1730 lines
82 KiB
Python
1730 lines
82 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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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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logger = logging.getLogger(__name__)
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from tabulate import tabulate
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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_3_2_B_4_2(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": 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 = 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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"sma5_1h": {
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"color": "white"
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},
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"sma5_1d": {
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"color": "blue"
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},
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"sma20": {
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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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"sma10": {
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"color": "blue"
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},
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"min12_1d": {
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"color": "red"
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},
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"max12_1d": {
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"color": 'red'
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},
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"min50": {
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"color": 'green'
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},
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"max50": {
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"color": 'green'
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}
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},
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"subplots": {
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"Pct": {
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"sma20_deriv1": {
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'color': "green"
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},
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"down_pct": {
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"color": "blue"
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},
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"down_pct_1h": {
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"color": "red"
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},
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"down_pct_1d": {
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"color": "red"
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}
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},
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"Rsi": {
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"rsi": {
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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_deriv": {
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"rsi_deriv1_1h": {
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"color": "red"
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},
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"rsi_deriv1_1d": {
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"color": "blue"
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},
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},
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"Down": {
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"down_count_1h": {
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"color": "green"
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},
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"up_count_1h": {
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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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"smooth": {
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'mid_smooth_deriv1_1d': {
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"color": "blue"
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},
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'mid_smooth_1h_deriv1': {
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"color": "red"
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},
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'mid_smooth_deriv2_1d': {
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"color": "pink"
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},
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'mid_smooth_1h_deriv2': {
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"color": "#da59a6"
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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_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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"last_buy": 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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}
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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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trades = list()
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max_profit_pairs = {}
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protection_percent_buy_lost = IntParameter(1, 10, default=5, space='protection')
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protection_fibo = IntParameter(1, 10, default=2, space='protection')
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sell_allow_decrease = DecimalParameter(0.005, 0.02, default=0.2, decimals=2, space='sell', optimize=True, load=True)
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# Probabilité de hausse pour futur_percent_3h (en %):
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# mid_smooth_1h_deriv1_bin B5 B4 B3 B2 B1 N0 H1 H2 H3 H4 H5
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# sma24_deriv1_1h_bin
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# B5 41.0 47.2 48.1 45.6 74.0 65.9 66.5 83.8 77.8 72.1 81.0
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# B4 41.2 35.8 48.4 46.5 59.9 60.2 75.8 79.4 84.6 83.0 78.5
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# B3 34.1 39.7 42.8 47.0 63.3 64.5 71.5 80.4 82.0 86.6 76.6
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# B2 27.5 27.9 32.3 33.2 61.9 67.1 70.8 79.5 81.3 73.6 81.9
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# B1 35.0 26.5 24.4 34.9 50.0 59.2 69.4 72.8 79.8 77.4 69.5
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# N0 30.6 19.9 23.6 30.8 41.9 59.2 67.5 70.6 74.0 63.0 75.0
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# H1 25.2 28.7 28.6 25.8 35.9 44.2 60.1 68.8 67.7 69.6 80.9
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# H2 29.8 20.8 23.9 30.4 34.4 37.5 52.7 66.1 69.8 67.5 62.9
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# H3 25.7 29.4 22.7 29.8 37.7 47.1 59.9 68.5 66.5 68.6 66.4
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# H4 30.6 27.5 25.1 22.6 30.8 34.1 50.9 59.8 57.0 68.6 63.7
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# H5 14.8 21.6 22.2 35.3 19.3 31.6 38.3 59.6 65.2 56.8 59.6
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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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# Récupération des labels ordonnés
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ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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label_to_index = {label: i for i, label in enumerate(ordered_labels)}
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# Données sous forme de dictionnaire
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# Bornes des quantiles pour
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mid_smooth_24_deriv1_bins = [-37.4852, -0.7541, -0.4233, -0.2510, -0.1338, -0.0389, 0.0496, 0.1464, 0.2660, 0.4384, 0.7697, 48.2985]
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sma144_deriv1_bins = [-0.2592, -0.0166, -0.0091, -0.0051, -0.0025, -0.0005, 0.0012, 0.0034, 0.0062, 0.0105, 0.0183, 0.2436]
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smooth24_sma144_deriv1_matrice = {
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'B5': [8.2, 4.1, 3.1, 3.4, 3.5, 3.0, 2.9, 2.8, 2.5, 3.0, 4.1],
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'B4': [24.9, 13.5, 11.8, 11.0, 9.0, 9.3, 9.1, 8.9, 8.1, 7.8, 11.4],
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'B3': [39.8, 24.7, 20.4, 18.8, 17.4, 16.0, 16.2, 14.7, 15.4, 15.5, 15.9],
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'B2': [54.8, 40.6, 32.7, 28.3, 25.9, 24.3, 23.1, 24.0, 23.4, 24.2, 21.1],
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'B1': [65.1, 52.9, 46.6, 44.7, 38.8, 37.7, 35.4, 33.6, 32.2, 33.1, 27.4],
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'N0': [73.1, 62.9, 61.1, 59.0, 56.1, 52.4, 49.5, 48.5, 42.7, 39.9, 35.3],
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'H1': [79.7, 72.5, 73.1, 72.6, 71.6, 69.8, 66.9, 63.8, 58.0, 53.2, 41.9],
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'H2': [81.7, 79.8, 79.6, 80.8, 79.3, 80.1, 78.1, 76.7, 72.5, 65.7, 52.8],
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'H3': [86.1, 87.7, 87.4, 87.8, 87.2, 86.5, 84.5, 84.4, 82.7, 78.8, 65.4],
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'H4': [92.6, 93.4, 94.0, 93.4, 94.3, 94.0, 93.8, 93.7, 92.7, 89.6, 79.9],
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'H5': [97.1, 97.5, 97.9, 98.2, 97.5, 98.2, 98.1, 98.2, 97.7, 97.4, 93.5],
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}
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smooth24_sma144_deriv1_matrice_df = pd.DataFrame(smooth24_sma144_deriv1_matrice, index=index_labels)
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# Extraction de la matrice numérique
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smooth24_sma144_deriv1_numeric_matrice = smooth24_sma144_deriv1_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# Bornes des quantiles pour
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mid_smooth_deriv2_24_bins = [-10.2968, -0.2061, -0.0996, -0.0559, -0.0292, -0.0093, 0.0083, 0.0281, 0.0550, 0.0999, 0.2072, 10.2252]
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# =========================================================================
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# variables pour probabilité 144 bougies
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mid_smooth_1h_bins = [-2.0622, -0.1618, -0.0717, -0.0353, -0.0135, 0.0, 0.0085, 0.0276, 0.0521, 0.0923, 0.1742, 2.3286]
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sma24_deriv1_1h_bins = [-0.84253877, -0.13177195, -0.07485074, -0.04293497, -0.02033502, -0.00215711,
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0.01411933, 0.03308264, 0.05661652, 0.09362708, 0.14898214, 0.50579505]
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smooth_smadiff_matrice = {
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"B5": [41.0, 41.2, 34.1, 27.5, 35.0, 30.6, 25.2, 29.8, 25.7, 30.6, 14.8],
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"B4": [47.2, 35.8, 39.7, 27.9, 26.5, 19.9, 28.7, 20.8, 29.4, 27.5, 21.6],
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"B3": [48.1, 48.4, 42.8, 32.3, 24.4, 23.6, 28.6, 23.9, 22.7, 25.1, 22.2],
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"B2": [45.6, 46.5, 47.0, 33.2, 34.9, 30.8, 25.8, 30.4, 29.8, 22.6, 35.3],
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"B1": [74.0, 59.9, 63.3, 61.9, 50.0, 41.9, 35.9, 34.4, 37.7, 30.8, 19.3],
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"N0": [65.9, 60.2, 64.5, 67.1, 59.2, 59.2, 44.2, 37.5, 47.1, 34.1, 31.6],
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"H1": [66.5, 75.8, 71.5, 70.8, 69.4, 67.5, 60.1, 52.7, 59.9, 50.9, 38.3],
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"H2": [83.8, 79.4, 80.4, 79.5, 72.8, 70.6, 68.8, 66.1, 68.5, 59.8, 59.6],
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"H3": [77.8, 84.6, 82.0, 81.3, 79.8, 74.0, 67.7, 69.8, 66.5, 57.0, 65.2],
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"H4": [72.1, 83.0, 86.6, 73.6, 77.4, 63.0, 69.6, 67.5, 68.6, 68.6, 56.8],
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"H5": [81.0, 78.5, 76.6, 81.9, 69.5, 75.0, 80.9, 62.9, 66.4, 63.7, 59.6]
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}
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smooth_smadiff_matrice_df = pd.DataFrame(smooth_smadiff_matrice, index=index_labels)
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# Extraction de la matrice numérique
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smooth_smadiff_numeric_matrice = smooth_smadiff_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# =========================================================================
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# variables pour probabilité
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smooth_pct_max_hour_matrice = {
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'B5': [43.5, 52.7, 62.3, 65.5, 86.9, 63.1, 81.5, 86.7, 90.2, 90.1, 93.0],
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'B4': [34.9, 46.3, 53.6, 60.4, 75.8, 83.3, 81.5, 83.0, 86.4, 86.9, 91.1],
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'B3': [20.5, 35.4, 43.7, 54.5, 69.7, 71.6, 80.4, 84.7, 86.7, 84.9, 85.9],
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'B2': [11.5, 25.4, 36.4, 47.9, 62.3, 65.7, 76.5, 82.0, 81.8, 82.8, 77.7],
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'B1': [3.6, 14.9, 26.8, 41.1, 55.6, 71.4, 74.3, 79.8, 80.8, 82.3, 75.1],
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'N0': [0.0, 6.9, 18.3, 32.0, 47.2, 62.1, 69.1, 74.8, 78.3, 76.6, 71.6],
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'H1': [0.7, 3.8, 9.4, 24.2, 40.6, 59.7, 67.8, 70.9, 73.4, 72.1, 70.0],
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'H2': [0.0, 0.6, 6.5, 13.6, 33.6, 51.7, 64.9, 70.2, 68.4, 67.8, 65.8],
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'H3': [1.4, 0.6, 2.6, 6.6, 23.3, 50.2, 56.2, 63.6, 65.7, 64.5, 64.7],
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'H4': [1.6, 0.3, 3.0, 3.2, 11.4, 32.7, 44.0, 54.9, 61.7, 60.6, 63.6],
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'H5': [1.8, 2.6, 0.6, 1.1, 9.7, 12.9, 26.2, 44.5, 52.6, 54.5, 56.2],
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}
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smooth_pct_max_hour_matrice_df = pd.DataFrame(smooth_pct_max_hour_matrice, index=index_labels)
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# Extraction de la matrice numérique
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smooth_pct_max_hour_numeric_matrice = smooth_pct_max_hour_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# =========================================================================
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# variables pour probabilité 144 bougies
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# Données sous forme de dictionnaire
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smooth_sma_24_diff_matrice = {
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"B5":[40.3, 52.1, 60.2, 68.6, 86.3, 76.5, 75.1, 83.5, 88.7, 96.3, 91.6],
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"B4":[26.6, 39.4, 48.1, 57.0, 76.7, 82.4, 79.6, 82.4, 91.8, 86.6, 87.8],
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"B3":[21.5, 27.7, 42.7, 53.2, 70.9, 76.6, 80.8, 79.4, 88.3, 88.0, 87.8],
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"B2":[15.1, 20.8, 32.9, 46.9, 59.1, 79.6, 82.5, 79.6, 80.8, 87.0, 85.5],
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"B1":[15.7, 15.4, 21.9, 29.4, 48.3, 66.6, 76.4, 77.8, 80.8, 83.5, 81.4],
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"N0":[15.0, 10.5, 20.1, 24.5, 36.9, 59.9, 68.8, 74.1, 77.7, 83.0, 75.7],
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"H1":[14.8, 10.7, 15.1, 21.0, 30.1, 47.3, 59.2, 70.4, 76.1, 82.7, 82.6],
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"H2":[7.9, 8.6, 13.6, 20.6, 27.0, 39.5, 55.2, 68.9, 69.0, 78.4, 83.4],
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"H3":[9.2, 6.2, 12.6, 21.7, 23.6, 33.1, 42.3, 57.8, 66.0, 71.9, 81.9],
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"H4":[4.8, 13.1, 16.3, 14.5, 19.5, 26.4, 35.6, 49.2, 63.2, 68.2, 71.6],
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"H5":[17.9, 25.7, 20.8, 17.8, 8.7, 18.5, 32.3, 37.7, 49.3, 59.8, 61.7]
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}
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smooth_sma_24_diff_matrice_df = pd.DataFrame(smooth_smadiff_matrice, index=index_labels)
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# Extraction de la matrice numérique
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smooth_sma_24_diff_numeric_matrice = smooth_sma_24_diff_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# Bornes des quantiles pour
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mid_smooth_1h_deriv1 = [-11.5091, -0.4887, -0.1902, -0.0823, -0.0281, -0.0008, 0.0110, 0.0439, 0.1066, 0.2349, 0.5440, 14.7943]
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# Bornes des quantiles pour
|
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mid_smooth_1h_deriv2 = [-6.2109, -0.2093, -0.0900, -0.0416, -0.0171, -0.0035, 0.0033, 0.0168, 0.0413, 0.0904, 0.2099, 6.2109]
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# =========================================================================
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# variables pour probabilité jour
|
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# Bornes des quantiles pour
|
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mid_smooth_1h_deriv1_1d_bins = [-11.5091, -0.4887, -0.1902, -0.0823, -0.0281, -0.0008, 0.0110, 0.0439, 0.1066, 0.2349, 0.5440, 14.7943]
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# Bornes des quantiles pour
|
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sma24_deriv1_1h_1d_bins = [-2.1101, -0.1413, -0.0768, -0.0433, -0.0196, -0.0028, 0.0120, 0.0304, 0.0560, 0.0933, 0.1568, 0.7793]
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smooth_1d_sma_2_diff_1d_matrice = {
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'B5': [42.5, 47.8, 52.7, 48.5, 54.2, 64.6, 70.8, 69.2, 72.3, 71.2, 79.9],
|
||
'B4': [34.1, 43.5, 45.7, 53.7, 52.6, 67.3, 63.9, 70.8, 73.5, 67.9, 82.9],
|
||
'B3': [33.7, 42.7, 45.8, 49.6, 49.0, 57.8, 64.7, 68.7, 70.7, 72.6, 87.1],
|
||
'B2': [30.0, 36.6, 40.5, 42.3, 51.2, 62.0, 64.4, 65.2, 69.8, 74.3, 84.9],
|
||
'B1': [21.4, 29.8, 33.6, 39.9, 49.4, 56.1, 59.9, 63.9, 71.0, 72.8, 79.6],
|
||
'N0': [19.8, 30.4, 34.5, 41.5, 42.2, 48.1, 61.7, 64.5, 73.7, 69.3, 79.4],
|
||
'H1': [22.7, 27.0, 36.9, 34.8, 46.3, 50.2, 58.9, 63.1, 65.8, 66.5, 80.0],
|
||
'H2': [23.1, 34.3, 32.2, 31.0, 38.8, 54.3, 53.6, 55.1, 60.3, 63.3, 77.4],
|
||
'H3': [17.0, 32.6, 37.4, 31.0, 35.1, 36.7, 45.2, 53.0, 55.4, 58.6, 71.8],
|
||
'H4': [22.7, 31.9, 28.0, 35.8, 36.3, 46.9, 53.9, 53.8, 58.8, 58.0, 67.6],
|
||
'H5': [18.8, 27.0, 32.1, 36.0, 41.9, 48.1, 49.8, 53.6, 57.2, 62.2, 65.2],
|
||
}
|
||
smooth_1d_sma_2_diff_1d_matrice_df = pd.DataFrame(smooth_smadiff_matrice, index=index_labels)
|
||
# Extraction de la matrice numérique
|
||
smooth_1d_sma_2_diff_1d_numeric_matrice = smooth_1d_sma_2_diff_1d_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
|
||
|
||
paliers = {}
|
||
|
||
# =========================================================================
|
||
# Parameters hyperopt
|
||
|
||
# buy_mid_smooth_3_deriv1 = DecimalParameter(-0.1, 0.1, decimals=2, default=-0.06, space='buy')
|
||
# buy_mid_smooth_24_deriv1 = DecimalParameter(-0.6, 0, decimals=2, default=-0.03, space='buy')
|
||
buy_horizon_predict_1h = IntParameter(1, 12, default=2, space='buy')
|
||
buy_level_predict_1h = IntParameter(2, 5, default=4, space='buy')
|
||
|
||
|
||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||
|
||
minutes = 0
|
||
if self.pairs[pair]['last_date'] != 0:
|
||
minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
last_candle_2 = dataframe.iloc[-2].squeeze()
|
||
last_candle_3 = dataframe.iloc[-3].squeeze()
|
||
# val = self.getProbaHausse144(last_candle)
|
||
|
||
# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
|
||
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')
|
||
|
||
if allow_to_buy:
|
||
self.trades = list()
|
||
self.pairs[pair]['first_buy'] = rate
|
||
self.pairs[pair]['last_buy'] = rate
|
||
self.pairs[pair]['max_touch'] = last_candle['close']
|
||
self.pairs[pair]['last_candle'] = last_candle
|
||
self.pairs[pair]['count_of_buys'] = 1
|
||
self.pairs[pair]['current_profit'] = 0
|
||
self.pairs[pair]['last_palier_index'] = -1
|
||
dispo = round(self.wallets.get_available_stake_amount())
|
||
self.printLineLog()
|
||
|
||
stake_amount = self.adjust_stake_amount(pair, last_candle)
|
||
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action=("Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
|
||
pair=pair,
|
||
rate=rate,
|
||
dispo=dispo,
|
||
profit=0,
|
||
trade_type=entry_tag,
|
||
buys=1,
|
||
stake=round(stake_amount, 2)
|
||
)
|
||
|
||
return allow_to_buy
|
||
|
||
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
||
time_in_force: str,
|
||
exit_reason: str, current_time, **kwargs, ) -> bool:
|
||
# allow_to_sell = (minutes > 30)
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
|
||
allow_to_sell = (last_candle['percent'] < 0)
|
||
|
||
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
|
||
|
||
if allow_to_sell:
|
||
self.trades = list()
|
||
self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries #self.pairs[pair]['count_of_buys']
|
||
self.pairs[pair]['last_sell'] = rate
|
||
self.pairs[pair]['last_trade'] = trade
|
||
self.pairs[pair]['last_candle'] = last_candle
|
||
self.pairs[pair]['max_profit'] = 0
|
||
self.trades = list()
|
||
dispo= round(self.wallets.get_available_stake_amount())
|
||
# print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="Sell " + str(minutes),
|
||
pair=pair,
|
||
trade_type=exit_reason,
|
||
rate=last_candle['close'],
|
||
dispo=dispo,
|
||
profit=round(trade.calc_profit(rate, amount), 2)
|
||
)
|
||
self.pairs[pair]['max_touch'] = 0
|
||
self.pairs[pair]['last_buy'] = 0
|
||
self.pairs[pair]['last_date'] = current_time
|
||
self.pairs[pair]['last_palier_index'] = -1
|
||
return (allow_to_sell) | (exit_reason == 'force_exit')
|
||
|
||
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
||
proposed_stake: float, min_stake: float, max_stake: float,
|
||
**kwargs) -> float:
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
|
||
current_candle = dataframe.iloc[-1].squeeze()
|
||
adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle)
|
||
|
||
# print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
|
||
|
||
# Use default stake amount.
|
||
return adjusted_stake_amount
|
||
|
||
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
|
||
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
last_candle_1h = dataframe.iloc[-13].squeeze()
|
||
before_last_candle = dataframe.iloc[-2].squeeze()
|
||
before_last_candle_2 = dataframe.iloc[-3].squeeze()
|
||
|
||
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['haclose'], self.pairs[pair]['last_max'])
|
||
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
|
||
baisse = self.pairs[pair]['max_profit'] - current_profit
|
||
mx = self.pairs[pair]['max_profit'] / 5
|
||
self.pairs[pair]['count_of_buys'] = count_of_buys
|
||
self.pairs[pair]['current_profit'] = current_profit
|
||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
|
||
|
||
# if (last_candle['mid_smooth_deriv1'] >= 0):
|
||
# return None
|
||
# if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
|
||
# return None
|
||
#
|
||
# if (last_candle['sma20_deriv1'] < 0 and before_last_candle['sma20_deriv1'] >= 0) and (current_profit > expected_profit):
|
||
# return 'Drv_' + str(count_of_buys)
|
||
|
||
|
||
if ((before_last_candle_2['mid_smooth_3_deriv1'] <= before_last_candle['mid_smooth_3_deriv1'])
|
||
& (before_last_candle['mid_smooth_3_deriv1'] >= last_candle['mid_smooth_3_deriv1'])) \
|
||
and (current_profit > expected_profit):
|
||
return 'Drv_' + str(count_of_buys)
|
||
|
||
# if (baisse > mx) & (current_profit > expected_profit):
|
||
# self.trades = list()
|
||
# return 'mx_' + str(count_of_buys)
|
||
# if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
|
||
# self.trades = list()
|
||
# return 'pft_' + str(count_of_buys)
|
||
|
||
self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch'])
|
||
|
||
def informative_pairs(self):
|
||
# get access to all pairs available in whitelist.
|
||
pairs = self.dp.current_whitelist()
|
||
informative_pairs = [(pair, '1d') for pair in pairs]
|
||
informative_pairs += [(pair, '1h') for pair in pairs]
|
||
|
||
return informative_pairs
|
||
|
||
from typing import List
|
||
|
||
def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float:
|
||
if pct <= thresholds[0]:
|
||
return factors[0]
|
||
if pct >= thresholds[-1]:
|
||
return factors[-1]
|
||
|
||
for i in range(1, len(thresholds)):
|
||
if pct <= thresholds[i]:
|
||
# interpolation linéaire entre thresholds[i-1] et thresholds[i]
|
||
return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / (
|
||
thresholds[i] - thresholds[i - 1])
|
||
|
||
# Juste au cas où (devrait jamais arriver)
|
||
return factors[-1]
|
||
|
||
def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
|
||
start_factor: float = 1.0, end_factor: float = 2.0) -> float:
|
||
if pct <= start_pct:
|
||
return start_factor
|
||
if pct >= end_pct:
|
||
return end_factor
|
||
# interpolation linéaire
|
||
return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
|
||
|
||
def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
|
||
last_candle=None):
|
||
# Afficher les colonnes une seule fois
|
||
if self.config.get('runmode') == 'hyperopt':
|
||
return
|
||
if self.columns_logged % 30 == 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}|{'Buys':>4}| {'Stake':>5} |"
|
||
f"Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d| drv2 |drv_2h|drv_2d|val144|val1h |"
|
||
)
|
||
self.printLineLog()
|
||
|
||
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 = round((last_candle['haclose'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
|
||
|
||
max_touch = '' #round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
|
||
pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) # round(100 * self.pairs[pair]['current_profit'], 1)
|
||
|
||
# if trade_type is not None:
|
||
# if np.isnan(last_candle['rsi_1d']):
|
||
# string = ' '
|
||
# else:
|
||
# string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_deriv1_1d']))
|
||
# trade_type = trade_type \
|
||
# + " " + string \
|
||
# + " " + str(int(last_candle['rsi_1h'])) \
|
||
# + " " + str(int(last_candle['rsi_deriv1_1h']))
|
||
|
||
# val144 = self.getProbaHausse144(last_candle)
|
||
# val1h = self.getProbaHausse1h(last_candle)
|
||
|
||
self.printLog(
|
||
f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
|
||
f"| {profit or '-':>8} | {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
|
||
f"| {int(self.pairs[pair]['last_max']) or '-':>7} |{buys or '-':>2}-{self.pairs[pair]['last_palier_index'] or '-':>2}|{stake or '-':>7}"
|
||
f"|{last_candle['tendency_12'] or '-':>3}|" #{last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
|
||
# 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(val144, 1) or '-' :>6}|{round(val1h, 1) or '-':>6}|"
|
||
f"{round(last_candle['sma20_deriv1'], 4) or '-' :>6}|{round(last_candle['sma5_deriv1_1d'], 4) or '-' :>6}"
|
||
)
|
||
|
||
def printLineLog(self):
|
||
# f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
|
||
self.printLog(
|
||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+"
|
||
f"{'-' * 3}"
|
||
#"+{'-' * 3}+{'-' * 3}
|
||
# f"+{'-' * 6}+{'-' * 6}+{'-' * 6}+{'-' * 6}+{'-' * 6}+{'-' * 6}+"
|
||
)
|
||
|
||
def printLog(self, str):
|
||
if not self.dp.runmode.value in ('backtest', 'hyperopt'):
|
||
logger.info(str)
|
||
else:
|
||
if not self.dp.runmode.value in ('hyperopt'):
|
||
print(str)
|
||
|
||
def add_tendency_column(self, dataframe: pd.DataFrame, suffixe='') -> pd.DataFrame:
|
||
def tag_by_derivatives(row):
|
||
d1 = row[f"mid_smooth{suffixe}_deriv1"]
|
||
d2 = row[f"mid_smooth{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['min'] = talib.MIN(dataframe['close'], timeperiod=200)
|
||
dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
|
||
|
||
dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50)
|
||
dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
|
||
dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
|
||
|
||
dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
|
||
dataframe['max200_diff'] = (dataframe['max200'] - dataframe['close']) / dataframe['close']
|
||
|
||
dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
|
||
dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
|
||
self.calculeDerivees(dataframe, 'sma10')
|
||
dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
|
||
self.calculeDerivees(dataframe, 'sma20')
|
||
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["mid_re_smooth_3"] = self.conditional_smoothing(dataframe['mid_smooth_3'].dropna(), threshold=0.0005).dropna()
|
||
self.calculeDerivees(dataframe, "mid_re_smooth_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)
|
||
self.calculeDerivees(dataframe, 'rsi')
|
||
|
||
# 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
|
||
|
||
# Compter les baisses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_24', window=24, degree=4, future_offset=12)
|
||
|
||
################### INFORMATIVE 1h
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
||
heikinashi = qtpylib.heikinashi(informative)
|
||
informative['haopen'] = heikinashi['open']
|
||
informative['haclose'] = heikinashi['close']
|
||
informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
|
||
|
||
# informative = self.calculateDerivation(informative, window=12)
|
||
|
||
# informative = self.apply_regression_derivatives(informative, column='mid', window=5, degree=4)
|
||
# informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
|
||
# informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
|
||
informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
|
||
informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
|
||
informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
|
||
|
||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||
informative['sma24'] = talib.SMA(informative, timeperiod=24)
|
||
self.calculeDerivees(informative, 'sma24')
|
||
# self.calculateDownAndUp(informative, limit=0.0012)
|
||
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
||
|
||
################### INFORMATIVE 1d
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
||
# informative = self.calculateDerivation(informative, window=5, factor_1=10000, factor_2=1000)
|
||
# informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
|
||
# informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
|
||
|
||
# informative = self.apply_regression_derivatives(informative, column='mid', window=5, degree=4)
|
||
informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
|
||
informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
|
||
informative['max3'] = talib.MAX(informative['close'], timeperiod=3)
|
||
informative['min3'] = talib.MIN(informative['close'], timeperiod=3)
|
||
|
||
# informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
|
||
# self.calculeDerivees(informative, 'rsi')
|
||
#
|
||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||
self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1)
|
||
|
||
# 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_1d'], window=24, degree=4)
|
||
|
||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||
|
||
dataframe['last_price'] = dataframe['close']
|
||
dataframe['first_price'] = dataframe['close']
|
||
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
|
||
# dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01
|
||
# dataframe['limit'] = dataframe['close']
|
||
count_buys = 0
|
||
if self.dp:
|
||
if self.dp.runmode.value in ('live', 'dry_run'):
|
||
self.getOpenTrades()
|
||
|
||
for trade in self.trades:
|
||
if trade.pair != pair:
|
||
continue
|
||
filled_buys = trade.select_filled_orders('buy')
|
||
count = 0
|
||
amount = 0
|
||
for buy in filled_buys:
|
||
if count == 0:
|
||
dataframe['first_price'] = buy.price
|
||
# 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
|
||
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
|
||
|
||
# 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() / dataframe['mid_smooth_1h']
|
||
dataframe["mid_smooth_1h_deriv2"] = 10 * dataframe["mid_smooth_1h_deriv1"].diff()
|
||
|
||
# dataframe['close_smooth_1h'] = self.conditional_smoothing(dataframe['mid'].rolling(window=3).mean().dropna(), threshold=0.0005)
|
||
# dataframe['smooth_1h'], dataframe['deriv1_1h'], dataframe['deriv2_1h'] = self.smooth_and_derivatives(dataframe['close_smooth_1h'])
|
||
# dataframe['deriv1_1h'] = 100 * dataframe['deriv1_1h'] / dataframe['mid_smooth_1h']
|
||
# dataframe['deriv2_1h'] = 1000 * dataframe['deriv2_1h'] / dataframe['mid_smooth_1h']
|
||
|
||
|
||
# dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=horizon_h).mean()
|
||
# dataframe['sma5_deriv1_1h'] = dataframe['sma5_deriv1_1h'].rolling(window=horizon_h).mean()
|
||
# dataframe['sma24_1h'] = dataframe['sma24_1h'].rolling(window=horizon_h).mean()
|
||
# dataframe['sma24_deriv1_1h'] = dataframe['sma24_deriv1_1h'].rolling(window=horizon_h).mean()
|
||
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_1h', window=horizon_h * 12, degree=4, future_offset=24)
|
||
|
||
# Suppose que df['close'] est ton prix de clôture
|
||
|
||
# dataframe['close_smooth_24'] = self.conditional_smoothing(dataframe['mid'].rolling(24).mean().dropna(), threshold=0.0015)
|
||
# dataframe['smooth_24'], dataframe['smooth_24_deriv1'], dataframe['smooth_24_deriv2'] = self.smooth_and_derivatives(dataframe['close_smooth_24'])
|
||
# dataframe['smooth_24_deriv1'] = 100 * dataframe['smooth_24_deriv1'] / dataframe['mid_smooth_24']
|
||
# dataframe['smooth_24_deriv2'] = 100 * dataframe['smooth_24_deriv2'] / dataframe['mid_smooth_24']
|
||
|
||
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 = 24 * 5
|
||
# dataframe['mid_smooth_1d'] = dataframe['mid_smooth_1d'].rolling(window=horizon_d * 5).mean()
|
||
# dataframe["mid_smooth_deriv1_1d"] = dataframe["mid_smooth_1d"].rolling(horizon_d).mean().diff() / horizon_d
|
||
# dataframe["mid_smooth_deriv2_1d"] = horizon_d * dataframe["mid_smooth_deriv1_1d"].rolling(horizon_d).mean().diff()
|
||
#
|
||
# dataframe['sma5_1d'] = dataframe['sma5_1d'].rolling(window=horizon_d * 5).mean()
|
||
# dataframe['sma5_deriv1_1d'] = dataframe['sma5_deriv1_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe['sma24_1d'] = dataframe['sma24_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe['sma24_deriv1_1d'] = dataframe['sma24_deriv1_1d'].rolling(window=horizon_d).mean()
|
||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_1d', window=24, degree=4, future_offset=12)
|
||
|
||
# dataframe['percent_with_previous_day'] = 100 * (dataframe['close'] - dataframe['close_1d']) / dataframe['close']
|
||
# dataframe['percent_with_max_hour'] = 100 * (dataframe['close'] - dataframe['max12_1h']) / dataframe['close']
|
||
#
|
||
# horizon_h = 24 * 5
|
||
# dataframe['futur_percent_1h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-12) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
# dataframe['futur_percent_3h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-36) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
# dataframe['futur_percent_5h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-60) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
# dataframe['futur_percent_12h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-144) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||
#
|
||
# dataframe['futur_percent_1d'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
|
||
# dataframe['futur_percent_3d'] = 100 * (dataframe['close'].shift(-3) - dataframe['close']) / dataframe['close']
|
||
#
|
||
# self.calculateProbabilite2Index(dataframe, ['futur_percent_1d'], 'sma24_deriv1_1h', 'sma5_1d')
|
||
|
||
return dataframe
|
||
|
||
def calculeDerivees(self, dataframe, indic, factor_1=100, factor_2=10):
|
||
dataframe[f"{indic}_deriv1"] = factor_1 * dataframe[f"{indic}"].diff() / dataframe[f"{indic}"]
|
||
dataframe[f"{indic}_deriv2"] = factor_2 * dataframe[f"{indic}_deriv1"].diff()
|
||
|
||
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='', 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, 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)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['percent'] > 0)
|
||
# & (dataframe['mid_smooth_deriv1'] >= dataframe['mid_smooth_deriv1'].shift(1))
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'down')
|
||
|
||
dataframe.loc[
|
||
(
|
||
# (dataframe['deriv2_1h'].shift(2) >= dataframe['deriv2_1h'].shift(1))
|
||
# & (dataframe['deriv2_1h'].shift(1) <= dataframe['deriv2_1h'])
|
||
# (dataframe['deriv1_1h'] >= -0.01)
|
||
# & (dataframe['deriv2_1h'] >= -0.00)
|
||
(dataframe['mid_smooth_3_deriv1'].shift(2) >= dataframe['mid_smooth_3_deriv1'].shift(1))
|
||
& (dataframe['mid_smooth_3_deriv1'].shift(1) <= dataframe['mid_smooth_3_deriv1'])
|
||
#
|
||
#
|
||
# (dataframe['mid_smooth_1h_deriv1'] >= 0)
|
||
# & (dataframe['mid_smooth_1h_deriv1'] >= 0)
|
||
# & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= 0)
|
||
# & (dataframe['mid_smooth_1h_deriv1'] >= dataframe['mid_smooth_1h_deriv1'].shift(1))
|
||
), ['enter_long', 'enter_tag']] = (1, 'smth')
|
||
|
||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||
|
||
self.paliers = self.get_dca_stakes()
|
||
|
||
if self.dp.runmode.value in ('backtest'):
|
||
today = datetime.now().strftime("%Y-%m-%d-%H:%M:%S")
|
||
dataframe.to_feather(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.feather")
|
||
#
|
||
# df = dataframe
|
||
#
|
||
# # Colonnes à traiter
|
||
# # futur_cols = ['futur_percent_1h', 'futur_percent_3h', 'futur_percent_5h', 'futur_percent_12h']
|
||
# futur_cols = ['futur_percent_1h']
|
||
#
|
||
# # Tranches équitables par quantiles
|
||
#
|
||
# indic_1 = 'mid_smooth_24_deriv1'
|
||
# indic_2 = 'sma144_deriv1'
|
||
# #indic_2 = 'percent_with_max_hour'
|
||
# # indic_1 = 'mid_smooth_1h_deriv1'
|
||
# # indic_2 = 'sma5_deriv1_1d'
|
||
#
|
||
# self.calculateProbabilite2Index(df, futur_cols, indic_1, indic_2)
|
||
|
||
return dataframe
|
||
|
||
def calculateProbabilite2Index(self, df, futur_cols, indic_1, indic_2):
|
||
# # Définition des tranches pour les dérivées
|
||
# bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
|
||
# labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
|
||
#
|
||
# # Ajout des colonnes bin (catégorisation)
|
||
# df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_1h_deriv1'], bins=bins_deriv, labels=labels)
|
||
# df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_deriv1_1d'], bins=bins_deriv, labels=labels)
|
||
#
|
||
# # Colonnes de prix futur à analyser
|
||
# futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
|
||
#
|
||
# # Calcul des moyennes et des effectifs
|
||
# grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
|
||
#
|
||
# pd.set_option('display.width', 200) # largeur max affichage
|
||
# pd.set_option('display.max_columns', None)
|
||
pd.set_option('display.max_columns', None)
|
||
pd.set_option('display.width', 300) # largeur max affichage
|
||
|
||
# nettoyage
|
||
# series = df[f"{indic_2}"].dropna()
|
||
# unique_vals = df[f"{indic_2}"].nunique()
|
||
# print(unique_vals)
|
||
# print(df[f"{indic_2}"])
|
||
n = len(self.labels)
|
||
|
||
df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=n, labels=self.labels, retbins=True,
|
||
duplicates='drop')
|
||
df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
|
||
duplicates='drop')
|
||
# Affichage formaté pour code Python
|
||
print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
||
print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
||
# Agrégation
|
||
grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
|
||
# Affichage
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
print(grouped.round(4))
|
||
# Ajout des probabilités de hausse
|
||
for col in futur_cols:
|
||
df[f"{col}_is_up"] = df[col] > 0
|
||
|
||
# Calcul de la proba de hausse
|
||
proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
|
||
|
||
print(f"\nProbabilité de hausse pour {col} (en %):")
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
print((proba_up * 100).round(1))
|
||
|
||
# Affichage formaté des valeurs comme tableau Python
|
||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||
df_formatted = (proba_up * 100).round(1)
|
||
|
||
print("data = {")
|
||
for index, row in df_formatted.iterrows():
|
||
row_values = ", ".join([f"{val:.1f}" for val in row])
|
||
print(f"'{index}': [{row_values}], ")
|
||
print("}")
|
||
|
||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['mid_smooth_deriv1'] == 0)
|
||
# & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
|
||
# ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
|
||
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() < 50): # 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()
|
||
last_candle_2 = dataframe.iloc[-3].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
|
||
|
||
if (len(dataframe) < 1):
|
||
print("skip dataframe")
|
||
return None
|
||
pair = trade.pair
|
||
if self.dp.runmode.value in ('dry_run'):
|
||
if pair not in ('BTC/USDT', 'BTC/USDC', 'XRP/USDT', 'XRP/USDC', 'ETH/USDT', 'ETH/USDC'):
|
||
# print(f"skip pair {pair}")
|
||
return None
|
||
else:
|
||
if pair not in ('BTC/USDT', 'BTC/USDC'):
|
||
# print(f"skip pair {pair}")
|
||
return None
|
||
|
||
# # déclenche un achat si bougie rouge importante
|
||
# stake_amount = self.config.get('stake_amount', 100)
|
||
# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
|
||
# current_time = current_time.astimezone(timezone.utc)
|
||
# seconds_since_filled = (current_time - trade.date_last_filled_utc).total_seconds()
|
||
# pct = (last_candle['close'] - last_candle['open']) / (last_candle['open']) * 100
|
||
# if (
|
||
# stake_amount
|
||
# and pct <= - 1.10 #self.red_candle_pct
|
||
# and min_stake < stake_amount < max_stake
|
||
# and seconds_since_filled > (60 * 5)
|
||
# # and (last_candle["sma24_deriv1_1h"] > - 0.02)
|
||
# # and seconds_since_filled > (1 * 3600)
|
||
# # and count_of_entries < 10
|
||
# ):
|
||
# trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="Adjust 1",
|
||
# dispo=dispo,
|
||
# pair=trade.pair,
|
||
# rate=current_rate,
|
||
# trade_type=trade_type,
|
||
# profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
|
||
# 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
|
||
#
|
||
# # déclenche un achat en conditions d'achat standard
|
||
# if (
|
||
# stake_amount
|
||
# and last_candle['close'] < last_candle['sma20']
|
||
# and last_candle['close'] < last_candle['open']
|
||
# and min_stake < stake_amount < max_stake
|
||
# and (last_candle["sma24_deriv1_1h"] > - 0.02)
|
||
# and seconds_since_filled > 23 * 3600 #self.staking_delay * 3600
|
||
# ):
|
||
# stake_amount = stake_amount * seconds_since_filled / (23 * 3600)
|
||
# trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="Adjust 2",
|
||
# dispo=dispo,
|
||
# pair=trade.pair,
|
||
# rate=current_rate,
|
||
# trade_type=trade_type,
|
||
# profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
|
||
# 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
|
||
|
||
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
|
||
pct_first = 0
|
||
if self.pairs[pair]['first_buy']:
|
||
pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
|
||
|
||
pct = 0.012
|
||
if count_of_buys == 1:
|
||
pct_max = current_profit
|
||
else:
|
||
if self.pairs[trade.pair]['last_buy']:
|
||
pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4)
|
||
else:
|
||
pct_max = - pct
|
||
lim = - pct - (count_of_buys * 0.001)
|
||
|
||
# index = self.get_palier_index(pct_first)
|
||
# if index is None:
|
||
# return None
|
||
# index = index -1
|
||
# lim, stake_amount = self.paliers[index] #- pct - (count_of_buys * 0.001)
|
||
|
||
# self.get_active_stake()
|
||
# val144 = self.getProbaHausse144(last_candle)
|
||
# val1h = self.getProbaHausse1h(last_candle)
|
||
# val = self.getProbaHausse144(last_candle)
|
||
|
||
# buy = False
|
||
# previous = 0
|
||
# # current_profit=-0.001998 count_of_buys=1 pct_first=0.000 pct_palier=-0.629 pct_max=-0.002 lim=0.000
|
||
#
|
||
# for pct_palier, stake_amount in self.paliers:
|
||
# if abs(pct_palier) > abs(pct_first):
|
||
# lim = pct_palier
|
||
# break
|
||
# previous = pct_palier
|
||
|
||
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_first={pct_first:.3f} pct_palier={pct_palier:.3f} pct_max={pct_max:.3f} lim={lim:.3f} ")
|
||
|
||
# if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
|
||
|
||
# ['mid_smooth_1h_deriv1']
|
||
# sans cdt° ==>Avg. stake amount 276.516 USDT │ Total trade volume 175760.342 USDT 315 │ 1.17 │ 1204.156 │ 60.21│ 1 day, 13:45:00 │ 314 0 1 99.7 │ 0.787 USDT 0.02% │
|
||
# > - 0.03 ==>Avg. stake amount 259.702 USDT │ Total trade volume 149302.88 USDT 285 │ 1.19 │ 974.542 │ 48.73│ 1 day, 17:45:00 │ 284 0 1 99.6 │ 0.787 USDT 0.03% │
|
||
# > - 0.03 ==>Avg. stake amount 253.535 USDT │ Total trade volume 145312.936 USDT 284 │ 1.19 │ 1014.898 │ 50.74| 1 day, 17:54:00 │ 283 0 1 99.6 │ 0.684 USDT 0.02% │
|
||
# > - 0.015 ==>Avg. stake amount 249.107 USDT │ Total trade volume 138186.861 USDT 275 │ 1.20 │ 901.976 │ 45.1 │ 1 day, 19:17:00 │ 274 0 1 99.6 │ 0.684 USDT 0.02%
|
||
|
||
condition = True #last_candle['mid_smooth_1h_deriv1'] > - 0.05 #(last_candle['mid_smooth_3_deriv1'] > self.buy_mid_smooth_3_deriv1.value) and (last_candle['mid_smooth_24_deriv1'] > self.buy_mid_smooth_24_deriv1.value)
|
||
# (last_candle['enter_long'] == 1 & (count_of_buys < 3)) \
|
||
# or ((before_last_candle['mid_re_smooth_3_deriv1'] <= 0) & (last_candle['mid_re_smooth_3_deriv1'] >= 0) & (3 <= count_of_buys < 6)) \
|
||
# or ((before_last_candle['mid_smooth_1h_deriv1'] <= 0) & (last_candle['mid_smooth_1h_deriv1'] >= 0) & (6 <= count_of_buys))
|
||
|
||
limit_buy = 20
|
||
if (count_of_buys < limit_buy) and condition and (pct_max < lim): # and val > self.buy_val_adjust.value and last_candle['mid_smooth_deriv1_1d'] > - 1):
|
||
try:
|
||
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_first={pct_first:.3f} pct_max={pct_max:.3f} lim={lim:.3f} index={index}")
|
||
# self.pairs[trade.pair]['last_palier_index'] = index
|
||
|
||
# Appel de la fonction
|
||
poly_func, x_future, y_future, count = self.polynomial_forecast(
|
||
dataframe['sma24_deriv1_1h'],
|
||
window=self.buy_horizon_predict_1h.value * 12,
|
||
degree=self.buy_level_predict_1h.value,
|
||
n_future=3)
|
||
|
||
if count < 3:
|
||
return None
|
||
|
||
max_amount = self.config.get('stake_amount', 100) * 2.5
|
||
# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
|
||
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
|
||
self.adjust_stake_amount(pair, last_candle) - 10 * pct_first / pct) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
|
||
|
||
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
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, 4), # round(current_profit * trade.stake_amount, 2),
|
||
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
|
||
except Exception as exception:
|
||
print(exception)
|
||
return None
|
||
|
||
# if (count_of_buys < limit_buy and pct_max > pct and current_profit > 0.004) \
|
||
# and (last_candle['rsi_deriv1_1h'] >= -5) \
|
||
# and (last_candle['tendency'] in ('P', 'H++', 'DH', 'H+')) \
|
||
# and (last_candle['mid_smooth_deriv1'] > 0.015):
|
||
# try:
|
||
# trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
|
||
# self.log_trade(
|
||
# last_candle=last_candle,
|
||
# date=current_time,
|
||
# action="Gain +",
|
||
# dispo=dispo,
|
||
# pair=trade.pair,
|
||
# rate=current_rate,
|
||
# trade_type=trade_type,
|
||
# profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
|
||
# 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
|
||
# except Exception as exception:
|
||
# print(exception)
|
||
# return None
|
||
|
||
return None
|
||
|
||
# def getProbaHausse144(self, last_candle):
|
||
# value_1 = self.getValuesFromTable(self.mid_smooth_24_deriv1_bins, last_candle['mid_smooth_24_deriv1'])
|
||
# value_2 = self.getValuesFromTable(self.sma144_deriv1_bins, last_candle['sma144_deriv1'])
|
||
#
|
||
# val = self.approx_val_from_bins(
|
||
# matrice=self.smooth24_sma144_deriv1_matrice_df,
|
||
# numeric_matrice=self.smooth24_sma144_deriv1_numeric_matrice,
|
||
# row_label=value_2,
|
||
# col_label=value_1)
|
||
# return val
|
||
#
|
||
# def getProbaHausse1h(self, last_candle):
|
||
# value_1 = self.getValuesFromTable(self.mid_smooth_1h_bins, last_candle['mid_smooth_1h_deriv1'])
|
||
# value_2 = self.getValuesFromTable(self.sma24_deriv1_1h_bins, last_candle['sma24_deriv1_1h'])
|
||
#
|
||
# val = self.approx_val_from_bins(matrice=self.smooth_smadiff_matrice_df, numeric_matrice=self.smooth_smadiff_numeric_matrice,
|
||
# row_label=value_2,
|
||
# col_label=value_1)
|
||
# return val
|
||
#
|
||
# def getProbaHausse1d(self, last_candle):
|
||
# value_1 = self.getValuesFromTable(self.mid_smooth_1h_bins, last_candle['mid_smooth_deriv1_1d'])
|
||
# value_2 = self.getValuesFromTable(self.sma24_deriv1_1h_bins, last_candle['sma5_deriv1_1d'])
|
||
#
|
||
# val = self.approx_val_from_bins(matrice=self.smooth_smadiff_matrice_df, numeric_matrice=self.smooth_smadiff_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', 100) # Montant de base configuré
|
||
|
||
first_price = self.pairs[pair]['first_buy']
|
||
if (first_price == 0):
|
||
first_price = last_candle['close']
|
||
|
||
last_max = last_candle['max12_1d']
|
||
pct = 5
|
||
if last_max > 0:
|
||
pct = 100 * (last_max - first_price) / last_max
|
||
thresholds = [2, 5, 10, 20]
|
||
factors = [1, 1.25, 1.5, 2.0]
|
||
|
||
factor = self.multi_step_interpolate(pct, thresholds, factors)
|
||
|
||
adjusted_stake_amount = base_stake_amount * factor #max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
||
|
||
return adjusted_stake_amount
|
||
|
||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||
|
||
expected_profit = 0.004 #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 interpolated_val_from_bins(self, row_pos, col_pos):
|
||
"""
|
||
Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
|
||
à partir de positions flottantes dans l'index (ligne) et les colonnes.
|
||
|
||
Parameters:
|
||
matrix_df (pd.DataFrame): Matrice des probabilités (index/colonnes = labels).
|
||
row_pos (float): Position réelle de la ligne (0 = B5, 10 = H5).
|
||
col_pos (float): Position réelle de la colonne (0 = B5, 10 = H5).
|
||
|
||
Returns:
|
||
float: Valeur interpolée, ou NaN si en dehors des bornes.
|
||
"""
|
||
|
||
# Labels ordonnés
|
||
n = len(self.labels)
|
||
|
||
# Vérification des limites
|
||
if not (0 <= row_pos <= n - 1) or not (0 <= col_pos <= n - 1):
|
||
return np.nan
|
||
|
||
# Conversion des labels -> matrice
|
||
matrix = self.smooth_smadiff_matrice_df.reindex(index=self.labels, columns=self.labels).values
|
||
|
||
# Coordonnées entières (inférieures)
|
||
i = int(np.floor(row_pos))
|
||
j = int(np.floor(col_pos))
|
||
|
||
# Coefficients pour interpolation
|
||
dx = row_pos - i
|
||
dy = col_pos - j
|
||
|
||
# Précautions sur les bords
|
||
if i >= n - 1: i = n - 2; dx = 1.0
|
||
if j >= n - 1: j = n - 2; dy = 1.0
|
||
|
||
# Récupération des 4 valeurs voisines
|
||
v00 = matrix[i][j]
|
||
v10 = matrix[i + 1][j]
|
||
v01 = matrix[i][j + 1]
|
||
v11 = matrix[i + 1][j + 1]
|
||
|
||
# Interpolation bilinéaire
|
||
interpolated = (
|
||
(1 - dx) * (1 - dy) * v00 +
|
||
dx * (1 - dy) * v10 +
|
||
(1 - dx) * dy * v01 +
|
||
dx * dy * v11
|
||
)
|
||
return interpolated
|
||
|
||
def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
|
||
"""
|
||
Renvoie une approximation de la valeur à partir des labels binaires (e.g. B5, H1)
|
||
en utilisant une interpolation simple basée sur les indices.
|
||
|
||
Parameters:
|
||
matrix_df (pd.DataFrame): Matrice avec les labels binaires en index et colonnes.
|
||
row_label (str): Label de la ligne (ex: 'B3').
|
||
col_label (str): Label de la colonne (ex: 'H2').
|
||
|
||
Returns:
|
||
float: Valeur approchée si possible, sinon NaN.
|
||
"""
|
||
|
||
# Vérification des labels
|
||
if row_label not in matrice.index or col_label not in matrice.columns:
|
||
return np.nan
|
||
|
||
# Index correspondant
|
||
row_idx = self.label_to_index.get(row_label)
|
||
col_idx = self.label_to_index.get(col_label)
|
||
|
||
# Approximation directe (aucune interpolation complexe ici, juste une lecture)
|
||
return numeric_matrice[row_idx, col_idx]
|
||
|
||
|
||
# @property
|
||
# def protections(self):
|
||
# return [
|
||
# {
|
||
# "method": "CooldownPeriod",
|
||
# "stop_duration_candles": 12
|
||
# }
|
||
# # {
|
||
# # "method": "MaxDrawdown",
|
||
# # "lookback_period_candles": self.lookback.value,
|
||
# # "trade_limit": self.trade_limit.value,
|
||
# # "stop_duration_candles": self.protection_stop.value,
|
||
# # "max_allowed_drawdown": self.protection_max_allowed_dd.value,
|
||
# # "only_per_pair": False
|
||
# # },
|
||
# # {
|
||
# # "method": "StoplossGuard",
|
||
# # "lookback_period_candles": 24,
|
||
# # "trade_limit": 4,
|
||
# # "stop_duration_candles": self.protection_stoploss_stop.value,
|
||
# # "only_per_pair": False
|
||
# # },
|
||
# # {
|
||
# # "method": "StoplossGuard",
|
||
# # "lookback_period_candles": 24,
|
||
# # "trade_limit": 4,
|
||
# # "stop_duration_candles": 2,
|
||
# # "only_per_pair": False
|
||
# # },
|
||
# # {
|
||
# # "method": "LowProfitPairs",
|
||
# # "lookback_period_candles": 6,
|
||
# # "trade_limit": 2,
|
||
# # "stop_duration_candles": 60,
|
||
# # "required_profit": 0.02
|
||
# # },
|
||
# # {
|
||
# # "method": "LowProfitPairs",
|
||
# # "lookback_period_candles": 24,
|
||
# # "trade_limit": 4,
|
||
# # "stop_duration_candles": 2,
|
||
# # "required_profit": 0.01
|
||
# # }
|
||
# ]
|
||
|
||
def conditional_smoothing(self, series, threshold=0.002):
|
||
smoothed = [series.iloc[0]]
|
||
for val in series.iloc[1:]:
|
||
last = smoothed[-1]
|
||
if abs(val - last) / last >= threshold:
|
||
smoothed.append(val)
|
||
else:
|
||
smoothed.append(last)
|
||
return pd.Series(smoothed, index=series.index)
|
||
|
||
def smooth_and_derivatives(self, series, window=25, polyorder=3):
|
||
series = series.copy()
|
||
if series.isna().sum() > 0:
|
||
series = series.fillna(method='ffill').fillna(method='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 get_stake_from_drawdown(self, pct: float, base_stake: float = 100.0, step: float = 0.04, growth: float = 1.15,
|
||
max_stake: float = 1000.0) -> float:
|
||
"""
|
||
Calcule la mise à allouer en fonction du drawdown.
|
||
|
||
:param pct: Drawdown en pourcentage (ex: -0.12 pour -12%)
|
||
:param base_stake: Mise de base (niveau 0)
|
||
:param step: Espacement entre paliers (ex: tous les -4%)
|
||
:param growth: Facteur de croissance par palier (ex: 1.15 pour +15%)
|
||
:param max_stake: Mise maximale à ne pas dépasser
|
||
:return: Montant à miser
|
||
"""
|
||
if pct >= 0:
|
||
return base_stake
|
||
|
||
level = int(abs(pct) / step)
|
||
stake = base_stake * (growth ** level)
|
||
return min(stake, max_stake)
|
||
|
||
def compute_adaptive_paliers(self, max_drawdown: float = 0.65, first_steps: list[float] = [0.01, 0.01, 0.015, 0.02], growth: float = 1.2) -> list[float]:
|
||
"""
|
||
Génère une liste de drawdowns négatifs avec des paliers plus rapprochés au début.
|
||
|
||
:param max_drawdown: Drawdown max (ex: 0.65 pour -65%)
|
||
:param first_steps: Liste des premiers paliers fixes en % (ex: [0.01, 0.01, 0.015])
|
||
:param growth: Facteur multiplicatif pour espacer les paliers suivants
|
||
:return: Liste de drawdowns négatifs (croissants)
|
||
"""
|
||
paliers = []
|
||
cumulated = 0.0
|
||
|
||
# Étapes initiales rapprochées
|
||
for step in first_steps:
|
||
cumulated += step
|
||
paliers.append(round(-cumulated, 4))
|
||
|
||
# Étapes suivantes plus espacées
|
||
step = first_steps[-1]
|
||
while cumulated < max_drawdown:
|
||
step *= growth
|
||
cumulated += step
|
||
if cumulated >= max_drawdown:
|
||
break
|
||
paliers.append(round(-cumulated, 4))
|
||
|
||
return paliers
|
||
|
||
def get_dca_stakes(self,
|
||
max_drawdown: float = 0.65,
|
||
base_stake: float = 100.0,
|
||
first_steps: list[float] = [0.01, 0.01, 0.015, 0.015],
|
||
growth: float = 1.2,
|
||
stake_growth: float = 1.15
|
||
) -> list[tuple[float, float]]:
|
||
"""
|
||
Génère les paliers de drawdown et leurs stakes associés.
|
||
|
||
:param max_drawdown: Maximum drawdown (ex: 0.65 pour -65%)
|
||
:param base_stake: Mise initiale
|
||
:param first_steps: Paliers de départ (plus resserrés)
|
||
:param growth: Multiplicateur d'espacement des paliers
|
||
:param stake_growth: Croissance multiplicative des mises
|
||
:return: Liste de tuples (palier_pct, stake)
|
||
[(-0.01, 100.0), (-0.02, 115.0), (-0.035, 132.25), (-0.05, 152.09), (-0.068, 174.9),
|
||
(-0.0896, 201.14), (-0.1155, 231.31), (-0.1466, 266.0), (-0.1839, 305.9), (-0.2287, 351.79),
|
||
(-0.2825, 404.56), (-0.347, 465.24), (-0.4244, 535.03), (-0.5173, 615.28), (-0.6287, 707.57)]
|
||
"""
|
||
paliers = [
|
||
(-0.01, 100.0), (-0.02, 115.0), (-0.035, 130), (-0.05, 150), (-0.07, 150),
|
||
(-0.10, 150), (-0.15, 150), (-0.20, 150), (-0.25, 150),
|
||
(-0.30, 200), (-0.40, 200),
|
||
(-0.50, 300), (-0.60, 400), (-0.70, 500), (-0.80, 1000)
|
||
]
|
||
|
||
# cumulated = 0.0
|
||
# stake = base_stake
|
||
#
|
||
# # Étapes initiales
|
||
# for step in first_steps:
|
||
# cumulated += step
|
||
# paliers.append((round(-cumulated, 4), round(stake, 2)))
|
||
# stake *= stake_growth
|
||
#
|
||
# # Étapes suivantes
|
||
# step = first_steps[-1]
|
||
# while cumulated < max_drawdown:
|
||
# step *= growth
|
||
# cumulated += step
|
||
# if cumulated >= max_drawdown:
|
||
# break
|
||
# paliers.append((round(-cumulated, 4), round(stake, 2)))
|
||
# stake *= stake_growth
|
||
|
||
return paliers
|
||
|
||
def get_active_stake(self, pct: float) -> float:
|
||
"""
|
||
Renvoie la mise correspondant au drawdown `pct`.
|
||
|
||
:param pct: drawdown courant (négatif, ex: -0.043)
|
||
:param paliers: liste de tuples (drawdown, stake)
|
||
:return: stake correspondant
|
||
"""
|
||
abs_pct = abs(pct)
|
||
stake = self.paliers[0][1] # stake par défaut
|
||
|
||
for palier, s in self.paliers:
|
||
if abs_pct >= abs(palier):
|
||
stake = s
|
||
else:
|
||
break
|
||
|
||
return stake
|
||
|
||
def get_palier_index(self, pct):
|
||
"""
|
||
Retourne l'index du palier franchi pour un pourcentage de baisse donné (pct).
|
||
On cherche le palier le plus profond atteint (dernier franchi).
|
||
"""
|
||
for i in reversed(range(len(self.paliers))):
|
||
seuil, _ = self.paliers[i]
|
||
#print(f"pct={pct} seuil={seuil}")
|
||
if pct <= seuil:
|
||
# print(pct)
|
||
return i
|
||
return None # Aucun palier atteint
|
||
|
||
# def poly_regression_predictions(self, series: pd.Series, window: int = 20, degree: int = 2, n_future: int = 3) -> pd.DataFrame:
|
||
# """
|
||
# Renvoie une DataFrame avec `n_future` colonnes contenant les extrapolations des n prochains points
|
||
# selon une régression polynomiale ajustée sur les `window` dernières valeurs.
|
||
# """
|
||
# result = pd.DataFrame(index=series.index)
|
||
# x = np.arange(window)
|
||
#
|
||
# for future_step in range(1, n_future + 1):
|
||
# result[f'poly_pred_t+{future_step}'] = np.nan
|
||
#
|
||
# for i in range(window - 1, len(series)):
|
||
# y = series.iloc[i - window + 1 : i + 1].values
|
||
#
|
||
# if np.any(pd.isna(y)):
|
||
# continue
|
||
#
|
||
# coeffs = np.polyfit(x, y, degree)
|
||
# poly = np.poly1d(coeffs)
|
||
#
|
||
# for future_step in range(1, n_future + 1):
|
||
# future_x = window - 1 + future_step # Extrapolation point
|
||
# result.loc[series.index[i], f'poly_pred_t+{future_step}'] = poly(future_x)
|
||
#
|
||
# return result
|
||
|
||
def polynomial_forecast(self, series: pd.Series, window: int = 20, degree: int = 2, n_future: int = 3):
|
||
"""
|
||
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 + n_future)
|
||
y_future = poly(x_future)
|
||
|
||
# Affichage de la fonction
|
||
# print("Fonction polynomiale trouvée :")
|
||
# print(poly_func)
|
||
|
||
count = 0
|
||
for future_step in [12, 24, 36]: #range(1, n_future + 1)
|
||
future_x = window - 1 + future_step
|
||
prediction = poly(future_x)
|
||
# result.loc[series.index[i], f'poly_pred_t+{future_step}'] = prediction
|
||
|
||
# ➕ Afficher les prédictions
|
||
# print(f" → t+{future_step}: x={future_x}, y={prediction:.2f}")
|
||
if prediction > 0:
|
||
count += 1
|
||
|
||
return poly, x_future, y_future, count
|