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.
- Positive → tendance haussière
- Négative → tendance baissière |
+ # | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**.
- Croisement du MACD au-dessus → signal d’achat
- Croisement du MACD en dessous → signal de vente |
+ # | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance.
- Positif et croissant → tendance haussière qui s’accélère
- Positif mais décroissant → ralentissement de la hausse
- Négatif et décroissant → baisse qui s’accélère
- 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.
- Positive → tendance haussière
- Négative → tendance baissière |
+ # | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**.
- Croisement du MACD au-dessus → signal d’achat
- Croisement du MACD en dessous → signal de vente |
+ # | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance.
- Positif et croissant → tendance haussière qui s’accélère
- Positif mais décroissant → ralentissement de la hausse
- Négatif et décroissant → baisse qui s’accélère
- 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
\ No newline at end of file