3626 lines
167 KiB
Python
3626 lines
167 KiB
Python
# Zeus Strategy: First Generation of GodStra Strategy with maximum
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# AVG/MID profit in USDT
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
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# --- Do not remove these libs ---
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from datetime import timedelta, datetime
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from freqtrade.persistence import Trade
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from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
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IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
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import pandas as pd
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import numpy as np
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import os
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import json
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from pandas import DataFrame
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from typing import Optional, Union, Tuple
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import math
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import logging
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import configparser
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from technical import pivots_points
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from pathlib import Path
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# --------------------------------
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# Add your lib to import here test git
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import ta
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import talib.abstract as talib
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import requests
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from datetime import timezone, timedelta
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from scipy.signal import savgol_filter
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from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
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from collections import Counter
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logger = logging.getLogger(__name__)
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# Machine Learning
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from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
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from sklearn.metrics import accuracy_score
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import joblib
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import matplotlib.pyplot as plt
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from sklearn.metrics import (
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classification_report,
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confusion_matrix,
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accuracy_score,
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roc_auc_score,
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roc_curve,
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precision_score, recall_score, precision_recall_curve,
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f1_score, mean_squared_error, r2_score
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)
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from sklearn.tree import export_text
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import inspect
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from sklearn.feature_selection import mutual_info_classif
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from sklearn.inspection import permutation_importance
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from lightgbm import LGBMClassifier
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.feature_selection import SelectFromModel
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from tabulate import tabulate
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from sklearn.model_selection import GridSearchCV
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from sklearn.feature_selection import VarianceThreshold
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import seaborn as sns
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from lightgbm import LGBMRegressor
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import lightgbm as lgb
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# Couleurs ANSI de base
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RED = "\033[31m"
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GREEN = "\033[32m"
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YELLOW = "\033[33m"
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BLUE = "\033[34m"
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MAGENTA = "\033[35m"
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CYAN = "\033[36m"
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RESET = "\033[0m"
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def pprint_df(dframe):
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print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False))
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def normalize(df):
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df = (df - df.min()) / (df.max() - df.min())
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return df
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class Zeus_LGBMRegressor(IStrategy):
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# Machine Learning
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# model_indicators = [
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# 'rsi', 'rsi_deriv1', 'rsi_deriv2', "max_rsi_12",
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# "bb_percent",
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# 'vol_24',
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# 'percent3',
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# 'sma5_dist', 'sma5_deriv1', 'sma5_deriv2',
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# 'sma24_dist', 'sma24_deriv1', 'sma24_deriv2',
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# 'sma60_dist', 'sma60_deriv1', 'sma60_deriv2',
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# 'down_pct', 'slope_norm',
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# 'min_max_60',
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# 'rsi_slope', 'adx_change', 'volatility_ratio',
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# 'slope_ratio', 'bb_width',
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# 'rsi_1h', 'rsi_deriv1_1h', 'rsi_deriv2_1h', "max_rsi_12_1h",
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# ]
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model = None
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model_indicators = ["ms-10", "ms-5", "ms-4", "ms-3", "ms-2", "ms-1", "ms-0"]
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# model_indicators = ['open', 'high', 'close', 'haclose', 'percent', 'sma5', 'sma12', 'sma24', 'sma24_deriv1', 'sma24_deriv2', 'sma48', 'sma48_deriv1', 'sma48_deriv2', 'sma60', 'sma60_dist', 'sma60_deriv1',
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# 'sma60_deriv2', 'mid_smooth_3_deriv2', 'mid_smooth_12_deriv1', 'mid_smooth_12_deriv2', 'mid_smooth_24', 'mid_smooth_24_deriv1', 'mid_smooth_24_deriv2', 'max_rsi_12', 'max_rsi_24', 'max12',
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# 'max60', 'min60', 'min_max_60', 'bb_lowerband', 'bb_upperband', 'bb_width', 'macd', 'macdsignal', 'macdhist', 'sma_20', 'sma_100', 'atr', 'atr_norm', 'adx', 'obv', 'vol_24', 'adx_change',
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# 'volatility_ratio', 'slope_ratio', 'mid_smooth_1h_deriv2', 'mid_smooth_5h', 'mid_smooth_5h_deriv1', 'mid_smooth_5h_deriv2']
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levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
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# startup_candle_count = 12 * 24 * 5
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# ROI table:
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minimal_roi = {
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"0": 0.564,
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"567": 0.273,
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"2814": 0.12,
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"7675": 0
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}
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stakes = 40
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# Stoploss:
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stoploss = -1 # 0.256
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# Custom stoploss
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use_custom_stoploss = False
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trailing_stop = True
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trailing_stop_positive = 0.15
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trailing_stop_positive_offset = 0.20
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trailing_only_offset_is_reached = True
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# Buy hypers
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timeframe = '5m'
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max_open_trades = 5
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max_amount = 40
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parameters = {}
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# DCA config
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position_adjustment_enable = True
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plot_config = {
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"main_plot": {
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"sma24_1h": {
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"color": "pink"
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},
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"sma5_1d": {
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"color": "blue"
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},
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# "sma24": {
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# "color": "yellow"
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# },
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"sma60": {
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"color": "green"
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},
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"bb_lowerband": {
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"color": "#da59a6"},
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"bb_upperband": {
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"color": "#da59a6",
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},
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# "sma12": {
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# "color": "blue"
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# },
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"mid_smooth_3_1h": {
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"color": "blue"
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}
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},
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"subplots": {
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"Rsi": {
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"max_rsi_24": {
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"color": "blue"
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},
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"max_rsi_24_1h": {
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"color": "pink"
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},
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# "rsi_1h": {
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# "color": "red"
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# },
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# "rsi_1d": {
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# "color": "blue"
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# }
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},
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"Rsi_deriv1": {
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"sma24_deriv1_1h": {
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"color": "pink"
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},
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"sma24_deriv1": {
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"color": "yellow"
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},
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"sma5_deriv1_1d": {
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"color": "blue"
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},
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"sma60_deriv1": {
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"color": "green"
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}
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},
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"Rsi_deriv2": {
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"sma24_deriv2_1h": {
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"color": "pink"
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},
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"sma24_deriv2": {
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"color": "yellow"
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},
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"sma5_deriv2_1d": {
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"color": "blue"
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},
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"sma60_deriv2": {
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"color": "green"
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}
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},
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"States": {
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"tdc_macd_1h": {
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"color": "cyan"
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},
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"sma24_state_1h": {
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"color": "pink"
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},
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"sma24_state": {
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"color": "yellow"
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},
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"sma5_state_1d": {
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"color": "blue"
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},
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"sma60_state": {
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"color": "green"
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}
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},
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'Macd': {
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"macd_rel_1d": {
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"color": "cyan"
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},
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"macdsignal_rel_1d": {
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"color": "pink"
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},
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"macdhist_rel_1d": {
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"color": "yellow"
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}
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}
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}
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}
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columns_logged = False
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pairs = {
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pair: {
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"first_buy": 0,
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"last_buy": 0.0,
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"last_min": 999999999999999.5,
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"last_max": 0,
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"trade_info": {},
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"max_touch": 0.0,
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"last_sell": 0.0,
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'count_of_buys': 0,
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'current_profit': 0,
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'expected_profit': 0,
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'previous_profit': 0,
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"last_candle": {},
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"last_count_of_buys": 0,
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'base_stake_amount': 0,
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'stop_buy': False,
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'last_date': 0,
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'stop': False,
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'max_profit': 0,
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'total_amount': 0,
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'has_gain': 0,
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'force_sell': False,
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'force_buy': False
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}
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for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
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"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
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}
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# 20 20 40 60 100 160 260 420
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# 50 50 100 300 500
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# fibo = [1, 1, 2, 3, 5, 8, 13, 21]
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# my fibo
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# 50 50 50 100 100 150 200 250 350 450 600 1050
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fibo = [1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21]
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baisse = [1, 2, 3, 5, 7, 10, 14, 19, 26, 35, 47, 63, 84]
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# Ma suite 1 1 1 2 2 3 4 5 7 9 12 16 21
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# Mise 50 50 50 100 100 150 200 250 350 450 600 800 1050
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# Somme Mises 50 100 150 250 350 500 700 950 1300 1750 2350 3150 4200
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# baisse 1 2 3 5 7 10 14 19 26 35 47 63 84
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# factors = [1, 1.1, 1.25, 1.5, 2.0, 3]
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# thresholds = [2, 5, 10, 20, 30, 50]
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factors = [0.5, 0.75, 1, 1.25, 1.5, 2]
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thresholds = [0, 2, 5, 10, 30, 45]
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trades = list()
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max_profit_pairs = {}
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# # sma24_deriv1_1d_stop_protection = DecimalParameter(-0.2, 0.2, default=0.05, decimals=2, space='protection',
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# # optimize=True, load=True)
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# sma5_deriv1_1d_stop_protection = DecimalParameter(-5, 0, default=0.5, decimals=1, space='protection',
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# optimize=True, load=True)
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# sma5_deriv2_1d_stop_protection = DecimalParameter(-5, 0, default=0.5, decimals=1, space='protection', optimize=True,
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# load=True)
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#
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# # sma24_deriv1_1d_start_protection = DecimalParameter(-0.2, 0.2, default=0.05, decimals=2, space='protection',
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# # optimize=True, load=True)
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# sma5_deriv1_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection',
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# optimize=True, load=True)
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# sma5_deriv2_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection',
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# optimize=True,
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# load=True)
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#
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mise_factor_buy = DecimalParameter(0.01, 0.1, default=0.05, decimals=2, space='buy', optimize=True, load=True)
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indicators = {'sma5', 'sma12', 'sma24', 'sma60'}
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indicators_percent = {'percent', 'percent3', 'percent12', 'percent24', 'percent_1h', 'percent3_1h', 'percent12_1h', 'percent24_1h'}
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mises = IntParameter(1, 50, default=5, space='buy', optimize=False, load=False)
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pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=True, load=True)
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pct_inc = DecimalParameter(0.0001, 0.003, default=0.0022, decimals=4, space='buy', optimize=True, load=True)
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indic_5m_slope_sup_buy = CategoricalParameter(indicators, default="sma60", space='buy')
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# indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
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# deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
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indic_5m_slope_inf_buy = CategoricalParameter(indicators, default="sma60", space='buy')
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# indic_deriv_5m_slop_inf_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
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# deriv_5m_slope_inf_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
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# indic_deriv1_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
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# indic_deriv2_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
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# indic_1h = CategoricalParameter(indicators, default="sma60", space='buy')
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# indic_deriv1_1h = DecimalParameter(-5, 5, default=0, decimals=1, space='buy', optimize=True, load=True)
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# indic_deriv2_1h = DecimalParameter(-10, 10, default=0, decimals=1, space='buy', optimize=True, load=True)
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# indic_1d_p = CategoricalParameter(indicators, default="sma60", space='protection')
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# indic_deriv1_1d_p_stop = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
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# indic_deriv2_1d_p_stop = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
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# indic_deriv1_1d_p_start = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
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# indic_deriv2_1d_p_start = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
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indic_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
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indic_deriv_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
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deriv_5m_slope_sup_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
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indic_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
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indic_deriv_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
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deriv_5m_slope_inf_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
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deriv1_buy_protect = DecimalParameter(-0.3, 0.1, default=-0.1, decimals=2, space='protection', optimize=True, load=True)
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rsi_buy_protect = IntParameter(50, 90, default=70, space='protection', optimize=True, load=True)
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indic_5m_slope_sup = CategoricalParameter(indicators, default="sma60", space='protection')
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indic_1h_slope_sup = CategoricalParameter(indicators, default="sma5", space='protection')
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# indic_percent_sell = CategoricalParameter(indicators_percent, default="sma60", space='sell')
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# percent_5m_sell = DecimalParameter(-0.1, -0.0, default=0, decimals=2, space='sell', optimize=True, load=True)
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# indic_deriv1_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
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# indic_deriv2_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
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# indic_deriv1_1h_sell = DecimalParameter(-5, 5, default=0, decimals=1, space='sell', optimize=True, load=True)
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# indic_deriv2_1h_sell = DecimalParameter(-10, 10, default=0, decimals=1, space='sell', optimize=True, load=True)
|
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labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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index_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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ordered_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
|
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label_to_index = {label: i for i, label in enumerate(ordered_labels)}
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# =========================================================================
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# paliers dérivées jour sma5
|
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sma5_deriv1 = [-1.1726, -0.2131, -0.1012, -0.0330, 0.0169, 0.0815, 0.2000, 4.0335]
|
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sma5_deriv2 = [-1.9190, -0.1388, -0.0644, -0.0202, 0.0209, 0.0646, 0.1377, 4.2987]
|
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sma5_derive1_2_matrice = {
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'B3': [8.6, 10.8, 34.6, 35.0, 58.8, 61.9, 91.2],
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'B2': [0.0, 12.5, 9.1, 57.1, 63.3, 79.3, 89.5],
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'B1': [6.1, 12.5, 22.0, 46.8, 61.5, 70.0, 100.0],
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'N0': [0.0, 10.7, 37.0, 43.5, 75.0, 75.9, 100.0],
|
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'H1': [0.0, 18.5, 32.4, 35.9, 76.8, 82.9, 92.0],
|
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'H2': [0.0, 21.9, 16.0, 39.5, 69.7, 83.3, 100.0],
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'H3': [9.5, 29.2, 41.2, 57.9, 53.8, 86.8, 92.3],
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}
|
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sma5_derive1_2_matrice_df = pd.DataFrame(sma5_derive1_2_matrice, index=index_labels)
|
||
# Extraction de la matrice numérique
|
||
sma5_derive1_2_numeric_matrice = sma5_derive1_2_matrice_df.reindex(index=ordered_labels,
|
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columns=ordered_labels).values
|
||
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||
# paliers = {}
|
||
|
||
# =========================================================================
|
||
# Parameters hyperopt
|
||
|
||
# buy_mid_smooth_3_deriv1 = DecimalParameter(-0.1, 0.1, decimals=2, default=-0.06, space='buy')
|
||
# buy_mid_smooth_24_deriv1 = DecimalParameter(-0.6, 0, decimals=2, default=-0.03, space='buy')
|
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# buy_horizon_predict_1h = IntParameter(1, 6, default=2, space='buy')
|
||
|
||
# buy_level_predict_1h = IntParameter(2, 5, default=4, space='buy')
|
||
|
||
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()
|
||
# val = self.getProbaHausse144(last_candle)
|
||
|
||
# trend = last_candle['trend_class']
|
||
# params = self.loadParamsFor(pair, trend)
|
||
|
||
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
|
||
# indic_deriv1_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv1_5m')
|
||
# indic_deriv2_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv2_5m')
|
||
|
||
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 #(not self.stop_all) #& (not self.all_down)
|
||
# and val > self.buy_val.value #not last_candle['tendency'] in (-1, -2) # (rate <= float(limit)) | (entry_tag == 'force_entry')
|
||
allow_to_buy = (condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
|
||
|
||
# if allow_to_buy:
|
||
# poly_func, x_future, y_future, count = self.polynomial_forecast(
|
||
# dataframe['mid_smooth_12'],
|
||
# window=self.buy_horizon_predict_1h.value * 12,
|
||
# degree=4,
|
||
# n_future=3)
|
||
#
|
||
# if count < 3:
|
||
# allow_to_buy = False
|
||
force = self.pairs[pair]['force_buy']
|
||
if self.pairs[pair]['force_buy']:
|
||
self.pairs[pair]['force_buy'] = False
|
||
allow_to_buy = True
|
||
else:
|
||
if not self.should_enter_trade(pair, last_candle, current_time):
|
||
allow_to_buy = False
|
||
|
||
if allow_to_buy:
|
||
self.trades = list()
|
||
self.pairs[pair]['first_buy'] = rate
|
||
self.pairs[pair]['last_buy'] = rate
|
||
self.pairs[pair]['max_touch'] = last_candle['close']
|
||
self.pairs[pair]['last_candle'] = last_candle
|
||
self.pairs[pair]['count_of_buys'] = 1
|
||
self.pairs[pair]['current_profit'] = 0
|
||
self.pairs[pair]['last_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()
|
||
|
||
profit =trade.calc_profit(rate)
|
||
force = self.pairs[pair]['force_sell']
|
||
allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
|
||
|
||
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
|
||
|
||
if allow_to_sell:
|
||
self.trades = list()
|
||
self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries # self.pairs[pair]['count_of_buys']
|
||
self.pairs[pair]['last_sell'] = rate
|
||
self.pairs[pair]['last_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:
|
||
# print(f"STOP triggered for {pair} ({exit_reason}) but condition blocked", "warning")
|
||
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()
|
||
before_last_candle_24 = dataframe.iloc[-25].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
|
||
|
||
# trend = last_candle['trend_class']
|
||
#
|
||
# indic_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_5m_sell')
|
||
# indic_deriv1_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_deriv1_5m_sell')
|
||
# indic_deriv2_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_deriv2_5m_sell')
|
||
|
||
if hours % 4 == 0:
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT",
|
||
dispo=dispo,
|
||
pair=pair,
|
||
rate=last_candle['close'],
|
||
trade_type='',
|
||
profit=round(profit, 2),
|
||
buys=count_of_buys,
|
||
stake=0
|
||
)
|
||
|
||
# if (last_candle['mid_smooth_deriv1'] >= 0):
|
||
# return None
|
||
# if (last_candle['tendency'] in (2, 1)) and (last_candle['rsi'] < 80):
|
||
# return None
|
||
#
|
||
# if (last_candle['sma24_deriv1'] < 0 and before_last_candle['sma24_deriv1'] >= 0) and (current_profit > expected_profit):
|
||
# return 'Drv_' + str(count_of_buys)
|
||
pair_name = self.getShortName(pair)
|
||
# if (current_profit > expected_profit) and last_candle['can_sell']:
|
||
# return 'Can_' + pair_name + '_' + str(count_of_buys)
|
||
# trend = last_candle['trend_class_1d']
|
||
# if (trend == "B-" or trend == "B--") and self.pairs[pair]['has_gain'] == 0: # and (last_candle[f"{indic_5m_sell}_deriv1"] <= indic_deriv1_5m_sell and last_candle[f"{indic_5m_sell}_deriv2"] <= indic_deriv2_5m_sell):
|
||
#
|
||
# if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 1 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0):
|
||
# self.pairs[pair]['stop'] = 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 "MAX_RSI"
|
||
#
|
||
# return None
|
||
|
||
# if (trend == "B-" or trend == "B--") and last_candle[f"{self.indic_5m_sell.value}_deriv1"] <= self.indic_deriv1_5m_sell.value \
|
||
# and last_candle[f"{self.indic_5m_sell.value}_deriv2"] <= self.indic_deriv2_5m_sell.value:
|
||
# return None
|
||
|
||
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 > max(5, expected_profit) 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 and last_candle['sma12_state'] <= 0 and last_candle['sma60_state'] <= - 1:
|
||
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'])
|
||
|
||
|
||
# if last_candle['mid_smooth_1h_deriv1'] < 0 and profit > expected_profit:
|
||
# self.pairs[pair]['force_sell'] = False
|
||
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
|
||
# return str(count_of_buys) + '_' + 'Drv3_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||
|
||
# if 4 <= count_of_buys <= 6:
|
||
# if ((before_last_candle_2['mid_smooth_12_deriv1'] <= before_last_candle['mid_smooth_12_deriv1'])
|
||
# & (before_last_candle['mid_smooth_12_deriv1'] >= last_candle['mid_smooth_12_deriv1'])) \
|
||
# and (current_profit > expected_profit):
|
||
# return 'Drv13_' + pair_name + '_' + str(count_of_buys)
|
||
#
|
||
# if 7 <= count_of_buys:
|
||
# if ((before_last_candle_24['sma24_deriv1_1h'] <= before_last_candle_12['sma24_deriv1_1h'])
|
||
# & (before_last_candle_12['sma24_deriv1_1h'] >= last_candle['sma24_deriv1_1h'])) \
|
||
# and (current_profit > expected_profit):
|
||
# return 'Drv24_' + pair_name + '_' + str(count_of_buys)
|
||
|
||
# if (baisse > mx) & (current_profit > expected_profit):
|
||
# self.trades = list()
|
||
# return 'mx_' + str(count_of_buys)
|
||
# if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
|
||
# self.trades = list()
|
||
# return 'pft_' + str(count_of_buys)
|
||
|
||
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 = '' # round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
|
||
pct_max = self.getPctFirstBuy(pair, last_candle)
|
||
|
||
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
|
||
|
||
dist_max = self.getDistMax(last_candle, pair)
|
||
|
||
# if trade_type is not None:
|
||
# if np.isnan(last_candle['rsi_1d']):
|
||
# string = ' '
|
||
# else:
|
||
# string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_deriv1_1d']))
|
||
# trade_type = trade_type \
|
||
# + " " + string \
|
||
# + " " + str(int(last_candle['rsi_1h'])) \
|
||
# + " " + str(int(last_candle['rsi_deriv1_1h']))
|
||
|
||
# val144 = self.getProbaHausse144(last_candle)
|
||
# val1h = self.getProbaHausse1h(last_candle)
|
||
val = self.getProbaHausseSma5d(last_candle)
|
||
|
||
pct60 = round(100 * self.getPct60D(pair, last_candle), 2)
|
||
|
||
color = GREEN if profit > 0 else RED
|
||
color_sma24 = GREEN if last_candle['sma24_deriv1_1d'] > 0 else RED
|
||
color_sma24_2 = GREEN if last_candle['sma24_deriv2_1d'] > 0 else RED
|
||
color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1_1d'] > 0 else RED
|
||
color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2_1d'] > 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.
|
||
# trend = last_candle['trend_class_1d']
|
||
#
|
||
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
|
||
# indic_deriv1_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv1_5m')
|
||
# indic_deriv2_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv2_5m')
|
||
#
|
||
# indic_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_5m_sell')
|
||
# indic_deriv1_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv1_5m_sell')
|
||
# indic_deriv2_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv2_5m_sell')
|
||
|
||
|
||
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_1d'], 2):>5}{RESET}"
|
||
f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1d'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1d'], 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}"
|
||
# f"|{last_candle['min60_1d']}|{last_candle['max60_1d']}"
|
||
# f"|{last_candle['mid_smooth_tdc_5_1d'] or '-':>3}|{last_candle['mid_smooth_tdc_5_1h'] or '-':>3}|{last_candle['mid_smooth_tdc_5'] or '-':>3}"
|
||
f"|{last_candle['mid_smooth_5_state_1d'] or '-':>3}|{last_candle['mid_smooth_24_state_1h'] or '-':>3}|{last_candle['mid_smooth_5_state_1h'] or '-':>3}|{last_candle['mid_smooth_5_state'] or '-':>3}"
|
||
f"|{last_candle['trend_class_1d']:>5} {last_candle['trend_class_1h']:>5}" # {indic_5m} {indic_deriv1_5m} {indic_deriv2_5m} {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}"
|
||
)
|
||
|
||
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 getDistMax(self, last_candle, pair):
|
||
mx = last_candle['max12_1d']
|
||
dist_max = round(100 * (mx - last_candle['close']) / mx, 0)
|
||
return dist_max
|
||
|
||
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']
|
||
|
||
dataframe = self.populateDataframe(dataframe, timeframe='5m')
|
||
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_24', window=24, degree=4, future_offset=12)
|
||
|
||
################### INFORMATIVE 1h
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
||
informative = self.populateDataframe(informative, timeframe='1h')
|
||
# informative['target_value'] = informative['sma5'].shift(-6).rolling(5).max() - informative['sma5'] * 1.005
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.trainModel(informative, metadata)
|
||
#
|
||
# # Préparer les features pour la prédiction
|
||
# features = informative[self.model_indicators].fillna(0)
|
||
#
|
||
# # Prédiction : probabilité que le prix monte
|
||
# probs = self.model.predict_proba(features)[:, 1]
|
||
#
|
||
# # Sauvegarder la probabilité pour l’analyse
|
||
# informative['ml_prob'] = probs
|
||
|
||
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')
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||
|
||
dataframe['last_price'] = dataframe['close']
|
||
dataframe['first_price'] = dataframe['close']
|
||
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
||
# dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01
|
||
# dataframe['limit'] = dataframe['close']
|
||
count_buys = 0
|
||
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()
|
||
|
||
# ===============================
|
||
# Lissage des valeurs Journalières
|
||
horizon_d = 12 * 5 * 24
|
||
# dataframe['rsi_1h'] = dataframe['rsi_1h'].rolling(12).mean()
|
||
# dataframe['rsi_deriv1_1h'] = dataframe['rsi_deriv1_1h'].rolling(12).mean()
|
||
# dataframe['rsi_deriv2_1h'] = dataframe['rsi_deriv2_1h'].rolling(12).mean()
|
||
|
||
# dataframe['mid_smooth_1d'] = dataframe['mid_smooth_1d'].rolling(window=horizon_d * 5).mean()
|
||
# dataframe["mid_smooth_deriv1_1d"] = dataframe["mid_smooth_1d"].rolling(horizon_d).mean().diff() / horizon_d
|
||
# dataframe["mid_smooth_deriv2_1d"] = horizon_d * dataframe["mid_smooth_deriv1_1d"].rolling(horizon_d).mean().diff()
|
||
#
|
||
# dataframe['sma5_1d'] = dataframe['sma5_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe['sma5_deriv1_1d'] = dataframe['sma5_deriv1_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe['sma24_1d'] = dataframe['sma24_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe['sma24_deriv1_1d'] = dataframe['sma24_deriv1_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_1d', window=24, degree=4, future_offset=12)
|
||
|
||
# dataframe['percent_with_previous_day'] = 100 * (dataframe['close'] - dataframe['close_1d']) / dataframe['close']
|
||
# dataframe['percent_with_max_hour'] = 100 * (dataframe['close'] - dataframe['max12_1h']) / dataframe['close']
|
||
#
|
||
# horizon_h = 24 * 5
|
||
# dataframe['futur_percent_1h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-12) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
# dataframe['futur_percent_3h'] = 100 * ((dataframe['close'].shift(-36) - dataframe['close']) / dataframe['close']).rolling(horizon_h).mean()
|
||
# dataframe['futur_percent_5h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-60) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
# dataframe['futur_percent_12h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-144) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
#
|
||
# dataframe['futur_percent_1d'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
|
||
# dataframe['futur_percent_3d'] = 100 * (dataframe['close'].shift(-3) - dataframe['close']) / dataframe['close']
|
||
#
|
||
# self.calculateProbabilite2Index(dataframe, ['futur_percent_1d'], 'sma24_deriv1_1h', 'sma5_1d')
|
||
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# print("##################")
|
||
# print("# STAT DAY vs HOUR")
|
||
# print("##################")
|
||
# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_1d'], indic_1='sma5_deriv1_1d',
|
||
# indic_2='sma5_deriv2_1d')
|
||
|
||
# dataframe['proba_hausse'] = dataframe.apply(lambda row: self.getProbaHausseEmaVolume(row), axis=1)
|
||
|
||
# dataframe['futur_percent_3'] = 100 * ((dataframe['sma5'].shift(-1) - dataframe['sma5']) / dataframe['sma5'])
|
||
# futur_cols = ['futur_percent_3']
|
||
# indic_1 = 'mid_smooth_1h_deriv1'
|
||
# indic_2 = 'mid_smooth_1h_deriv2'
|
||
# self.calculateProbabilite2Index(dataframe, futur_cols, indic_1, indic_2)
|
||
|
||
# dataframe = dataframe.resample('sma12_1h').ffill()
|
||
# dataframe = dataframe.resample('sma24_1h').ffill()
|
||
|
||
# mises = IntParameter(1, 50, default=5, space='buy', optimize=False, load=False)
|
||
#
|
||
# 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)
|
||
#
|
||
# indic_5m_slope_sup = CategoricalParameter(indicators, default="sma60", space='buy')
|
||
|
||
indic_5m_protect = self.indic_5m_slope_sup.value
|
||
indic_1h_protect = self.indic_1h_slope_sup.value + '_1h'
|
||
|
||
dataframe['stop_buying_deb'] = ((dataframe['max_rsi_12_1d'] > self.rsi_buy_protect.value) | (dataframe['sma24_deriv1_1h'] < self.deriv1_buy_protect.value)) & (qtpylib.crossed_below(dataframe[indic_5m_protect], dataframe[indic_1h_protect]))
|
||
dataframe['stop_buying_end'] = (dataframe[indic_1h_protect].shift(24) > dataframe[indic_1h_protect].shift(12)) & (dataframe[indic_1h_protect].shift(12) < dataframe[indic_1h_protect])
|
||
|
||
latched = np.zeros(len(dataframe), dtype=bool)
|
||
|
||
for i in range(1, len(dataframe)):
|
||
if dataframe['stop_buying_deb'].iloc[i]:
|
||
latched[i] = True
|
||
elif dataframe['stop_buying_end'].iloc[i]:
|
||
latched[i] = False
|
||
else:
|
||
latched[i] = latched[i - 1]
|
||
|
||
dataframe['stop_buying'] = latched
|
||
|
||
if self.dp.runmode.value in ('backtest'):
|
||
self.trainModel(dataframe, metadata)
|
||
|
||
self.model = joblib.load('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):
|
||
pd.set_option('display.max_rows', None)
|
||
pd.set_option('display.max_columns', None)
|
||
pd.set_option("display.width", 200)
|
||
|
||
# # 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("_1h") and not c.endswith("_1d")
|
||
# 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
|
||
|
||
dataframe["ms-10"] = dataframe["mid_smooth_24_deriv1"].shift(10)
|
||
dataframe["ms-5"] = dataframe["mid_smooth_24_deriv1"].shift(5)
|
||
dataframe["ms-4"] = dataframe["mid_smooth_24_deriv1"].shift(4)
|
||
dataframe["ms-3"] = dataframe["mid_smooth_24_deriv1"].shift(3)
|
||
dataframe["ms-2"] = dataframe["mid_smooth_24_deriv1"].shift(2)
|
||
dataframe["ms-1"] = dataframe["mid_smooth_24_deriv1"].shift(1)
|
||
dataframe["ms-0"] = dataframe["mid_smooth_24_deriv1"]
|
||
# dataframe["ms+10"] = dataframe["mid_smooth_24"].shift(-11)
|
||
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["mid_smooth_24_deriv1"].shift(-11) # > 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=(10,6)) #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 = "/home/souti/freqtrade/user_data/plots/Matrice_de_correlation_temperature.png"
|
||
plt.savefig(output_path, bbox_inches="tight", dpi=150)
|
||
plt.close(fig)
|
||
|
||
print(f"✅ Matrice enregistrée : {output_path}")
|
||
|
||
# # Exemple d'utilisation :
|
||
# selected_corr = self.select_uncorrelated_features(df, target="target", top_n=30, corr_threshold=0.7)
|
||
# print("===== 🎯 FEATURES SÉLECTIONNÉES =====")
|
||
# print(selected_corr)
|
||
|
||
|
||
# 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)
|
||
|
||
# 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))
|
||
|
||
# 5️⃣ Entraînement du modèle
|
||
# train_model = RandomForestClassifier(n_estimators=200, random_state=42)
|
||
# train_model = RandomForestClassifier(
|
||
# n_estimators=300,
|
||
# max_depth=12,
|
||
# # min_samples_split=4,
|
||
# # min_samples_leaf=2,
|
||
# # max_features='sqrt',
|
||
# # random_state=42,
|
||
# # n_jobs=-1,
|
||
# # n_jobs=-1,
|
||
# class_weight='balanced'
|
||
# )
|
||
# 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 = LGBMClassifier(
|
||
# n_estimators=800,
|
||
# learning_rate=0.02,
|
||
# max_depth=10,
|
||
# num_leaves=31,
|
||
# subsample=0.8,
|
||
# colsample_bytree=0.8,
|
||
# reg_alpha=0.2,
|
||
# reg_lambda=0.4,
|
||
# class_weight='balanced',
|
||
# random_state=42,
|
||
# )
|
||
|
||
train_model.fit(X_train, y_train)
|
||
|
||
# 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)
|
||
|
||
# 3️⃣ Calibration ensuite (facultative)
|
||
# calibrated = CalibratedClassifierCV(train_model, method='sigmoid', cv=5)
|
||
# calibrated.fit(X_train[selected_features], y_train)
|
||
# print(calibrated)
|
||
|
||
# # calibration
|
||
# train_model = CalibratedClassifierCV(train_model, method='sigmoid', cv=5)
|
||
# # Sélection
|
||
# sfm = SelectFromModel(train_model, threshold="median")
|
||
# sfm.fit(X_train, y_train)
|
||
# selected_features = X_train.columns[sfm.get_support()]
|
||
# print(selected_features)
|
||
|
||
train_model.fit(X_train, y_train)
|
||
# y_pred = train_model.predict(X_test)
|
||
# y_proba = train_model.predict_proba(X_test)[:, 1]
|
||
# print(classification_report(y_test, y_pred))
|
||
# print(confusion_matrix(y_test, y_pred))
|
||
# print("\nRapport de classification :\n", classification_report(y_test, y_pred))
|
||
# print("\nMatrice de confusion :\n", confusion_matrix(y_test, y_pred))
|
||
|
||
# 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)
|
||
|
||
# best_f1 = 0
|
||
# best_t = 0.5
|
||
# for t in [0.3, 0.4, 0.5, 0.6, 0.7]:
|
||
# y_pred_thresh = (y_proba > t).astype(int)
|
||
# score = f1_score(y_test, y_pred_thresh)
|
||
# print(f"Seuil {t:.1f} → F1: {score:.3f}")
|
||
# if score > best_f1:
|
||
# best_f1 = score
|
||
# best_t = t
|
||
#
|
||
# print(f"✅ Meilleur seuil trouvé: {best_t} avec F1={best_f1:.3f}")
|
||
|
||
# 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, 'rf_model.pkl')
|
||
print("✅ Modèle sauvegardé sous rf_model.pkl")
|
||
|
||
# X = dataframe des features (après shift/rolling/indicators)
|
||
# y = target binaire ou décimale
|
||
# model = ton modèle entraîné (RandomForestClassifier ou Regressor)
|
||
|
||
# # --- 1️⃣ Mutual Information (MI) ---
|
||
# mi_scores = mutual_info_classif(X.fillna(0), y)
|
||
# mi_series = pd.Series(mi_scores, index=X.columns, name='MI')
|
||
#
|
||
# # --- 2️⃣ Permutation Importance (PI) ---
|
||
# pi_result = permutation_importance(train_model, X, y, n_repeats=10, random_state=42, n_jobs=-1)
|
||
# pi_series = pd.Series(pi_result.importances_mean, index=X.columns, name='PI')
|
||
#
|
||
# # --- 3️⃣ Combinaison dans un seul dataframe ---
|
||
# importance_df = pd.concat([mi_series, pi_series], axis=1)
|
||
# importance_df = importance_df.sort_values(by='PI', ascending=False) # tri par importance modèle
|
||
# print(importance_df)
|
||
#
|
||
# importance_df.plot(kind='bar', figsize=(10, 5))
|
||
# plt.title("Mutual Info vs Permutation Importance")
|
||
# plt.ylabel("Score")
|
||
# plt.show()
|
||
|
||
self.analyze_model(train_model, X_train, X_test, y_train, y_test)
|
||
|
||
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, 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 = "user_data/plots"
|
||
os.makedirs(output_dir, exist_ok=True)
|
||
|
||
# ---- Prédictions ----
|
||
probs = model.predict(X_test)
|
||
# probs = model.predict_proba(X_test)[:, 1] if hasattr(model, "predict_proba") else preds
|
||
|
||
# # ---- Performances globales ----
|
||
# print("===== 📊 ÉVALUATION DU MODÈLE =====")
|
||
# print("Colonnes du modèle :", model.feature_names_in_)
|
||
# print("Colonnes X_test :", list(X_test.columns))
|
||
# print(f"Accuracy: {accuracy_score(y_test, preds):.3f}")
|
||
# print(f"ROC AUC : {roc_auc_score(y_test, probs):.3f}")
|
||
#
|
||
# print("TN (True Negative) / FP (False Positive)")
|
||
# print("FN (False Negative) / TP (True Positive)")
|
||
# print("\nRapport de classification :\n", classification_report(y_test, preds))
|
||
|
||
# | Élément | Valeur | Signification |
|
||
# | ------------------- | ------ | ----------------------------------------------------------- |
|
||
# | TN (True Negative) | 983 | Modèle a correctement prédit 0 (pas d’achat) |
|
||
# | FP (False Positive) | 43 | Modèle a prédit 1 alors que c’était 0 (faux signal d’achat) |
|
||
# | FN (False Negative) | 108 | Modèle a prédit 0 alors que c’était 1 (manqué un achat) |
|
||
# | TP (True Positive) | 19 | Modèle a correctement prédit 1 (bon signal d’achat) |
|
||
|
||
# ---- Matrice de confusion ----
|
||
# cm = confusion_matrix(y_test, preds)
|
||
# print("Matrice de confusion :\n", cm)
|
||
#
|
||
# plt.figure(figsize=(4, 4))
|
||
# plt.imshow(cm, cmap="Blues")
|
||
# plt.title("Matrice de confusion")
|
||
# plt.xlabel("Prédit")
|
||
# plt.ylabel("Réel")
|
||
# for i in range(2):
|
||
# for j in range(2):
|
||
# plt.text(j, i, cm[i, j], ha="center", va="center", color="black")
|
||
# # plt.show()
|
||
# plt.savefig(os.path.join(output_dir, "Matrice de confusion.png"), bbox_inches="tight")
|
||
# plt.close()
|
||
|
||
# ---- 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)
|
||
|
||
# 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])
|
||
|
||
# # ---- Précision selon le seuil ----
|
||
# thresholds = np.linspace(0.1, 0.9, 9)
|
||
# print("\n===== ⚙️ PERFORMANCE SELON SEUIL =====")
|
||
# for t in thresholds:
|
||
# preds_t = (probs > t).astype(int)
|
||
# acc = accuracy_score(y_test, preds_t)
|
||
# print(f"Seuil {t:.1f} → précision {acc:.3f}")
|
||
|
||
# # ---- ROC Curve ----
|
||
# fpr, tpr, _ = roc_curve(y_test, probs)
|
||
# plt.figure(figsize=(5, 4))
|
||
# plt.plot(fpr, tpr, label="ROC curve")
|
||
# plt.plot([0, 1], [0, 1], linestyle="--", color="gray")
|
||
# plt.xlabel("Taux de faux positifs")
|
||
# plt.ylabel("Taux de vrais positifs")
|
||
# plt.title("Courbe ROC")
|
||
# plt.legend()
|
||
# # plt.show()
|
||
# plt.savefig(os.path.join(output_dir, "Courbe ROC.png"), bbox_inches="tight")
|
||
# plt.close()
|
||
|
||
# # ---- Interprétation SHAP (optionnelle) ----
|
||
# try:
|
||
# import shap
|
||
#
|
||
# print("\n===== 💡 ANALYSE SHAP =====")
|
||
# explainer = shap.TreeExplainer(model)
|
||
# shap_values = explainer.shap_values(X_test)
|
||
# # shap.summary_plot(shap_values[1], X_test)
|
||
# # Vérifie le type de sortie de shap_values
|
||
# if isinstance(shap_values, list):
|
||
# # Cas des modèles de classification (plusieurs classes)
|
||
# shap_values_to_plot = shap_values[0] if len(shap_values) == 1 else shap_values[1]
|
||
# else:
|
||
# shap_values_to_plot = shap_values
|
||
#
|
||
# # Ajustement des dimensions au besoin
|
||
# if shap_values_to_plot.shape[1] != X_test.shape[1]:
|
||
# print(f"⚠️ Mismatch dimensions SHAP ({shap_values_to_plot.shape[1]}) vs X_test ({X_test.shape[1]})")
|
||
# min_dim = min(shap_values_to_plot.shape[1], X_test.shape[1])
|
||
# shap_values_to_plot = shap_values_to_plot[:, :min_dim]
|
||
# X_to_plot = X_test.iloc[:, :min_dim]
|
||
# else:
|
||
# X_to_plot = X_test
|
||
#
|
||
# plt.figure(figsize=(12, 4))
|
||
# shap.summary_plot(shap_values_to_plot, X_to_plot, show=False)
|
||
# plt.savefig(os.path.join(output_dir, "shap_summary.png"), bbox_inches="tight")
|
||
# plt.close()
|
||
# except ImportError:
|
||
# print("\n(SHAP non installé — `pip install shap` pour activer l’analyse SHAP.)")
|
||
|
||
# y_proba = model.predict_proba(X_test)[:, 1]
|
||
y_proba = model.predict(X_test)
|
||
|
||
# Trace ou enregistre le graphique
|
||
# self.plot_threshold_analysis(y_test, y_proba, step=0.05, save_path="/home/souti/freqtrade/user_data/plots/threshold_analysis.png")
|
||
|
||
# y_test : vraies classes (0 / 1)
|
||
# y_proba : probabilités de la classe 1 prédites par ton modèle
|
||
# Exemple : y_proba = model.predict_proba(X_test)[:, 1]
|
||
|
||
# seuils = np.arange(0.0, 1.01, 0.05)
|
||
# precisions, recalls, f1s = [], [], []
|
||
#
|
||
# for seuil in seuils:
|
||
# y_pred = (y_proba >= seuil).astype(int)
|
||
# precisions.append(precision_score(y_test, y_pred))
|
||
# recalls.append(recall_score(y_test, y_pred))
|
||
# f1s.append(f1_score(y_test, y_pred))
|
||
#
|
||
# plt.figure(figsize=(10, 6))
|
||
# plt.plot(seuils, precisions, label='Précision', marker='o')
|
||
# plt.plot(seuils, recalls, label='Rappel', marker='o')
|
||
# plt.plot(seuils, f1s, label='F1-score', marker='o')
|
||
#
|
||
# # Ajoute un point pour le meilleur F1
|
||
# best_idx = np.argmax(f1s)
|
||
# plt.scatter(seuils[best_idx], f1s[best_idx], color='red', s=80, label=f'Max F1 ({seuils[best_idx]:.2f})')
|
||
#
|
||
# plt.title("Performance du modèle selon le seuil de probabilité")
|
||
# plt.xlabel("Seuil de probabilité (classe 1)")
|
||
# plt.ylabel("Score")
|
||
# plt.grid(True, alpha=0.3)
|
||
# plt.legend()
|
||
# plt.savefig("/home/souti/freqtrade/user_data/plots/seuil_de_probabilite.png", bbox_inches='tight')
|
||
# plt.show()
|
||
|
||
# print(f"✅ Meilleur F1 : {f1s[best_idx]:.3f} au seuil {seuils[best_idx]:.2f}")
|
||
|
||
# --- 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 ---
|
||
plot_dir = "/home/souti/freqtrade/user_data/plots"
|
||
os.makedirs(plot_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(plot_dir, "LightGBM_regression_pred_vs_real.png")
|
||
plt.savefig(plot_path, bbox_inches="tight", dpi=200)
|
||
plt.close()
|
||
|
||
print(f"✅ Graphique sauvegardé : {plot_path}")
|
||
|
||
# save_dir = "/home/souti/freqtrade/user_data/plots/"
|
||
# os.makedirs(save_dir, exist_ok=True)
|
||
|
||
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(plot_dir, "lgbm_tree_0.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(plot_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)
|
||
|
||
plt.figure(figsize=(10, 5))
|
||
plt.plot(X_test, y_pred, color="red", label="modèle LGBM")
|
||
plt.title("Fonction apprise par LGBMRegressor")
|
||
plt.xlabel("x")
|
||
plt.ylabel("y")
|
||
plt.legend()
|
||
plt.savefig(os.path.join(plot_dir, "lgbm_function_curve.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
ax = lgb.plot_importance(model, max_num_features=30, figsize=(12, 6))
|
||
plt.title("Importance des features - LGBM")
|
||
plt.savefig(os.path.join(plot_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(plot_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(plot_dir, "ytest_vs_ypred.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
print("\n===== ✅ FIN DE L’ANALYSE =====")
|
||
|
||
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'):
|
||
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)
|
||
dataframe["percent12"] = dataframe['close'].pct_change(12)
|
||
dataframe["percent24"] = dataframe['close'].pct_change(24)
|
||
|
||
# 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['max60'] = talib.MAX(dataframe['close'], timeperiod=60)
|
||
dataframe['min60'] = talib.MIN(dataframe['close'], timeperiod=60)
|
||
dataframe['min_max_60'] = ((dataframe['max60'] - dataframe['close']) / dataframe['min60'])
|
||
# dataframe['min36'] = talib.MIN(dataframe['close'], timeperiod=36)
|
||
# dataframe['max36'] = talib.MAX(dataframe['close'], timeperiod=36)
|
||
# dataframe['pct36'] = 100 * (dataframe['max36'] - dataframe['min36']) / dataframe['min36']
|
||
# dataframe['maxpct36'] = talib.MAX(dataframe['pct36'], timeperiod=36)
|
||
# Bollinger Bands
|
||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||
dataframe['bb_lowerband'] = bollinger['lower']
|
||
dataframe['bb_middleband'] = bollinger['mid']
|
||
dataframe['bb_upperband'] = bollinger['upper']
|
||
dataframe["bb_percent"] = (
|
||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
||
)
|
||
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["sma5"]
|
||
|
||
# dataframe["bb_width"] = (
|
||
# (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
|
||
# )
|
||
|
||
# Calcul MACD
|
||
macd, macdsignal, macdhist = talib.MACD(
|
||
dataframe['close'],
|
||
fastperiod=12,
|
||
slowperiod=26,
|
||
signalperiod=9
|
||
)
|
||
|
||
# | Nom | Formule / définition | Signification |
|
||
# | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
# | **MACD** (`macd`) | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue. <br> - Positive → tendance haussière <br> - Négative → tendance baissière |
|
||
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente |
|
||
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - Négatif mais croissant → ralentissement de la baisse |
|
||
|
||
# Ajouter dans le dataframe
|
||
dataframe['macd'] = macd
|
||
dataframe['macdsignal'] = macdsignal
|
||
dataframe['macdhist'] = macdhist
|
||
|
||
# Regarde dans le futur
|
||
# # --- Rendre relatif sur chaque série (-1 → 1) ---
|
||
# for col in ['macd', 'macdsignal', 'macdhist']:
|
||
# series = dataframe[col]
|
||
# valid = series[~np.isnan(series)] # ignorer NaN
|
||
# min_val = valid.min()
|
||
# max_val = valid.max()
|
||
# span = max_val - min_val if max_val != min_val else 1
|
||
# dataframe[f'{col}_rel'] = 2 * ((series - min_val) / span) - 1
|
||
#
|
||
# dataframe['tdc_macd'] = self.macd_tendance_int(
|
||
# dataframe,
|
||
# macd_col='macd_rel',
|
||
# signal_col='macdsignal_rel',
|
||
# hist_col='macdhist_rel'
|
||
# )
|
||
|
||
# ------------------------------------------------------------------------------------
|
||
# rolling SMA indicators (used for trend detection too)
|
||
s_short = self.DEFAULT_PARAMS['sma_short']
|
||
s_long = self.DEFAULT_PARAMS['sma_long']
|
||
|
||
dataframe[f'sma_{s_short}'] = dataframe['close'].rolling(window=s_short).mean()
|
||
dataframe[f'sma_{s_long}'] = dataframe['close'].rolling(window=s_long).mean()
|
||
|
||
# --- pente brute ---
|
||
dataframe['slope'] = dataframe['sma24'].diff()
|
||
|
||
# --- lissage EMA ---
|
||
dataframe['slope_smooth'] = dataframe['slope'].ewm(span=10, adjust=False).mean()
|
||
|
||
# # RSI
|
||
# window = 14
|
||
# delta = dataframe['close'].diff()
|
||
# up = delta.clip(lower=0)
|
||
# down = -1 * delta.clip(upper=0)
|
||
# ma_up = up.rolling(window=window).mean()
|
||
# ma_down = down.rolling(window=window).mean()
|
||
# rs = ma_up / ma_down.replace(0, 1e-9)
|
||
# dataframe['rsi'] = 100 - (100 / (1 + rs))
|
||
#
|
||
# # EMA example
|
||
# dataframe['ema'] = dataframe['close'].ewm(span=self.DEFAULT_PARAMS['ema_period'], adjust=False).mean()
|
||
#
|
||
# # ATR (simple implementation)
|
||
# high_low = dataframe['high'] - dataframe['low']
|
||
# high_close = (dataframe['high'] - dataframe['close'].shift()).abs()
|
||
# low_close = (dataframe['low'] - dataframe['close'].shift()).abs()
|
||
# tr = DataFrame({'hl': high_low, 'hc': high_close, 'lc': low_close}).max(axis=1)
|
||
# dataframe['atr'] = tr.rolling(window=self.DEFAULT_PARAMS['atr_period']).mean()
|
||
|
||
###########################
|
||
# df = ton DataFrame OHLCV avec colonnes: open, high, low, close, volume
|
||
# Assure-toi qu'il est trié par date croissante
|
||
|
||
# --- Volatilité normalisée ---
|
||
dataframe['atr'] = ta.volatility.AverageTrueRange(
|
||
high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
|
||
).average_true_range()
|
||
dataframe['atr_norm'] = dataframe['atr'] / dataframe['close']
|
||
|
||
# --- Force de tendance ---
|
||
dataframe['adx'] = ta.trend.ADXIndicator(
|
||
high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
|
||
).adx()
|
||
|
||
# --- Volume directionnel (On Balance Volume) ---
|
||
dataframe['obv'] = ta.volume.OnBalanceVolumeIndicator(
|
||
close=dataframe['close'], volume=dataframe['volume']
|
||
).on_balance_volume()
|
||
|
||
# --- Volatilité récente (écart-type des rendements) ---
|
||
dataframe['vol_24'] = dataframe['percent'].rolling(24).std()
|
||
|
||
# Compter les baisses / hausses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
# df : ton dataframe OHLCV + indicateurs existants
|
||
# Assurez-vous que les colonnes suivantes existent :
|
||
# 'max_rsi_12', 'roc_24', 'bb_percent_1h'
|
||
|
||
# --- Filtrage des NaN initiaux ---
|
||
# dataframe = dataframe.dropna()
|
||
|
||
dataframe['rsi_slope'] = dataframe['rsi'].diff(3) / 3 # vitesse moyenne du RSI
|
||
dataframe['adx_change'] = dataframe['adx'] - dataframe['adx'].shift(12) # évolution de la tendance
|
||
dataframe['volatility_ratio'] = dataframe['atr_norm'] / dataframe['bb_width']
|
||
|
||
dataframe["rsi_diff"] = dataframe["rsi"] - dataframe["rsi"].shift(3)
|
||
dataframe["slope_ratio"] = dataframe["sma5_deriv1"] / (dataframe["sma60_deriv1"] + 1e-9)
|
||
dataframe["divergence"] = (dataframe["rsi_deriv1"] * dataframe["sma5_deriv1"]) < 0
|
||
|
||
###########################
|
||
|
||
dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
|
||
self.calculeDerivees(dataframe, 'volume', timeframe=timeframe, ema_period=12)
|
||
|
||
self.setTrends(dataframe)
|
||
|
||
return dataframe
|
||
|
||
def feature_auc_scores(self, X, y):
|
||
aucs = {}
|
||
for col in X.columns:
|
||
try:
|
||
aucs[col] = roc_auc_score(y, X[col].ffill().fillna(0))
|
||
except Exception:
|
||
aucs[col] = np.nan
|
||
return pd.Series(aucs).sort_values(ascending=False)
|
||
|
||
def macd_tendance_int(self, dataframe: pd.DataFrame,
|
||
macd_col='macd',
|
||
signal_col='macdsignal',
|
||
hist_col='macdhist',
|
||
eps=0.0) -> pd.Series:
|
||
"""
|
||
Renvoie la tendance MACD sous forme d'entiers.
|
||
2 : Haussier
|
||
1 : Ralentissement hausse
|
||
0 : Neutre
|
||
-1 : Ralentissement baisse
|
||
-2 : Baissier
|
||
"""
|
||
|
||
# | Nom | Formule / définition | Signification |
|
||
# | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
# | **MACD** (`macd`) | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue. <br> - Positive → tendance haussière <br> - Négative → tendance baissière |
|
||
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente |
|
||
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - Négatif mais croissant → ralentissement de la baisse |
|
||
|
||
# | Situation | MACD | Signal | Hist | Interprétation |
|
||
# | -------------------------- | ---------- | --------- | -------- | ------------------------------------------ |
|
||
# | MACD > 0, Hist croissant | au-dessus | croissant | Haussier | Momentum fort → tendance haussière |
|
||
# | MACD > 0, Hist décroissant | au-dessus | en baisse | Momentum | La hausse ralentit, prudence |
|
||
# | MACD < 0, Hist décroissant | en dessous | en baisse | Baissier | Momentum fort → tendance baissière |
|
||
# | MACD < 0, Hist croissant | en dessous | en hausse | Rebond ? | La baisse ralentit → possible retournement |
|
||
|
||
# Créer une série de 0 par défaut
|
||
tendance = pd.Series(0, index=dataframe.index)
|
||
|
||
# Cas MACD > signal
|
||
mask_up = dataframe[macd_col] > dataframe[signal_col] + eps
|
||
mask_up_hist_pos = mask_up & (dataframe[hist_col] > 0)
|
||
mask_up_hist_neg = mask_up & (dataframe[hist_col] <= 0)
|
||
|
||
tendance[mask_up_hist_pos] = 2 # Haussier
|
||
tendance[mask_up_hist_neg] = 1 # Ralentissement hausse
|
||
|
||
# Cas MACD < signal
|
||
mask_down = dataframe[macd_col] < dataframe[signal_col] - eps
|
||
mask_down_hist_neg = mask_down & (dataframe[hist_col] < 0)
|
||
mask_down_hist_pos = mask_down & (dataframe[hist_col] >= 0)
|
||
|
||
tendance[mask_down_hist_neg] = -2 # Baissier
|
||
tendance[mask_down_hist_pos] = -1 # Ralentissement baisse
|
||
|
||
# Les NaN deviennent neutre
|
||
tendance[dataframe[[macd_col, signal_col, hist_col]].isna().any(axis=1)] = 0
|
||
|
||
return tendance
|
||
|
||
def calculateDownAndUp(self, dataframe, limit=0.0001):
|
||
dataframe['down'] = dataframe['hapercent'] <= limit
|
||
dataframe['up'] = dataframe['hapercent'] >= limit
|
||
dataframe['down_count'] = - dataframe['down'].astype(int) * (
|
||
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
|
||
dataframe['up_count'] = dataframe['up'].astype(int) * (
|
||
dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
|
||
# Créer une colonne vide
|
||
dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
|
||
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
|
||
|
||
def calculateDerivation(self, dataframe, window=12, suffixe='', timeframe='5m'):
|
||
dataframe[f"mid_smooth{suffixe}"] = dataframe['mid'].rolling(window).mean()
|
||
dataframe = self.calculeDerivees(dataframe, f"mid_smooth{suffixe}", timeframe=timeframe, ema_period=window)
|
||
return dataframe
|
||
|
||
def calculeDerivees(
|
||
self,
|
||
dataframe: pd.DataFrame,
|
||
name: str,
|
||
suffixe: str = '',
|
||
window: int = 100,
|
||
coef: float = 0.15,
|
||
ema_period: int = 10,
|
||
verbose: bool = True,
|
||
timeframe: str = '5m'
|
||
) -> pd.DataFrame:
|
||
"""
|
||
Calcule deriv1/deriv2 (relative simple), applique EMA, calcule tendency
|
||
avec epsilon adaptatif basé sur rolling percentiles.
|
||
"""
|
||
|
||
d1_col = f"{name}{suffixe}_deriv1"
|
||
d2_col = f"{name}{suffixe}_deriv2"
|
||
# d1s_col = f"{name}{suffixe}_deriv1_smooth"
|
||
# d2s_col = f"{name}{suffixe}_deriv2_smooth"
|
||
tendency_col = f"{name}{suffixe}_state"
|
||
|
||
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] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
|
||
# lissage EMA
|
||
dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
|
||
|
||
# dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
|
||
|
||
dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
|
||
dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
|
||
|
||
# epsilon adaptatif via rolling percentile
|
||
p_low_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.05)
|
||
p_high_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.95)
|
||
p_low_d2 = dataframe[d2_col].rolling(window=window, min_periods=1).quantile(0.05)
|
||
p_high_d2 = dataframe[d2_col].rolling(window=window, min_periods=1).quantile(0.95)
|
||
|
||
eps_d1_series = ((p_low_d1.abs() + p_high_d1.abs()) / 2) * coef
|
||
eps_d2_series = ((p_low_d2.abs() + p_high_d2.abs()) / 2) * coef
|
||
|
||
# fallback global eps
|
||
global_eps_d1 = (abs(dataframe[d1_col].quantile(0.05)) + abs(dataframe[d1_col].quantile(0.95))) / 2 * coef
|
||
global_eps_d2 = (abs(dataframe[d2_col].quantile(0.05)) + abs(dataframe[d2_col].quantile(0.95))) / 2 * coef
|
||
|
||
eps_d1_series = eps_d1_series.fillna(global_eps_d1).replace(0, global_eps_d1)
|
||
eps_d2_series = eps_d2_series.fillna(global_eps_d2).replace(0, global_eps_d2)
|
||
|
||
# if verbose and self.dp.runmode.value in ('backtest'):
|
||
# stats = dataframe[[d1_col, d2_col]].agg(['min', 'max']).T
|
||
# stats['abs_max'] = dataframe[[d1_col, d2_col]].abs().max(axis=0)
|
||
# print(f"---- Derivatives stats {timeframe}----")
|
||
# print(stats)
|
||
# print(f"rolling window = {window}, coef = {coef}, ema_period = {ema_period}")
|
||
# print("---------------------------")
|
||
|
||
# mapping tendency
|
||
def tag_by_derivatives(row):
|
||
idx = int(row.name)
|
||
d1v = float(row[d1_col])
|
||
d2v = float(row[d2_col])
|
||
eps1 = float(eps_d1_series.iloc[idx])
|
||
eps2 = float(eps_d2_series.iloc[idx])
|
||
|
||
# # mapping état → codes 3 lettres explicites
|
||
# # | Ancien état | Nouveau code 3 lettres | Interprétation |
|
||
# # | ----------- | ---------------------- | --------------------- |
|
||
# # | 4 | HAU | Hausse Accélérée |
|
||
# # | 3 | HSR | Hausse Ralentissement |
|
||
# # | 2 | HST | Hausse Stable |
|
||
# # | 1 | DHB | Départ Hausse |
|
||
# # | 0 | PAL | Palier / neutre |
|
||
# # | -1 | DBD | Départ Baisse |
|
||
# # | -2 | BSR | Baisse Ralentissement |
|
||
# # | -3 | BST | Baisse Stable |
|
||
# # | -4 | BAS | Baisse Accélérée |
|
||
|
||
# Palier strict
|
||
if abs(d1v) <= eps1 and abs(d2v) <= eps2:
|
||
return 0
|
||
# Départ si d1 ~ 0 mais d2 signale direction
|
||
if abs(d1v) <= eps1:
|
||
return 1 if d2v > eps2 else -1 if d2v < -eps2 else 0
|
||
# Hausse
|
||
if d1v > eps1:
|
||
return 4 if d2v > eps2 else 3
|
||
# Baisse
|
||
if d1v < -eps1:
|
||
return -4 if d2v < -eps2 else -2
|
||
return 0
|
||
|
||
dataframe[tendency_col] = dataframe.apply(tag_by_derivatives, axis=1)
|
||
|
||
# if timeframe == '1h' and verbose and self.dp.runmode.value in ('backtest'):
|
||
# print("##################")
|
||
# print(f"# STAT {timeframe} {name}{suffixe}")
|
||
# print("##################")
|
||
# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent'], indic_1=f"{name}{suffixe}_deriv1", indic_2=f"{name}{suffixe}_deriv2")
|
||
|
||
return dataframe
|
||
|
||
def getOpenTrades(self):
|
||
# if len(self.trades) == 0:
|
||
self.trades = Trade.get_open_trades()
|
||
return self.trades
|
||
|
||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
pair = metadata['pair']
|
||
# trend = self.getTrend(dataframe)
|
||
# # params = self.loadParamsFor(pair, trend)
|
||
#
|
||
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
|
||
# indic_deriv1_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv1_5m')
|
||
# indic_deriv2_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv2_5m')
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['mid_smooth_3'].shift(1) < dataframe['mid_smooth_3'])
|
||
# & (dataframe['hapercent'] > 0)
|
||
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
|
||
# & (dataframe['open'] <= dataframe['bb_middleband'])
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'smth')
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['sma24_deriv2'].shift(1) < 0)
|
||
# & (dataframe['sma24_deriv2'] > 0)
|
||
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'invert')
|
||
#
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['sma24_deriv1'] > 0)
|
||
# & (dataframe['sma60_deriv1'].shift(1) < 0)
|
||
# & (dataframe['sma60_deriv1'] > 0)
|
||
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'raise')
|
||
#
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['sma60_deriv1'].shift(1) < 0)
|
||
# & (dataframe['sma24_deriv2'] > 0)
|
||
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'stg_inv')
|
||
#
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['mid_smooth_24'].shift(24) >= dataframe['mid_smooth_24'].shift(12))
|
||
# & (dataframe['mid_smooth_24'].shift(12) <= dataframe['mid_smooth_24'])
|
||
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
|
||
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
|
||
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'smth3_inv')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['percent3'] < -0.03)
|
||
& (dataframe['percent'] > 0)
|
||
), ['enter_long', 'enter_tag']] = (1, 'pct3')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe["sma24"].shift(2) >= dataframe["sma24"].shift(1))
|
||
& (dataframe["sma24"].shift(1) <= dataframe["sma24"])
|
||
& (dataframe['percent3_1h'] < -0.03)
|
||
), ['enter_long', 'enter_tag']] = (1, 'pct3_1h')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(1))
|
||
& (dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_sup_buy.value}"])
|
||
& (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
|
||
& (dataframe['stop_buying'] == False)
|
||
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# & (dataframe[f"hapercent"] >= -0.001)
|
||
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_sup.value}_sup")
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(1))
|
||
& (dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_inf_buy.value}"])
|
||
& (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
|
||
& (dataframe['stop_buying'] == False)
|
||
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# & (dataframe[f"hapercent"] >= -0.001)
|
||
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_inf_buy.value}_inf")
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['stop_buying'] == False)
|
||
& (dataframe['stop_buying'].shift(1) == True)
|
||
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# & (dataframe[f"hapercent"] >= -0.001)
|
||
), ['enter_long', 'enter_tag']] = (1, f"end")
|
||
|
||
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 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_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['mid_smooth_deriv1'] == 0)
|
||
# & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
|
||
# ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
|
||
|
||
# {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}
|
||
dataframe.loc[
|
||
(
|
||
(dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1))
|
||
& (dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_sup_sell.value}"])
|
||
& (dataframe[f"{self.indic_deriv_5m_slope_sup_sell.value}_deriv1"] < self.deriv_5m_slope_sup_sell.value)
|
||
& (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
|
||
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_sup_sell.value}_sup")
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1))
|
||
& (dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_inf_sell.value}"])
|
||
& (dataframe[f"{self.indic_deriv_5m_slope_inf_sell.value}_deriv1"] < self.deriv_5m_slope_inf_sell.value)
|
||
& (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
|
||
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_inf_sell.value}_inf")
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['stop_buying'] == True)
|
||
& (dataframe['stop_buying'].shift(1) == False)
|
||
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
|
||
# & (dataframe[f"sma60_deriv1"] >= -0.2)
|
||
# & (dataframe[f"hapercent"] >= -0.001)
|
||
), ['enter_long', 'enter_tag']] = (1, f"start")
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe[f"{self.indic_percent_sell.value}"] < self.percent_5m_sell.value)
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_percent_sell.value}")
|
||
|
||
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() < 0): # 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()
|
||
before_last_candle_12 = dataframe.iloc[-13].squeeze()
|
||
before_last_candle_24 = dataframe.iloc[-25].squeeze()
|
||
last_candle_3 = dataframe.iloc[-4].squeeze()
|
||
last_candle_previous_1h = dataframe.iloc[-13].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)
|
||
# lim = self.getLimitBuy(pair, last_candle, pct)
|
||
# lim = - (0.012 * (1 + round(count_of_buys / 5)) + 0.001 * (count_of_buys - 1))
|
||
# lim = - (0.012 + 0.001 * (count_of_buys - 1) + (0.002 * count_of_buys if count_of_buys > 10 else 0.001 * count_of_buys if count_of_buys > 5 else 0))
|
||
|
||
else:
|
||
pct = 0.05
|
||
lim = - pct - (count_of_buys * 0.0025)
|
||
# lim = self.getLimitBuy(pair, last_candle, pct)
|
||
|
||
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['sma60_deriv1'] > 0 and last_candle['hapercent'] > 0 and last_candle['stop_buying'] == False) \
|
||
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
|
||
|
||
# if 6 <= count_of_buys:
|
||
# if not ((before_last_candle_24['sma24_deriv1_1h'] > before_last_candle_12['sma24_deriv1_1h'])
|
||
# & (before_last_candle_12['sma24_deriv1_1h'] < last_candle['sma24_deriv1_1h'])):
|
||
# return None
|
||
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_first={pct_first:.3f} pct_max={pct_max:.3f} lim={lim:.3f} index={index}")
|
||
# self.pairs[trade.pair]['last_palier_index'] = index
|
||
|
||
# # Appel de la fonction
|
||
# poly_func, x_future, y_future, count = self.polynomial_forecast(
|
||
# dataframe['mid_smooth_12'],
|
||
# window=self.buy_horizon_predict_1h.value * 12,
|
||
# degree=4)
|
||
#
|
||
# if count < 3:
|
||
# 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 = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||
self.pairs[pair]['total_amount'] += 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 +'
|
||
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 getPct60D(self, pair, last_candle):
|
||
return round((last_candle['max60_1d'] - last_candle['min60_1d']) / last_candle['max60_1d'], 4)
|
||
|
||
def getPctClose60D(self, pair, last_candle):
|
||
if last_candle['close'] > last_candle['max12_1d']:
|
||
return 1
|
||
if last_candle['close'] < last_candle['min12_1d']:
|
||
return 0
|
||
return round(
|
||
(last_candle['close'] - last_candle['min12_1d']) / (last_candle['max12_1d'] - last_candle['min12_1d']), 4)
|
||
|
||
def getLimitBuy(self, pair, last_candle, first_pct):
|
||
count_of_buys = self.pairs[pair]['count_of_buys']
|
||
pct60 = self.getPct60D(pair, last_candle) # exemple 0.3 pour 30%
|
||
if (pct60 < 0.05):
|
||
lim = - first_pct - (count_of_buys * 0.001 * 0.05 / 0.05)
|
||
else:
|
||
# 0.1
|
||
# 0.4
|
||
lim = - first_pct - (count_of_buys * 0.001 * pct60 / 0.05)
|
||
|
||
return lim
|
||
|
||
# def getProbaHausseEmaVolume(self, last_candle):
|
||
# value_1 = self.getValuesFromTable(self.ema_volume, last_candle['ema_volume'])
|
||
# value_2 = self.getValuesFromTable(self.mid_smooth_1h_deriv1, last_candle['mid_smooth_1h_deriv1'])
|
||
#
|
||
# val = self.approx_val_from_bins(
|
||
# matrice=self.ema_volume_mid_smooth_1h_deriv1_matrice_df,
|
||
# numeric_matrice=self.ema_volume_mid_smooth_1h_deriv1_numeric_matrice,
|
||
# row_label=value_2,
|
||
# col_label=value_1
|
||
# )
|
||
# return val
|
||
|
||
def getProbaHausseSma5d(self, last_candle):
|
||
value_1 = self.getValuesFromTable(self.sma5_deriv1, last_candle['sma5_deriv1_1d'])
|
||
value_2 = self.getValuesFromTable(self.sma5_deriv2, last_candle['sma5_deriv2_1d'])
|
||
|
||
# print(f"{last_candle['sma5_deriv1_1d']} => {value_1} / {last_candle['sma5_deriv2_1d']} => {value_2}")
|
||
|
||
val = self.approx_val_from_bins(
|
||
matrice=self.sma5_derive1_2_matrice_df,
|
||
numeric_matrice=self.sma5_derive1_2_numeric_matrice,
|
||
row_label=value_2,
|
||
col_label=value_1
|
||
)
|
||
return val
|
||
|
||
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
|
||
# pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2)
|
||
|
||
if True: # not pair in ('BTC/USDT', 'BTC/USDC'):
|
||
# factors = [1, 1.2, 1.3, 1.4]
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
# pctClose60 = self.getPctClose60D(pair, last_candle)
|
||
# dist_max = self.getDistMax(last_candle, pair)
|
||
# factor = self.multi_step_interpolate(dist_max, self.thresholds, self.factors)
|
||
factor = 1 #65 / min(65, last_candle['rsi_1d'])
|
||
if last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']:
|
||
factor = 2
|
||
|
||
adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
|
||
else:
|
||
adjusted_stake_amount = self.pairs[pair]['first_amount']
|
||
else:
|
||
first_price = self.pairs[pair]['first_buy']
|
||
if (first_price == 0):
|
||
first_price = last_candle['close']
|
||
|
||
last_max = last_candle['max12_1d']
|
||
pct = 5
|
||
if last_max > 0:
|
||
pct = 100 * (last_max - first_price) / last_max
|
||
|
||
factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
|
||
adjusted_stake_amount = base_stake_amount * factor # max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
||
|
||
# pct = 100 * abs(self.getPctFirstBuy(pair, last_candle))
|
||
#
|
||
# factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
|
||
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
self.pairs[pair]['first_amount'] = adjusted_stake_amount
|
||
|
||
return adjusted_stake_amount
|
||
|
||
def calculateAmountSliding(self, pair, last_candle):
|
||
val = last_candle['close']
|
||
min_sliding = min(last_candle['min60_1d'], val)
|
||
max_sliding = max(last_candle['max60_1d'], val)
|
||
min_abs = self.pairs[pair]['last_min']
|
||
max_abs = self.pairs[pair]['last_max']
|
||
full = self.wallets.get_total_stake_amount()
|
||
stake = full / self.stakes
|
||
|
||
out_min = stake / 2
|
||
out_max = stake * 2
|
||
# Clamp sliding range within absolute bounds
|
||
min_sliding = max(min_sliding, min_abs)
|
||
max_sliding = min(max_sliding, max_abs)
|
||
|
||
# Avoid division by zero
|
||
if max_sliding == min_sliding:
|
||
return out_max # Or midpoint, or default value
|
||
|
||
# Inverse linear interpolation
|
||
position = (val - min_sliding) / (max_sliding - min_sliding)
|
||
return out_max - position * (out_max - out_min)
|
||
|
||
def calculatePctSliding(self, pair, last_candle):
|
||
val = last_candle['close']
|
||
min_sliding = last_candle['min60_1d']
|
||
max_sliding = last_candle['max60_1d']
|
||
min_abs = self.pairs[pair]['last_min']
|
||
max_abs = self.pairs[pair]['last_max']
|
||
out_min = 0.025
|
||
out_max = 0.08
|
||
# Clamp sliding range within absolute bounds
|
||
min_sliding = max(min_sliding, min_abs)
|
||
max_sliding = min(max_sliding, max_abs)
|
||
|
||
# Avoid division by zero
|
||
if max_sliding == min_sliding:
|
||
return out_max # Or midpoint, or default value
|
||
|
||
# Inverse linear interpolation
|
||
position = (val - min_sliding) / (max_sliding - min_sliding)
|
||
return out_max - position * (out_max - out_min)
|
||
|
||
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']
|
||
# if self.pairs[pair]['count_of_buys'] > 6:
|
||
# pct_to_max = 0.006 * self.pairs[pair]['count_of_buys']
|
||
# pctClose60 = self.getPctClose60D(pair, last_candle)
|
||
|
||
# max_60 = last_candle['max60_1d']
|
||
# if last_candle['close'] < max_60:
|
||
# pct_to_max = 0.25 * (max_60 - last_candle['close']) / max_60
|
||
# pct_to_max = pct_to_max * (2 - pctClose60)
|
||
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
|
||
|
||
# print(
|
||
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
|
||
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
|
||
|
||
# ✅ Première dérivée(variation ou pente)
|
||
# Positive: la courbe est croissante → tendance haussière.
|
||
# Négative: la courbe est décroissante → tendance baissière.
|
||
# Proche de 0: la courbe est plate → marché stable ou en transition.
|
||
#
|
||
# Applications:
|
||
# Détecter les points d’inflexion(changement de tendance) quand elle s’annule.\
|
||
# Analyser la vitesse d’un mouvement(plus elle est forte, plus le mouvement est impulsif).
|
||
#
|
||
# ✅ Seconde dérivée(accélération ou concavité)
|
||
# Positive: la pente augmente → accélération de la hausse ou ralentissement de la baisse.
|
||
# Négative: la pente diminue → accélération de la baisse ou ralentissement de la hausse.
|
||
# Changement de signe: indique souvent un changement de courbure, utile pour prévoir des retournements.
|
||
#
|
||
# Exemples:
|
||
# 🟢 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.
|
||
#
|
||
# Filtrer les signaux: ne prendre un signal haussier que si dérivée1 > 0 et dérivée2 > 0.
|
||
# Détecter les zones de retournement: quand dérivée1 ≈ 0 et que dérivée2 change de signe.
|
||
def calculateRegression(self,
|
||
dataframe: DataFrame,
|
||
column='close',
|
||
window=50,
|
||
degree=3,
|
||
future_offset: int = 10 # projection à n bougies après
|
||
) -> DataFrame:
|
||
df = dataframe.copy()
|
||
|
||
regression_fit = []
|
||
regression_future_fit = []
|
||
|
||
regression_fit = []
|
||
regression_future_fit = []
|
||
|
||
for i in range(len(df)):
|
||
if i < window:
|
||
regression_fit.append(np.nan)
|
||
regression_future_fit.append(np.nan)
|
||
continue
|
||
|
||
# Fin de la fenêtre d’apprentissage
|
||
end_index = i
|
||
start_index = i - window
|
||
y = df[column].iloc[start_index:end_index].values
|
||
|
||
# Si les données sont insuffisantes (juste par précaution)
|
||
if len(y) < window:
|
||
regression_fit.append(np.nan)
|
||
regression_future_fit.append(np.nan)
|
||
continue
|
||
|
||
# x centré pour meilleure stabilité numérique
|
||
x = np.linspace(-1, 1, window)
|
||
coeffs = np.polyfit(x, y, degree)
|
||
poly = np.poly1d(coeffs)
|
||
|
||
# Calcul point présent (dernier de la fenêtre)
|
||
x_now = x[-1]
|
||
regression_fit.append(poly(x_now))
|
||
|
||
# Calcul point futur, en ajustant si on dépasse la fin
|
||
remaining = len(df) - i - 1
|
||
effective_offset = min(future_offset, remaining)
|
||
x_future = x_now + (effective_offset / window) * 2 # respect du même pas
|
||
regression_future_fit.append(poly(x_future))
|
||
|
||
df[f"{column}_regression"] = regression_fit
|
||
# 2. Dérivée première = différence entre deux bougies successives
|
||
df[f"{column}_regression_deriv1"] = round(100 * df[f"{column}_regression"].diff() / df[f"{column}_regression"], 4)
|
||
|
||
# 3. Dérivée seconde = différence de la dérivée première
|
||
df[f"{column}_regression_deriv2"] = round(10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||
|
||
df[f"{column}_future_{future_offset}"] = regression_future_fit
|
||
|
||
# # 2. Dérivée première = différence entre deux bougies successives
|
||
# df[f"{column}_future_{future_offset}_deriv1"] = round(100 * df[f"{column}_future_{future_offset}"].diff() / df[f"{column}_future_{future_offset}"], 4)
|
||
#
|
||
# # 3. Dérivée seconde = différence de la dérivée première
|
||
# df[f"{column}_future_{future_offset}_deriv2"] = round(10 * df[f"{column}_future_{future_offset}_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||
|
||
return df
|
||
|
||
def getValuesFromTable(self, values, value):
|
||
for i in range(len(values) - 1):
|
||
if values[i] <= value < values[i + 1]:
|
||
return self.labels[i]
|
||
return self.labels[-1] # cas limite pour la borne max
|
||
|
||
# def interpolated_val_from_bins(self, row_pos, col_pos):
|
||
# """
|
||
# Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
|
||
# à partir de positions flottantes dans l'index (ligne) et les colonnes.
|
||
#
|
||
# Parameters:
|
||
# matrix_df (pd.DataFrame): Matrice des probabilités (index/colonnes = labels).
|
||
# row_pos (float): Position réelle de la ligne (0 = B5, 10 = H5).
|
||
# col_pos (float): Position réelle de la colonne (0 = B5, 10 = H5).
|
||
#
|
||
# Returns:
|
||
# float: Valeur interpolée, ou NaN si en dehors des bornes.
|
||
# """
|
||
#
|
||
# # Labels ordonnés
|
||
# n = len(self.labels)
|
||
#
|
||
# # Vérification des limites
|
||
# if not (0 <= row_pos <= n - 1) or not (0 <= col_pos <= n - 1):
|
||
# return np.nan
|
||
#
|
||
# # Conversion des labels -> matrice
|
||
# matrix = self.smooth_smadiff_matrice_df.reindex(index=self.labels, columns=self.labels).values
|
||
#
|
||
# # Coordonnées entières (inférieures)
|
||
# i = int(np.floor(row_pos))
|
||
# j = int(np.floor(col_pos))
|
||
#
|
||
# # Coefficients pour interpolation
|
||
# dx = row_pos - i
|
||
# dy = col_pos - j
|
||
#
|
||
# # Précautions sur les bords
|
||
# if i >= n - 1: i = n - 2; dx = 1.0
|
||
# if j >= n - 1: j = n - 2; dy = 1.0
|
||
#
|
||
# # Récupération des 4 valeurs voisines
|
||
# v00 = matrix[i][j]
|
||
# v10 = matrix[i + 1][j]
|
||
# v01 = matrix[i][j + 1]
|
||
# v11 = matrix[i + 1][j + 1]
|
||
#
|
||
# # Interpolation bilinéaire
|
||
# interpolated = (
|
||
# (1 - dx) * (1 - dy) * v00 +
|
||
# dx * (1 - dy) * v10 +
|
||
# (1 - dx) * dy * v01 +
|
||
# dx * dy * v11
|
||
# )
|
||
# return interpolated
|
||
|
||
def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
|
||
"""
|
||
Renvoie une approximation de la valeur à partir des labels binaires (e.g. B5, H1)
|
||
en utilisant une interpolation simple basée sur les indices.
|
||
|
||
Parameters:
|
||
matrix_df (pd.DataFrame): Matrice avec les labels binaires en index et colonnes.
|
||
row_label (str): Label de la ligne (ex: 'B3').
|
||
col_label (str): Label de la colonne (ex: 'H2').
|
||
|
||
Returns:
|
||
float: Valeur approchée si possible, sinon NaN.
|
||
"""
|
||
|
||
# Vérification des labels
|
||
if row_label not in matrice.index or col_label not in matrice.columns:
|
||
return np.nan
|
||
|
||
# Index correspondant
|
||
row_idx = self.label_to_index.get(row_label)
|
||
col_idx = self.label_to_index.get(col_label)
|
||
|
||
# Approximation directe (aucune interpolation complexe ici, juste une lecture)
|
||
return numeric_matrice[row_idx, col_idx]
|
||
|
||
@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 conditional_smoothing(self, series, threshold=0.002):
|
||
smoothed = [series.iloc[0]]
|
||
for val in series.iloc[1:]:
|
||
last = smoothed[-1]
|
||
if abs(val - last) / last >= threshold:
|
||
smoothed.append(val)
|
||
else:
|
||
smoothed.append(last)
|
||
return pd.Series(smoothed, index=series.index)
|
||
|
||
def causal_savgol(self, series, window=25, polyorder=3):
|
||
result = []
|
||
half_window = window # Fenêtre complète dans le passé
|
||
for i in range(len(series)):
|
||
if i < half_window:
|
||
result.append(np.nan)
|
||
continue
|
||
window_series = series[i - half_window:i]
|
||
if window_series.isna().any():
|
||
result.append(np.nan)
|
||
continue
|
||
coeffs = np.polyfit(range(window), window_series, polyorder)
|
||
poly = np.poly1d(coeffs)
|
||
result.append(poly(window - 1))
|
||
return pd.Series(result, index=series.index)
|
||
|
||
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 compute_adaptive_paliers(self, max_drawdown: float = 0.65, first_steps: list[float] = [0.01, 0.01, 0.015, 0.02],
|
||
growth: float = 1.2) -> list[float]:
|
||
"""
|
||
Génère une liste de drawdowns négatifs avec des paliers plus rapprochés au début.
|
||
|
||
:param max_drawdown: Drawdown max (ex: 0.65 pour -65%)
|
||
:param first_steps: Liste des premiers paliers fixes en % (ex: [0.01, 0.01, 0.015])
|
||
:param growth: Facteur multiplicatif pour espacer les paliers suivants
|
||
:return: Liste de drawdowns négatifs (croissants)
|
||
"""
|
||
paliers = []
|
||
cumulated = 0.0
|
||
|
||
# Étapes initiales rapprochées
|
||
for step in first_steps:
|
||
cumulated += step
|
||
paliers.append(round(-cumulated, 4))
|
||
|
||
# Étapes suivantes plus espacées
|
||
step = first_steps[-1]
|
||
while cumulated < max_drawdown:
|
||
step *= growth
|
||
cumulated += step
|
||
if cumulated >= max_drawdown:
|
||
break
|
||
paliers.append(round(-cumulated, 4))
|
||
|
||
return paliers
|
||
|
||
# def get_dca_stakes(self,
|
||
# max_drawdown: float = 0.65,
|
||
# base_stake: float = 100.0,
|
||
# first_steps: list[float] = [0.01, 0.01, 0.015, 0.015],
|
||
# growth: float = 1.2,
|
||
# stake_growth: float = 1.15
|
||
# ) -> list[tuple[float, float]]:
|
||
# """
|
||
# Génère les paliers de drawdown et leurs stakes associés.
|
||
#
|
||
# :param max_drawdown: Maximum drawdown (ex: 0.65 pour -65%)
|
||
# :param base_stake: Mise initiale
|
||
# :param first_steps: Paliers de départ (plus resserrés)
|
||
# :param growth: Multiplicateur d'espacement des paliers
|
||
# :param stake_growth: Croissance multiplicative des mises
|
||
# :return: Liste de tuples (palier_pct, stake)
|
||
# [(-0.01, 100.0), (-0.02, 115.0), (-0.035, 132.25), (-0.05, 152.09), (-0.068, 174.9),
|
||
# (-0.0896, 201.14), (-0.1155, 231.31), (-0.1466, 266.0), (-0.1839, 305.9), (-0.2287, 351.79),
|
||
# (-0.2825, 404.56), (-0.347, 465.24), (-0.4244, 535.03), (-0.5173, 615.28), (-0.6287, 707.57)]
|
||
# """
|
||
# paliers = [
|
||
# (-0.01, 100.0), (-0.02, 115.0), (-0.035, 130), (-0.05, 150), (-0.07, 150),
|
||
# (-0.10, 150), (-0.15, 150), (-0.20, 150), (-0.25, 150),
|
||
# (-0.30, 200), (-0.40, 200),
|
||
# (-0.50, 300), (-0.60, 400), (-0.70, 500), (-0.80, 1000)
|
||
# ]
|
||
#
|
||
# # cumulated = 0.0
|
||
# # stake = base_stake
|
||
# #
|
||
# # # Étapes initiales
|
||
# # for step in first_steps:
|
||
# # cumulated += step
|
||
# # paliers.append((round(-cumulated, 4), round(stake, 2)))
|
||
# # stake *= stake_growth
|
||
# #
|
||
# # # Étapes suivantes
|
||
# # step = first_steps[-1]
|
||
# # while cumulated < max_drawdown:
|
||
# # step *= growth
|
||
# # cumulated += step
|
||
# # if cumulated >= max_drawdown:
|
||
# # break
|
||
# # paliers.append((round(-cumulated, 4), round(stake, 2)))
|
||
# # stake *= stake_growth
|
||
#
|
||
# return paliers
|
||
|
||
# def get_active_stake(self, pct: float) -> float:
|
||
# """
|
||
# Renvoie la mise correspondant au drawdown `pct`.
|
||
#
|
||
# :param pct: drawdown courant (négatif, ex: -0.043)
|
||
# :param paliers: liste de tuples (drawdown, stake)
|
||
# :return: stake correspondant
|
||
# """
|
||
# abs_pct = abs(pct)
|
||
# stake = self.paliers[0][1] # stake par défaut
|
||
#
|
||
# for palier, s in self.paliers:
|
||
# if abs_pct >= abs(palier):
|
||
# stake = s
|
||
# else:
|
||
# break
|
||
#
|
||
# return stake
|
||
|
||
# def get_palier_index(self, pct):
|
||
# """
|
||
# Retourne l'index du palier franchi pour un pourcentage de baisse donné (pct).
|
||
# On cherche le palier le plus profond atteint (dernier franchi).
|
||
# """
|
||
# for i in reversed(range(len(self.paliers))):
|
||
# seuil, _ = self.paliers[i]
|
||
# #print(f"pct={pct} seuil={seuil}")
|
||
# if pct <= seuil:
|
||
# # print(pct)
|
||
# return i
|
||
# return None # Aucun palier atteint
|
||
|
||
# def poly_regression_predictions(self, series: pd.Series, window: int = 20, degree: int = 2, n_future: int = 3) -> pd.DataFrame:
|
||
# """
|
||
# Renvoie une DataFrame avec `n_future` colonnes contenant les extrapolations des n prochains points
|
||
# selon une régression polynomiale ajustée sur les `window` dernières valeurs.
|
||
# """
|
||
# result = pd.DataFrame(index=series.index)
|
||
# x = np.arange(window)
|
||
#
|
||
# for future_step in range(1, n_future + 1):
|
||
# result[f'poly_pred_t+{future_step}'] = np.nan
|
||
#
|
||
# for i in range(window - 1, len(series)):
|
||
# y = series.iloc[i - window + 1 : i + 1].values
|
||
#
|
||
# if np.any(pd.isna(y)):
|
||
# continue
|
||
#
|
||
# coeffs = np.polyfit(x, y, degree)
|
||
# poly = np.poly1d(coeffs)
|
||
#
|
||
# for future_step in range(1, n_future + 1):
|
||
# future_x = window - 1 + future_step # Extrapolation point
|
||
# result.loc[series.index[i], f'poly_pred_t+{future_step}'] = poly(future_x)
|
||
#
|
||
# return result
|
||
|
||
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 calculateStats2(self, df, index, target):
|
||
# # Nombre de tranches (modifiable)
|
||
# n_bins_indice = 11
|
||
# n_bins_valeur = 11
|
||
#
|
||
# # Tranches dynamiques
|
||
# # df['indice_tranche'] = pd.qcut(df[f"{index}"], q=n_bins_indice, duplicates='drop')
|
||
# # df['valeur_tranche'] = pd.qcut(df[f"{target}"], q=n_bins_valeur, duplicates='drop')
|
||
#
|
||
# df[f"{index}_bin"], bins_1h = pd.qcut(df[f"{index}"], q=n_bins_indice, labels=self.labels, retbins=True,
|
||
# duplicates='drop')
|
||
# df[f"{target}_bin"], bins_1d = pd.qcut(df[f"{target}"], q=n_bins_valeur, labels=self.labels, retbins=True,
|
||
# duplicates='drop')
|
||
# # Affichage formaté pour code Python
|
||
# print(f"Bornes des quantiles pour {index} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
||
# print(f"Bornes des quantiles pour {target} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
||
#
|
||
# # Tableau croisé (compte)
|
||
# tableau = pd.crosstab(df[f"{index}_bin"], df[f"{target}_bin"])
|
||
#
|
||
# # Facultatif : en pourcentages
|
||
# tableau_pct = tableau.div(tableau.sum(axis=1), axis=0) * 100
|
||
#
|
||
# # Affichage
|
||
# print("Répartition brute :")
|
||
# print(tableau)
|
||
# print("\nRépartition en % par ligne :")
|
||
# print(tableau_pct.round(2))
|
||
|
||
def calculateStats(self, df, index, target):
|
||
# Nombre de tranches (modifiable)
|
||
n_bins_indice = 11
|
||
n_bins_valeur = 11
|
||
|
||
# Créer les tranches dynamiques
|
||
df['indice_tranche'] = pd.qcut(df[index], q=n_bins_indice, duplicates='drop')
|
||
df['valeur_tranche'] = pd.qcut(df[target], q=n_bins_valeur, duplicates='drop')
|
||
|
||
# Créer un tableau croisé avec la moyenne des valeurs
|
||
pivot_mean = df.pivot_table(
|
||
index='indice_tranche',
|
||
columns='valeur_tranche',
|
||
values=target, # <-- c'est la colonne qu'on agrège
|
||
aggfunc='mean' # <-- on calcule la moyenne
|
||
)
|
||
|
||
# Résultat
|
||
# print("Moyenne des valeurs par double-tranche :")
|
||
# print(pivot_mean.round(2))
|
||
|
||
def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
|
||
limit = 3
|
||
|
||
# return last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']
|
||
|
||
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
|
||
|
||
# if last_candle['sma5_deriv1_1h'] < -0.02:
|
||
# return False
|
||
#
|
||
# if last_candle['mid_smooth_1h_deriv2'] < -2 or last_candle['sma5_deriv2_1h'] < -2:
|
||
# return False
|
||
#
|
||
# if last_candle['sma5_deriv1_1h'] < 0.0 and last_candle['sma5_deriv2_1h'] < -0.0:
|
||
# return False
|
||
#
|
||
# if last_candle['mid_smooth_1h_deriv1'] < 0.0 and last_candle['mid_smooth_1h_deriv2'] < -0.0 and last_candle[
|
||
# 'sma5_deriv2_1h'] < 0:
|
||
# return False
|
||
|
||
# if pair.startswith('BTC'):
|
||
# return True # BTC toujours autorisé
|
||
|
||
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)
|
||
|
||
val = self.getProbaHausseSma5d(last_candle)
|
||
if val < 15:
|
||
return False
|
||
|
||
# if count_decrease == len(non_btc_pairs):
|
||
# self.should_enter_trade_count += 1
|
||
# char="."
|
||
# print(f"should_enter_trade canceled all pairs decreased {'':{char}>{self.should_enter_trade_count}}")
|
||
# return False
|
||
# if (last_candle['mid_smooth_1h_deriv1'] < -0.0 and last_candle['sma24_deriv1_1h'] < -0.0):
|
||
# return False
|
||
|
||
# if (last_candle['sma5_deriv1_1d'] < -0.1 and last_candle['sma5_deriv2_1d'] < -0) \
|
||
# or last_candle['sma5_deriv2_1d'] < -0.2:
|
||
# return False
|
||
|
||
if last_candle['mid_smooth_1h_deriv1'] < -0.02: # and last_candle['mid_smooth_1h_deriv2'] > 0):
|
||
return False
|
||
|
||
# if self.pairs[pair]['count_of_buys'] >= 3:
|
||
# if (last_candle['sma24_deriv1_1d'] < self.sma24_deriv1_1d_protection.value
|
||
# and last_candle['sma5_deriv1_1d'] < self.sma5_deriv1_1d_protection.value \
|
||
# and last_candle['sma5_deriv2_1d'] < -0.05):
|
||
# # or (last_candle['sma5_deriv1_1d'] < -0.1 and last_candle['sma24_deriv1_1h'] < -0.1):
|
||
# self.pairs[pair]['stop'] = True
|
||
# 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
|
||
|
||
@staticmethod
|
||
def check_derivatives_vectorized(dataframe, deriv_pairs, thresholds):
|
||
"""
|
||
Retourne True si toutes les dérivées respectent leur seuil.
|
||
"""
|
||
mask = pd.Series(True, index=dataframe.index)
|
||
for d1_col, d2_col in deriv_pairs:
|
||
d1_thresh = thresholds.get(d1_col, 0)
|
||
d2_thresh = thresholds.get(d2_col, 0)
|
||
mask &= (dataframe[d1_col] >= d1_thresh) & (dataframe[d2_col] >= d2_thresh)
|
||
return mask
|
||
|
||
# ----------------------------------------------------------------------------------------------
|
||
# fallback defaults (used when no JSON exists)
|
||
PARAMS_DIR = 'params'
|
||
|
||
DEFAULT_PARAMS = {
|
||
"rsi_buy": 30,
|
||
"rsi_sell": 70,
|
||
"ema_period": 21,
|
||
"sma_short": 20,
|
||
"sma_long": 100,
|
||
"atr_period": 14,
|
||
"atr_multiplier": 1.5,
|
||
"stake_amount": None, # use exchange default
|
||
"stoploss": -0.10,
|
||
"minimal_roi": {"0": 0.10}
|
||
}
|
||
|
||
def __init__(self, config: dict) -> None:
|
||
super().__init__(config)
|
||
# self.parameters = self.load_params_tree("user_data/strategies/params/")
|
||
|
||
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)
|
||
|
||
# --- classification dynamique via quantiles ---
|
||
|
||
q = df['slope_norm'].quantile([0.125, 0.375, 0.625, 0.875]).values
|
||
q1, q2, q3, q4 = q
|
||
|
||
def classify_expanding(series):
|
||
trend_class = []
|
||
for i in range(len(series)):
|
||
past_values = series[:i + 1] # uniquement le passé
|
||
q = past_values.quantile([0.125, 0.375, 0.625, 0.875]).values
|
||
q1, q2, q3, q4 = q
|
||
v = series.iloc[i]
|
||
if v <= q1:
|
||
trend_class.append(-2)
|
||
elif v <= q2:
|
||
trend_class.append(-1)
|
||
elif v <= q3:
|
||
trend_class.append(0)
|
||
elif v <= q4:
|
||
trend_class.append(1)
|
||
else:
|
||
trend_class.append(2)
|
||
return trend_class
|
||
|
||
dataframe['slope_norm'] = df['slope_norm']
|
||
# dataframe['trend_class'] = df['slope_norm'].apply(classify)
|
||
dataframe['trend_class'] = None
|
||
|
||
# Rolling sur la fenêtre passée
|
||
dataframe['trend_class'] = classify_expanding(dataframe['slope_norm'])
|
||
|
||
|
||
# # -------------------------- Trend detection (M2) --------------------------
|
||
# def getTrend(self, dataframe: DataFrame) -> str:
|
||
# """
|
||
# M2: SMA50 / SMA200 golden/death cross
|
||
# - bull: sma50 > sma200
|
||
# - bear: sma50 < sma200
|
||
# - range: sma50 ~= sma200 (within a small pct)
|
||
#
|
||
# Uses only past data (no future lookahead).
|
||
# """
|
||
# if dataframe is None or len(dataframe) < max(self.DEFAULT_PARAMS['sma_short'], self.DEFAULT_PARAMS['sma_long']) + 2:
|
||
# return 'RANGE'
|
||
#
|
||
# sma_short = dataframe['close'].rolling(window=self.DEFAULT_PARAMS['sma_short']).mean()
|
||
# sma_long = dataframe['close'].rolling(window=self.DEFAULT_PARAMS['sma_long']).mean()
|
||
#
|
||
# cur_short = sma_short.iloc[-1]
|
||
# cur_long = sma_long.iloc[-1]
|
||
#
|
||
# # small relative threshold to avoid constant flips
|
||
# if cur_long == 0 or cur_short == 0:
|
||
# return 'RANGE'
|
||
#
|
||
# rel = abs(cur_short - cur_long) / cur_long
|
||
# threshold = 0.01 # 1% by default; tweak as needed
|
||
#
|
||
# if rel <= threshold:
|
||
# return 'RANGE'
|
||
# if cur_short > cur_long:
|
||
# return 'BULL'
|
||
# return 'BEAR'
|
||
|
||
# # -------------------------- Parameter loading --------------------------
|
||
# def loadParamsFor(self, pair: str, trend: str) -> dict:
|
||
# """Load JSON from params/<PAIR>/<trend>.json with fallback to DEFAULT_PARAMS."""
|
||
# pair_safe = pair.replace('/', '-') # folder name convention: BTC-USDT
|
||
# # cache key
|
||
# cache_key = f"{pair_safe}:{trend}"
|
||
# if cache_key in self._params_cache:
|
||
# return self._params_cache[cache_key]
|
||
#
|
||
# path = os.path.join(self.PARAMS_DIR, pair_safe, f"{trend}.json")
|
||
# if os.path.isfile(path):
|
||
# try:
|
||
# with open(path, 'r') as f:
|
||
# params = json.load(f)
|
||
# # merge with defaults so missing keys won't break
|
||
# merged = {**self.DEFAULT_PARAMS, **params}
|
||
# self._params_cache[cache_key] = merged
|
||
# logger.info(f"Loaded params for {pair} {trend} from {path}")
|
||
# return merged
|
||
# except Exception as e:
|
||
# logger.exception(f"Failed to load params {path}: {e}")
|
||
#
|
||
# # fallback
|
||
# logger.info(f"Using DEFAULT_PARAMS for {pair} {trend}")
|
||
# self._params_cache[cache_key] = dict(self.DEFAULT_PARAMS)
|
||
# return self._params_cache[cache_key]
|
||
|
||
|
||
def load_params_tree(self, base_path="user_data/strategies/params/"):
|
||
base = Path(base_path)
|
||
params_tree = {}
|
||
if not base.exists():
|
||
raise FileNotFoundError(f"Base path '{base_path}' not found.")
|
||
|
||
for pair_dir in base.iterdir():
|
||
if not pair_dir.is_dir():
|
||
continue
|
||
pair = self.getShortName(pair_dir.name) # ex : BTC-USDT
|
||
params_tree.setdefault(pair, {})
|
||
|
||
for trend_dir in pair_dir.iterdir():
|
||
if not trend_dir.is_dir():
|
||
continue
|
||
trend = trend_dir.name # ex : bull / bear / range
|
||
params_tree[pair].setdefault(trend, [])
|
||
|
||
for file in trend_dir.glob("*-hyperopt_result.json"):
|
||
filename = file.name
|
||
|
||
# Extraire START et END
|
||
try:
|
||
prefix = filename.replace("-hyperopt_result.json", "")
|
||
start, end = prefix.split("-", 1) # split en 2
|
||
except Exception:
|
||
start = None
|
||
end = None
|
||
|
||
# Lire le JSON
|
||
try:
|
||
with open(file, "r") as f:
|
||
content = json.load(f)
|
||
except Exception as err:
|
||
content = {"error": str(err)}
|
||
|
||
params_tree[pair][trend].append({
|
||
"start": start,
|
||
"end": end,
|
||
"file": str(file),
|
||
"content": content,
|
||
})
|
||
for pair, trends in params_tree.items():
|
||
for trend, entries in trends.items():
|
||
if entries:
|
||
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
|
||
# indic_deriv1_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv1_5m')
|
||
# indic_deriv2_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv2_5m')
|
||
#
|
||
# indic_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_5m_sell')
|
||
# indic_deriv1_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv1_5m_sell')
|
||
# indic_deriv2_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv2_5m_sell')
|
||
|
||
print(f"{pair} -> {trend}") # {indic_5m} {indic_deriv1_5m} {indic_deriv2_5m} {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}")
|
||
# for entry in entries:
|
||
# print(entry)
|
||
|
||
return params_tree
|
||
|
||
def getParamValue(self, pair, trend, space, param):
|
||
pair = self.getShortName(pair)
|
||
return self.parameters[pair][trend][0]['content']['params'][space][param]
|
||
|
||
|
||
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
|