1540 lines
69 KiB
Python
1540 lines
69 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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from pandas import DataFrame
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from typing import Optional, Union, Tuple
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from typing import List
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import logging
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import configparser
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from technical import pivots_points
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# --------------------------------
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# Add your lib to import here test git
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import ta
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import talib.abstract as talib
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import requests
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from datetime import timezone, timedelta
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from scipy.signal import savgol_filter
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from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
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from collections import Counter
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logger = logging.getLogger(__name__)
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from tabulate import tabulate
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# Couleurs ANSI de base
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RED = "\033[31m"
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GREEN = "\033[32m"
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YELLOW = "\033[33m"
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BLUE = "\033[34m"
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MAGENTA = "\033[35m"
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CYAN = "\033[36m"
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RESET = "\033[0m"
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def pprint_df(dframe):
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print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False))
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def normalize(df):
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df = (df - df.min()) / (df.max() - df.min())
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return df
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class Zeus_8_1d(IStrategy):
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levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
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startup_candle_count = 12 * 24 * 2
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# ROI table:
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minimal_roi = {
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"0": 10
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}
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stakes = 40
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# Stoploss:
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stoploss = -1 # 0.256
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# Custom stoploss
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use_custom_stoploss = 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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# 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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"mid_smooth_5": {
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"color": "blue"
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},
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"mid_smooth_12": {
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"color": "green"
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},
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"mid_smooth_24": {
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"color": "yellow"
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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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},
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"subplots": {
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"Rsi": {
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"max_rsi_12": {
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"color": "blue"
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},
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},
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"Deriv1": {
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"mid_smooth_5_deriv1": {
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"color": "blue"
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},
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"mid_smooth_12_deriv1": {
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"color": "green"
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},
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"mid_smooth_24_deriv1": {
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"color": "yellow"
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},
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},
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"Deriv2": {
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"mid_smooth_5_deriv2": {
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"color": "blue"
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},
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"mid_smooth_12_deriv2": {
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"color": "green"
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},
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"mid_smooth_24_deriv2": {
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"color": "yellow"
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},
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},
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"Down": {
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"percage_upperband": {
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"color": "green"
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},
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"percage_up": {
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"color": "blue"
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}
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}
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# "Diff": {
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# "sma10_deriv1": {
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# "color": "#74effc"
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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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"last_candle": {},
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"last_trade": None,
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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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'last_palier_index': -1,
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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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# =========================================================================
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# Parameters hyperopt
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mise_factor_buy = DecimalParameter(0.01, 0.2, default=0.05, decimals=2, space='buy', optimize=True, load=True)
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# Récupération des labels ordonnés
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# labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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# index_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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# ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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index_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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ordered_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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label_to_index = {label: i for i, label in enumerate(ordered_labels)}
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# =========================================================================
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# paliers dérivées jour sma5
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sma5_deriv1 = [-1.1726, -0.2131, -0.1012, -0.0330, 0.0169, 0.0815, 0.2000, 4.0335]
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sma5_deriv2 = [-1.9190, -0.1388, -0.0644, -0.0202, 0.0209, 0.0646, 0.1377, 4.2987]
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sma5_derive1_2_matrice = {
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'B3': [8.6, 10.8, 34.6, 35.0, 58.8, 61.9, 91.2],
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'B2': [0.0, 12.5, 9.1, 57.1, 63.3, 79.3, 89.5],
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'B1': [6.1, 12.5, 22.0, 46.8, 61.5, 70.0, 100.0],
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'N0': [0.0, 10.7, 37.0, 43.5, 75.0, 75.9, 100.0],
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'H1': [0.0, 18.5, 32.4, 35.9, 76.8, 82.9, 92.0],
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'H2': [0.0, 21.9, 16.0, 39.5, 69.7, 83.3, 100.0],
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'H3': [9.5, 29.2, 41.2, 57.9, 53.8, 86.8, 92.3],
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}
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sma5_derive1_2_matrice_df = pd.DataFrame(sma5_derive1_2_matrice, index=index_labels)
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# Extraction de la matrice numérique
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sma5_derive1_2_numeric_matrice = sma5_derive1_2_matrice_df.reindex(index=ordered_labels,
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columns=ordered_labels).values
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# paliers = {}
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indicateur_achat_vente = 'mid_smooth_12'
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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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# val = self.getProbaHausse144(last_candle)
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# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
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allow_to_buy = not self.pairs[pair]['stop'] # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
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force = self.pairs[pair]['force_buy']
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if self.pairs[pair]['force_buy']:
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self.pairs[pair]['force_buy'] = False
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allow_to_buy = True
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else:
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if not self.should_enter_trade(pair, last_candle, current_time):
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allow_to_buy = False
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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_palier_index'] = -1
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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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force = self.pairs[pair]['force_sell']
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allow_to_sell = (last_candle['percent'] < 0) #or force
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minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
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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_trade'] = trade
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self.pairs[pair]['last_candle'] = last_candle
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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(trade.calc_profit(rate, amount), 2)
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)
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self.pairs[pair]['max_profit'] = 0
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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]['last_palier_index'] = -1
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self.pairs[pair]['last_trade'] = trade
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self.pairs[pair]['current_trade'] = None
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return (allow_to_sell) | (exit_reason == 'force_exit')
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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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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)
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# print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
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# Use default stake amount.
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return adjusted_stake_amount
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def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
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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_1h = dataframe.iloc[-13].squeeze()
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before_last_candle = dataframe.iloc[-2].squeeze()
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before_last_candle_2 = dataframe.iloc[-3].squeeze()
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before_last_candle_12 = dataframe.iloc[-13].squeeze()
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before_last_candle_24 = dataframe.iloc[-25].squeeze()
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expected_profit = self.expectedProfit(pair, last_candle)
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# print(f"current_time={current_time} current_profit={current_profit} expected_profit={expected_profit}")
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max_touch_before = self.pairs[pair]['max_touch']
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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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self.pairs[pair]['current_trade'] = trade
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count_of_buys = trade.nr_of_successful_entries
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profit = round(current_profit * trade.stake_amount, 1)
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self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
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max_profit = self.pairs[pair]['max_profit']
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baisse = 0
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if profit > 0:
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baisse = 100 * abs(max_profit - profit) / max_profit
|
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mx = max_profit / 5
|
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self.pairs[pair]['count_of_buys'] = count_of_buys
|
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self.pairs[pair]['current_profit'] = profit
|
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dispo = round(self.wallets.get_available_stake_amount())
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hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
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days_since_first_buy = (current_time - trade.open_date_utc).days
|
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hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
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if hours % 4 == 0:
|
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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="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT",
|
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dispo=dispo,
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pair=pair,
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rate=last_candle['close'],
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trade_type='',
|
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profit=profit,
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buys='',
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stake=0
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)
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pair_name = self.getShortName(pair)
|
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# if baisse > 20 and before_last_candle[self.indicateur_achat_vente] > last_candle[self.indicateur_achat_vente] :
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# self.pairs[pair]['force_sell'] = False
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# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
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# return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
|
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if last_candle['mid_smooth_5_deriv1'] <= -0.1 and profit > expected_profit and last_candle['rsi'] > 65:
|
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self.pairs[pair]['force_sell'] = False
|
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self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
|
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return 'RSI_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
|
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|
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if last_candle['mid_smooth_24_deriv1'] <= -0.1 \
|
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and profit > expected_profit:
|
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self.pairs[pair]['force_sell'] = False
|
||
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
|
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return 'Drv3_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain']) + '_' + str(round(baisse, 2))
|
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self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
|
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|
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def getShortName(self, pair):
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return pair.replace("/USDT", '').replace("/USDC", '')
|
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|
||
def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float:
|
||
if pct <= thresholds[0]:
|
||
return factors[0]
|
||
if pct >= thresholds[-1]:
|
||
return factors[-1]
|
||
|
||
for i in range(1, len(thresholds)):
|
||
if pct <= thresholds[i]:
|
||
# interpolation linéaire entre thresholds[i-1] et thresholds[i]
|
||
return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / (
|
||
thresholds[i] - thresholds[i - 1])
|
||
|
||
# Juste au cas où (devrait jamais arriver)
|
||
return factors[-1]
|
||
|
||
def 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} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
|
||
f"Tdc|{'val':>6}| RSI |s201d|s5_1d|s5_2d|s51h|s52h"
|
||
)
|
||
self.printLineLog()
|
||
df = pd.DataFrame.from_dict(self.pairs, orient='index')
|
||
colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index', 'current_trade',
|
||
'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
|
||
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
|
||
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
|
||
|
||
print(df_filtered)
|
||
|
||
self.columns_logged += 1
|
||
date = str(date)[:16] if date else "-"
|
||
limit = None
|
||
# if buys is not None:
|
||
# limit = round(last_rate * (1 - self.fibo[buys] / 100), 4)
|
||
|
||
rsi = ''
|
||
rsi_pct = ''
|
||
sma5_1d = ''
|
||
sma5_1h = ''
|
||
|
||
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
|
||
|
||
last_lost = self.getLastLost(last_candle, pair)
|
||
|
||
if buys is None:
|
||
buys = ''
|
||
|
||
max_touch = '' # round(last_candle['max12'], 1) #round(self.pairs[pair]['max_touch'], 1)
|
||
pct_max = self.getPctFirstBuy(pair, last_candle)
|
||
|
||
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
|
||
|
||
dist_max = self.getDistMax(last_candle, pair)
|
||
val = self.getProbaHausseSma5d(last_candle)
|
||
|
||
pct60 = round(100 * self.getPct60D(pair, last_candle), 2)
|
||
|
||
color = GREEN if profit > 0 else RED
|
||
color_sma20 = GREEN if last_candle['sma20_deriv1'] > 0 else RED
|
||
color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1'] > 0 else RED
|
||
color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2'] > 0 else RED
|
||
color_sma5_1h = GREEN if last_candle['sma60_deriv1'] > 0 else RED
|
||
color_sma5_2h = GREEN if last_candle['sma60_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.
|
||
|
||
# tdc last_candle['tendency_12']
|
||
self.printLog(
|
||
f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
|
||
f"|{color}{profit or '-':>10}{RESET}| {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
|
||
f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
|
||
f"|{ last_candle['tendency_12'] or '-':>3}|"
|
||
f"{round(val, 1) or '-' :>6}|"
|
||
f"{round(last_candle['rsi'], 0):>7}|{color_sma20}{round(last_candle['sma20_deriv1'], 2):>5}{RESET}"
|
||
f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2'], 2):>5}{RESET}"
|
||
f"|{color_sma5_1h}{round(last_candle['sma60_deriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma60_deriv2'], 2):>5}{RESET}"
|
||
# f"|{last_candle['min60']}|{last_candle['max60']}"
|
||
)
|
||
|
||
def getLastLost(self, last_candle, pair):
|
||
last_lost = round((last_candle['close'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
|
||
return last_lost
|
||
|
||
def getDistMax(self, last_candle, pair):
|
||
mx = last_candle['max12']
|
||
dist_max = round(100 * (mx - last_candle['close']) / mx, 0)
|
||
return dist_max
|
||
|
||
def printLineLog(self):
|
||
self.printLog(
|
||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
|
||
f"{'-' * 3}"
|
||
# "+{'-' * 3}+{'-' * 3}
|
||
f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
|
||
)
|
||
|
||
def printLog(self, str):
|
||
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
|
||
return;
|
||
if not self.dp.runmode.value in ('backtest', 'hyperopt', 'lookahead-analysis'):
|
||
logger.info(str)
|
||
else:
|
||
if not self.dp.runmode.value in ('hyperopt'):
|
||
print(str)
|
||
|
||
def add_tendency_column(self, dataframe: pd.DataFrame, name, suffixe='') -> pd.DataFrame:
|
||
def tag_by_derivatives(row):
|
||
d1 = row[f"{name}{suffixe}_deriv1"]
|
||
d2 = row[f"{name}{suffixe}_deriv2"]
|
||
d1_lim_inf = -0.01
|
||
d1_lim_sup = 0.01
|
||
if d1 >= d1_lim_inf and d1 <= d1_lim_sup: # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
|
||
return 'P' # Palier
|
||
if d1 == 0.0:
|
||
return 'DH' if d2 > 0 else 'DB' # Depart Hausse / Départ Baisse
|
||
if d1 > d1_lim_sup:
|
||
return 'H++' if d2 > 0 else 'H+' # Acceleration Hausse / Ralentissement Hausse
|
||
if d1 < d1_lim_inf:
|
||
return 'B--' if d2 < 0 else 'B-' # Accéleration Baisse / Ralentissement Baisse
|
||
return 'Mid'
|
||
|
||
dataframe[f"tendency{suffixe}"] = dataframe.apply(tag_by_derivatives, axis=1)
|
||
return dataframe
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# Add all ta features
|
||
pair = metadata['pair']
|
||
|
||
heikinashi = qtpylib.heikinashi(dataframe)
|
||
dataframe['haopen'] = heikinashi['open']
|
||
dataframe['haclose'] = heikinashi['close']
|
||
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
|
||
dataframe['hapercent3'] = (dataframe['haclose'] - dataframe['haopen'].shift(3)) / dataframe['haclose'].shift(3)
|
||
|
||
dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
|
||
self.calculeDerivees(dataframe, 'sma5', horizon=10)
|
||
dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
|
||
self.calculeDerivees(dataframe, 'sma10', horizon=10)
|
||
dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
|
||
self.calculeDerivees(dataframe, 'sma20', horizon=20)
|
||
dataframe['sma60'] = talib.SMA(dataframe, timeperiod=60)
|
||
self.calculeDerivees(dataframe, 'sma60', horizon=60)
|
||
dataframe['sma144'] = talib.SMA(dataframe, timeperiod=144)
|
||
self.calculeDerivees(dataframe, 'sma144')
|
||
|
||
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
|
||
dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
|
||
dataframe["percent5"] = (dataframe["close"] - dataframe["open"].shift(5)) / dataframe["open"].shift(5)
|
||
dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
|
||
|
||
dataframe = self.calculateDerivation(dataframe, window=3, suffixe="_3")
|
||
# dataframe = self.calculateDerivation(dataframe, window=3, suffixe="_6")
|
||
|
||
dataframe["mid_re_smooth_3"] = self.conditional_smoothing(dataframe['mid_smooth_3'].dropna(),
|
||
threshold=0.0005).dropna()
|
||
self.calculeDerivees(dataframe, "mid_re_smooth_3", horizon=3)
|
||
dataframe = self.calculateDerivation(dataframe, window=12, suffixe="_12")
|
||
dataframe = self.calculateDerivation(dataframe, window=24, suffixe="_24", factor_1=1000, factor_2=10)
|
||
|
||
# print(metadata['pair'])
|
||
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
|
||
dataframe['max_rsi_12'] = talib.MAX(dataframe['rsi'], timeperiod=12)
|
||
self.calculeDerivees(dataframe, 'rsi', horizon=12)
|
||
|
||
dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
|
||
dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
|
||
dataframe['max60'] = talib.MAX(dataframe['close'], timeperiod=60)
|
||
dataframe['min60'] = talib.MIN(dataframe['close'], timeperiod=60)
|
||
|
||
# Bollinger Bands
|
||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||
dataframe['bb_lowerband'] = bollinger['lower']
|
||
dataframe['bb_middleband'] = bollinger['mid']
|
||
dataframe['bb_upperband'] = bollinger['upper']
|
||
dataframe["bb_percent"] = (
|
||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
||
)
|
||
dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"])
|
||
# Normalization
|
||
|
||
dataframe['bb_upperband5'] = dataframe['bb_upperband'].rolling(window=5).mean()
|
||
dataframe['bb_lowerband5'] = dataframe['bb_lowerband'].rolling(window=5).mean()
|
||
|
||
dataframe = self.calculateDerivation(dataframe, window=5, suffixe="_5")
|
||
dataframe = self.calculateDerivation(dataframe, window=12, suffixe="_12")
|
||
|
||
dataframe['percage_up'] = dataframe['high'] > dataframe['bb_upperband']
|
||
|
||
dataframe['percage_upperband'] = dataframe['percage_up'].astype(int) * (
|
||
dataframe['percage_up'].groupby((dataframe['percage_up'] != dataframe['percage_up'].shift()).cumsum()).cumcount() + 1)
|
||
|
||
# informative['futur_percent_3d'] = 100 * (informative['close'].shift(-3) - informative['close']) / informative['close']
|
||
#
|
||
# self.calculateProbabilite2Index(informative, ['futur_percent'], 'rsi_deriv1', 'rsi')
|
||
# # self.calculateProbabilite2Index(dataframe, ['futur_percent_3d'], 'rsi_deriv1', 'sma5')
|
||
|
||
|
||
# informative['close_smooth'] = self.conditional_smoothing(informative['mid'].dropna(), threshold=0.0015).dropna()
|
||
# informative['smooth'], informative['deriv1'], informative['deriv2'] = self.smooth_and_derivatives(informative['close_smooth'])
|
||
# informative['deriv1'] = 100 * informative['deriv1'] / informative['mid']
|
||
# informative['deriv2'] = 1000 * informative['deriv2'] / informative['mid']
|
||
|
||
# poly_func, x_future, y_future, count = self.polynomial_forecast(informative['sma5_deriv1'], window=24, degree=4)
|
||
|
||
# informative['futur_percent_3'] = 100 * ((informative['sma5'].shift(-1) - informative['sma5']) / informative['sma5'])
|
||
# futur_cols = ['futur_percent_3']
|
||
# indic_1 = 'sma5_deriv1'
|
||
# indic_2 = 'sma5_deriv2'
|
||
|
||
# self.calculateProbabilite2Index(informative, futur_cols, indic_1, indic_2)
|
||
|
||
# self.calculePlateaux(informative, 14, 0.01)
|
||
dataframe['last_price'] = dataframe['close']
|
||
dataframe['first_price'] = dataframe['close']
|
||
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
||
# dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01
|
||
# dataframe['limit'] = dataframe['close']
|
||
count_buys = 0
|
||
if self.dp:
|
||
if self.dp.runmode.value in ('live', 'dry_run'):
|
||
self.getOpenTrades()
|
||
|
||
for trade in self.trades:
|
||
if trade.pair != pair:
|
||
continue
|
||
filled_buys = trade.select_filled_orders('buy')
|
||
count = 0
|
||
amount = 0
|
||
for buy in filled_buys:
|
||
if count == 0:
|
||
dataframe['first_price'] = buy.price
|
||
self.pairs[pair]['first_buy'] = buy.price
|
||
# 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)
|
||
# dataframe['amount'] = amount
|
||
|
||
# Compter les baisses / hausses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
horizon_h = 12
|
||
|
||
dataframe['close_smooth'] = self.conditional_smoothing(dataframe['mid'].rolling(3).mean().dropna(),
|
||
threshold=0.001)
|
||
dataframe['smooth'], dataframe['deriv1'], dataframe['deriv2'] = self.smooth_and_derivatives(
|
||
dataframe['close_smooth'])
|
||
dataframe['deriv1'] = 100 * dataframe['deriv1'] / dataframe['mid']
|
||
dataframe['deriv2'] = 100 * dataframe['deriv2'] / dataframe['mid']
|
||
|
||
# ===============================
|
||
# Lissage des valeurs Journalières
|
||
horizon_d = 12 * 5 * 24
|
||
dataframe['ema_volume'] = 20 * (dataframe['volume'] * dataframe['percent']) / (
|
||
abs(dataframe['volume'].shift(1)) + abs(dataframe['volume'].shift(2)))
|
||
|
||
self.calculeDerivees(dataframe, 'ema_volume', factor_1=10, factor_2=1, horizon=14)
|
||
|
||
return dataframe
|
||
|
||
def calculeDerivees(self, dataframe, indic, factor_1=100, factor_2=10, horizon=5):
|
||
dataframe[f"{indic}_deriv1"] = (factor_1 * dataframe[f"{indic}"].diff() / dataframe[f"{indic}"]).rolling(horizon).mean()
|
||
dataframe[f"{indic}_deriv2"] = (factor_2 * dataframe[f"{indic}_deriv1"].diff()).rolling(horizon).mean()
|
||
|
||
def calculateDownAndUp(self, dataframe, limit=0.0001):
|
||
dataframe['down'] = dataframe['mid_smooth_12_deriv1'] < limit # dataframe['hapercent'] <= limit
|
||
dataframe['up'] = dataframe['mid_smooth_12_deriv1'] > limit # 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='', factor_1=100, factor_2=10):
|
||
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
|
||
# 1. Calcul du lissage par moyenne mobile médiane
|
||
dataframe[f"mid_smooth{suffixe}"] = dataframe['haclose'].rolling(window=window).mean()
|
||
# 2. Dérivée première = différence entre deux bougies successives
|
||
dataframe[f"mid_smooth{suffixe}_deriv1"] = round(
|
||
factor_1 * dataframe[f"mid_smooth{suffixe}"].rolling(window=3).mean().diff() / dataframe[
|
||
f"mid_smooth{suffixe}"], 4)
|
||
|
||
# 3. Dérivée seconde = différence de la dérivée première
|
||
dataframe[f"mid_smooth{suffixe}_deriv2"] = round(
|
||
factor_2 * dataframe[f"mid_smooth{suffixe}_deriv1"].rolling(window=3).mean().diff(), 4)
|
||
dataframe = self.add_tendency_column(dataframe, "mid_smooth", suffixe)
|
||
return dataframe
|
||
|
||
def getOpenTrades(self):
|
||
# if len(self.trades) == 0:
|
||
self.trades = Trade.get_open_trades()
|
||
return self.trades
|
||
|
||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
pair = metadata['pair']
|
||
|
||
# self.getOpenTrades()
|
||
# expected_profit = self.expectedProfit(pair, dataframe.iloc[-1])
|
||
# self.getBinanceOrderBook(pair, dataframe)
|
||
|
||
# Calcul de la pente (différence entre deux bougies consécutives)
|
||
dataframe['bb_ub_slope'] = dataframe['bb_upperband'].diff()
|
||
|
||
# Détection d'une inversion (changement de signe de la pente)
|
||
# inversion = (
|
||
# (dataframe['bb_ub_slope'].shift(1).rolling(5).apply(
|
||
# lambda x: any(x[i] > 0 and x[i + 1] < 0 for i in range(len(x) - 1)))) == 1
|
||
# )
|
||
# On regarde si la bande supérieure a atteint un maximum il y a k bougies
|
||
# lookback = 5
|
||
# inversion = (dataframe['bb_upperband'] == dataframe['bb_upperband'].rolling(lookback).max())
|
||
|
||
# pente de la bb_upperband
|
||
dataframe['bb_ub_slope'] = dataframe['bb_upperband5'].diff()
|
||
|
||
# évènement "inversion vers le bas" (pente passe de >0 à <=0) sur chaque bougie
|
||
cross_down = (dataframe['bb_ub_slope'].shift(1) > 0) & (dataframe['bb_ub_slope'] <= 0)
|
||
dataframe['bb_cross_down'] = 10000 * cross_down * dataframe['bb_width'] \
|
||
* (dataframe['bb_lowerband'] - dataframe['bb_lowerband'].shift(1)) / dataframe[
|
||
'bb_lowerband']
|
||
|
||
# vrai si AU MOINS une inversion a eu lieu dans les 5 bougies *précédentes* (on exclut l'actuelle)
|
||
inversion_last5 = cross_down.shift(1).rolling(5, min_periods=1).max().astype(bool)
|
||
dataframe['inversion_last5'] = inversion_last5
|
||
|
||
N = 24 # nombre minimum de bougies avant inversion
|
||
rise_threshold = 1.0 # % de hausse à ne pas dépasser
|
||
|
||
# # Calcul de la hausse minimale avant inversion
|
||
# def compute_rise(idx):
|
||
# if idx < N:
|
||
# return 0
|
||
# low_before = dataframe['close'].iloc[idx - N:idx].min() # min des N bougies avant inversion
|
||
# return (dataframe['close'].iloc[idx] / low_before - 1) * 100
|
||
#
|
||
# rise = [compute_rise(i) for i in range(len(dataframe))]
|
||
# dataframe['rise_before_inversion'] = rise
|
||
#
|
||
# # Filtre : inversion sans forte hausse avant
|
||
# valid_inversion = inversion_last5 & (dataframe['rise_before_inversion'] <= rise_threshold)
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['percent'] > 0)
|
||
# & (dataframe['mid_smooth_12_deriv1'] >= dataframe['mid_smooth_12_deriv1'].shift(1))
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'down')
|
||
factor = 1.01
|
||
if pair == "BTC/USDT" or pair == "BTC/USDC":
|
||
factor = factor / 2
|
||
dataframe.loc[
|
||
(
|
||
# (valid_inversion & inversion_last5 )
|
||
# (dataframe['mid_smooth_12'].shift(2) > dataframe['mid_smooth_12'].shift(1))
|
||
# (dataframe['mid_smooth_24_deriv1'].shift(1) <= 0)
|
||
(dataframe['mid_smooth_24_deriv1'] >= 0.05)
|
||
& (dataframe['mid_smooth_24_deriv2'] > 0)
|
||
# & (dataframe['hapercent'] > 0)
|
||
#& (dataframe['max_rsi_12'] < 50)
|
||
# & (dataframe['open'] <= dataframe['bb_middleband'])
|
||
|
||
), ['enter_long', 'enter_tag']] = (1, 'smth_12')
|
||
|
||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||
|
||
return dataframe
|
||
|
||
def calculateProbabilite2Index(self, df, futur_cols, indic_1, indic_2):
|
||
# # Définition des tranches pour les dérivées
|
||
# bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
|
||
# labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
|
||
#
|
||
# # Ajout des colonnes bin (catégorisation)
|
||
# df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_1h_deriv1'], bins=bins_deriv, labels=labels)
|
||
# df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_12_deriv1'], bins=bins_deriv, labels=labels)
|
||
#
|
||
# # Colonnes de prix futur à analyser
|
||
# futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
|
||
#
|
||
# # Calcul des moyennes et des effectifs
|
||
# grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
|
||
#
|
||
# pd.set_option('display.width', 200) # largeur max affichage
|
||
# pd.set_option('display.max_columns', None)
|
||
pd.set_option('display.max_columns', None)
|
||
pd.set_option('display.width', 300) # largeur max affichage
|
||
|
||
# nettoyage
|
||
# series = df[f"{indic_2}"].dropna()
|
||
# unique_vals = df[f"{indic_2}"].nunique()
|
||
# print(unique_vals)
|
||
# print(df[f"{indic_2}"])
|
||
n = len(self.labels)
|
||
|
||
df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=n, labels=self.labels, retbins=True,
|
||
duplicates='drop')
|
||
df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
|
||
duplicates='drop')
|
||
# Affichage formaté pour code Python
|
||
print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
||
print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
||
# Agrégation
|
||
grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
|
||
# Affichage
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
print(grouped.round(4))
|
||
# Ajout des probabilités de hausse
|
||
for col in futur_cols:
|
||
df[f"{col}_is_up"] = df[col] > 0
|
||
|
||
# Calcul de la proba de hausse
|
||
proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
|
||
|
||
print(f"\nProbabilité de hausse pour {col} (en %):")
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
print((proba_up * 100).round(1))
|
||
|
||
# Affichage formaté des valeurs comme tableau Python
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
df_formatted = (proba_up * 100).round(1)
|
||
|
||
print("data = {")
|
||
for index, row in df_formatted.iterrows():
|
||
row_values = ", ".join([f"{val:.1f}" for val in row])
|
||
print(f"'{index}': [{row_values}], ")
|
||
print("}")
|
||
|
||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['mid_smooth_12_deriv1'] == 0)
|
||
# & (dataframe['mid_smooth_12_deriv1'].shift(1) > 0)
|
||
# ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
|
||
return dataframe
|
||
|
||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||
current_rate: float, current_profit: float, min_stake: float,
|
||
max_stake: float, **kwargs):
|
||
# ne rien faire si ordre deja en cours
|
||
if trade.has_open_orders:
|
||
# print("skip open orders")
|
||
return None
|
||
if (self.wallets.get_available_stake_amount() < 0): # or trade.stake_amount >= max_stake:
|
||
return 0
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
before_last_candle_12 = dataframe.iloc[-13].squeeze()
|
||
before_last_candle_24 = dataframe.iloc[-25].squeeze()
|
||
last_candle_3 = dataframe.iloc[-4].squeeze()
|
||
last_candle_previous_1h = dataframe.iloc[-13].squeeze()
|
||
# prépare les données
|
||
current_time = current_time.astimezone(timezone.utc)
|
||
open_date = trade.open_date.astimezone(timezone.utc)
|
||
dispo = round(self.wallets.get_available_stake_amount())
|
||
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
current_time_utc = current_time.astimezone(timezone.utc)
|
||
open_date = trade.open_date.astimezone(timezone.utc)
|
||
days_since_open = (current_time_utc - open_date).days
|
||
pair = trade.pair
|
||
|
||
pct_first = 0
|
||
|
||
total_counts = sum(
|
||
pair_data['count_of_buys'] for pair_data in self.pairs.values() if not pair in ('BTC/USDT', 'BTC/USDC'))
|
||
|
||
if self.pairs[pair]['first_buy']:
|
||
pct_first = self.getPctFirstBuy(pair, last_candle)
|
||
|
||
pct = 0.012
|
||
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 pair in ('BTC/USDT', 'BTC/USDC') or count_of_buys <= 2:
|
||
lim = - pct - (count_of_buys * 0.001)
|
||
# lim = self.getLimitBuy(pair, last_candle, pct)
|
||
# lim = - (0.012 * (1 + round(count_of_buys / 5)) + 0.001 * (count_of_buys - 1))
|
||
# lim = - (0.012 + 0.001 * (count_of_buys - 1) + (0.002 * count_of_buys if count_of_buys > 10 else 0.001 * count_of_buys if count_of_buys > 5 else 0))
|
||
|
||
else:
|
||
pct = 0.05
|
||
lim = - pct - (count_of_buys * 0.0025)
|
||
# lim = self.getLimitBuy(pair, last_candle, pct)
|
||
|
||
if (len(dataframe) < 1):
|
||
# print("skip dataframe")
|
||
return None
|
||
|
||
if not self.should_enter_trade(pair, last_candle, current_time):
|
||
return None
|
||
|
||
condition = (last_candle['sma5_deriv1'] > 0) # and \
|
||
|
||
if condition and (pct_max < lim):
|
||
try:
|
||
|
||
if self.pairs[pair]['has_gain']:
|
||
self.pairs[pair]['force_sell'] = True
|
||
|
||
last_lost = self.getLastLost(last_candle, pair)
|
||
|
||
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) - 10 * pct_first / self.mise_factor_buy.value) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
|
||
|
||
if self.wallets.get_available_stake_amount() > stake_amount:
|
||
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||
self.pairs[pair]['total_amount'] += 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(current_profit * trade.stake_amount, 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', 'last_trade', 'last_palier_index', '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
|
||
|
||
last_lost = self.getLastLost(last_candle, pair)
|
||
|
||
if (False and hours > 6 and last_candle['mid_smooth_5_deriv1'] > 0):
|
||
try:
|
||
stake_amount = self.pairs[pair]['first_amount'] / 4
|
||
if self.wallets.get_available_stake_amount() > stake_amount:
|
||
self.pairs[pair]['has_gain'] += 1
|
||
|
||
trade_type = 'Gain +'
|
||
self.pairs[trade.pair]['count_of_buys'] += 1
|
||
self.pairs[pair]['total_amount'] += stake_amount
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="🟡 Gain +",
|
||
dispo=dispo,
|
||
pair=trade.pair,
|
||
rate=current_rate,
|
||
trade_type=str(round(pct_max, 4)),
|
||
profit=round(current_profit * trade.stake_amount, 1),
|
||
buys=trade.nr_of_successful_entries + 1,
|
||
stake=round(stake_amount, 2)
|
||
)
|
||
self.pairs[trade.pair]['last_buy'] = current_rate
|
||
self.pairs[trade.pair]['max_touch'] = last_candle['close']
|
||
self.pairs[trade.pair]['last_candle'] = last_candle
|
||
return stake_amount
|
||
return None
|
||
except Exception as exception:
|
||
# print(exception)
|
||
return None
|
||
|
||
return None
|
||
|
||
def getPctFirstBuy(self, pair, last_candle):
|
||
return round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
|
||
|
||
def getPctLastBuy(self, pair, last_candle):
|
||
return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
|
||
|
||
def getPct60D(self, pair, last_candle):
|
||
return round((last_candle['max60'] - last_candle['min60']) / last_candle['max60'], 4)
|
||
|
||
def getPctClose60D(self, pair, last_candle):
|
||
if last_candle['close'] > last_candle['max12']:
|
||
return 1
|
||
if last_candle['close'] < last_candle['min12']:
|
||
return 0
|
||
return round(
|
||
(last_candle['close'] - last_candle['min12']) / (last_candle['max12'] - last_candle['min12']), 4)
|
||
|
||
def getLimitBuy(self, pair, last_candle, first_pct):
|
||
count_of_buys = self.pairs[pair]['count_of_buys']
|
||
pct60 = self.getPct60D(pair, last_candle) # exemple 0.3 pour 30%
|
||
if (pct60 < 0.05):
|
||
lim = - first_pct - (count_of_buys * 0.001 * 0.05 / 0.05)
|
||
else:
|
||
# 0.1
|
||
# 0.4
|
||
lim = - first_pct - (count_of_buys * 0.001 * pct60 / 0.05)
|
||
|
||
return lim
|
||
|
||
def getProbaHausseSma5d(self, last_candle):
|
||
value_1 = self.getValuesFromTable(self.sma5_deriv1, last_candle['sma5_deriv1'])
|
||
value_2 = self.getValuesFromTable(self.sma5_deriv2, last_candle['sma5_deriv2'])
|
||
|
||
val = self.approx_val_from_bins(
|
||
matrice=self.sma5_derive1_2_matrice_df,
|
||
numeric_matrice=self.sma5_derive1_2_numeric_matrice,
|
||
row_label=value_2,
|
||
col_label=value_1
|
||
)
|
||
return val
|
||
|
||
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||
# Calculer le minimum des 14 derniers jours
|
||
base_stake_amount = self.config.get('stake_amount') # Montant de base configuré
|
||
# pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2)
|
||
|
||
if True: # not pair in ('BTC/USDT', 'BTC/USDC'):
|
||
# factors = [1, 1.2, 1.3, 1.4]
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
# pctClose60 = self.getPctClose60D(pair, last_candle)
|
||
dist_max = self.getDistMax(last_candle, pair)
|
||
factor = self.multi_step_interpolate(dist_max, self.thresholds, self.factors)
|
||
|
||
adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
|
||
else:
|
||
adjusted_stake_amount = self.pairs[pair]['first_amount']
|
||
else:
|
||
first_price = self.pairs[pair]['first_buy']
|
||
if (first_price == 0):
|
||
first_price = last_candle['close']
|
||
|
||
last_max = last_candle['max12']
|
||
pct = 5
|
||
if last_max > 0:
|
||
pct = 100 * (last_max - first_price) / last_max
|
||
|
||
factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
|
||
adjusted_stake_amount = base_stake_amount * factor # max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
||
|
||
# pct = 100 * abs(self.getPctFirstBuy(pair, last_candle))
|
||
#
|
||
# factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
|
||
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
self.pairs[pair]['first_amount'] = adjusted_stake_amount
|
||
|
||
return adjusted_stake_amount
|
||
|
||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||
lim = 0.01
|
||
pct = 0.002
|
||
if pair == "BTC/USDT" or pair == "BTC/USDC":
|
||
lim = 0.005
|
||
pct = 0.001
|
||
pct_to_max = lim + pct * self.pairs[pair]['count_of_buys']
|
||
# if self.pairs[pair]['count_of_buys'] > 6:
|
||
# pct_to_max = 0.006 * self.pairs[pair]['count_of_buys']
|
||
# pctClose60 = self.getPctClose60D(pair, last_candle)
|
||
|
||
# max_60 = last_candle['max60']
|
||
# if last_candle['close'] < max_60:
|
||
# pct_to_max = 0.25 * (max_60 - last_candle['close']) / max_60
|
||
# pct_to_max = pct_to_max * (2 - pctClose60)
|
||
expected_profit = lim #* self.pairs[pair]['total_amount'] #min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
|
||
|
||
# print(
|
||
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
|
||
return expected_profit
|
||
|
||
def calculateUpDownPct(self, dataframe, key):
|
||
down_pct_values = np.full(len(dataframe), np.nan)
|
||
# Remplir la colonne avec les bons calculs
|
||
for i in range(len(dataframe)):
|
||
shift_value = abs(int(dataframe[key].iloc[i])) # Récupérer le shift actuel
|
||
if i - shift_value > 1: # Vérifier que le shift ne dépasse pas l'index
|
||
down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
|
||
dataframe['close'].iloc[i - shift_value]
|
||
return down_pct_values
|
||
|
||
# ✅ Première dérivée(variation ou pente)
|
||
# Positive: la courbe est croissante → tendance haussière.
|
||
# Négative: la courbe est décroissante → tendance baissière.
|
||
# Proche de 0: la courbe est plate → marché stable ou en transition.
|
||
#
|
||
# Applications:
|
||
# Détecter les points d’inflexion(changement de tendance) quand elle s’annule.\
|
||
# Analyser la vitesse d’un mouvement(plus elle est forte, plus le mouvement est impulsif).
|
||
#
|
||
# ✅ Seconde dérivée(accélération ou concavité)
|
||
# Positive: la pente augmente → accélération de la hausse ou ralentissement de la baisse.
|
||
# Négative: la pente diminue → accélération de la baisse ou ralentissement de la hausse.
|
||
# Changement de signe: indique souvent un changement de courbure, utile pour prévoir des retournements.
|
||
#
|
||
# Exemples:
|
||
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
|
||
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
|
||
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
|
||
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
|
||
#
|
||
# Filtrer les signaux: ne prendre un signal haussier que si dérivée1 > 0 et dérivée2 > 0.
|
||
# Détecter les zones de retournement: quand dérivée1 ≈ 0 et que dérivée2 change de signe.
|
||
def calculateRegression(self,
|
||
dataframe: DataFrame,
|
||
column='close',
|
||
window=50,
|
||
degree=3,
|
||
future_offset: int = 10 # projection à n bougies après
|
||
) -> DataFrame:
|
||
df = dataframe.copy()
|
||
|
||
regression_fit = []
|
||
regression_future_fit = []
|
||
|
||
regression_fit = []
|
||
regression_future_fit = []
|
||
|
||
for i in range(len(df)):
|
||
if i < window:
|
||
regression_fit.append(np.nan)
|
||
regression_future_fit.append(np.nan)
|
||
continue
|
||
|
||
# Fin de la fenêtre d’apprentissage
|
||
end_index = i
|
||
start_index = i - window
|
||
y = df[column].iloc[start_index:end_index].values
|
||
|
||
# Si les données sont insuffisantes (juste par précaution)
|
||
if len(y) < window:
|
||
regression_fit.append(np.nan)
|
||
regression_future_fit.append(np.nan)
|
||
continue
|
||
|
||
# x centré pour meilleure stabilité numérique
|
||
x = np.linspace(-1, 1, window)
|
||
coeffs = np.polyfit(x, y, degree)
|
||
poly = np.poly1d(coeffs)
|
||
|
||
# Calcul point présent (dernier de la fenêtre)
|
||
x_now = x[-1]
|
||
regression_fit.append(poly(x_now))
|
||
|
||
# Calcul point futur, en ajustant si on dépasse la fin
|
||
remaining = len(df) - i - 1
|
||
effective_offset = min(future_offset, remaining)
|
||
x_future = x_now + (effective_offset / window) * 2 # respect du même pas
|
||
regression_future_fit.append(poly(x_future))
|
||
|
||
df[f"{column}_regression"] = regression_fit
|
||
# 2. Dérivée première = différence entre deux bougies successives
|
||
df[f"{column}_regression_deriv1"] = round(100 * df[f"{column}_regression"].diff() / df[f"{column}_regression"],
|
||
4)
|
||
|
||
# 3. Dérivée seconde = différence de la dérivée première
|
||
df[f"{column}_regression_deriv2"] = round(
|
||
10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||
|
||
df[f"{column}_future_{future_offset}"] = regression_future_fit
|
||
|
||
# # 2. Dérivée première = différence entre deux bougies successives
|
||
# df[f"{column}_future_{future_offset}_deriv1"] = round(100 * df[f"{column}_future_{future_offset}"].diff() / df[f"{column}_future_{future_offset}"], 4)
|
||
#
|
||
# # 3. Dérivée seconde = différence de la dérivée première
|
||
# df[f"{column}_future_{future_offset}_deriv2"] = round(10 * df[f"{column}_future_{future_offset}_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||
|
||
return df
|
||
|
||
def getValuesFromTable(self, values, value):
|
||
for i in range(len(values) - 1):
|
||
if values[i] <= value < values[i + 1]:
|
||
return self.labels[i]
|
||
return self.labels[-1] # cas limite pour la borne max
|
||
|
||
def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
|
||
"""
|
||
Renvoie une approximation de la valeur à partir des labels binaires (e.g. B5, H1)
|
||
en utilisant une interpolation simple basée sur les indices.
|
||
|
||
Parameters:
|
||
matrix_df (pd.DataFrame): Matrice avec les labels binaires en index et colonnes.
|
||
row_label (str): Label de la ligne (ex: 'B3').
|
||
col_label (str): Label de la colonne (ex: 'H2').
|
||
|
||
Returns:
|
||
float: Valeur approchée si possible, sinon NaN.
|
||
"""
|
||
|
||
# Vérification des labels
|
||
if row_label not in matrice.index or col_label not in matrice.columns:
|
||
return np.nan
|
||
|
||
# Index correspondant
|
||
row_idx = self.label_to_index.get(row_label)
|
||
col_idx = self.label_to_index.get(col_label)
|
||
|
||
# Approximation directe (aucune interpolation complexe ici, juste une lecture)
|
||
return numeric_matrice[row_idx, col_idx]
|
||
|
||
@property
|
||
def protections(self):
|
||
return [
|
||
# {
|
||
# "method": "CooldownPeriod",
|
||
# "stop_duration_candles": 12
|
||
# }
|
||
# {
|
||
# "method": "MaxDrawdown",
|
||
# "lookback_period_candles": self.lookback.value,
|
||
# "trade_limit": self.trade_limit.value,
|
||
# "stop_duration_candles": self.protection_stop.value,
|
||
# "max_allowed_drawdown": self.protection_max_allowed_dd.value,
|
||
# "only_per_pair": False
|
||
# },
|
||
# {
|
||
# "method": "StoplossGuard",
|
||
# "lookback_period_candles": 24,
|
||
# "trade_limit": 4,
|
||
# "stop_duration_candles": self.protection_stoploss_stop.value,
|
||
# "only_per_pair": False
|
||
# },
|
||
# {
|
||
# "method": "StoplossGuard",
|
||
# "lookback_period_candles": 24,
|
||
# "trade_limit": 4,
|
||
# "stop_duration_candles": 2,
|
||
# "only_per_pair": False
|
||
# },
|
||
# {
|
||
# "method": "LowProfitPairs",
|
||
# "lookback_period_candles": 6,
|
||
# "trade_limit": 2,
|
||
# "stop_duration_candles": 60,
|
||
# "required_profit": 0.02
|
||
# },
|
||
# {
|
||
# "method": "LowProfitPairs",
|
||
# "lookback_period_candles": 24,
|
||
# "trade_limit": 4,
|
||
# "stop_duration_candles": 2,
|
||
# "required_profit": 0.01
|
||
# }
|
||
]
|
||
|
||
def conditional_smoothing(self, series, threshold=0.002):
|
||
smoothed = [series.iloc[0]]
|
||
for val in series.iloc[1:]:
|
||
last = smoothed[-1]
|
||
if abs(val - last) / last >= threshold:
|
||
smoothed.append(val)
|
||
else:
|
||
smoothed.append(last)
|
||
return pd.Series(smoothed, index=series.index)
|
||
|
||
def smooth_and_derivatives(self, series, window=25, polyorder=3):
|
||
series = series.copy()
|
||
if series.isna().sum() > 0:
|
||
series = series.ffill().bfill() # Si tu veux éviter toute NaN
|
||
|
||
smooth = self.causal_savgol(series, window=window, polyorder=polyorder)
|
||
deriv1 = np.diff(smooth, prepend=smooth[0])
|
||
deriv2 = np.diff(deriv1, prepend=deriv1[0])
|
||
|
||
return pd.Series(smooth, index=series.index), pd.Series(deriv1, index=series.index), pd.Series(deriv2,
|
||
index=series.index)
|
||
|
||
def causal_savgol(self, series, window=25, polyorder=3):
|
||
result = []
|
||
half_window = window # Fenêtre complète dans le passé
|
||
for i in range(len(series)):
|
||
if i < half_window:
|
||
result.append(np.nan)
|
||
continue
|
||
window_series = series[i - half_window:i]
|
||
if window_series.isna().any():
|
||
result.append(np.nan)
|
||
continue
|
||
coeffs = np.polyfit(range(window), window_series, polyorder)
|
||
poly = np.poly1d(coeffs)
|
||
result.append(poly(window - 1))
|
||
return pd.Series(result, index=series.index)
|
||
|
||
def 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 calculateStats(self, df, index, target):
|
||
# Nombre de tranches (modifiable)
|
||
n_bins_indice = 11
|
||
n_bins_valeur = 11
|
||
|
||
# Créer les tranches dynamiques
|
||
df['indice_tranche'] = pd.qcut(df[index], q=n_bins_indice, duplicates='drop')
|
||
df['valeur_tranche'] = pd.qcut(df[target], q=n_bins_valeur, duplicates='drop')
|
||
|
||
# Créer un tableau croisé avec la moyenne des valeurs
|
||
pivot_mean = df.pivot_table(
|
||
index='indice_tranche',
|
||
columns='valeur_tranche',
|
||
values=target, # <-- c'est la colonne qu'on agrège
|
||
aggfunc='mean' # <-- on calcule la moyenne
|
||
)
|
||
|
||
# Résultat
|
||
# print("Moyenne des valeurs par double-tranche :")
|
||
# print(pivot_mean.round(2))
|
||
|
||
def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
|
||
|
||
return True
|
||
limit = 3
|
||
|
||
# 🟢 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['mid_smooth_5_deriv1'] > -0.9 and last_candle['sma5_deriv1'] > 0 and last_candle['sma5_deriv2'] > 0:
|
||
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['sma5_deriv1'] < -0.2 or last_candle['sma5_deriv2'] < -3):
|
||
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
|
||
|
||
# if pair.startswith('BTC'):
|
||
# return True # BTC toujours autorisé
|
||
|
||
#return True
|
||
|
||
# Filtrer les paires non-BTC
|
||
non_btc_pairs = [p for p in self.pairs if not p.startswith('BTC')]
|
||
|
||
# Compter les positions actives sur les paires non-BTC
|
||
max_nb_trades = 0
|
||
total_non_btc = 0
|
||
max_pair = ''
|
||
limit_amount = 250
|
||
max_amount = 0
|
||
for p in non_btc_pairs:
|
||
max_nb_trades = max(max_nb_trades, self.pairs[p]['count_of_buys'])
|
||
max_amount = max(max_amount, self.pairs[p]['total_amount'])
|
||
|
||
for p in non_btc_pairs:
|
||
if (max_nb_trades == self.pairs[p]['count_of_buys'] and max_nb_trades > limit):
|
||
# if (max_amount == self.pairs[p]['total_amount'] and max_amount > limit_amount):
|
||
max_pair = p
|
||
total_non_btc += self.pairs[p]['count_of_buys']
|
||
|
||
pct_max = self.getPctFirstBuy(pair, last_candle) # self.getPctLastBuy(pair, last_candle)
|
||
|
||
val = self.getProbaHausseSma5d(last_candle)
|
||
if val < 15:
|
||
return False
|
||
|
||
return True
|
||
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 calculePlateaux(self, informative: pd.DataFrame, plateau_duration, plateau_tolerance) -> pd.DataFrame:
|
||
|
||
# 1. Détection plateau
|
||
informative['rolling_min'] = informative['close'].rolling(plateau_duration).min()
|
||
informative['rolling_max'] = informative['close'].rolling(plateau_duration).max()
|
||
informative['plateau_amplitude'] = (informative['rolling_max'] - informative['rolling_min']) / informative[
|
||
'rolling_min']
|
||
informative['plateau'] = informative['plateau_amplitude'] < plateau_tolerance
|
||
|
||
# 2. Détection "fin de plateau"
|
||
# informative['plateau_end'] = (informative['plateau'] & ~informative['plateau'].shift(-1).fillna(False).astype(bool))
|
||
next_plateau = informative['plateau'].shift(-1)
|
||
next_plateau = next_plateau.fillna(False).astype(bool)
|
||
informative['plateau_end'] = informative['plateau'] & ~next_plateau
|
||
|
||
# 3. Enregistrer dernier plateau (min/max)
|
||
last_min = None
|
||
last_max = None
|
||
last_status = []
|
||
|
||
for i, row in informative.iterrows():
|
||
if row['plateau_end']:
|
||
last_min = row['rolling_min']
|
||
last_max = row['rolling_max']
|
||
|
||
if last_min is not None and last_max is not None:
|
||
if row['close'] > last_max:
|
||
breakout = "up"
|
||
distance = (row['close'] - last_max) / last_max
|
||
elif row['close'] < last_min:
|
||
breakout = "down"
|
||
distance = (last_min - row['close']) / last_min
|
||
else:
|
||
breakout = "inside"
|
||
distance = 0
|
||
else:
|
||
breakout = None
|
||
distance = None
|
||
|
||
last_status.append((breakout, distance))
|
||
|
||
informative['breakout_status'] = [s[0] for s in last_status]
|
||
informative['breakout_distance'] = [s[1] for s in last_status]
|
||
|
||
return informative
|