1859 lines
83 KiB
Python
1859 lines
83 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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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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from datetime import timezone, timedelta
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logger = logging.getLogger(__name__)
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# Machine Learning
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from sklearn.model_selection import train_test_split
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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 SelectFromModel
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from tabulate import tabulate
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from sklearn.feature_selection import VarianceThreshold
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import seaborn as sns
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import lightgbm as lgb
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from sklearn.model_selection import cross_val_score
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import optuna.visualization as vis
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import optuna
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from lightgbm import LGBMRegressor
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor
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from sklearn.preprocessing import StandardScaler, PolynomialFeatures
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from sklearn.pipeline import make_pipeline
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from sklearn.svm import SVR
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.preprocessing import StandardScaler
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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# Tensorflow
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras import layers, models
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from tensorflow.keras.models import load_model
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from keras.utils import plot_model
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from keras.models import Sequential
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from keras.layers import LSTM, Dense
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from sklearn.preprocessing import MinMaxScaler
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import LSTM, Dense
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from tensorflow.keras.optimizers import Adam
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # désactive complètement le GPU
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os.environ["TF_XLA_FLAGS"] = "--tf_xla_enable_xla_devices=false"
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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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import warnings
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warnings.filterwarnings(
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"ignore",
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message=r".*No further splits with positive gain.*"
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)
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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_TensorFlow(IStrategy):
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startup_candle_count = 24 * 12
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# Machine Learning
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model = None
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model_indicators = []
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indicator_target = 'mid_smooth_12_deriv1'
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# Tensorflow
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lookback = 20
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future_steps = 1
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y_no_scale = True
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path = f"user_data/plots/"
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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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# 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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'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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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=True, load=True)
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ml_prob_buy = DecimalParameter(-0.5, 0.5, default=0.0, decimals=2, space='buy', optimize=True, load=True)
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ml_prob_sell = DecimalParameter(-0.5, 0.5, default=0.0, decimals=2, space='sell', optimize=True, load=True)
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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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rsi_deb_protect = IntParameter(50, 90, default=70, space='protection', optimize=True, load=True)
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rsi_end_protect = IntParameter(20, 60, default=55, space='protection', optimize=True, load=True)
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sma24_deriv1_deb_protect = DecimalParameter(-4, 4, default=-2, decimals=1, space='protection', optimize=True, load=True)
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sma24_deriv1_end_protect = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
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# =========================================================================
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should_enter_trade_count = 0
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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minutes = 0
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if self.pairs[pair]['last_date'] != 0:
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minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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last_candle_2 = dataframe.iloc[-2].squeeze()
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last_candle_3 = dataframe.iloc[-3].squeeze()
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condition = True #(last_candle[f"{indic_5m}_deriv1"] >= indic_deriv1_5m) and (last_candle[f"{indic_5m}_deriv2"] >= indic_deriv2_5m)
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allow_to_buy = True #(condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
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if allow_to_buy:
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self.trades = list()
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self.pairs[pair]['first_buy'] = rate
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self.pairs[pair]['last_buy'] = rate
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self.pairs[pair]['max_touch'] = last_candle['close']
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self.pairs[pair]['last_candle'] = last_candle
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self.pairs[pair]['count_of_buys'] = 1
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self.pairs[pair]['current_profit'] = 0
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self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
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self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
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dispo = round(self.wallets.get_available_stake_amount())
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self.printLineLog()
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stake_amount = self.adjust_stake_amount(pair, last_candle)
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self.pairs[pair]['total_amount'] = stake_amount
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
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pair=pair,
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rate=rate,
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dispo=dispo,
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profit=0,
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trade_type=entry_tag,
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buys=1,
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stake=round(stake_amount, 2)
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)
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return allow_to_buy
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
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time_in_force: str,
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exit_reason: str, current_time, **kwargs, ) -> bool:
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# allow_to_sell = (minutes > 30)
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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minutes = int(round((current_time - trade.open_date_utc).seconds / 60, 0))
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profit =trade.calc_profit(rate)
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force = self.pairs[pair]['force_sell']
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allow_to_sell = minutes > 30 and (last_candle['hapercent'] < 0 ) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
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if allow_to_sell:
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self.trades = list()
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self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries # self.pairs[pair]['count_of_buys']
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self.pairs[pair]['last_sell'] = rate
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self.pairs[pair]['last_candle'] = last_candle
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self.pairs[pair]['max_profit'] = 0
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self.pairs[pair]['previous_profit'] = 0
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self.trades = list()
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dispo = round(self.wallets.get_available_stake_amount())
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# print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action="🟥Sell " + str(minutes),
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pair=pair,
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trade_type=exit_reason,
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rate=last_candle['close'],
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dispo=dispo,
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profit=round(profit, 2)
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)
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self.pairs[pair]['force_sell'] = False
|
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self.pairs[pair]['has_gain'] = 0
|
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self.pairs[pair]['current_profit'] = 0
|
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self.pairs[pair]['total_amount'] = 0
|
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self.pairs[pair]['count_of_buys'] = 0
|
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self.pairs[pair]['max_touch'] = 0
|
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self.pairs[pair]['last_buy'] = 0
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self.pairs[pair]['last_date'] = current_time
|
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self.pairs[pair]['current_trade'] = None
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# else:
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# self.printLog(f"{current_time} SELL triggered for {pair} ({exit_reason} profit={profit} minutes={minutes} percent={last_candle['hapercent']}) but condition blocked")
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return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss')
|
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|
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
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proposed_stake: float, min_stake: float, max_stake: float,
|
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**kwargs) -> float:
|
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|
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dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
|
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current_candle = dataframe.iloc[-1].squeeze()
|
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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()
|
||
|
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expected_profit = self.expectedProfit(pair, last_candle)
|
||
# print(f"current_time={current_time} current_profit={current_profit} expected_profit={expected_profit}")
|
||
|
||
max_touch_before = self.pairs[pair]['max_touch']
|
||
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||
self.pairs[pair]['current_trade'] = trade
|
||
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
|
||
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
||
max_profit = self.pairs[pair]['max_profit']
|
||
baisse = 0
|
||
if profit > 0:
|
||
baisse = 1 - (profit / max_profit)
|
||
mx = max_profit / 5
|
||
self.pairs[pair]['count_of_buys'] = count_of_buys
|
||
self.pairs[pair]['current_profit'] = profit
|
||
|
||
dispo = round(self.wallets.get_available_stake_amount())
|
||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||
|
||
if hours % 4 == 0:
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="🔴 CURRENT" if self.pairs[pair]['stop'] or last_candle['stop_buying_1h'] else "🟢 CURRENT",
|
||
dispo=dispo,
|
||
pair=pair,
|
||
rate=last_candle['close'],
|
||
trade_type='',
|
||
profit=round(profit, 2),
|
||
buys=count_of_buys,
|
||
stake=0
|
||
)
|
||
|
||
pair_name = self.getShortName(pair)
|
||
if last_candle['max_rsi_24'] > 85 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0.05:
|
||
self.pairs[pair]['force_sell'] = False
|
||
self.pairs[pair]['force_buy'] = False #(self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
|
||
return str(count_of_buys) + '_' + 'Rsi85_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||
|
||
if self.pairs[pair]['force_sell']:
|
||
self.pairs[pair]['force_sell'] = False
|
||
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
|
||
return str(count_of_buys) + '_' + 'Frc_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||
|
||
if profit > 0 and baisse > 0.30:
|
||
self.pairs[pair]['force_sell'] = False
|
||
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
|
||
return str(count_of_buys) + '_' + 'B30_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||
|
||
# if max_profit > 0.5 * count_of_buys and baisse > 0.15:
|
||
# self.pairs[pair]['force_sell'] = False
|
||
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
|
||
# return str(count_of_buys) + '_' + 'B15_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||
|
||
if (last_candle['sma5_1h'] - before_last_candle_12['sma5_1h']) / last_candle['sma5_1h'] > 0.0002:
|
||
return None
|
||
|
||
factor = 1
|
||
if (self.getShortName(pair) == 'BTC'):
|
||
factor = 0.5
|
||
# if baisse > 2 and baisse > factor * self.pairs[pair]['total_amount'] / 100:
|
||
# self.pairs[pair]['force_sell'] = False
|
||
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
|
||
# return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
|
||
#
|
||
# if 1 <= count_of_buys <= 3:
|
||
if last_candle['max_rsi_24'] > 75 and profit > expected_profit and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0:
|
||
self.pairs[pair]['force_sell'] = False
|
||
return str(count_of_buys) + '_' + 'Rsi75_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
|
||
|
||
|
||
self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
|
||
|
||
def getShortName(self, pair):
|
||
return pair.replace("/USDT", '').replace("/USDC", '').replace("_USDC", '').replace("_USDT", '')
|
||
|
||
def informative_pairs(self):
|
||
# get access to all pairs available in whitelist.
|
||
pairs = self.dp.current_whitelist()
|
||
informative_pairs = [(pair, '1d') for pair in pairs]
|
||
informative_pairs += [(pair, '1h') for pair in pairs]
|
||
|
||
return informative_pairs
|
||
|
||
def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
|
||
last_candle=None):
|
||
# Afficher les colonnes une seule fois
|
||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||
return
|
||
if self.columns_logged % 10 == 0:
|
||
self.printLog(
|
||
f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} "
|
||
f"| {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
|
||
f"{'rsi':>6}|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|tdc1d|tdc1h"
|
||
)
|
||
self.printLineLog()
|
||
df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||
colonnes_a_exclure = ['last_candle',
|
||
'trade_info', 'last_date', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
|
||
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||
|
||
print(df_filtered)
|
||
|
||
self.columns_logged += 1
|
||
date = str(date)[:16] if date else "-"
|
||
limit = None
|
||
# if buys is not None:
|
||
# limit = round(last_rate * (1 - self.fibo[buys] / 100), 4)
|
||
|
||
rsi = ''
|
||
rsi_pct = ''
|
||
# if last_candle is not None:
|
||
# if (not np.isnan(last_candle['rsi_1d'])) and (not np.isnan(last_candle['rsi_1h'])):
|
||
# rsi = str(int(last_candle['rsi_1d'])) + " " + str(int(last_candle['rsi_1h']))
|
||
# if (not np.isnan(last_candle['rsi_pct_1d'])) and (not np.isnan(last_candle['rsi_pct_1h'])):
|
||
# rsi_pct = str(int(10000 * last_candle['bb_mid_pct_1d'])) + " " + str(
|
||
# int(last_candle['rsi_pct_1d'])) + " " + str(int(last_candle['rsi_pct_1h']))
|
||
|
||
# first_rate = self.percent_threshold.value
|
||
# last_rate = self.threshold.value
|
||
# action = self.color_line(action, action)
|
||
sma5_1d = ''
|
||
sma5_1h = ''
|
||
|
||
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
|
||
|
||
last_lost = self.getLastLost(last_candle, pair)
|
||
|
||
if buys is None:
|
||
buys = ''
|
||
|
||
max_touch = ''
|
||
pct_max = self.getPctFirstBuy(pair, last_candle)
|
||
|
||
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
|
||
|
||
dist_max = ''
|
||
|
||
color = GREEN if profit > 0 else RED
|
||
color_sma24 = GREEN if last_candle['sma24_deriv1_1h'] > 0 else RED
|
||
color_sma24_2 = GREEN if last_candle['sma24_deriv2_1h'] > 0 else RED
|
||
color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1_1h'] > 0 else RED
|
||
color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2_1h'] > 0 else RED
|
||
color_sma5_1h = GREEN if last_candle['sma60_deriv1'] > 0 else RED
|
||
color_sma5_2h = GREEN if last_candle['sma60_deriv2'] > 0 else RED
|
||
color_smooth_1h = GREEN if last_candle['mid_smooth_1h_deriv1'] > 0 else RED
|
||
color_smooth2_1h = GREEN if last_candle['mid_smooth_1h_deriv2'] > 0 else RED
|
||
|
||
last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(
|
||
self.pairs[pair]['last_max'], 3)
|
||
last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(
|
||
self.pairs[pair]['last_min'], 3)
|
||
|
||
profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
|
||
|
||
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
|
||
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
|
||
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
|
||
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
|
||
self.printLog(
|
||
f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
|
||
f"|{color}{profit or '-':>10}{RESET}| {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
|
||
f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
|
||
# f"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
|
||
# f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
|
||
f"{round(last_candle['max_rsi_24'], 1) or '-' :>6}|"
|
||
f"{dist_max:>7}|{color_sma24}{round(last_candle['sma24_deriv1_1h'], 2):>5}{RESET}"
|
||
f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1h'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1h'], 2):>5}{RESET}"
|
||
f"|{color_sma5_1h}{round(last_candle['sma60_deriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma60_deriv2'], 2):>5}{RESET}"
|
||
f"|{color_smooth_1h}{round(last_candle['mid_smooth_1h_deriv1'], 2):>5}{RESET}|{color_smooth2_1h}{round(last_candle['mid_smooth_1h_deriv2'], 2):>5}{RESET}"
|
||
)
|
||
|
||
def getLastLost(self, last_candle, pair):
|
||
last_lost = round((last_candle['close'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
|
||
return last_lost
|
||
|
||
def printLineLog(self):
|
||
# f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
|
||
self.printLog(
|
||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
|
||
f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
|
||
)
|
||
|
||
def printLog(self, str):
|
||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||
return;
|
||
if not self.dp.runmode.value in ('backtest', 'hyperopt', 'lookahead-analysis'):
|
||
logger.info(str)
|
||
else:
|
||
if not self.dp.runmode.value in ('hyperopt'):
|
||
print(str)
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# Add all ta features
|
||
pair = metadata['pair']
|
||
short_pair = self.getShortName(pair)
|
||
self.path = f"user_data/plots/{short_pair}/"
|
||
|
||
dataframe = self.populateDataframe(dataframe, timeframe='5m')
|
||
|
||
# ################### INFORMATIVE 1d
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
||
informative = self.populateDataframe(informative, timeframe='1d')
|
||
# informative = self.calculateRegression(informative, 'mid', lookback=15)
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||
|
||
################### INFORMATIVE 1h
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
||
informative = self.populateDataframe(informative, timeframe='1h')
|
||
informative = self.calculateRegression(informative, 'mid', lookback=5)
|
||
|
||
# # TENSOR FLOW
|
||
# self.model_indicators = self.listUsableColumns(informative)
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.tensorFlowTrain(informative, lookback=self.lookback, future_steps = self.future_steps)
|
||
#
|
||
# self.tensorFlowPredict(informative)
|
||
#
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.kerasGenerateGraphs(informative)
|
||
|
||
informative['stop_buying_deb'] = ((informative['max_rsi_24'] > self.rsi_deb_protect.value)
|
||
& (informative['sma24_deriv1'] < self.sma24_deriv1_deb_protect.value)
|
||
)
|
||
informative['stop_buying_end'] = ((informative['max_rsi_24'] < self.rsi_end_protect.value)
|
||
& (informative['sma24_deriv1'] > self.sma24_deriv1_end_protect.value)
|
||
)
|
||
|
||
latched = np.zeros(len(informative), dtype=bool)
|
||
|
||
for i in range(1, len(informative)):
|
||
if informative['stop_buying_deb'].iloc[i]:
|
||
latched[i] = True
|
||
elif informative['stop_buying_end'].iloc[i]:
|
||
latched[i] = False
|
||
else:
|
||
latched[i] = latched[i - 1]
|
||
|
||
informative['stop_buying'] = latched
|
||
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
||
|
||
dataframe['last_price'] = dataframe['close']
|
||
dataframe['first_price'] = dataframe['close']
|
||
if self.dp:
|
||
if self.dp.runmode.value in ('live', 'dry_run'):
|
||
self.getOpenTrades()
|
||
|
||
for trade in self.trades:
|
||
if trade.pair != pair:
|
||
continue
|
||
filled_buys = trade.select_filled_orders('buy')
|
||
count = 0
|
||
amount = 0
|
||
for buy in filled_buys:
|
||
if count == 0:
|
||
dataframe['first_price'] = buy.price
|
||
self.pairs[pair]['first_buy'] = buy.price
|
||
self.pairs[pair]['first_amount'] = buy.price * buy.filled
|
||
# dataframe['close01'] = buy.price * 1.01
|
||
|
||
# Order(id=2396, trade=1019, order_id=29870026652, side=buy, filled=0.00078, price=63921.01,
|
||
# status=closed, date=2024-08-26 02:20:11)
|
||
dataframe['last_price'] = buy.price
|
||
self.pairs[pair]['last_buy'] = buy.price
|
||
count = count + 1
|
||
amount += buy.price * buy.filled
|
||
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
||
count_buys = count
|
||
# dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
|
||
self.pairs[pair]['total_amount'] = amount
|
||
|
||
# dataframe['mid_smooth_tag'] = qtpylib.crossed_below(dataframe['mid_smooth_24_deriv1'], dataframe['mid_smooth_deriv2_24'])
|
||
|
||
# ===============================
|
||
# lissage des valeurs horaires
|
||
dataframe['mid_smooth_1h'] = dataframe['mid'].rolling(window=6).mean()
|
||
dataframe["mid_smooth_1h_deriv1"] = 100 * dataframe["mid_smooth_1h"].diff().rolling(window=6).mean() / \
|
||
dataframe['mid_smooth_1h']
|
||
dataframe["mid_smooth_1h_deriv2"] = 100 * dataframe["mid_smooth_1h_deriv1"].diff().rolling(window=6).mean()
|
||
|
||
dataframe['mid_smooth_5h'] = talib.EMA(dataframe, timeperiod=60) # dataframe['mid'].rolling(window=60).mean()
|
||
dataframe["mid_smooth_5h_deriv1"] = 100 * dataframe["mid_smooth_5h"].diff().rolling(window=60).mean() / \
|
||
dataframe['mid_smooth_5h']
|
||
dataframe["mid_smooth_5h_deriv2"] = 100 * dataframe["mid_smooth_5h_deriv1"].diff().rolling(window=60).mean()
|
||
|
||
dataframe = self.calculateRegression(dataframe, 'mid', lookback=10, future_steps=10, model_type="poly")
|
||
dataframe = self.calculateRegression(dataframe, 'sma24', lookback=12, future_steps=12)
|
||
|
||
# self.model_indicators = self.listUsableColumns(dataframe)
|
||
|
||
# # TENSOR FLOW
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.tensorFlowTrain(dataframe, future_steps = self.future_steps)
|
||
#
|
||
# self.tensorFlowPredict(dataframe)
|
||
#
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.kerasGenerateGraphs(dataframe)
|
||
|
||
return dataframe
|
||
|
||
def listUsableColumns(self, dataframe):
|
||
# Étape 1 : sélectionner numériques
|
||
numeric_cols = dataframe.select_dtypes(include=['int64', 'float64']).columns
|
||
# Étape 2 : enlever constantes
|
||
# usable_cols = [c for c in numeric_cols if dataframe[c].nunique() > 1
|
||
# and (c.endswith("_deriv1") or not c.endswith("deriv1_1h"))
|
||
# and not c.endswith("_count")
|
||
# ]
|
||
usable_cols = [c for c in numeric_cols if dataframe[c].nunique() > 1
|
||
and not c.endswith("_state")
|
||
# and not c.endswith("_1d")
|
||
# and not c.endswith("_1h")
|
||
and not c.endswith("_count")
|
||
# and not c.startswith("open") and not c.startswith("close")
|
||
# and not c.startswith("low") and not c.startswith("high")
|
||
# and not c.startswith("haopen") and not c.startswith("haclose")
|
||
# and not c.startswith("bb_lower") and not c.startswith("bb_upper")
|
||
# and not c.startswith("bb_middle")
|
||
and not c.endswith("_class") and not c.endswith("_price")
|
||
and not c.startswith('stop_buying')]
|
||
# Étape 3 : remplacer inf et NaN par 0
|
||
# usable_cols = [
|
||
# 'hapercent', 'percent', 'percent3', 'percent12',
|
||
# 'percent24',
|
||
# 'sma5_dist', 'sma5_deriv1', 'sma12_dist', 'sma12_deriv1',
|
||
# 'sma24_dist', 'sma24_deriv1', 'sma48_dist', 'sma48_deriv1', 'sma60_dist', 'sma60_deriv1', 'sma60_deriv2',
|
||
# 'mid_smooth_3_deriv1', 'mid_smooth_5_dist',
|
||
# 'mid_smooth_5_deriv1', 'mid_smooth_12_dist',
|
||
# 'mid_smooth_12_deriv1', 'mid_smooth_24_dist',
|
||
# 'mid_smooth_24_deriv1',
|
||
# 'rsi', 'max_rsi_12', 'max_rsi_24',
|
||
# 'rsi_dist', 'rsi_deriv1',
|
||
# 'min_max_60', 'bb_percent', 'bb_width',
|
||
# 'macd', 'macdsignal', 'macdhist', 'slope',
|
||
# 'slope_smooth', 'atr', 'atr_norm', 'adx', 'obv', 'vol_24',
|
||
# 'rsi_slope', 'adx_change', 'volatility_ratio', 'rsi_diff',
|
||
# 'slope_ratio', 'volume_sma_deriv', 'volume_dist', 'volume_deriv1',
|
||
# 'slope_norm',
|
||
# # 'mid_smooth_1h_deriv1',
|
||
# # 'mid_smooth_5h_deriv1', 'mid_smooth_5h_deriv2', 'mid_future_pred_cons',
|
||
# # 'sma24_future_pred_cons'
|
||
# ]
|
||
|
||
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
|
||
return self.model_indicators
|
||
|
||
def populateDataframe(self, dataframe, timeframe='5m'):
|
||
dataframe = dataframe.copy()
|
||
heikinashi = qtpylib.heikinashi(dataframe)
|
||
dataframe['haopen'] = heikinashi['open']
|
||
dataframe['haclose'] = heikinashi['close']
|
||
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
|
||
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
|
||
|
||
dataframe["percent"] = dataframe['mid'].pct_change()
|
||
dataframe["percent3"] = dataframe['mid'].pct_change(3).rolling(3).mean()
|
||
dataframe["percent12"] = dataframe['mid'].pct_change(12).rolling(12).mean()
|
||
dataframe["percent24"] = dataframe['mid'].pct_change(24).rolling(24).mean()
|
||
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# dataframe['futur_percent'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
|
||
|
||
dataframe['sma5'] = dataframe['mid'].ewm(span=5, adjust=False).mean() #dataframe["mid"].rolling(window=5).mean()
|
||
self.calculeDerivees(dataframe, 'sma5', timeframe=timeframe, ema_period=5)
|
||
dataframe['sma12'] = dataframe['mid'].ewm(span=12, adjust=False).mean() #dataframe["mid"].rolling(window=12).mean()
|
||
self.calculeDerivees(dataframe, 'sma12', timeframe=timeframe, ema_period=12)
|
||
dataframe['sma24'] = dataframe['mid'].ewm(span=24, adjust=False).mean() #dataframe["mid"].rolling(window=24).mean()
|
||
self.calculeDerivees(dataframe, 'sma24', timeframe=timeframe, ema_period=24)
|
||
dataframe['sma48'] = dataframe['mid'].ewm(span=48, adjust=False).mean() #dataframe["mid"].rolling(window=48).mean()
|
||
self.calculeDerivees(dataframe, 'sma48', timeframe=timeframe, ema_period=48)
|
||
dataframe['sma60'] = dataframe['mid'].ewm(span=60, adjust=False).mean() #dataframe["mid"].rolling(window=60).mean()
|
||
self.calculeDerivees(dataframe, 'sma60', timeframe=timeframe, ema_period=60)
|
||
|
||
dataframe = self.calculateDerivation(dataframe, window=3, suffixe="_3",timeframe=timeframe)
|
||
dataframe = self.calculateDerivation(dataframe, window=5, suffixe="_5",timeframe=timeframe)
|
||
dataframe = self.calculateDerivation(dataframe, window=12, suffixe="_12",timeframe=timeframe)
|
||
dataframe = self.calculateDerivation(dataframe, window=24, suffixe="_24", timeframe=timeframe)
|
||
# print(metadata['pair'])
|
||
dataframe['rsi'] = talib.RSI(dataframe['mid'], 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['mid'], timeperiod=12)
|
||
dataframe['min12'] = talib.MIN(dataframe['mid'], timeperiod=12)
|
||
dataframe['max60'] = talib.MAX(dataframe['mid'], timeperiod=60)
|
||
dataframe['min60'] = talib.MIN(dataframe['mid'], timeperiod=60)
|
||
dataframe['min_max_60'] = ((dataframe['max60'] - dataframe['mid']) / 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["mid"] - 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['mid'],
|
||
fastperiod=12,
|
||
slowperiod=26,
|
||
signalperiod=9
|
||
)
|
||
|
||
# | Nom | Formule / définition | Signification |
|
||
# | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
# | **MACD** (`macd`) | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue. <br> - Positive → tendance haussière <br> - Négative → tendance baissière |
|
||
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente |
|
||
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - Négatif mais croissant → ralentissement de la baisse |
|
||
|
||
# Ajouter dans le dataframe
|
||
dataframe['macd'] = macd
|
||
dataframe['macdsignal'] = macdsignal
|
||
dataframe['macdhist'] = macdhist
|
||
|
||
# Regarde dans le futur
|
||
# # --- Rendre relatif sur chaque série (-1 → 1) ---
|
||
# for col in ['macd', 'macdsignal', 'macdhist']:
|
||
# series = dataframe[col]
|
||
# valid = series[~np.isnan(series)] # ignorer NaN
|
||
# min_val = valid.min()
|
||
# max_val = valid.max()
|
||
# span = max_val - min_val if max_val != min_val else 1
|
||
# dataframe[f'{col}_rel'] = 2 * ((series - min_val) / span) - 1
|
||
#
|
||
# dataframe['tdc_macd'] = self.macd_tendance_int(
|
||
# dataframe,
|
||
# macd_col='macd_rel',
|
||
# signal_col='macdsignal_rel',
|
||
# hist_col='macdhist_rel'
|
||
# )
|
||
|
||
# --- pente brute ---
|
||
dataframe['slope'] = dataframe['sma24'].diff()
|
||
|
||
# --- lissage EMA ---
|
||
dataframe['slope_smooth'] = dataframe['slope'].ewm(span=10, adjust=False).mean()
|
||
|
||
# --- Volatilité normalisée ---
|
||
dataframe['atr'] = ta.volatility.AverageTrueRange(
|
||
high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
|
||
).average_true_range()
|
||
dataframe['atr_norm'] = dataframe['atr'] / dataframe['close']
|
||
|
||
# --- Force de tendance ---
|
||
dataframe['adx'] = ta.trend.ADXIndicator(
|
||
high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
|
||
).adx()
|
||
|
||
# --- Volume directionnel (On Balance Volume) ---
|
||
dataframe['obv'] = ta.volume.OnBalanceVolumeIndicator(close=dataframe['mid'], volume=dataframe['volume']).on_balance_volume()
|
||
|
||
# --- Volatilité récente (écart-type des rendements) ---
|
||
dataframe['vol_24'] = dataframe['percent'].rolling(24).std()
|
||
|
||
# Compter les baisses / hausses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
# df : ton dataframe OHLCV + indicateurs existants
|
||
# Assurez-vous que les colonnes suivantes existent :
|
||
# 'max_rsi_12', 'roc_24', 'bb_percent_1h'
|
||
|
||
# --- Filtrage des NaN initiaux ---
|
||
# dataframe = dataframe.dropna()
|
||
|
||
dataframe['rsi_slope'] = dataframe['rsi'].diff(3) / 3 # vitesse moyenne du RSI
|
||
dataframe['adx_change'] = dataframe['adx'] - dataframe['adx'].shift(12) # évolution de la tendance
|
||
dataframe['volatility_ratio'] = dataframe['atr_norm'] / dataframe['bb_width']
|
||
|
||
dataframe["rsi_diff"] = dataframe["rsi"] - dataframe["rsi"].shift(3)
|
||
dataframe["slope_ratio"] = dataframe["sma5_deriv1"] / (dataframe["sma60_deriv1"] + 1e-9)
|
||
dataframe["divergence"] = (dataframe["rsi_deriv1"] * dataframe["sma5_deriv1"]) < 0
|
||
|
||
###########################
|
||
|
||
dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
|
||
self.calculeDerivees(dataframe, 'volume', timeframe=timeframe, ema_period=12)
|
||
|
||
self.setTrends(dataframe)
|
||
|
||
return dataframe
|
||
|
||
def feature_auc_scores(self, X, y):
|
||
aucs = {}
|
||
for col in X.columns:
|
||
try:
|
||
aucs[col] = roc_auc_score(y, X[col].ffill().fillna(0))
|
||
except Exception:
|
||
aucs[col] = np.nan
|
||
return pd.Series(aucs).sort_values(ascending=False)
|
||
|
||
def macd_tendance_int(self, dataframe: pd.DataFrame,
|
||
macd_col='macd',
|
||
signal_col='macdsignal',
|
||
hist_col='macdhist',
|
||
eps=0.0) -> pd.Series:
|
||
"""
|
||
Renvoie la tendance MACD sous forme d'entiers.
|
||
2 : Haussier
|
||
1 : Ralentissement hausse
|
||
0 : Neutre
|
||
-1 : Ralentissement baisse
|
||
-2 : Baissier
|
||
"""
|
||
|
||
# | Nom | Formule / définition | Signification |
|
||
# | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
# | **MACD** (`macd`) | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue. <br> - Positive → tendance haussière <br> - Négative → tendance baissière |
|
||
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente |
|
||
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - Négatif mais croissant → ralentissement de la baisse |
|
||
|
||
# | Situation | MACD | Signal | Hist | Interprétation |
|
||
# | -------------------------- | ---------- | --------- | -------- | ------------------------------------------ |
|
||
# | MACD > 0, Hist croissant | au-dessus | croissant | Haussier | Momentum fort → tendance haussière |
|
||
# | MACD > 0, Hist décroissant | au-dessus | en baisse | Momentum | La hausse ralentit, prudence |
|
||
# | MACD < 0, Hist décroissant | en dessous | en baisse | Baissier | Momentum fort → tendance baissière |
|
||
# | MACD < 0, Hist croissant | en dessous | en hausse | Rebond ? | La baisse ralentit → possible retournement |
|
||
|
||
# Créer une série de 0 par défaut
|
||
tendance = pd.Series(0, index=dataframe.index)
|
||
|
||
# Cas MACD > signal
|
||
mask_up = dataframe[macd_col] > dataframe[signal_col] + eps
|
||
mask_up_hist_pos = mask_up & (dataframe[hist_col] > 0)
|
||
mask_up_hist_neg = mask_up & (dataframe[hist_col] <= 0)
|
||
|
||
tendance[mask_up_hist_pos] = 2 # Haussier
|
||
tendance[mask_up_hist_neg] = 1 # Ralentissement hausse
|
||
|
||
# Cas MACD < signal
|
||
mask_down = dataframe[macd_col] < dataframe[signal_col] - eps
|
||
mask_down_hist_neg = mask_down & (dataframe[hist_col] < 0)
|
||
mask_down_hist_pos = mask_down & (dataframe[hist_col] >= 0)
|
||
|
||
tendance[mask_down_hist_neg] = -2 # Baissier
|
||
tendance[mask_down_hist_pos] = -1 # Ralentissement baisse
|
||
|
||
# Les NaN deviennent neutre
|
||
tendance[dataframe[[macd_col, signal_col, hist_col]].isna().any(axis=1)] = 0
|
||
|
||
return tendance
|
||
|
||
def calculateDownAndUp(self, dataframe, limit=0.0001):
|
||
dataframe['down'] = dataframe['hapercent'] <= limit
|
||
dataframe['up'] = dataframe['hapercent'] >= limit
|
||
dataframe['down_count'] = - dataframe['down'].astype(int) * (
|
||
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
|
||
dataframe['up_count'] = dataframe['up'].astype(int) * (
|
||
dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
|
||
# Créer une colonne vide
|
||
dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
|
||
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
|
||
|
||
def calculateDerivation(self, dataframe, window=12, suffixe='', timeframe='5m'):
|
||
dataframe[f"mid_smooth{suffixe}"] = dataframe['mid'].rolling(window).mean()
|
||
dataframe = self.calculeDerivees(dataframe, f"mid_smooth{suffixe}", timeframe=timeframe, ema_period=window)
|
||
return dataframe
|
||
|
||
def calculeDerivees(
|
||
self,
|
||
dataframe: pd.DataFrame,
|
||
name: str,
|
||
suffixe: str = '',
|
||
window: int = 100,
|
||
coef: float = 0.15,
|
||
ema_period: int = 10,
|
||
verbose: bool = True,
|
||
timeframe: str = '5m'
|
||
) -> pd.DataFrame:
|
||
"""
|
||
Calcule deriv1/deriv2 (relative simple), applique EMA, calcule tendency
|
||
avec epsilon adaptatif basé sur rolling percentiles.
|
||
"""
|
||
|
||
d1_col = f"{name}{suffixe}_deriv1"
|
||
d2_col = f"{name}{suffixe}_deriv2"
|
||
factor1 = 100 * (ema_period / 5)
|
||
factor2 = 10 * (ema_period / 5)
|
||
|
||
dataframe[f"{name}{suffixe}_inv"] = (dataframe[f"{name}{suffixe}"].shift(2) >= dataframe[f"{name}{suffixe}"].shift(1)) \
|
||
& (dataframe[f"{name}{suffixe}"].shift(1) <= dataframe[f"{name}{suffixe}"])
|
||
# --- Distance à la moyenne mobile ---
|
||
dataframe[f"{name}{suffixe}_dist"] = (dataframe['close'] - dataframe[f"{name}{suffixe}"]) / dataframe[f"{name}{suffixe}"]
|
||
|
||
# dérivée relative simple
|
||
dataframe[d1_col] = 1000 * (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
|
||
dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
|
||
return dataframe
|
||
|
||
def getOpenTrades(self):
|
||
# if len(self.trades) == 0:
|
||
self.trades = Trade.get_open_trades()
|
||
return self.trades
|
||
|
||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['lstm_pred'] > 0)
|
||
), ['enter_long', 'enter_tag']] = (1, f"future")
|
||
|
||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||
|
||
if self.dp.runmode.value in ('backtest'):
|
||
dataframe.to_feather(f"user_data/backtest_results/{metadata['pair'].replace('/', '_')}_df.feather")
|
||
|
||
return dataframe
|
||
|
||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['lstm_pred'] < 0) & (dataframe['hapercent'] < 0)
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"sma60_future")
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (
|
||
# (
|
||
# (dataframe['mid_future_pred_cons'].shift(2) < dataframe['mid_future_pred_cons'].shift(1))
|
||
# & (dataframe['mid_future_pred_cons'].shift(1) > dataframe['mid_future_pred_cons'])
|
||
# )
|
||
# # | (dataframe['mid_smooth_12_deriv1'] < 0)
|
||
# )
|
||
# & (dataframe['sma60_future_pred_cons'] < dataframe['sma60_future_pred_cons'].shift(1))
|
||
# & (dataframe['hapercent'] < 0)
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"sma60_future")
|
||
|
||
#
|
||
# dataframe.loc[
|
||
# (
|
||
# (
|
||
# (dataframe['mid_future_pred_cons'].shift(2) < dataframe['mid_future_pred_cons'].shift(1))
|
||
# & (dataframe['mid_future_pred_cons'].shift(1) > dataframe['mid_future_pred_cons'])
|
||
#
|
||
# )
|
||
# # & (dataframe['mid_future_pred_cons'] > dataframe['max12'])
|
||
# & (dataframe['hapercent'] < 0)
|
||
#
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"max12")
|
||
|
||
return dataframe
|
||
|
||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||
current_rate: float, current_profit: float, min_stake: float,
|
||
max_stake: float, **kwargs):
|
||
# ne rien faire si ordre deja en cours
|
||
if trade.has_open_orders:
|
||
# print("skip open orders")
|
||
return None
|
||
if (self.wallets.get_available_stake_amount() < 10): # or trade.stake_amount >= max_stake:
|
||
return 0
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
before_last_candle = dataframe.iloc[-2].squeeze()
|
||
# prépare les données
|
||
current_time = current_time.astimezone(timezone.utc)
|
||
open_date = trade.open_date.astimezone(timezone.utc)
|
||
dispo = round(self.wallets.get_available_stake_amount())
|
||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
current_time_utc = current_time.astimezone(timezone.utc)
|
||
open_date = trade.open_date.astimezone(timezone.utc)
|
||
days_since_open = (current_time_utc - open_date).days
|
||
pair = trade.pair
|
||
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||
last_lost = self.getLastLost(last_candle, pair)
|
||
pct_first = 0
|
||
|
||
total_counts = sum(
|
||
pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
|
||
|
||
if self.pairs[pair]['first_buy']:
|
||
pct_first = self.getPctFirstBuy(pair, last_candle)
|
||
|
||
pct = self.pct.value
|
||
if count_of_buys == 1:
|
||
pct_max = current_profit
|
||
else:
|
||
if self.pairs[trade.pair]['last_buy']:
|
||
pct_max = self.getPctLastBuy(pair, last_candle)
|
||
else:
|
||
pct_max = - pct
|
||
|
||
if (self.getShortName(pair) == 'BTC') or count_of_buys <= 2:
|
||
lim = - pct - (count_of_buys * self.pct_inc.value)
|
||
else:
|
||
pct = 0.05
|
||
lim = - pct - (count_of_buys * 0.0025)
|
||
|
||
if (len(dataframe) < 1):
|
||
# print("skip dataframe")
|
||
return None
|
||
|
||
if not self.should_enter_trade(pair, last_candle, current_time):
|
||
return None
|
||
|
||
condition = (last_candle['enter_long'] and last_candle['stop_buying_1h'] == False and last_candle['hapercent'] > 0)
|
||
# and last_candle['sma60_deriv1'] > 0
|
||
# or last_candle['enter_tag'] == 'pct3' \
|
||
# or last_candle['enter_tag'] == 'pct3_1h'
|
||
|
||
# if (self.getShortName(pair) != 'BTC' and count_of_buys > 3):
|
||
# condition = before_last_candle_24['mid_smooth_3_1h'] > before_last_candle_12['mid_smooth_3_1h'] and before_last_candle_12['mid_smooth_3_1h'] < last_candle['mid_smooth_3_1h'] #and last_candle['mid_smooth_3_deriv1_1h'] < -1.5
|
||
|
||
limit_buy = 40
|
||
if (count_of_buys < limit_buy) and condition and (pct_max < lim):
|
||
try:
|
||
|
||
if self.pairs[pair]['has_gain'] and profit > 0:
|
||
self.pairs[pair]['force_sell'] = True
|
||
return None
|
||
|
||
max_amount = self.config.get('stake_amount') * 2.5
|
||
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
|
||
self.adjust_stake_amount(pair, last_candle) * abs(last_lost / self.mise_factor_buy.value))
|
||
|
||
if stake_amount > 0:
|
||
trade_type = "Loss " + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||
self.pairs[pair]['total_amount'] += stake_amount
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="🟧 Loss -",
|
||
dispo=dispo,
|
||
pair=trade.pair,
|
||
rate=current_rate,
|
||
trade_type=trade_type,
|
||
profit=round(profit, 1),
|
||
buys=trade.nr_of_successful_entries + 1,
|
||
stake=round(stake_amount, 2)
|
||
)
|
||
|
||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||
|
||
# df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||
# colonnes_a_exclure = ['last_candle', 'stop',
|
||
# 'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
|
||
# df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||
# # df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||
#
|
||
# print(df_filtered)
|
||
|
||
return stake_amount
|
||
return None
|
||
except Exception as exception:
|
||
print(exception)
|
||
return None
|
||
|
||
if (profit > self.pairs[pair]['previous_profit'] and profit > self.pairs[pair]['expected_profit'] and hours > 6
|
||
# and last_candle['sma60_deriv1'] > 0
|
||
and last_candle['max_rsi_12_1h'] < 75
|
||
# and last_candle['rsi_1d'] < 58
|
||
# and last_candle['stop_buying'] == False
|
||
# and last_candle['mid_smooth_5_deriv1_1d'] > 0
|
||
and self.wallets.get_available_stake_amount() > 0
|
||
):
|
||
try:
|
||
self.pairs[pair]['previous_profit'] = profit
|
||
stake_amount = min(self.wallets.get_available_stake_amount(), self.pairs[pair]['first_amount'])
|
||
if stake_amount > 0:
|
||
self.pairs[pair]['has_gain'] += 1
|
||
|
||
trade_type = 'Gain +' + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||
self.pairs[pair]['total_amount'] += stake_amount
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="🟡 Gain +",
|
||
dispo=dispo,
|
||
pair=trade.pair,
|
||
rate=current_rate,
|
||
trade_type=str(round(pct_max, 4)),
|
||
profit=round(profit, 1),
|
||
buys=trade.nr_of_successful_entries + 1,
|
||
stake=round(stake_amount, 2)
|
||
)
|
||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||
return stake_amount
|
||
return None
|
||
except Exception as exception:
|
||
print(exception)
|
||
return None
|
||
|
||
return None
|
||
|
||
def getPctFirstBuy(self, pair, last_candle):
|
||
return round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
|
||
|
||
def getPctLastBuy(self, pair, last_candle):
|
||
return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
|
||
|
||
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||
# Calculer le minimum des 14 derniers jours
|
||
nb_pairs = len(self.dp.current_whitelist())
|
||
|
||
base_stake_amount = self.config.get('stake_amount') / (self.mises.value) # * nb_pairs) # Montant de base configuré
|
||
|
||
# factors = [1, 1.2, 1.3, 1.4]
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
factor = 1 #65 / min(65, last_candle['rsi_1d'])
|
||
if last_candle['open'] < last_candle['sma5_1h'] and last_candle['mid_smooth_12_deriv1'] > 0:
|
||
factor = 2
|
||
|
||
adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
|
||
else:
|
||
adjusted_stake_amount = self.pairs[pair]['first_amount']
|
||
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
self.pairs[pair]['first_amount'] = adjusted_stake_amount
|
||
|
||
return adjusted_stake_amount
|
||
|
||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||
lim = 0.01
|
||
pct = 0.002
|
||
if (self.getShortName(pair) == 'BTC'):
|
||
lim = 0.005
|
||
pct = 0.001
|
||
pct_to_max = lim + pct * self.pairs[pair]['count_of_buys']
|
||
expected_profit = lim * self.pairs[pair]['total_amount'] # min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
|
||
|
||
self.pairs[pair]['expected_profit'] = expected_profit
|
||
|
||
return expected_profit
|
||
|
||
def calculateUpDownPct(self, dataframe, key):
|
||
down_pct_values = np.full(len(dataframe), np.nan)
|
||
# Remplir la colonne avec les bons calculs
|
||
for i in range(len(dataframe)):
|
||
shift_value = abs(int(dataframe[key].iloc[i])) # Récupérer le shift actuel
|
||
if i - shift_value > 1: # Vérifier que le shift ne dépasse pas l'index
|
||
down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
|
||
dataframe['close'].iloc[i - shift_value]
|
||
return down_pct_values
|
||
|
||
@property
|
||
def protections(self):
|
||
return [
|
||
{
|
||
"method": "CooldownPeriod",
|
||
"stop_duration_candles": 12
|
||
}
|
||
# {
|
||
# "method": "MaxDrawdown",
|
||
# "lookback_period_candles": self.lookback.value,
|
||
# "trade_limit": self.trade_limit.value,
|
||
# "stop_duration_candles": self.protection_stop.value,
|
||
# "max_allowed_drawdown": self.protection_max_allowed_dd.value,
|
||
# "only_per_pair": False
|
||
# },
|
||
# {
|
||
# "method": "StoplossGuard",
|
||
# "lookback_period_candles": 24,
|
||
# "trade_limit": 4,
|
||
# "stop_duration_candles": self.protection_stoploss_stop.value,
|
||
# "only_per_pair": False
|
||
# },
|
||
# {
|
||
# "method": "StoplossGuard",
|
||
# "lookback_period_candles": 24,
|
||
# "trade_limit": 4,
|
||
# "stop_duration_candles": 2,
|
||
# "only_per_pair": False
|
||
# },
|
||
# {
|
||
# "method": "LowProfitPairs",
|
||
# "lookback_period_candles": 6,
|
||
# "trade_limit": 2,
|
||
# "stop_duration_candles": 60,
|
||
# "required_profit": 0.02
|
||
# },
|
||
# {
|
||
# "method": "LowProfitPairs",
|
||
# "lookback_period_candles": 24,
|
||
# "trade_limit": 4,
|
||
# "stop_duration_candles": 2,
|
||
# "required_profit": 0.01
|
||
# }
|
||
]
|
||
|
||
def get_stake_from_drawdown(self, pct: float, base_stake: float = 100.0, step: float = 0.04, growth: float = 1.15,
|
||
max_stake: float = 1000.0) -> float:
|
||
"""
|
||
Calcule la mise à allouer en fonction du drawdown.
|
||
|
||
:param pct: Drawdown en pourcentage (ex: -0.12 pour -12%)
|
||
:param base_stake: Mise de base (niveau 0)
|
||
:param step: Espacement entre paliers (ex: tous les -4%)
|
||
:param growth: Facteur de croissance par palier (ex: 1.15 pour +15%)
|
||
:param max_stake: Mise maximale à ne pas dépasser
|
||
:return: Montant à miser
|
||
"""
|
||
if pct >= 0:
|
||
return base_stake
|
||
|
||
level = int(abs(pct) / step)
|
||
stake = base_stake * (growth ** level)
|
||
return min(stake, max_stake)
|
||
|
||
def polynomial_forecast(self, series: pd.Series, window: int = 20, degree: int = 2, steps=[12, 24, 36]):
|
||
"""
|
||
Calcule une régression polynomiale sur les `window` dernières valeurs de la série,
|
||
puis prédit les `n_future` prochaines valeurs.
|
||
|
||
:param series: Série pandas (ex: dataframe['close'])
|
||
:param window: Nombre de valeurs récentes utilisées pour ajuster le polynôme
|
||
:param degree: Degré du polynôme (ex: 2 pour quadratique)
|
||
:param n_future: Nombre de valeurs futures à prédire
|
||
:return: tuple (poly_function, x_vals, y_pred), où y_pred contient les prédictions futures
|
||
"""
|
||
if len(series) < window:
|
||
raise ValueError("La série est trop courte pour la fenêtre spécifiée.")
|
||
|
||
recent_y = series.iloc[-window:].values
|
||
x = np.arange(window)
|
||
|
||
coeffs = np.polyfit(x, recent_y, degree)
|
||
poly = np.poly1d(coeffs)
|
||
|
||
x_future = np.arange(window, window + len(steps))
|
||
y_future = poly(x_future)
|
||
|
||
# Affichage de la fonction
|
||
# print("Fonction polynomiale trouvée :")
|
||
# print(poly)
|
||
|
||
current = series.iloc[-1]
|
||
count = 0
|
||
for future_step in steps: # range(1, n_future + 1)
|
||
future_x = window - 1 + future_step
|
||
prediction = poly(future_x)
|
||
# series.loc[series.index[future_x], f'poly_pred_t+{future_step}'] = prediction
|
||
|
||
# ➕ Afficher les prédictions
|
||
# print(f"{current} → t+{future_step}: x={future_x}, y={prediction:.2f}")
|
||
if prediction > 0: # current:
|
||
count += 1
|
||
|
||
return poly, x_future, y_future, count
|
||
|
||
def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
|
||
limit = 3
|
||
|
||
# if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == -1:
|
||
# dispo = round(self.wallets.get_available_stake_amount())
|
||
# self.pairs[pair]['stop'] = False
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="🟢RESTART",
|
||
# dispo=dispo,
|
||
# pair=pair,
|
||
# rate=last_candle['close'],
|
||
# trade_type='',
|
||
# profit=0,
|
||
# buys=self.pairs[pair]['count_of_buys'],
|
||
# stake=0
|
||
# )
|
||
|
||
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
|
||
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
|
||
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
|
||
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
|
||
|
||
# if not pair.startswith('BTC'):
|
||
dispo = round(self.wallets.get_available_stake_amount())
|
||
|
||
# if self.pairs[pair]['stop'] \
|
||
# and last_candle[f"{self.indic_1d_p.value}_deriv1_1h"] >= self.indic_deriv1_1d_p_start.value \
|
||
# and last_candle[f"{self.indic_1d_p.value}_deriv2_1h"] >= self.indic_deriv2_1d_p_start.value:
|
||
# self.pairs[pair]['stop'] = False
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="🟢RESTART",
|
||
# dispo=dispo,
|
||
# pair=pair,
|
||
# rate=last_candle['close'],
|
||
# trade_type='',
|
||
# profit=0,
|
||
# buys=self.pairs[pair]['count_of_buys'],
|
||
# stake=0
|
||
# )
|
||
# else:
|
||
# if self.pairs[pair]['stop'] == False \
|
||
# and last_candle[f"{self.indic_1d_p.value}_deriv1_1h"] <= self.indic_deriv1_1d_p_stop.value \
|
||
# and last_candle[f"{self.indic_1d_p.value}_deriv2_1h"] <= self.indic_deriv2_1d_p_stop.value:
|
||
# self.pairs[pair]['stop'] = True
|
||
# # if self.pairs[pair]['current_profit'] > 0:
|
||
# # self.pairs[pair]['force_sell'] = True
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="🔴STOP",
|
||
# dispo=dispo,
|
||
# pair=pair,
|
||
# rate=last_candle['close'],
|
||
# trade_type='',
|
||
# profit=self.pairs[pair]['current_profit'],
|
||
# buys=self.pairs[pair]['count_of_buys'],
|
||
# stake=0
|
||
# )
|
||
# return False
|
||
# if self.pairs[pair]['stop']:
|
||
# return False
|
||
|
||
return True
|
||
|
||
# Filtrer les paires non-BTC
|
||
non_btc_pairs = [p for p in self.pairs if not p.startswith('BTC')]
|
||
|
||
# Compter les positions actives sur les paires non-BTC
|
||
max_nb_trades = 0
|
||
total_non_btc = 0
|
||
max_pair = ''
|
||
limit_amount = 250
|
||
max_amount = 0
|
||
for p in non_btc_pairs:
|
||
max_nb_trades = max(max_nb_trades, self.pairs[p]['count_of_buys'])
|
||
max_amount = max(max_amount, self.pairs[p]['total_amount'])
|
||
|
||
for p in non_btc_pairs:
|
||
if (max_nb_trades == self.pairs[p]['count_of_buys'] and max_nb_trades > limit):
|
||
# if (max_amount == self.pairs[p]['total_amount'] and max_amount > limit_amount):
|
||
max_pair = p
|
||
total_non_btc += self.pairs[p]['count_of_buys']
|
||
|
||
pct_max = self.getPctFirstBuy(pair, last_candle) # self.getPctLastBuy(pair, last_candle)
|
||
|
||
if last_candle['mid_smooth_1h_deriv1'] < -0.02: # and last_candle['mid_smooth_1h_deriv2'] > 0):
|
||
return False
|
||
|
||
self.should_enter_trade_count = 0
|
||
|
||
# if max_pair != pair and self.pairs[pair]['total_amount'] > 300:
|
||
# return False
|
||
|
||
if (max_pair != '') & (self.pairs[pair]['count_of_buys'] >= limit):
|
||
trade = self.pairs[max_pair]['current_trade']
|
||
current_time = current_time.astimezone(timezone.utc)
|
||
open_date = trade.open_date.astimezone(timezone.utc)
|
||
current_time_utc = current_time.astimezone(timezone.utc)
|
||
days_since_open = (current_time_utc - open_date).days
|
||
pct_max_max = self.getPctFirstBuy(max_pair, last_candle)
|
||
# print(f"days_since_open {days_since_open} max_pair={max_pair} pair={pair}")
|
||
return max_pair == pair or pct_max < - 0.25 or (
|
||
pct_max_max < - 0.15 and max_pair != pair and days_since_open > 30)
|
||
else:
|
||
return True
|
||
|
||
def setTrends(self, dataframe: DataFrame):
|
||
SMOOTH_WIN=10
|
||
df = dataframe.copy()
|
||
|
||
# # --- charger les données ---
|
||
# df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
|
||
|
||
# --- calcul SMA14 ---
|
||
# df['sma'] = talib.SMA(df, timeperiod=20) # ta.trend.sma_indicator(df['close'], 14)
|
||
|
||
# --- pente brute ---
|
||
df['slope'] = df['sma12'].diff()
|
||
|
||
# --- lissage EMA ---
|
||
df['slope_smooth'] = df['slope'].ewm(span=SMOOTH_WIN, adjust=False).mean()
|
||
|
||
# df["slope_smooth"] = savgol_filter(df["slope_smooth"], window_length=21, polyorder=3)
|
||
|
||
# --- normalisation relative ---
|
||
df['slope_norm'] = 10000 * df['slope_smooth'] / df['close']
|
||
# df['slope_norm'].fillna(0, inplace=True)
|
||
df['slope_norm'] = df['slope_norm'].fillna(0)
|
||
dataframe['slope_norm'] = df['slope_norm']
|
||
|
||
def make_model(self, model_type="linear", degree=2, random_state=0):
|
||
model_type = model_type.lower()
|
||
if model_type == "linear":
|
||
return LinearRegression()
|
||
if model_type == "poly":
|
||
return make_pipeline(StandardScaler(), PolynomialFeatures(degree=degree, include_bias=False),
|
||
LinearRegression())
|
||
if model_type == "svr":
|
||
return make_pipeline(StandardScaler(), SVR(kernel="rbf", C=1.0, epsilon=0.1))
|
||
if model_type == "rf":
|
||
return RandomForestRegressor(n_estimators=100, random_state=random_state, n_jobs=1)
|
||
if model_type == "lgbm":
|
||
if not _HAS_LGBM:
|
||
raise RuntimeError("lightgbm n'est pas installé")
|
||
return LGBMRegressor(n_estimators=100, random_state=random_state)
|
||
raise ValueError(f"model_type inconnu: {model_type}")
|
||
|
||
def calculateRegressionNew(self, df, indic, lookback=20, future_steps=5, model_type="linear"):
|
||
df = df.copy()
|
||
pred_col = f"{indic}_future_pred_cons"
|
||
df[pred_col] = np.nan
|
||
|
||
X_idx = np.arange(lookback).reshape(-1, 1)
|
||
|
||
values = df[indic].values
|
||
n = len(values)
|
||
|
||
model = LinearRegression()
|
||
|
||
for i in range(lookback, n - future_steps):
|
||
window = values[i - lookback:i]
|
||
|
||
# cible = vraie valeur future
|
||
y_target = values[i + future_steps]
|
||
|
||
if np.isnan(window).any() or np.isnan(y_target):
|
||
continue
|
||
|
||
# entraînement
|
||
model.fit(X_idx, window)
|
||
|
||
# prédiction de la valeur future
|
||
future_x = np.array([[lookback + future_steps - 1]])
|
||
pred_future = model.predict(future_x)[0]
|
||
|
||
# la prédiction concerne i + future_steps
|
||
df.iloc[i + future_steps, df.columns.get_loc(pred_col)] = pred_future
|
||
|
||
return df
|
||
|
||
# ==========================================================
|
||
# NOUVELLE VERSION : calcule AUSSI les dernières valeurs !
|
||
# ==========================================================
|
||
def calculateRegression(
|
||
self,
|
||
df,
|
||
indic,
|
||
lookback=30,
|
||
future_steps=5,
|
||
model_type="linear",
|
||
degree=2,
|
||
weight_mode="exp",
|
||
weight_strength=2,
|
||
clip_k=2.0,
|
||
blend_alpha=0.7,
|
||
):
|
||
|
||
values = df[indic].values.astype(float)
|
||
n = len(values)
|
||
colname = f"{indic}_future_pred_cons"
|
||
|
||
df[colname] = np.nan
|
||
|
||
# pré-calcul des fenêtres
|
||
windows = np.lib.stride_tricks.sliding_window_view(values, lookback)
|
||
# windows[k] = valeurs de [k .. k+lookback-1]
|
||
|
||
# indices valides d’entraînement
|
||
trainable_end = n - future_steps
|
||
|
||
# créer une fois le modèle
|
||
model = self.make_model(model_type=model_type, degree=degree)
|
||
|
||
# ================
|
||
# BOUCLE TRAINING
|
||
# ================
|
||
for i in range(lookback, trainable_end):
|
||
|
||
window = values[i - lookback:i]
|
||
if np.isnan(window).any():
|
||
continue
|
||
|
||
# delta future réelle
|
||
y_target = values[i + future_steps] - values[i]
|
||
|
||
# features = positions dans la fenêtre : 0..lookback-1
|
||
X_window = np.arange(lookback).reshape(-1, 1)
|
||
|
||
# sample weights
|
||
if weight_mode == "exp":
|
||
weights = np.linspace(0.1, 1, lookback) ** weight_strength
|
||
else:
|
||
weights = None
|
||
|
||
# entraînement
|
||
try:
|
||
model.fit(X_window, window, sample_weight=weights)
|
||
except Exception:
|
||
model.fit(X_window, window)
|
||
|
||
# prédiction de la valeur future (position lookback+future_steps-1)
|
||
y_pred_value = model.predict(
|
||
np.array([[lookback + future_steps - 1]])
|
||
)[0]
|
||
|
||
pred_delta = y_pred_value - values[i]
|
||
|
||
# clipping par volatilité locale
|
||
local_std = np.std(window)
|
||
max_change = clip_k * (local_std if local_std > 0 else 1e-9)
|
||
pred_delta = np.clip(pred_delta, -max_change, max_change)
|
||
|
||
# blend
|
||
final_pred_value = (
|
||
blend_alpha * (values[i] + pred_delta)
|
||
+ (1 - blend_alpha) * values[i]
|
||
)
|
||
|
||
df.iloc[i, df.columns.get_loc(colname)] = final_pred_value
|
||
|
||
# ==========================================================
|
||
# 🔥 CALCUL DES DERNIÈRES VALEURS MANQUANTES 🔥
|
||
# ==========================================================
|
||
|
||
# Il reste les indices : [n - future_steps … n - 1]
|
||
for i in range(trainable_end, n):
|
||
|
||
# fenêtre glissante de fin
|
||
if i - lookback < 0:
|
||
continue
|
||
|
||
window = values[i - lookback:i]
|
||
if np.isnan(window).any():
|
||
continue
|
||
|
||
# features
|
||
X_window = np.arange(lookback).reshape(-1, 1)
|
||
|
||
try:
|
||
model.fit(X_window, window)
|
||
except:
|
||
continue
|
||
|
||
# prédiction d’une continuation locale : future_steps = 1 en fin
|
||
y_pred_value = model.predict(np.array([[lookback]]))[0]
|
||
pred_delta = y_pred_value - values[i - 1]
|
||
|
||
final_pred_value = (
|
||
blend_alpha * (values[i - 1] + pred_delta)
|
||
+ (1 - blend_alpha) * values[i - 1]
|
||
)
|
||
|
||
df.iloc[i, df.columns.get_loc(colname)] = final_pred_value
|
||
|
||
return df
|
||
|
||
def kerasGenerateGraphs(self, dataframe):
|
||
model = self.model
|
||
self.kerasGenerateGraphModel(model)
|
||
self.kerasGenerateGraphPredictions(model, dataframe, self.lookback)
|
||
self.kerasGenerateGraphPoids(model)
|
||
|
||
def kerasGenerateGraphModel(self, model):
|
||
plot_model(
|
||
model,
|
||
to_file=f"{self.path}/lstm_model.png",
|
||
show_shapes=True,
|
||
show_layer_names=True
|
||
)
|
||
|
||
def kerasGenerateGraphPredictions(self, model, dataframe, lookback):
|
||
preds = self.tensorFlowGeneratePredictions(dataframe, lookback, model)
|
||
|
||
# plot
|
||
plt.figure(figsize=(36, 8))
|
||
plt.plot(dataframe[self.indicator_target].values, label=self.indicator_target)
|
||
plt.plot(preds, label="lstm_pred")
|
||
plt.legend()
|
||
plt.savefig(f"{self.path}/lstm_predictions.png")
|
||
plt.close()
|
||
|
||
def kerasGenerateGraphPoids(self, model):
|
||
for i, layer in enumerate(model.layers):
|
||
weights = layer.get_weights() # liste de tableaux numpy
|
||
|
||
# Sauvegarde SAFE : tableau d’objets
|
||
np.save(
|
||
f"{self.path}/layer_{i}_weights.npy",
|
||
np.array(weights, dtype=object)
|
||
)
|
||
|
||
# Exemple lecture et heatmap
|
||
weights_layer0 = np.load(
|
||
f"{self.path}/layer_{i}_weights.npy",
|
||
allow_pickle=True
|
||
)
|
||
|
||
# Choisir un poids 2D
|
||
W = None
|
||
for w in weights_layer0:
|
||
if isinstance(w, np.ndarray) and w.ndim == 2:
|
||
W = w
|
||
break
|
||
|
||
if W is None:
|
||
print(f"Aucune matrice 2D dans layer {i} (rien à afficher).")
|
||
return
|
||
|
||
plt.figure(figsize=(8, 6))
|
||
sns.heatmap(W, cmap="viridis")
|
||
plt.title(f"Poids 2D du layer {i}")
|
||
plt.savefig(f"{self.path}/layer{i}_weights.png")
|
||
plt.close()
|
||
|
||
# -------------------
|
||
# Entraînement
|
||
# -------------------
|
||
def tensorFlowTrain(self, dataframe, future_steps=1, lookback=50, epochs=40, batch_size=32):
|
||
X_seq, y_seq = self.tensorFlowPrepareDataFrame(dataframe, future_steps, lookback)
|
||
|
||
# 6) Modèle LSTM
|
||
self.model = Sequential([
|
||
LSTM(64, return_sequences=False, input_shape=(lookback, X_seq.shape[2])),
|
||
Dense(32, activation="relu"),
|
||
Dense(1)
|
||
])
|
||
|
||
self.model.compile(loss='mse', optimizer=Adam(learning_rate=1e-4))
|
||
self.model.fit(X_seq, y_seq, epochs=epochs, batch_size=batch_size, verbose=1)
|
||
|
||
# 7) Sauvegarde
|
||
self.model.save(f"{self.path}/lstm_model.keras")
|
||
# joblib.dump(self.scaler_X, f"{self.path}/lstm_scaler_X.pkl")
|
||
# joblib.dump(self.scaler_y, f"{self.path}/lstm_scaler_y.pkl")
|
||
|
||
def tensorFlowPrepareDataFrame(self, dataframe, future_steps, lookback):
|
||
target = self.indicator_target
|
||
# 1) Détecter NaN / Inf et nettoyer
|
||
feature_columns = self.model_indicators # [col for col in dataframe.columns if col != target]
|
||
df = dataframe.copy()
|
||
df.replace([np.inf, -np.inf], np.nan, inplace=True)
|
||
df.dropna(subset=feature_columns + [target], inplace=True)
|
||
# 2) Séparer features et cible
|
||
X_values = df[feature_columns].values
|
||
y_values = df[target].values.reshape(-1, 1)
|
||
# 3) Gestion colonnes constantes (éviter division par zéro)
|
||
for i in range(X_values.shape[1]):
|
||
if X_values[:, i].max() == X_values[:, i].min():
|
||
X_values[:, i] = 0.0
|
||
if y_values.max() == y_values.min():
|
||
y_values[:] = 0.0
|
||
# 4) Normalisation
|
||
self.scaler_X = MinMaxScaler()
|
||
X_scaled = self.scaler_X.fit_transform(X_values)
|
||
|
||
if self.y_no_scale:
|
||
y_scaled = y_values
|
||
else:
|
||
self.scaler_y = MinMaxScaler()
|
||
y_scaled = self.scaler_y.fit_transform(y_values)
|
||
|
||
# 5) Création des fenêtres glissantes
|
||
X_seq = []
|
||
y_seq = []
|
||
for i in range(len(X_scaled) - lookback - future_steps):
|
||
X_seq.append(X_scaled[i:i + lookback])
|
||
y_seq.append(y_scaled[i + lookback + future_steps])
|
||
X_seq = np.array(X_seq)
|
||
y_seq = np.array(y_seq)
|
||
# Vérification finale
|
||
if np.isnan(X_seq).any() or np.isnan(y_seq).any():
|
||
raise ValueError("X_seq ou y_seq contient encore des NaN")
|
||
if np.isinf(X_seq).any() or np.isinf(y_seq).any():
|
||
raise ValueError("X_seq ou y_seq contient encore des Inf")
|
||
return X_seq, y_seq
|
||
|
||
# -------------------
|
||
# Prédiction
|
||
# -------------------
|
||
def tensorFlowPredict(self, dataframe, future_steps=1, lookback=50):
|
||
feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
|
||
# charger le modèle si pas déjà chargé
|
||
if self.model is None:
|
||
self.model = load_model(f"{self.path}/lstm_model.keras", compile=False)
|
||
# self.scaler_X = joblib.load(f"{self.path}/lstm_scaler_X.pkl")
|
||
# self.scaler_y = joblib.load(f"{self.path}/lstm_scaler_y.pkl")
|
||
|
||
X_seq, y_seq = self.tensorFlowPrepareDataFrame(dataframe, future_steps, lookback)
|
||
|
||
preds = self.tensorFlowGeneratePredictions(dataframe, lookback, self.model)
|
||
|
||
dataframe["lstm_pred"] = preds
|
||
dataframe["lstm_pred_deriv1"] = dataframe["lstm_pred"].diff()
|
||
return dataframe
|
||
|
||
def tensorFlowGeneratePredictions(self, dataframe, lookback, model):
|
||
# features = toutes les colonnes sauf la cible
|
||
feature_columns = self.model_indicators # [col for col in dataframe.columns if col != self.indicator_target]
|
||
X_values = dataframe[feature_columns].values
|
||
|
||
# normalisation (avec le scaler utilisé à l'entraînement)
|
||
X_scaled = self.scaler_X.transform(X_values)
|
||
# créer les séquences glissantes
|
||
X_seq = []
|
||
for i in range(len(X_scaled) - lookback):
|
||
X_seq.append(X_scaled[i:i + lookback])
|
||
X_seq = np.array(X_seq)
|
||
|
||
# prédictions
|
||
y_pred_scaled = model.predict(X_seq, verbose=0).flatten()
|
||
if self.y_no_scale:
|
||
y_pred = y_pred_scaled
|
||
else:
|
||
y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
|
||
# alignement avec les données
|
||
preds = [np.nan] * len(dataframe)
|
||
start = lookback
|
||
end = start + len(y_pred)
|
||
# preds[start:end] = y_pred[:end - start]
|
||
preds[start:start + len(y_pred)] = y_pred
|
||
return preds |