1,
+                        stake=round(stake_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
+                    return stake_amount
+                return None
+            except Exception as exception:
+                print(exception)
+                return None
+
+        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 adjust_stake_amount(self, pair: str, last_candle: DataFrame):
+        # Calculer le minimum des 14 derniers jours
+        nb_pairs = len(self.dp.current_whitelist())
+
+        base_stake_amount = self.config.get('stake_amount') / (self.mises.value) # * nb_pairs) # Montant de base configuré
+
+        # factors = [1, 1.2, 1.3, 1.4]
+        if self.pairs[pair]['count_of_buys'] == 0:
+            factor = 1 #65 / min(65, last_candle['rsi_1d'])
+            if last_candle['open'] < last_candle['sma5_1h'] and last_candle['mid_smooth_12_deriv1'] > 0:
+                factor = 2
+
+            adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
+        else:
+            adjusted_stake_amount = self.pairs[pair]['first_amount']
+
+        if self.pairs[pair]['count_of_buys'] == 0:
+            self.pairs[pair]['first_amount'] = adjusted_stake_amount
+
+        return adjusted_stake_amount
+
+    def expectedProfit(self, pair: str, last_candle: DataFrame):
+        lim = 0.01
+        pct = 0.002
+        if (self.getShortName(pair) == 'BTC'):
+            lim = 0.005
+            pct = 0.001
+        pct_to_max = lim + pct * self.pairs[pair]['count_of_buys']
+        expected_profit = lim * self.pairs[pair]['total_amount']  # min(3 * lim, max(lim, pct_to_max))  # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
+
+        self.pairs[pair]['expected_profit'] = expected_profit
+
+        return expected_profit
+
+    def calculateUpDownPct(self, dataframe, key):
+        down_pct_values = np.full(len(dataframe), np.nan)
+        # Remplir la colonne avec les bons calculs
+        for i in range(len(dataframe)):
+            shift_value = abs(int(dataframe[key].iloc[i]))  # Récupérer le shift actuel
+            if i - shift_value > 1:  # Vérifier que le shift ne dépasse pas l'index
+                down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
+                                     dataframe['close'].iloc[i - shift_value]
+        return down_pct_values
+
+    @property
+    def protections(self):
+        return [
+            {
+                "method": "CooldownPeriod",
+                "stop_duration_candles": 12
+            }
+            # {
+            #     "method": "MaxDrawdown",
+            #     "lookback_period_candles": self.lookback.value,
+            #     "trade_limit": self.trade_limit.value,
+            #     "stop_duration_candles": self.protection_stop.value,
+            #     "max_allowed_drawdown": self.protection_max_allowed_dd.value,
+            #     "only_per_pair": False
+            # },
+            # {
+            #     "method": "StoplossGuard",
+            #     "lookback_period_candles": 24,
+            #     "trade_limit": 4,
+            #     "stop_duration_candles": self.protection_stoploss_stop.value,
+            #     "only_per_pair": False
+            # },
+            # {
+            #     "method": "StoplossGuard",
+            #     "lookback_period_candles": 24,
+            #     "trade_limit": 4,
+            #     "stop_duration_candles": 2,
+            #     "only_per_pair": False
+            # },
+            # {
+            #     "method": "LowProfitPairs",
+            #     "lookback_period_candles": 6,
+            #     "trade_limit": 2,
+            #     "stop_duration_candles": 60,
+            #     "required_profit": 0.02
+            # },
+            # {
+            #     "method": "LowProfitPairs",
+            #     "lookback_period_candles": 24,
+            #     "trade_limit": 4,
+            #     "stop_duration_candles": 2,
+            #     "required_profit": 0.01
+            # }
+        ]
+
+    def get_stake_from_drawdown(self, pct: float, base_stake: float = 100.0, step: float = 0.04, growth: float = 1.15,
+                                max_stake: float = 1000.0) -> float:
+        """
+        Calcule la mise à allouer en fonction du drawdown.
+
+        :param pct: Drawdown en pourcentage (ex: -0.12 pour -12%)
+        :param base_stake: Mise de base (niveau 0)
+        :param step: Espacement entre paliers (ex: tous les -4%)
+        :param growth: Facteur de croissance par palier (ex: 1.15 pour +15%)
+        :param max_stake: Mise maximale à ne pas dépasser
+        :return: Montant à miser
+        """
+        if pct >= 0:
+            return base_stake
+
+        level = int(abs(pct) / step)
+        stake = base_stake * (growth ** level)
+        return min(stake, max_stake)
+
+    def polynomial_forecast(self, series: pd.Series, window: int = 20, degree: int = 2, steps=[12, 24, 36]):
+        """
+        Calcule une régression polynomiale sur les `window` dernières valeurs de la série,
+        puis prédit les `n_future` prochaines valeurs.
+
+        :param series: Série pandas (ex: dataframe['close'])
+        :param window: Nombre de valeurs récentes utilisées pour ajuster le polynôme
+        :param degree: Degré du polynôme (ex: 2 pour quadratique)
+        :param n_future: Nombre de valeurs futures à prédire
+        :return: tuple (poly_function, x_vals, y_pred), où y_pred contient les prédictions futures
+        """
+        if len(series) < window:
+            raise ValueError("La série est trop courte pour la fenêtre spécifiée.")
+
+        recent_y = series.iloc[-window:].values
+        x = np.arange(window)
+
+        coeffs = np.polyfit(x, recent_y, degree)
+        poly = np.poly1d(coeffs)
+
+        x_future = np.arange(window, window + len(steps))
+        y_future = poly(x_future)
+
+        # Affichage de la fonction
+        # print("Fonction polynomiale trouvée :")
+        # print(poly)
+
+        current = series.iloc[-1]
+        count = 0
+        for future_step in steps:  # range(1, n_future + 1)
+            future_x = window - 1 + future_step
+            prediction = poly(future_x)
+            # series.loc[series.index[future_x], f'poly_pred_t+{future_step}'] = prediction
+
+            # ➕ Afficher les prédictions
+            # print(f"{current} → t+{future_step}: x={future_x}, y={prediction:.2f}")
+            if prediction > 0:  # current:
+                count += 1
+
+        return poly, x_future, y_future, count
+
+    def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
+        limit = 3
+
+        # if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == -1:
+        #     dispo = round(self.wallets.get_available_stake_amount())
+        #     self.pairs[pair]['stop'] = False
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="🟢RESTART",
+        #         dispo=dispo,
+        #         pair=pair,
+        #         rate=last_candle['close'],
+        #         trade_type='',
+        #         profit=0,
+        #         buys=self.pairs[pair]['count_of_buys'],
+        #         stake=0
+        #     )
+
+        #     🟢 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.
+
+        # if not pair.startswith('BTC'):
+        dispo = round(self.wallets.get_available_stake_amount())
+
+        # if self.pairs[pair]['stop'] \
+        #         and last_candle[f"{self.indic_1d_p.value}_deriv1_1h"] >= self.indic_deriv1_1d_p_start.value \
+        #         and last_candle[f"{self.indic_1d_p.value}_deriv2_1h"] >= self.indic_deriv2_1d_p_start.value:
+        #     self.pairs[pair]['stop'] = False
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="🟢RESTART",
+        #         dispo=dispo,
+        #         pair=pair,
+        #         rate=last_candle['close'],
+        #         trade_type='',
+        #         profit=0,
+        #         buys=self.pairs[pair]['count_of_buys'],
+        #         stake=0
+        #     )
+        # else:
+        #     if self.pairs[pair]['stop'] == False \
+        #             and last_candle[f"{self.indic_1d_p.value}_deriv1_1h"] <= self.indic_deriv1_1d_p_stop.value \
+        #             and last_candle[f"{self.indic_1d_p.value}_deriv2_1h"] <= self.indic_deriv2_1d_p_stop.value:
+        #         self.pairs[pair]['stop'] = True
+        #         # if self.pairs[pair]['current_profit'] > 0:
+        #         #     self.pairs[pair]['force_sell'] = True
+        #         self.log_trade(
+        #             last_candle=last_candle,
+        #             date=current_time,
+        #             action="🔴STOP",
+        #             dispo=dispo,
+        #             pair=pair,
+        #             rate=last_candle['close'],
+        #             trade_type='',
+        #             profit=self.pairs[pair]['current_profit'],
+        #             buys=self.pairs[pair]['count_of_buys'],
+        #             stake=0
+        #         )
+        #         return False
+        # if self.pairs[pair]['stop']:
+        #     return False
+
+        return True
+
+        # Filtrer les paires non-BTC
+        non_btc_pairs = [p for p in self.pairs if not p.startswith('BTC')]
+
+        # Compter les positions actives sur les paires non-BTC
+        max_nb_trades = 0
+        total_non_btc = 0
+        max_pair = ''
+        limit_amount = 250
+        max_amount = 0
+        for p in non_btc_pairs:
+            max_nb_trades = max(max_nb_trades, self.pairs[p]['count_of_buys'])
+            max_amount = max(max_amount, self.pairs[p]['total_amount'])
+
+        for p in non_btc_pairs:
+            if (max_nb_trades == self.pairs[p]['count_of_buys'] and max_nb_trades > limit):
+                # if (max_amount == self.pairs[p]['total_amount'] and max_amount > limit_amount):
+                max_pair = p
+            total_non_btc += self.pairs[p]['count_of_buys']
+
+        pct_max = self.getPctFirstBuy(pair, last_candle)  # self.getPctLastBuy(pair, last_candle)
+
+        if last_candle['mid_smooth_1h_deriv1'] < -0.02:  # and last_candle['mid_smooth_1h_deriv2'] > 0):
+            return False
+
+        self.should_enter_trade_count = 0
+
+        # if max_pair != pair and self.pairs[pair]['total_amount'] > 300:
+        #     return False
+
+        if (max_pair != '') & (self.pairs[pair]['count_of_buys'] >= limit):
+            trade = self.pairs[max_pair]['current_trade']
+            current_time = current_time.astimezone(timezone.utc)
+            open_date = trade.open_date.astimezone(timezone.utc)
+            current_time_utc = current_time.astimezone(timezone.utc)
+            days_since_open = (current_time_utc - open_date).days
+            pct_max_max = self.getPctFirstBuy(max_pair, last_candle)
+            # print(f"days_since_open {days_since_open} max_pair={max_pair} pair={pair}")
+            return max_pair == pair or pct_max < - 0.25 or (
+                    pct_max_max < - 0.15 and max_pair != pair and days_since_open > 30)
+        else:
+            return True
+
+    def select_uncorrelated_features(self, df, target, top_n=20, corr_threshold=0.7):
+        """
+        Sélectionne les features les plus corrélées avec target,
+        tout en supprimant celles trop corrélées entre elles.
+        """
+        # 1️⃣ Calcul des corrélations absolues avec la cible
+        corr = df.corr(numeric_only=True)
+        corr_target = corr[target].abs().sort_values(ascending=False)
+
+        # 2️⃣ Prend les N features les plus corrélées avec la cible (hors target)
+        features = corr_target.drop(target).head(top_n).index.tolist()
+
+        # 3️⃣ Évite les features trop corrélées entre elles
+        selected = []
+        for feat in features:
+            too_correlated = False
+            for sel in selected:
+                if abs(corr.loc[feat, sel]) > corr_threshold:
+                    too_correlated = True
+                    break
+            if not too_correlated:
+                selected.append(feat)
+
+        # 4️⃣ Retourne un DataFrame propre avec les valeurs de corrélation
+        selected_corr = pd.DataFrame({
+            "feature": selected,
+            "corr_with_target": [corr.loc[f, target] for f in selected]
+        }).sort_values(by="corr_with_target", key=np.abs, ascending=False)
+
+        return selected_corr
+
+    def graphFonctionApprise(self, path, X_test, y_test, y_pred):
+        # Exemple : trier les valeurs de X_test et les prédictions
+        x_sorted = np.argsort(X_test.iloc[:, 0])
+        x = X_test.iloc[:, 0].iloc[x_sorted]
+        y_true = y_test.iloc[x_sorted]
+        y_pred = y_pred[x_sorted]
+
+        plt.figure(figsize=(12, 6))
+        plt.plot(x, y_true, label="Réel", color="blue", alpha=0.7)
+        plt.plot(x, y_pred, label="Prédit (LGBM)", color="red", alpha=0.7)
+
+        plt.title("Fonction apprise par LGBMRegressor")
+        plt.xlabel("Feature principale")
+        plt.ylabel("Valeur prédite")
+        plt.legend()
+        plt.grid(True)
+
+        out_path = f"{self.path}/lgbm_function.png"
+        plt.savefig(out_path, bbox_inches="tight")
+        plt.close()
+
+        print(f"Graphique sauvegardé : {out_path}")
+
+    def graphFonctionAppriseFeature(self, path, X_test, y_test, y_pred):
+        plt.figure(figsize=(14, 8))
+        colors = sns.color_palette("coolwarm", n_colors=X_test.shape[1])
+
+        # Conversion en DataFrame pour manip plus simple
+        df = X_test.copy()
+        df["y_pred"] = y_pred
+
+        # --- filtrage sur y_pred (ou sur chaque feature si tu veux)
+        mean = df["y_pred"].mean()
+        std = df["y_pred"].std()
+
+        df = df[(df["y_pred"] >= mean - 2 * std) & (df["y_pred"] <= mean + 2 * std)]
+
+        # --- tracé
+        for i, col in enumerate(X_test.columns):
+            plt.plot(df[col], df["y_pred"], '.', color=colors[i], alpha=0.4, label=col)
+
+        plt.title("Fonction apprise par LGBMRegressor (filtrée à ±2σ)")
+        plt.xlabel("Valeur feature")
+        plt.ylabel("Valeur prédite")
+        plt.legend(loc="right")
+        plt.grid(True)
+
+        out_path = f"{self.path}/lgbm_features.png"
+        plt.savefig(out_path, bbox_inches="tight")
+        plt.close()
+
+        print(f"Graphique sauvegardé : {out_path}")
+
+    def optuna(self, path, X_train, X_test, y_train, y_test):
+        # Suppose que X_train, y_train sont déjà définis
+        # ou sinon :
+        # X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        print("Description")
+        print(X_train.describe().T.sort_values("std"))
+        def objective(trial):
+            params = {
+                'objective': 'regression',
+                'metric': 'rmse',
+                'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
+                'learning_rate': trial.suggest_float('learning_rate', 0.005, 0.2, log=True),
+                'max_depth': trial.suggest_int('max_depth', 3, 15),
+                'num_leaves': trial.suggest_int('num_leaves', 20, 300),
+                'subsample': trial.suggest_float('subsample', 0.5, 1.0),
+                'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),
+                'reg_alpha': trial.suggest_float('reg_alpha', 0.0, 10.0),
+                'reg_lambda': trial.suggest_float('reg_lambda', 0.0, 10.0),
+                'random_state': 42,
+            }
+
+            model = LGBMRegressor(**params)
+            model.fit(X_train, y_train)
+
+            # On peut aussi valider sur un split interne
+            preds = model.predict(X_test)
+            rmse = np.sqrt(mean_squared_error(y_test, preds))
+            return rmse
+
+        # Crée une étude Optuna
+        study = optuna.create_study(direction="minimize")  # on veut minimiser l'erreur
+        study.optimize(objective, n_trials=50, show_progress_bar=True)
+
+        # 🔹 Afficher les meilleurs résultats
+        print("✅ Meilleurs hyperparamètres trouvés :")
+        print(study.best_params)
+        print(f"Meilleur RMSE : {study.best_value:.4f}")
+
+        # 🔹 Sauvegarder les résultats
+        optuna_path = f"{self.path}/optuna_lgbm_results.txt"
+        with open(optuna_path, "w") as f:
+            f.write(f"Best params:\n{study.best_params}\n")
+            f.write(f"Best RMSE: {study.best_value:.4f}\n")
+
+        print(f"Résultats sauvegardés dans : {optuna_path}")
+
+        # 🔹 Créer le modèle final avec les meilleurs paramètres
+        print("🚀 Entraînement du modèle LightGBM...")
+
+        # -- Appliquer le filtrage --
+        X_train_filtered = self.filter_features(X_train, y_train)
+        best_model = LGBMRegressor(**study.best_params)
+        best_model.fit(X_train_filtered, y_train)
+
+        # fig1 = vis.plot_optimization_history(study)
+        # fig1.write_image("/home/souti/freqtrade/user_data/plots/optuna_history.png")
+        #
+        # fig2 = vis.plot_param_importances(study)
+        # fig2.write_image("/home/souti/freqtrade/user_data/plots/optuna_importance.png")
+
+        return best_model, X_train_filtered
+
+    def filter_features(self, X: pd.DataFrame, y: pd.Series, corr_threshold: float = 0.95):
+        """Filtre les colonnes peu utiles ou redondantes"""
+        print("🔍 Filtrage automatique des features...")
+
+        # 1️⃣ Supprimer les colonnes constantes
+        vt = VarianceThreshold(threshold=1e-5)
+        X_var = pd.DataFrame(vt.fit_transform(X), columns=X.columns[vt.get_support()])
+        print(f" - {len(X.columns) - X_var.shape[1]} colonnes supprimées (variance faible)")
+
+        # 2️⃣ Supprimer les colonnes très corrélées entre elles
+        corr = X_var.corr().abs()
+        upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool))
+        drop_cols = [column for column in upper.columns if any(upper[column] > corr_threshold)]
+        X_corr = X_var.drop(columns=drop_cols, errors='ignore')
+        print(f" - {len(drop_cols)} colonnes supprimées (corrélation > {corr_threshold})")
+
+        # 3️⃣ Facultatif : supprimer les colonnes entièrement NaN
+        X_clean = X_corr.dropna(axis=1, how='all')
+
+        print(f"✅ {X_clean.shape[1]} colonnes conservées après filtrage.\n")
+        return X_clean
+
+    def setTrends(self, dataframe: DataFrame):
+        SMOOTH_WIN=10
+        df = dataframe.copy()
+
+        # # --- charger les données ---
+        # df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
+
+        # --- calcul SMA14 ---
+        # df['sma'] = talib.SMA(df, timeperiod=20)  # ta.trend.sma_indicator(df['close'], 14)
+
+        # --- pente brute ---
+        df['slope'] = df['sma12'].diff()
+
+        # --- lissage EMA ---
+        df['slope_smooth'] = df['slope'].ewm(span=SMOOTH_WIN, adjust=False).mean()
+
+        # df["slope_smooth"] = savgol_filter(df["slope_smooth"], window_length=21, polyorder=3)
+
+        # --- normalisation relative ---
+        df['slope_norm'] = 10000 * df['slope_smooth'] / df['close']
+        # df['slope_norm'].fillna(0, inplace=True)
+        df['slope_norm'] = df['slope_norm'].fillna(0)
+        dataframe['slope_norm'] = df['slope_norm']
+
+
+    try:
+        from lightgbm import LGBMRegressor
+        _HAS_LGBM = True
+    except Exception:
+        _HAS_LGBM = False
+
+    def make_model(self, model_type="linear", degree=2, random_state=0):
+        model_type = model_type.lower()
+        if model_type == "linear":
+            return LinearRegression()
+        if model_type == "poly":
+            return make_pipeline(StandardScaler(), PolynomialFeatures(degree=degree, include_bias=False),
+                                 LinearRegression())
+        if model_type == "svr":
+            return make_pipeline(StandardScaler(), SVR(kernel="rbf", C=1.0, epsilon=0.1))
+        if model_type == "rf":
+            return RandomForestRegressor(n_estimators=100, random_state=random_state, n_jobs=1)
+        if model_type == "lgbm":
+            if not _HAS_LGBM:
+                raise RuntimeError("lightgbm n'est pas installé")
+            return LGBMRegressor(n_estimators=100, random_state=random_state)
+        raise ValueError(f"model_type inconnu: {model_type}")
+
+    def calculateRegressionNew(self, df, indic, lookback=20, future_steps=5, model_type="linear"):
+        df = df.copy()
+        pred_col = f"{indic}_future_pred_cons"
+        df[pred_col] = np.nan
+
+        X_idx = np.arange(lookback).reshape(-1, 1)
+
+        values = df[indic].values
+        n = len(values)
+
+        model = LinearRegression()
+
+        for i in range(lookback, n - future_steps):
+            window = values[i - lookback:i]
+
+            # cible = vraie valeur future
+            y_target = values[i + future_steps]
+
+            if np.isnan(window).any() or np.isnan(y_target):
+                continue
+
+            # entraînement
+            model.fit(X_idx, window)
+
+            # prédiction de la valeur future
+            future_x = np.array([[lookback + future_steps - 1]])
+            pred_future = model.predict(future_x)[0]
+
+            # la prédiction concerne i + future_steps
+            df.iloc[i + future_steps, df.columns.get_loc(pred_col)] = pred_future
+
+        return df
+
+    # ==========================================================
+    #   NOUVELLE VERSION : calcule AUSSI les dernières valeurs !
+    # ==========================================================
+    def calculateRegression(
+            self,
+            df,
+            indic,
+            lookback=30,
+            future_steps=5,
+            model_type="linear",
+            degree=2,
+            weight_mode="exp",
+            weight_strength=2,
+            clip_k=2.0,
+            blend_alpha=0.7,
+    ):
+
+        values = df[indic].values.astype(float)
+        n = len(values)
+        colname = f"{indic}_future_pred_cons"
+
+        df[colname] = np.nan
+
+        # pré-calcul des fenêtres
+        windows = np.lib.stride_tricks.sliding_window_view(values, lookback)
+        # windows[k] = valeurs de [k .. k+lookback-1]
+
+        # indices valides d’entraînement
+        trainable_end = n - future_steps
+
+        # créer une fois le modèle
+        model = self.make_model(model_type=model_type, degree=degree)
+
+        # ================
+        #  BOUCLE TRAINING
+        # ================
+        for i in range(lookback, trainable_end):
+
+            window = values[i - lookback:i]
+            if np.isnan(window).any():
+                continue
+
+            # delta future réelle
+            y_target = values[i + future_steps] - values[i]
+
+            # features = positions dans la fenêtre : 0..lookback-1
+            X_window = np.arange(lookback).reshape(-1, 1)
+
+            # sample weights
+            if weight_mode == "exp":
+                weights = np.linspace(0.1, 1, lookback) ** weight_strength
+            else:
+                weights = None
+
+            # entraînement
+            try:
+                model.fit(X_window, window, sample_weight=weights)
+            except Exception:
+                model.fit(X_window, window)
+
+            # prédiction de la valeur future (position lookback+future_steps-1)
+            y_pred_value = model.predict(
+                np.array([[lookback + future_steps - 1]])
+            )[0]
+
+            pred_delta = y_pred_value - values[i]
+
+            # clipping par volatilité locale
+            local_std = np.std(window)
+            max_change = clip_k * (local_std if local_std > 0 else 1e-9)
+            pred_delta = np.clip(pred_delta, -max_change, max_change)
+
+            # blend
+            final_pred_value = (
+                    blend_alpha * (values[i] + pred_delta)
+                    + (1 - blend_alpha) * values[i]
+            )
+
+            df.iloc[i, df.columns.get_loc(colname)] = final_pred_value
+
+        # ==========================================================
+        #           🔥 CALCUL DES DERNIÈRES VALEURS MANQUANTES 🔥
+        # ==========================================================
+
+        # Il reste les indices : [n - future_steps … n - 1]
+        for i in range(trainable_end, n):
+
+            # fenêtre glissante de fin
+            if i - lookback < 0:
+                continue
+
+            window = values[i - lookback:i]
+            if np.isnan(window).any():
+                continue
+
+            # features
+            X_window = np.arange(lookback).reshape(-1, 1)
+
+            try:
+                model.fit(X_window, window)
+            except:
+                continue
+
+            # prédiction d’une continuation locale : future_steps = 1 en fin
+            y_pred_value = model.predict(np.array([[lookback]]))[0]
+            pred_delta = y_pred_value - values[i - 1]
+
+            final_pred_value = (
+                    blend_alpha * (values[i - 1] + pred_delta)
+                    + (1 - blend_alpha) * values[i - 1]
+            )
+
+            df.iloc[i, df.columns.get_loc(colname)] = final_pred_value
+
+        return df
+
+    # def calculateRegression(self,
+    #                         df,
+    #                         indic,
+    #                         lookback=30,
+    #                         future_steps=5,
+    #                         model_type="linear",
+    #                         degree=2,
+    #                         random_state=0,
+    #                         weight_mode="exp",  # "exp", "linear" ou None
+    #                         weight_strength=0.2,  # plus c’est grand, plus les dernières bougies comptent
+    #                         ):
+    #     """
+    #     Ajoute une régression glissante qui prévoit la valeur future à horizon 'future_steps',
+    #     avec pondération des dernières valeurs si weight_mode != None.
+    #     """
+    #     df = df.copy()
+    #     colname = f"{indic}_future_pred_{model_type}"
+    #     df[colname] = np.nan
+    #
+    #     values = df[indic].values
+    #     n = len(values)
+    #     X_window = np.arange(lookback).reshape(-1, 1)
+    #
+    #     # génération du schéma de pondération
+    #     if weight_mode == "exp":
+    #         # exponentiel → les derniers points pèsent beaucoup plus
+    #         weights = np.exp(np.linspace(-weight_strength, weight_strength, lookback))
+    #     elif weight_mode == "linear":
+    #         # poids linéaire → 1..lookback
+    #         weights = np.linspace(0.5, 1.0, lookback)
+    #     else:
+    #         weights = np.ones(lookback)
+    #
+    #     for i in range(lookback, n - future_steps):
+    #         y_window = values[i - lookback:i]
+    #         if np.isnan(y_window).any():
+    #             continue
+    #
+    #         model = self.make_model(model_type=model_type, degree=degree, random_state=random_state)
+    #
+    #         try:
+    #             model.fit(X_window, y_window, sample_weight=weights)
+    #         except TypeError:
+    #             # certains modèles (RF) ne supportent pas sample_weight dans ce contexte
+    #             model.fit(X_window, y_window)
+    #         except Exception:
+    #             continue
+    #
+    #         X_pred = np.array([[lookback + future_steps - 1]])
+    #         try:
+    #             pred = model.predict(X_pred)[0]
+    #         except Exception:
+    #             continue
+    #
+    #         df.iloc[i, df.columns.get_loc(colname)] = pred
+    #
+    #     return df
+
+    # def calculateRegression(self, df, indic, lookback=30, future_steps=5):
+    #     """
+    #     Ajoute un indicateur {indic}_future_pred qui contient,
+    #     pour chaque bougie n, la valeur attendue à n + future_steps
+    #     selon une régression linéaire sur les lookback dernières bougies.
+    #     """
+    #     df = df.copy()
+    #     df[f"{indic}_future_pred"] = np.nan
+    #
+    #     values = df[indic].values
+    #     n = len(values)
+    #
+    #     model = LinearRegression()
+    #
+    #     for i in range(lookback, n - future_steps):
+    #         # Fenêtre d’apprentissage
+    #         X = np.arange(lookback).reshape(-1, 1)
+    #         y = values[i - lookback:i]
+    #
+    #         model.fit(X, y)
+    #
+    #         # Prédiction future
+    #         next_X = np.array([[lookback + future_steps - 1]])
+    #         future_pred = model.predict(next_X)[0]
+    #
+    #         # On insère la prédiction à la position actuelle (n)
+    #         df.iloc[i, df.columns.get_loc(f"{indic}_future_pred")] = future_pred
+    #
+    #     return df
+
+    def add_future_quantiles(self, dataframe, indic, lookback=30, future_steps=5, quantiles=[0.1, 0.5, 0.9]):
+
+        working_columns = self.listUsableColumns(dataframe)
+
+        df = dataframe[self.model_indicators].copy()
+        n = len(df)
+        target = self.indicator_target + "_future"
+
+        df[target] = dataframe[self.indicator_target].shift(-24) # > df['sma24'] * 1.003).astype(int)
+        df[target] = df[target].fillna(0) #.astype(int)
+
+        # Créer les colonnes pour chaque quantile
+        for q in quantiles:
+            df[f"{indic}_future_q{int(q * 100)}"] = np.nan
+
+        # Préparer toutes les fenêtres X
+        X = np.array([df[indic].iloc[i - lookback:i].values for i in range(lookback, n - future_steps)])
+        y_idx = np.arange(lookback, n - future_steps) + future_steps  # index des valeurs futures
+
+        # Imputer les NaN
+        imputer = SimpleImputer(strategy='median')
+        X_imputed = imputer.fit_transform(X)
+
+        # Pour chaque quantile, créer un modèle et prédire
+        for q in quantiles:
+            model = HistGradientBoostingRegressor(loss='quantile', quantile=q, max_iter=100)
+            # Entrainer chaque ligne X_imputed à prédire la dernière valeur de la fenêtre + future_steps
+            # Ici, comme on prédit delta future par fenêtre, on peut utiliser la valeur cible correspondante
+            y = df[indic].iloc[y_idx].values
+            model.fit(X_imputed, y)
+            y_pred = model.predict(X_imputed)
+
+            # Écrire les prédictions dans le dataframe
+            df.iloc[lookback:n - future_steps, df.columns.get_loc(f"{indic}_future_q{int(q * 100)}")] = y_pred
+
+        df_plot = df.iloc[lookback:-future_steps]
+        self.plot_future_quantiles_band(df_plot, indic=self.indicator_target, quantiles=[0.1, 0.5, 0.9])
+        # self.compute_quantile_confidence(df_plot, indic=self.indicator_target, quantiles=[0.1, 0.5, 0.9])
+
+        # fig, ax = plt.subplots(figsize=(20, 20))
+        # for q in quantiles:
+        #     plt.plot(stats.index.astype(str), stats[q], marker='o', label=f"Q{int(q * 100)}")
+        # plt.xticks(rotation=45)
+        # plt.xlabel(f"{indic} bins")
+        # plt.ylabel(f"Quantiles")
+        # plt.title(f"Distribution quantile de {indic}")
+        # plt.legend()
+        # plt.grid(True)
+        # plt.tight_layout()
+        # # plt.show()
+        # # --- Sauvegarde ---
+        # output_path = f"{path}/Distribution_quantile.png"
+        # plt.savefig(output_path, bbox_inches="tight", dpi=150)
+        # plt.close(fig)
+        #
+        # target = "future_return"
+        quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
+        for indicator in working_columns:
+            df["bin"] = pd.qcut(df[indicator], q=20, duplicates="drop")
+            stats = df.groupby("bin")[target].quantile(quantiles).unstack()
+
+            fig, ax = plt.subplots(figsize=(10, 10))
+            # plt.figure(figsize=(12, 6))
+            for q in stats.columns:
+                plt.plot(stats.index.astype(str), stats[q], marker='o', label=f"Q{int(q * 100)}")
+
+            plt.xticks(rotation=45)
+            plt.xlabel(f"{indicator} bins")
+            plt.ylabel(f"Quantiles of {target}")
+            plt.title(f"Distribution quantile de {target} selon {indicator}")
+            plt.legend()
+            plt.grid(True)
+            plt.tight_layout()
+            # --- Sauvegarde ---
+            output_path = f"{self.path}/Distribution_{indicator}.png"
+            plt.savefig(output_path, bbox_inches="tight", dpi=150)
+            plt.close(fig)
+            # plt.show()
+
+        return df
+
+    def plot_future_quantiles_band(self, df, indic, quantiles=[0.1, 0.5, 0.9], lookback=30, future_steps=5):
+        """
+        df: DataFrame contenant la colonne réelle et les colonnes de quantiles
+        indic: nom de la colonne cible (ex: 'mid')
+        quantiles: liste des quantiles prédits
+        """
+        # plt.figure(figsize=(16, 6))
+        fig, ax = plt.subplots(figsize=(96, 30))
+
+        # Série réelle
+        plt.plot(df[indic], label=f"{indic} réel", color='black', linewidth=1.2)
+
+        # Récupérer les colonnes de quantiles
+        cols_q = [f"{indic}_future_q{int(q * 100)}" for q in quantiles]
+
+        # Vérifier que tous les quantiles existent
+        cols_q = [c for c in cols_q if c in df.columns]
+
+        if len(cols_q) < 2:
+            print("Au moins deux quantiles sont nécessaires pour afficher les bandes")
+            return
+
+        # Ordre : q_min, q_median, q_max
+        df_plot = df[cols_q]
+
+        # Couleur pour la bande
+        color = sns.color_palette("coolwarm", n_colors=1)[0]
+
+        # Tracer la bande entre min et max quantiles
+        plt.fill_between(df.index,
+                         df_plot.iloc[:, 0],  # quantile bas (ex: 10%)
+                         df_plot.iloc[:, -1],  # quantile haut (ex: 90%)
+                         color=color,
+                         alpha=0.3,
+                         label=f"Intervalle {quantiles[0] * 100}-{quantiles[-1] * 100}%")
+
+        # Tracer la médiane
+        if len(cols_q) >= 3:
+            plt.plot(df_plot.iloc[:, 1], color=color, linestyle='--', linewidth=1, label="Quantile médian")
+
+        plt.title(f"Prédiction futures valeurs de {indic} avec intervalle de quantiles")
+        plt.xlabel("Index / Bougies")
+        plt.ylabel(indic)
+        plt.legend()
+        plt.grid(True)
+        # plt.show()
+        # --- Sauvegarde ---
+        output_path = f"{self.path}/Prédiction futures valeurs de {indic}.png"
+        plt.savefig(output_path, bbox_inches="tight", dpi=150)
+        plt.close(fig)
+
+    def compute_quantile_confidence(self, df, indic, quantiles=[0.1, 0.5, 0.9]):
+        """
+        df: DataFrame contenant les colonnes des quantiles
+        indic: nom de la colonne réelle
+        quantiles: liste des quantiles prédits
+        Retourne une série score [-1,1], positif = au-dessus de la médiane, négatif = en dessous
+        """
+
+        # df['quantile_conf'] = compute_quantile_confidence(df_plot, indic='mid')
+        #
+        # # Exemple de signal simple
+        # df['buy_signal'] = df['quantile_conf'] < -0.5  # valeur sous la médiane + bande étroite
+        # df['sell_signal'] = df['quantile_conf'] > 0.5  # valeur au-dessus de la médiane + bande étroite
+
+        col_low = f"{indic}_future_q{int(quantiles[0] * 100)}"
+        col_med = f"{indic}_future_q{int(quantiles[1] * 100)}"
+        col_high = f"{indic}_future_q{int(quantiles[2] * 100)}"
+
+        # largeur de bande (incertitude)
+        band_width = df[col_high] - df[col_low] + 1e-9  # éviter division par 0
+
+        # distance normalisée à la médiane
+        score = (df[indic] - df[col_med]) / band_width
+
+        # clipper le score dans [-1,1] pour éviter les valeurs extrêmes
+        score = np.clip(score, -1, 1)
+
+        # plt.figure(figsize=(16, 6))
+        fig, ax = plt.subplots(figsize=(16, 6))
+        plt.plot(df[indic], color='black', label='Valeur réelle')
+        plt.fill_between(df.index,
+                         df[f"{indic}_future_q10"],
+                         df[f"{indic}_future_q90"],
+                         alpha=0.3, color='blue', label='Intervalle 10%-90%')
+        plt.plot(df[f"{indic}_future_q50"], linestyle='--', color='blue', label='Médiane')
+
+        # Ajouter le score comme couleur de fond
+        plt.scatter(df.index, df[indic], c=df['quantile_conf'], cmap='coolwarm', s=20)
+        plt.colorbar(label='Score de confiance')
+        plt.title("Prédiction + score de confiance quantile")
+        plt.legend()
+        plt.grid(True)
+        # plt.show()
+        # --- Sauvegarde ---
+        output_path = f"{self.path}/Prédiction score confiance de {indic}.png"
+        plt.savefig(output_path, bbox_inches="tight", dpi=150)
+        plt.close(fig)
+
+        return score
+
+    # def loadTensorFlow(self, dataframe, metadata, lookback=50, future_steps=1):
+    #     self.model = load_model(f"{self.path}/lstm_model.keras", compile=False)
+    #
+    #     # features = toutes les colonnes sauf la cible
+    #     feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
+    #     X_values = dataframe[feature_columns].values
+    #
+    #     # normalisation avec le même scaler que l'entraînement
+    #     scaler_X = MinMaxScaler()
+    #     scaler_X.fit(X_values)  # ou charger les paramètres si sauvegardés
+    #     X_scaled = scaler_X.transform(X_values)
+    #
+    #     # création des fenêtres glissantes
+    #     X = np.lib.stride_tricks.sliding_window_view(X_scaled, window_shape=(self.lookback, X_scaled.shape[1]))
+    #     # np.lib.stride_tricks.sliding_window_view ne supporte pas directement 2D → il vaut mieux utiliser une boucle :
+    #     X_seq = []
+    #     for i in range(len(X_scaled) - self.lookback):
+    #         X_seq.append(X_scaled[i:i + self.lookback])
+    #     X_seq = np.array(X_seq)
+    #
+    #     # prédiction
+    #     y_pred = self.model.predict(X_seq, verbose=0).flatten()
+    #
+    #     # alignement avec les données
+    #     preds = [np.nan] * len(dataframe)
+    #     start = self.lookback
+    #     end = start + len(y_pred)
+    #     preds[start:end] = y_pred[:end - start]
+    #
+    #     dataframe["lstm_pred"] = preds
+    #
+    # def trainTensorFlow(self, dataframe, metadata, lookback=50, future_steps=1):
+    #     # 1) définir la cible
+    #     y_values = dataframe[self.indicator_target].values.reshape(-1, 1)
+    #
+    #     # 2) définir les features (toutes les colonnes sauf la cible)
+    #     feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
+    #     X_values = dataframe[feature_columns].values
+    #
+    #     # 3) normalisation
+    #     scaler_X = MinMaxScaler()
+    #     X_scaled = scaler_X.fit_transform(X_values)
+    #
+    #     scaler_y = MinMaxScaler()
+    #     y_scaled = scaler_y.fit_transform(y_values)
+    #
+    #     # 4) création des fenêtres glissantes
+    #     X = []
+    #     y = []
+    #     for i in range(len(X_scaled) - lookback - future_steps):
+    #         X.append(X_scaled[i:i + lookback])
+    #         y.append(y_scaled[i + lookback + future_steps])
+    #
+    #     X = np.array(X)
+    #     y = np.array(y)
+    #
+    #     # 5) définition du modèle LSTM
+    #     model = Sequential([
+    #         LSTM(64, return_sequences=False, input_shape=(lookback, X.shape[2])),
+    #         Dense(32, activation="relu"),
+    #         Dense(1)
+    #     ])
+    #
+    #     model.compile(loss="mse", optimizer="adam")
+    #     model.fit(X, y, epochs=20, batch_size=32, verbose=1)
+    #
+    #     # 6) sauvegarde
+    #     model.save(f"{self.path}/lstm_model.keras")
+    #     np.save(f"{self.path}/lstm_scaler_X.npy", scaler_X.data_max_)
+    #     np.save(f"{self.path}/lstm_scaler_y.npy", scaler_y.data_max_)
+    #     # pour restaurer
+    #
+    #     # df = dataframe[self.model_indicators].copy()
+    #     #
+    #     # # Construction dataset X / y
+    #     # X = []
+    #     # y = []
+    #     #
+    #     # prices = df[self.indicator_target].values
+    #     #
+    #     # for i in range(lookback, len(prices) - future_steps):
+    #     #     X.append(prices[i - lookback:i])
+    #     #     y.append(prices[i + future_steps])
+    #     #
+    #     # X = np.array(X).reshape(-1, lookback, 1)
+    #     # y = np.array(y)
+    #     #
+    #     # # --- Définition du modèle ---
+    #     # model = models.Sequential([
+    #     #     layers.Input((lookback, 1)),
+    #     #     layers.LSTM(64),
+    #     #     layers.Dense(32, activation="relu"),
+    #     #     layers.Dense(1)
+    #     # ])
+    #     #
+    #     # model.compile(optimizer="adam", loss="mse")
+    #     # model.summary()
+    #     #
+    #     # # --- Entraînement ---
+    #     # model.fit(X, y, epochs=20, batch_size=32, verbose=1)
+    #     #
+    #     # # --- Sauvegarde ---
+    #     # model.save(f"{self.path}/lstm_model.keras", include_optimizer=False)
+    #     #
+    #     print("Modèle entraîné et sauvegardé → lstm_model.h5")
+
+    def kerasGenerateGraphs(self, dataframe):
+        model = self.model
+        self.kerasGenerateGraphModel(model)
+        self.kerasGenerateGraphPredictions(model, dataframe, self.lookback)
+        self.kerasGenerateGraphPoids(model)
+
+    def kerasGenerateGraphModel(self, model):
+        plot_model(
+            model,
+            to_file=f"{self.path}/lstm_model.png",
+            show_shapes=True,
+            show_layer_names=True
+        )
+
+    def kerasGenerateGraphPredictions(self, model, dataframe, lookback):
+        preds = self.tensorFlowGeneratePredictions(dataframe, lookback, model)
+
+        # plot
+        plt.figure(figsize=(36, 8))
+        plt.plot(dataframe[self.indicator_target].values, label=self.indicator_target)
+        plt.plot(preds, label="lstm_pred")
+        plt.legend()
+        plt.savefig(f"{self.path}/lstm_predictions.png")
+        plt.close()
+
+    def kerasGenerateGraphPoids(self, model):
+        for i, layer in enumerate(model.layers):
+            weights = layer.get_weights()  # liste de tableaux numpy
+
+            # Sauvegarde SAFE : tableau d’objets
+            np.save(
+                f"{self.path}/layer_{i}_weights.npy",
+                np.array(weights, dtype=object)
+            )
+
+            # Exemple lecture et heatmap
+            weights_layer0 = np.load(
+                f"{self.path}/layer_{i}_weights.npy",
+                allow_pickle=True
+            )
+
+            # Choisir un poids 2D
+            W = None
+            for w in weights_layer0:
+                if isinstance(w, np.ndarray) and w.ndim == 2:
+                    W = w
+                    break
+
+            if W is None:
+                print(f"Aucune matrice 2D dans layer {i} (rien à afficher).")
+                return
+
+            plt.figure(figsize=(8, 6))
+            sns.heatmap(W, cmap="viridis")
+            plt.title(f"Poids 2D du layer {i}")
+            plt.savefig(f"{self.path}/layer{i}_weights.png")
+            plt.close()
+
+    # -------------------
+    # Entraînement
+    # -------------------
+    def trainTensorFlow(self, dataframe, future_steps=1, lookback=50, epochs=40, batch_size=32):
+        X_seq, y_seq = self.tensorFlowPrepareDataFrame(dataframe, future_steps, lookback)
+
+        # 6) Modèle LSTM
+        self.model = Sequential([
+            LSTM(64, return_sequences=False, input_shape=(lookback, X_seq.shape[2])),
+            Dense(32, activation="relu"),
+            Dense(1)
+        ])
+
+        self.model.compile(loss='mse', optimizer=Adam(learning_rate=1e-4))
+        self.model.fit(X_seq, y_seq, epochs=epochs, batch_size=batch_size, verbose=1)
+
+        # 7) Sauvegarde
+        self.model.save(f"{self.path}/lstm_model.keras")
+        np.save(f"{self.path}/lstm_scaler_X.npy", self.scaler_X.data_max_)
+        np.save(f"{self.path}/lstm_scaler_y.npy", self.scaler_y.data_max_)
+
+    def tensorFlowPrepareDataFrame(self, dataframe, future_steps, lookback):
+        target = self.indicator_target
+        # 1) Détecter NaN / Inf et nettoyer
+        feature_columns = self.model_indicators  # [col for col in dataframe.columns if col != target]
+        df = dataframe.copy()
+        df.replace([np.inf, -np.inf], np.nan, inplace=True)
+        df.dropna(subset=feature_columns + [target], inplace=True)
+        # 2) Séparer features et cible
+        X_values = df[feature_columns].values
+        y_values = df[target].values.reshape(-1, 1)
+        # 3) Gestion colonnes constantes (éviter division par zéro)
+        for i in range(X_values.shape[1]):
+            if X_values[:, i].max() == X_values[:, i].min():
+                X_values[:, i] = 0.0
+        if y_values.max() == y_values.min():
+            y_values[:] = 0.0
+        # 4) Normalisation
+        self.scaler_X = MinMaxScaler()
+        X_scaled = self.scaler_X.fit_transform(X_values)
+        self.scaler_y = MinMaxScaler()
+        y_scaled = self.scaler_y.fit_transform(y_values)
+        # 5) Création des fenêtres glissantes
+        X_seq = []
+        y_seq = []
+        for i in range(len(X_scaled) - lookback - future_steps):
+            X_seq.append(X_scaled[i:i + lookback])
+            y_seq.append(y_scaled[i + lookback + future_steps])
+        X_seq = np.array(X_seq)
+        y_seq = np.array(y_seq)
+        # Vérification finale
+        if np.isnan(X_seq).any() or np.isnan(y_seq).any():
+            raise ValueError("X_seq ou y_seq contient encore des NaN")
+        if np.isinf(X_seq).any() or np.isinf(y_seq).any():
+            raise ValueError("X_seq ou y_seq contient encore des Inf")
+        return X_seq, y_seq
+
+    # -------------------
+    # Prédiction
+    # -------------------
+    def predictTensorFlow(self, dataframe, future_steps=1, lookback=50):
+        feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
+        # charger le modèle si pas déjà chargé
+        if self.model is None:
+            self.model = load_model(f"{self.path}/lstm_model.keras", compile=False)
+            X_seq, y_seq = self.tensorFlowPrepareDataFrame(dataframe, future_steps, lookback)
+
+        preds = self.tensorFlowGeneratePredictions(dataframe, lookback, self.model)
+
+        # # features = toutes les colonnes sauf la cible
+        # feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
+        # X_values = dataframe[feature_columns].values
+        #
+        # # normalisation (avec le scaler utilisé à l'entraînement)
+        # X_scaled = self.scaler_X.transform(X_values)
+        #
+        # # créer les séquences glissantes
+        # X_seq = []
+        # for i in range(len(X_scaled) - self.lookback):
+        #     X_seq.append(X_scaled[i:i + self.lookback])
+        # X_seq = np.array(X_seq)
+        #
+        # # prédictions
+        # y_pred_scaled = self.model.predict(X_seq, verbose=0).flatten()
+        # y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
+        #
+        # # alignement avec les données
+        # preds = [np.nan] * len(dataframe)
+        # start = self.lookback
+        # end = start + len(y_pred)
+        # # preds[start:end] = y_pred[:end - start]
+        # preds[start:start + len(y_pred)] = y_pred
+        #
+        # # # features
+        # # X_values = dataframe[feature_columns].values
+        # # X_scaled = self.scaler_X.transform(X_values)
+        # #
+        # # # création des fenêtres
+        # # X_seq = []
+        # # for i in range(len(X_scaled) - self.lookback):
+        # #     X_seq.append(X_scaled[i:i + self.lookback])
+        # # X_seq = np.array(X_seq)
+        # #
+        # # # prédiction
+        # # y_pred_scaled = self.model.predict(X_seq, verbose=0).flatten()
+        # # y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
+        # #
+        # # # alignement avec le dataframe
+        # # preds = [np.nan] * len(dataframe)
+        # # start = self.lookback
+        # # end = start + len(y_pred)
+        # # preds[start:end] = y_pred[:end-start]
+        # # # preds[start:start + len(y_pred)] = y_pred
+
+        dataframe["lstm_pred"] = preds
+        return dataframe
+
+    def tensorFlowGeneratePredictions(self, dataframe, lookback, model):
+        # features = toutes les colonnes sauf la cible
+        feature_columns = self.model_indicators  # [col for col in dataframe.columns if col != self.indicator_target]
+        X_values = dataframe[feature_columns].values
+
+        # normalisation (avec le scaler utilisé à l'entraînement)
+        X_scaled = self.scaler_X.transform(X_values)
+        # créer les séquences glissantes
+        X_seq = []
+        for i in range(len(X_scaled) - lookback):
+            X_seq.append(X_scaled[i:i + lookback])
+        X_seq = np.array(X_seq)
+
+
+        # prédictions
+        y_pred_scaled = model.predict(X_seq, verbose=0).flatten()
+        y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
+        # alignement avec les données
+        preds = [np.nan] * len(dataframe)
+        start = lookback
+        end = start + len(y_pred)
+        # preds[start:end] = y_pred[:end - start]
+        preds[start:start + len(y_pred)] = y_pred
+        return preds
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