1539 lines
70 KiB
Python
1539 lines
70 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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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 = 24
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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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"sma20_smooth": {
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"color": "green"
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},
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"sma20_smooth_2": {
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"color": "red",
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},
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"sma20_smooth_3": {
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"color": "blue",
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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_pct": {
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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_diff": {
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"rsi_diff_1h": {
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"color": "red"
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},
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"rsi_diff_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_diff": {
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# "color": "#74effc"
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# }
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# },
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"smooth": {
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'sma5_diff_sum_1h': {
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"color": "green"
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},
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'sma5_diff2_sum_1h': {
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"color": "blue"
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},
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'mid_smooth_deriv1_1d': {
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"color": "blue"
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},
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'mid_smooth_deriv1_1h': {
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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_deriv2_1h': {
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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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}
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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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def min_max_scaling(self, series: pd.Series) -> pd.Series:
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"""Normaliser les données en les ramenant entre 0 et 100."""
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return 100 * (series - series.min()) / (series.max() - series.min())
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def z_score_scaling(self, series: pd.Series) -> pd.Series:
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"""Normaliser les données en utilisant Z-Score Scaling."""
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return (series - series.mean()) / series.std()
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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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# count_buys = 0
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# trade = self.getTrade(pair)
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# if trade:
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# filled_buys = trade.select_filled_orders('buy')
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# count_buys = len(filled_buys)
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minutes = 0
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if self.pairs[pair]['last_date'] != 0:
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minutes = round((current_time - self.pairs[pair]['last_date']).total_seconds() / 60,0)
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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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# last_candle_12 = dataframe.iloc[-13].squeeze()
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# if (last_candle['close'] < self.pairs[pair]['last_sell'] * 0.99 or minutes > 60 * 5) & (self.pairs[pair]['stop']):
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# print(f"restart {pair} last_sell={self.pairs[pair]['last_sell'] * 0.99} minutes={minutes}")
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# self.pairs[pair]['stop'] = False
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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'] #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
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self.trades = list()
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dispo = round(self.wallets.get_available_stake_amount())
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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.printLineLog()
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stake_amount = self.adjust_stake_amount(pair, last_candle)
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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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allow_to_sell = (last_candle['percent'] < 0)
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minutes = 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.pairs[pair]['max_profit'] = 0
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self.trades = list()
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dispo= round(self.wallets.get_available_stake_amount())
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# print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action="Sell " + str(round(minutes,0)),
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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_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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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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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['haclose'], self.pairs[pair]['last_max'])
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count_of_buys = trade.nr_of_successful_entries
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self.pairs[pair]['count_of_buys'] = count_of_buys
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self.pairs[pair]['current_profit'] = current_profit
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self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
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pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
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# if (last_candle['close'] > last_candle['max3_1d']) and \
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# (current_profit >= expected_profit or (last_candle['rsi'] >= 80 and current_profit > 0)) & (last_candle['percent'] < 0.0) \
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# and ((last_candle['rsi'] >= 75) or before_last_candle['rsi'] >= 75)\
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# and (count_of_buys <= 2):
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# self.trades = list()
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# # self.pairs[pair]['stop'] = True
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# return 'rsi_' + str(count_of_buys)
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if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
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# and (last_candle['tendency_1h'] in ('H++', 'H+')):
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# and (last_candle['tendency_1d'] in ('H++', 'H+')) :
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return None
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# if (count_of_buys >= 4 and last_candle['sma5_diff_1h'] > 0):
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# return None
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# if (last_candle['rsi_1d'] > 50) & (last_candle['percent12'] < 0.0):
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baisse = self.pairs[pair]['max_profit'] - current_profit
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mx = self.pairs[pair]['max_profit'] / 5
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if (baisse > mx) & (current_profit > expected_profit): #last_candle['min_max200'] / 3):
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self.trades = list()
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return 'mx_' + str(count_of_buys)
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if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
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self.trades = list()
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return 'pft_' + str(count_of_buys)
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# if (last_candle['percent3'] < -0.002) & (last_candle['percent12'] < 0) & (
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# current_profit > last_candle['min_max200'] / 3):
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# self.trades = list()
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# return 'mnmx_' + str(count_of_buys)
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# if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
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# self.trades = list()
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# return 'profit_' + str(count_of_buys)
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self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch'])
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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informative_pairs = [(pair, '1d') for pair in pairs]
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informative_pairs += [(pair, '1h') for pair in pairs]
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return informative_pairs
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from typing import List
|
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|
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def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float:
|
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if pct <= thresholds[0]:
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return factors[0]
|
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if pct >= thresholds[-1]:
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return factors[-1]
|
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|
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for i in range(1, len(thresholds)):
|
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if pct <= thresholds[i]:
|
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# interpolation linéaire entre thresholds[i-1] et thresholds[i]
|
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return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / (
|
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thresholds[i] - thresholds[i - 1])
|
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|
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# Juste au cas où (devrait jamais arriver)
|
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return factors[-1]
|
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|
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def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
|
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start_factor: float = 1.0, end_factor: float = 2.0) -> float:
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if pct <= start_pct:
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return start_factor
|
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if pct >= end_pct:
|
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return end_factor
|
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# interpolation linéaire
|
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return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
|
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|
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def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
|
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last_candle=None):
|
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# Afficher les colonnes une seule fois
|
||
if self.config.get('runmode') == 'hyperopt':
|
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return
|
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if self.columns_logged % 30 == 0:
|
||
# print(
|
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# f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
|
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# )
|
||
self.printLog(
|
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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|Tdc|Tdh|Tdd|"
|
||
)
|
||
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_diff_1d']))
|
||
trade_type = trade_type \
|
||
+ " " + string \
|
||
+ " " + str(int(last_candle['rsi_1h'])) \
|
||
+ " " + str(int(last_candle['rsi_diff_1h']))
|
||
|
||
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"| {round(self.pairs[pair]['last_max'], 0) or '-':>7} |{buys or '-':>4}|{stake or '-':>7}"
|
||
f"|{round(last_candle['sma5_diff_sum_1h'], 2) or '-':>6}|{round(last_candle['sma5_diff_sum_1d'], 2) or '-':>6}"
|
||
f"|{last_candle['tendency'] or '-':>3}|{last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
|
||
f"|{round(last_candle['mid_smooth_deriv1']) or '-':>3}|{round(last_candle['mid_smooth_deriv1_1h']) or '-':>5}|{round(last_candle['mid_smooth_deriv1_1d']) or '-' :>5}"
|
||
# f"|{round(last_candle['mid_smooth_deriv2']) or '-' :>3 }|{round(last_candle['mid_smooth_deriv2_1h']) or '-':>5}|{round(last_candle['mid_smooth_deriv2_1d']) or '-':>5}"
|
||
)
|
||
|
||
def printLineLog(self):
|
||
self.printLog(
|
||
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+"
|
||
)
|
||
|
||
def printLog(self, str):
|
||
if not self.dp.runmode.value in ('backtest', 'hyperopt'):
|
||
logger.info(str)
|
||
else:
|
||
print(str)
|
||
|
||
def add_tendency_column(self, dataframe: pd.DataFrame) -> pd.DataFrame:
|
||
def tag_by_derivatives(row):
|
||
d1 = row['mid_smooth_deriv1']
|
||
d2 = row['mid_smooth_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['tendency'] = 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['pct_change'] = dataframe['close'].pct_change(5)
|
||
dataframe = self.calculateTendency(dataframe)
|
||
|
||
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['max144'] = talib.MAX(dataframe['close'], timeperiod=144)
|
||
dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
|
||
|
||
dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200']
|
||
dataframe['max200_diff'] = (dataframe['max200'] - dataframe['close']) / dataframe['close']
|
||
dataframe['max50_diff'] = (dataframe['max50'] - dataframe['close']) / dataframe['close']
|
||
|
||
dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
|
||
dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
|
||
dataframe['sma10_diff'] = 100 * dataframe['sma10'].diff() / dataframe['sma10']
|
||
dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
|
||
dataframe['sma20_pct'] = 100 * dataframe['sma20'].diff() / dataframe['sma20']
|
||
dataframe['sma20_smooth'] = dataframe['sma20'].ewm(span=5).mean()
|
||
dataframe['sma20_smooth_2'] = dataframe['sma20'].rolling(window=5, center=True).median()
|
||
dataframe['sma20_smooth_3'] = self.smooth_series(dataframe['sma20'], alpha_low=0.05, alpha_high=0.3, threshold=0.2)
|
||
|
||
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["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
|
||
dataframe["percent48"] = (dataframe["close"] - dataframe["open"].shift(48)) / dataframe["open"].shift(48)
|
||
dataframe["percent_max_144"] = (dataframe["close"] - dataframe["max144"]) / dataframe["close"]
|
||
|
||
# print(metadata['pair'])
|
||
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
|
||
dataframe['rsi_diff'] = dataframe['rsi'].diff()
|
||
dataframe['rsi_diff_2'] = dataframe['rsi_diff'].diff()
|
||
|
||
dataframe['rsi60'] = talib.RSI(dataframe['close'], timeperiod=60)
|
||
dataframe['rsi60_diff'] = dataframe['rsi60'].rolling(60).mean().diff()
|
||
dataframe['rsi60_diff2'] = dataframe['rsi60_diff'].rolling(60).mean().diff()
|
||
|
||
# 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['average_line'] = dataframe['close'].mean()
|
||
dataframe['average_line_50'] = talib.MIDPOINT(dataframe['close'], timeperiod=50)
|
||
|
||
dataframe['average_line_288'] = talib.MIDPOINT(dataframe['close'], timeperiod=288)
|
||
dataframe['average_line_288_098'] = dataframe['average_line_288'] * 0.98
|
||
dataframe['average_line_288_099'] = dataframe['average_line_288'] * 0.99
|
||
# # Sort the close prices to find the 4 lowest values
|
||
# sorted_close_prices = dataframe['close'].rolling(576).sort_values()
|
||
# lowest_4 = sorted_close_prices.head(20)
|
||
#
|
||
# dataframe['lowest_4_average'] = lowest_4.mean()
|
||
# # Propagate this mean value across the entire dataframe
|
||
# # dataframe['lowest_4_average'] = dataframe['lowest_4_average'].iloc[0]
|
||
#
|
||
# # # Sort the close prices to find the 4 highest values
|
||
# sorted_close_prices = dataframe['close'].rolling(288).sort_values(ascending=False)
|
||
# highest_4 = sorted_close_prices.head(20)
|
||
#
|
||
# # # Calculate the mean of the 4 highest values
|
||
# dataframe['highest_4_average'] = highest_4.mean()
|
||
|
||
# Compter les baisses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
# dataframe = self.apply_regression_derivatives(dataframe, column='mid', window=24, degree=3)
|
||
|
||
# Normaliser les données de 'close'
|
||
# normalized_close = self.min_max_scaling(dataframe['close'])
|
||
################### 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.calculateTendency(informative, 3)
|
||
# informative = self.apply_regression_derivatives(informative, column='mid', window=5, degree=3)
|
||
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['rsi_diff'] = informative['rsi'].diff()
|
||
informative['rsi_sum'] = (informative['rsi'].rolling(7).sum() - 350) / 7
|
||
informative['rsi_sum_diff'] = informative['rsi_sum'].diff()
|
||
informative['rsi_diff_2'] = informative['rsi_diff'].diff()
|
||
informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
|
||
informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
|
||
|
||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||
informative['sma5_diff'] = 100 * informative['sma5'].diff() / informative['sma5']
|
||
informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
|
||
informative['sma5_diff_sum'] = (informative['sma5_pct'].rolling(5).sum()) / 5
|
||
informative['sma5_diff2_sum'] = informative['sma5_diff_sum'].diff()
|
||
|
||
self.calculateDownAndUp(informative, limit=0.0012)
|
||
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.test_signal_success(informative, percent=0.01, window_size=24)
|
||
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# condition = (informative['sma5'].shift(2) > informative['sma5'].shift(1)) \
|
||
# & (informative['sma5'].shift(1) < informative['sma5']) \
|
||
# & (informative['down_pct'].shift(3) < -0.015)
|
||
#
|
||
# self.test_signal_success(informative, condition, percent=0.01, window_size=3)
|
||
# self.test_signal_success(informative, condition, percent=0.01, window_size=5)
|
||
# self.test_signal_success(informative, condition, percent=0.01, window_size=10)
|
||
|
||
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.calculateTendency(informative, 3)
|
||
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=3)
|
||
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)
|
||
informative['rsi_diff'] = informative['rsi'].diff()
|
||
informative['rsi_sum'] = (informative['rsi'].rolling(7).sum() - 350) / 7
|
||
informative['rsi_diff_2'] = informative['rsi_diff'].diff()
|
||
|
||
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
||
informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
|
||
informative['sma5_diff_sum'] = (informative['sma5_pct'].rolling(5).sum()) / 5
|
||
informative['sma5_diff2_sum'] = informative['sma5_diff_sum'].diff()
|
||
|
||
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
|
||
print(trade)
|
||
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
|
||
print(buy)
|
||
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
|
||
print(f"amount= {amount}")
|
||
|
||
# # trades = Trade.get_trades([Trade.is_open is False]).all()
|
||
# trades = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
|
||
# if trades:
|
||
# trade = trades[-1]
|
||
# print('closed trade pair is : ')
|
||
# print(trade)
|
||
# dataframe['expected_profit'] = (1 + self.expectedProfit(pair, dataframe.iloc[-1])) * dataframe[
|
||
# 'last_price']
|
||
# dataframe['lbp'] = dataframe['last_price']
|
||
# dataframe['lbp_3'] = dataframe['lbp'] * 0.97 # 3
|
||
# dataframe['lbp_6'] = dataframe['lbp'] * 0.94 # 6
|
||
# dataframe['lbp_9'] = dataframe['lbp'] * 0.90 # 10
|
||
# dataframe['lbp_12'] = dataframe['lbp'] * 0.85 # 15
|
||
# dataframe['lbp_20'] = dataframe['lbp'] * 0.8 # 20
|
||
# dataframe['fbp'] = trade.open_rate
|
||
# # else:
|
||
# # last_trade = self.get_trades(pair=pair).order_by('-close_date').first()
|
||
# # filled_buys = last_trade.select_filled_orders('buy')
|
||
# # print(last_trade)
|
||
# # for buy in filled_buys:
|
||
# # print(filled_buys)
|
||
|
||
# dataframe['buy_level'] = dataframe['lowest_4_average'] * (1 - self.levels[count_buys] / 100)
|
||
# ----------------------------------------------------------
|
||
# Calcul de la variation entre deux bougies successives
|
||
# dataframe['price_change'] = dataframe['close'].diff()
|
||
|
||
# # Marquer les bougies en baisse
|
||
# dataframe['is_down'] = dataframe['price_change'] < 0
|
||
#
|
||
# # Identifier les blocs consécutifs de baisses
|
||
# # dataframe['drop_id'] = (dataframe['is_down'] != dataframe['is_down'].shift(1)).cumsum()
|
||
# dataframe['drop_id'] = np.where(dataframe['is_down'],
|
||
# (dataframe['is_down'] != dataframe['is_down'].shift(12)).cumsum(), np.nan)
|
||
#
|
||
# # Identifier uniquement les blocs de baisse
|
||
# dataframe['drop_id'] = dataframe['drop_id'].where(dataframe['is_down'])
|
||
# # Grouper par les chutes détectées
|
||
# drop_info = dataframe.groupby('drop_id').agg(
|
||
# start=('close', 'first'), # Prix au début de la chute
|
||
# end=('close', 'last'), # Prix à la fin de la chute
|
||
# start_index=('close', 'idxmin'), # Début de la chute (index)
|
||
# end_index=('close', 'idxmax'), # Fin de la chute (index)
|
||
# )
|
||
#
|
||
# # Calcul de l'ampleur de la chute en %
|
||
# drop_info['drop_amplitude_pct'] = ((drop_info['end'] - drop_info['start']) / drop_info['start']) * 100
|
||
# # Filtrer les chutes avec une amplitude supérieure à 3%
|
||
# drop_info = drop_info[drop_info['drop_amplitude_pct'] < -3]
|
||
|
||
# **************
|
||
|
||
# Identifier le prix de début et de fin de chaque chute
|
||
# drop_stats = dataframe.groupby('drop_id').agg(
|
||
# start_price=('close', 'first'), # Prix au début de la chute
|
||
# end_price=('close', 'last'), # Prix à la fin de la chute
|
||
# )
|
||
|
||
# Calculer l'amplitude en %
|
||
# drop_stats['amplitude_pct'] = ((drop_stats['end_price'] - drop_stats['start_price']) / drop_stats[
|
||
# 'start_price']) * 100
|
||
# # drop_stats = drop_stats[drop_stats['amplitude_pct'] < -1]
|
||
# # Associer les amplitudes calculées à chaque drop_id dans dataframe
|
||
# dataframe = dataframe.merge(drop_stats[['amplitude_pct']], on='drop_id', how='left')
|
||
# # Remplir les lignes sans drop_id par 0
|
||
# dataframe['amplitude_pct'] = dataframe['amplitude_pct'].fillna(0)
|
||
# dataframe['amplitude_pct_60'] = dataframe['amplitude_pct'].rolling(60).sum()
|
||
# ----------------------------------------------------------
|
||
|
||
# self.getBinanceOrderBook(pair, dataframe)
|
||
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.test_signal_success(dataframe, 0.005)
|
||
|
||
dataframe['futur_price_1h'] = dataframe['close'].shift(-12)
|
||
dataframe['futur_price_2h'] = dataframe['close'].shift(-24)
|
||
dataframe['futur_price_3h'] = dataframe['close'].shift(-36)
|
||
|
||
return dataframe
|
||
|
||
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 calculateTendency(self, dataframe, window=12):
|
||
dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
|
||
# 2. Calcul du lissage sur 200 bougies par moyenne mobile médiane
|
||
dataframe['mid_smooth'] = dataframe['close'].rolling(window=window, center=True, min_periods=1).median().rolling(
|
||
3).mean()
|
||
# 2. Dérivée première = différence entre deux bougies successives
|
||
dataframe['mid_smooth_deriv1'] = round(100 * dataframe['mid_smooth'].diff() / dataframe['mid_smooth'], 4)
|
||
# 3. Dérivée seconde = différence de la dérivée première
|
||
dataframe['mid_smooth_deriv2'] = round(100 * dataframe['mid_smooth_deriv1'].diff().rolling(3).mean(), 4)
|
||
dataframe = self.add_tendency_column(dataframe)
|
||
return dataframe
|
||
|
||
def getOpenTrades(self):
|
||
# if len(self.trades) == 0:
|
||
print('search open trades')
|
||
self.trades = Trade.get_open_trades()
|
||
return self.trades
|
||
|
||
# def getTrade(self, pair):
|
||
# trades = self.getOpenTrades()
|
||
# trade_for_pair = None
|
||
# for trade in trades:
|
||
# if trade.pair == pair:
|
||
# trade_for_pair = trade
|
||
# break
|
||
# return trade_for_pair
|
||
|
||
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()
|
||
|
||
print("---------------" + pair + "----------------")
|
||
print('adjust stake amount ' + str(self.adjust_stake_amount(pair, dataframe.iloc[-1])))
|
||
# print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
|
||
print('calcul expected_profit ' + str(expected_profit))
|
||
|
||
buy_level = dataframe['average_line_50'] #dataframe['buy_level'] # self.get_buy_level(pair, dataframe)
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['max200_diff'] >= 0.01)
|
||
& (dataframe['percent12'] < -0.002)
|
||
# & (dataframe['pct_change'] < 0)
|
||
& (dataframe['open'] < dataframe['average_line_288_099'])
|
||
& (dataframe['open'] < dataframe['average_line_50'])
|
||
# & (dataframe['percent'] >= -0.0005)
|
||
& (dataframe['min12'].shift(2) == dataframe['min12'])
|
||
& (dataframe['up_count'] > 0)
|
||
& (dataframe["bb_width"] > 0.01)
|
||
), ['enter_long', 'enter_tag']] = (1, 'mx200')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(
|
||
(dataframe['percent12'] < -0.015) |
|
||
(dataframe['percent24'] < -0.022) |
|
||
(dataframe['percent48'] < -0.030)
|
||
)
|
||
& (dataframe['close'] <= dataframe['min50'] * 1.002)
|
||
& (dataframe['open'] < dataframe['average_line_50'])
|
||
& (
|
||
(dataframe['close'] < dataframe['min12'] * 1.002)
|
||
| (dataframe['percent12'] < -0.022)
|
||
| (dataframe['percent24'] < -0.022)
|
||
)
|
||
& (
|
||
(dataframe['min50'].shift(2) == dataframe['min50'])
|
||
| (dataframe['percent12'] < -0.022)
|
||
| (dataframe['percent24'] < -0.022)
|
||
)
|
||
& (dataframe['up_count'] > 0)
|
||
& (dataframe["bb_width"] > 0.01)
|
||
), ['enter_long', 'enter_tag']] = (1, 'pct12')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['close'] <= dataframe['min200'] * 1.002)
|
||
& (dataframe["bb_width"] > 0.01)
|
||
& (dataframe['min_max200'] > 0.015)
|
||
# & (dataframe['pct_change'] < 0)
|
||
& (dataframe['haopen'] < buy_level)
|
||
& (dataframe['open'] < dataframe['average_line_288'])
|
||
& (dataframe['up_count'] > 0)
|
||
), ['enter_long', 'enter_tag']] = (1, 'mnmx200')
|
||
# dataframe.loc[
|
||
# (
|
||
# (dataframe['close'].shift(2) <= dataframe['min200'])
|
||
# & (dataframe['pct_change'] < 0)
|
||
# & (dataframe['min200'].shift(2) == dataframe['min200'])
|
||
# & (dataframe['close'] < dataframe['lowest_4_average'])
|
||
# & (dataframe['up_count'] > 0)
|
||
# ), ['enter_long', 'enter_tag']] = (1, 'min200')
|
||
|
||
dataframe.loc[
|
||
(
|
||
# (dataframe['rsi_1h'] < 70)
|
||
# & (dataframe['rsi_diff_1h'] > -5)
|
||
# (dataframe["bb_width"] > 0.01)
|
||
(dataframe['down_count'].shift(1) < - 1)
|
||
& (dataframe['down_count'] == 0)
|
||
& (dataframe['mid_smooth_deriv1'] >= -0.01)
|
||
# & (dataframe['tendency'] != "B--")
|
||
# & (dataframe['tendency'] != "B-")
|
||
), ['enter_long', 'enter_tag']] = (1, 'down')
|
||
|
||
dataframe.loc[
|
||
(
|
||
(dataframe['low'] < dataframe['min200'])
|
||
& (dataframe['min50'] == dataframe['min50'].shift(3))
|
||
#
|
||
& (dataframe['tendency'] != "B-")
|
||
), ['enter_long', 'enter_tag']] = (1, 'low')
|
||
|
||
|
||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||
|
||
if self.dp.runmode.value in ('backtest'):
|
||
dataframe.to_feather(f"user_data/data/binance/{metadata['pair'].replace('/', '_')}_df.feather")
|
||
|
||
return dataframe
|
||
|
||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
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()
|
||
last_candle_3 = dataframe.iloc[-4].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 = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||
|
||
if (len(dataframe) < 1):
|
||
print("skip dataframe")
|
||
|
||
return None
|
||
pair = trade.pair
|
||
if pair not in ('BTC/USDT', 'BTC/USDC', 'XRP/USDT', 'XRP/USDC', 'ETH/USDT', 'ETH/USDC'):
|
||
print(f"skip pair {pair}")
|
||
return None
|
||
count_of_buys = trade.nr_of_successful_entries
|
||
|
||
# if 'buy' in last_candle:
|
||
# condition = (last_candle['buy'] == 1)
|
||
# else:
|
||
# condition = False
|
||
# self.protection_nb_buy_lost.value
|
||
|
||
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)
|
||
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_max={pct_max:.3f} lim={lim:.3f} rsi_diff_1f={last_candle['rsi_diff_1h']}")
|
||
|
||
# if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
|
||
limit_buy = 20
|
||
if (count_of_buys < limit_buy) \
|
||
and ((last_candle['enter_long'] == 1)
|
||
or (last_candle['percent48'] < - 0.03)
|
||
or ((last_candle['min50'] == last_candle_3['min50']) and (last_candle['low'] <= last_candle['min50']))
|
||
) \
|
||
and (last_candle['rsi_diff_1h'] >= -5) \
|
||
and (last_candle['tendency'] in ('P', 'H++', 'DH', 'H+')) \
|
||
and (last_candle['sma5_diff_sum_1d'] > -1 or count_of_buys <= 9) \
|
||
and (pct_max < lim):
|
||
try:
|
||
|
||
# and (last_candle['mid_smooth_deriv1_1d'] > -1000 or last_candle['mid_smooth_deriv1_1h'] > 200) \
|
||
# and (last_candle['mid_smooth_deriv1_1d'] > -1500) \
|
||
# and not (last_candle['mid_smooth_deriv1_1d'] < - 500 and last_candle['mid_smooth_deriv1_1h'] < 0) \
|
||
|
||
max_amount = self.config.get('stake_amount', 100) * 2.5
|
||
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
|
||
return None
|
||
|
||
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']
|
||
# if self.pairs[pair]['last_max'] == 0:
|
||
# self.pairs[pair]['last_max'] = last_candle['max12_1d']
|
||
# print(f"last_max set to {last_max}")
|
||
#
|
||
# if self.pairs[pair]['last_max'] > 0:
|
||
# last_max = self.pairs[pair]['last_max']
|
||
# print(f"last_max is {last_max}")
|
||
|
||
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 adjust_exit_price(self, dataframe: DataFrame):
|
||
# # Calculer le max des 14 derniers jours
|
||
# min_14_days = dataframe['lowest_1d']
|
||
# max_14_days = dataframe['highest_1d']
|
||
# entry_price = dataframe['fbp']
|
||
# current_price = dataframe['close']
|
||
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
||
# exit_price = (1 + percent) * entry_price
|
||
#
|
||
# print(f"Exit price ajusté price={current_price:.4f} max_14={max_14_days:.4f} exit_price={exit_price:.4f}")
|
||
#
|
||
# return exit_price
|
||
|
||
# def adjust_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||
# current_rate: float, current_profit: float, **kwargs) -> float:
|
||
# dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||
# # print(dataframe)
|
||
# last_candle = dataframe.iloc[-1].squeeze()
|
||
#
|
||
# # Utiliser l'ATR pour ajuster le stoploss
|
||
# atr_stoploss = current_rate - (last_candle['atr'] * 1.5) # Stoploss à 1.5x l'ATR
|
||
#
|
||
# # Retourner le stoploss dynamique en pourcentage du prix actuel
|
||
# return (atr_stoploss / current_rate) - 1
|
||
|
||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||
|
||
# first_price = last_candle['first_price']
|
||
# first_max = 0.01
|
||
# if first_price < last_candle['max200']:
|
||
# first_max = (last_candle['max200'] - first_price) / first_price
|
||
|
||
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}")
|
||
|
||
# self.analyze_conditions(pair, dataframe)
|
||
return expected_profit
|
||
|
||
# def adjust_exit_price(self, dataframe: DataFrame):
|
||
# # Calculer le max des 14 derniers jours
|
||
# min_14_days = dataframe['lowest_1d']
|
||
# max_14_days = dataframe['highest_1d']
|
||
# entry_price = dataframe['fbp']
|
||
# current_price = dataframe['close']
|
||
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
||
# exit_price = (1 + percent) * entry_price
|
||
#
|
||
# print(f"Exit price ajusté price={current_price} max_14={max_14_days} exit_price={exit_price}")
|
||
#
|
||
# return exit_price
|
||
|
||
# def adjust_entry_price(self, dataframe: DataFrame):
|
||
# # Calculer le max des 14 derniers jours
|
||
# min_14_days = dataframe['lowest_1d']
|
||
# max_14_days = dataframe['highest_1d']
|
||
# current_price = dataframe['close']
|
||
# percent = 0.5 * (max_14_days - min_14_days) / min_14_days
|
||
# entry_price = (1 + percent) * entry_price
|
||
#
|
||
# print(f"Entry price ajusté price={current_price} max_14={max_14_days} exit_price={entry_price}")
|
||
#
|
||
# return entry_price
|
||
|
||
# def adjust_stake_amount(self, dataframe: DataFrame):
|
||
# # Calculer le minimum des 14 derniers jours
|
||
# middle = dataframe['middle_1d']
|
||
#
|
||
# # Récupérer la dernière cotation actuelle (peut être le dernier point de la série)
|
||
# current_price = dataframe['close']
|
||
#
|
||
# # Calculer l'écart entre la cotation actuelle et le minimum des 14 derniers jours
|
||
# difference = middle - current_price
|
||
# # Ajuster la stake_amount en fonction de l'écart
|
||
# # Par exemple, augmenter la stake_amount proportionnellement à l'écart
|
||
# base_stake_amount = self.config.get('stake_amount', 100) # Montant de base configuré
|
||
#
|
||
# multiplier = 1 - (difference / current_price) # Exemple de logique d'ajustement
|
||
#
|
||
# adjusted_stake_amount = max(base_stake_amount / 2.5, base_stake_amount * multiplier)
|
||
#
|
||
# # difference = 346.07000000000016
|
||
# # price = 2641.75
|
||
# # min_14 = 2295.68
|
||
# # amount = 56.5500141951358
|
||
#
|
||
# print(f"Stack amount ajusté difference={difference} price={current_price} middle={middle} multiplier={multiplier} amount={adjusted_stake_amount}")
|
||
#
|
||
# return adjusted_stake_amount
|
||
|
||
def getBinanceOrderBook(self, pair, dataframe: DataFrame):
|
||
"""Fetch the order book (depth) from Binance."""
|
||
# print(dataframe)
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
symbol = pair.replace('/', '')
|
||
|
||
try:
|
||
url = f"https://api.binance.com/api/v3/depth?symbol={symbol}&limit=5000"
|
||
response = requests.get(url)
|
||
data = response.json()
|
||
|
||
# Extract bids and asks from the order book
|
||
asks, bids = self.calculateSMA(20, data['asks'], data['bids']) # Ventes List of [price, quantity]
|
||
# bids = data['bids']
|
||
# asks = data['asks'] # Achats List of [price, quantity]
|
||
|
||
# Process the depth data as you need it
|
||
# bid_volume = sum([float(bid[1]) for bid in bids]) # Sum of all bid volumes
|
||
# $ * nb / $ => nb
|
||
bid_volume = sum([float(bid[0]) * float(bid[1]) / float(last_candle['close']) for bid in bids[:10]])
|
||
# ask_volume = sum([float(ask[1]) for ask in asks]) # Sum of all ask volumes
|
||
ask_volume = sum([float(ask[0]) * float(ask[1]) / float(last_candle['close']) for ask in asks[:10]])
|
||
|
||
# Example: add the difference in volumes as an indicator
|
||
if bid_volume and ask_volume:
|
||
self.updateLastValue(dataframe, 'depth_bid_ask_diff', round(bid_volume - ask_volume, 2))
|
||
else:
|
||
self.updateLastValue(dataframe, 'depth_bid_ask_diff', 0)
|
||
|
||
# probabilité baisse hausse sur les n premiers élements
|
||
for start in [0, 50, 100, 150]:
|
||
self.updateLastValue(dataframe, 'prob_hausse_' + str(start + 50),
|
||
self.calculateProbaNb(asks, bids, start, start + 50))
|
||
# dataframe['prob_hausse_' + str(nb)] = self.calculateProbaNb(asks, bids, nb)
|
||
# Analyse des prix moyens pondérés par les volumes (VWAP) :
|
||
#
|
||
# Le VWAP (Volume Weighted Average Price) peut être utilisé pour comprendre la pression directionnelle.
|
||
# Si le VWAP basé sur les ordres d'achat est plus élevé que celui des ordres de vente,
|
||
# cela peut indiquer une probabilité de hausse.
|
||
nb = 50
|
||
|
||
bid_vwap = sum([float(bid[0]) * float(bid[1]) for bid in bids[:nb]]) / sum(
|
||
[float(bid[1]) for bid in bids[:nb]])
|
||
ask_vwap = sum([float(ask[0]) * float(ask[1]) for ask in asks[:nb]]) / sum(
|
||
[float(ask[1]) for ask in asks[:nb]])
|
||
|
||
if bid_vwap > ask_vwap:
|
||
self.updateLastValue(dataframe, 'vwap_hausse',
|
||
round(100 * (bid_vwap - ask_vwap) / (bid_vwap + ask_vwap), 2))
|
||
else:
|
||
self.updateLastValue(dataframe, 'vwap_hausse',
|
||
- round(100 * (ask_vwap - bid_vwap) / (bid_vwap + ask_vwap), 2))
|
||
|
||
current_price = last_candle['close'] # le prix actuel du marché
|
||
|
||
# Calcul du seuil de variation de 1%
|
||
lower_threshold = current_price * 0.99
|
||
upper_threshold = current_price * 1.01
|
||
|
||
# Volumes d'achat (bids) sous 1% du prix actuel
|
||
bid_volume_1percent = sum(
|
||
[float(bid[1]) for bid in bids if current_price >= float(bid[0]) >= lower_threshold])
|
||
|
||
# Volumes de vente (asks) au-dessus de 1% du prix actuel
|
||
ask_volume_1percent = sum(
|
||
[float(ask[1]) for ask in asks if current_price <= float(ask[0]) <= upper_threshold])
|
||
|
||
# Estimation de la probabilité basée sur le déséquilibre des volumes
|
||
total_volume = bid_volume_1percent + ask_volume_1percent
|
||
if total_volume > 0:
|
||
prob_hausse = bid_volume_1percent / total_volume
|
||
prob_baisse = ask_volume_1percent / total_volume
|
||
else:
|
||
prob_hausse = prob_baisse = 0
|
||
|
||
self.updateLastValue(dataframe, 'proba_hausse_1%', round(prob_hausse * 100, 2))
|
||
self.updateLastValue(dataframe, 'proba_baisse_1%', round(prob_baisse * 100, 2))
|
||
print(f"Probabilité de hausse de 1%: {prob_hausse * 100:.2f}%")
|
||
print(f"Probabilité de baisse de 1%: {prob_baisse * 100:.2f}%")
|
||
|
||
self.calculateResistance(pair, asks, dataframe)
|
||
self.calculateSupport(pair, bids, dataframe)
|
||
|
||
dataframe['r_s'] = 100 * (dataframe['r_min'] - dataframe['s_min']) / dataframe['s_min']
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error fetching order book: {e}")
|
||
return None, None
|
||
|
||
def calculateProbaNb(self, asks, bids, start, nb):
|
||
top_bids = sum([float(bid[1]) for bid in bids[start:nb]])
|
||
top_asks = sum([float(ask[1]) for ask in asks[start:nb]])
|
||
if top_bids > top_asks:
|
||
proba = round(100 * (top_bids - top_asks) / (top_bids + top_asks), 2)
|
||
else:
|
||
proba = - round(100 * (top_asks - top_bids) / (top_bids + top_asks), 2)
|
||
return proba
|
||
|
||
def calculateResistance(self, pair, asks, dataframe: DataFrame):
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
|
||
# Filtrage +-5%
|
||
current_price = float(last_candle['close'])
|
||
lower_bound = current_price * 0.95
|
||
upper_bound = current_price * 1.05
|
||
ask_prices = [float(ask[0]) for ask in asks]
|
||
ask_volumes = [float(ask[1]) for ask in asks]
|
||
ask_df = pd.DataFrame({'price': ask_prices, 'volume': ask_volumes})
|
||
filtered_ask_df = ask_df[(ask_df['price'] >= lower_bound) & (ask_df['price'] <= upper_bound)]
|
||
# Trier le DataFrame sur la colonne 'volume' en ordre décroissant
|
||
sorted_ask_df = filtered_ask_df.sort_values(by='volume', ascending=False)
|
||
|
||
# Ne garder que les 3 premières lignes (les 3 plus gros volumes)
|
||
top_3_asks = sorted_ask_df.head(3)
|
||
print(top_3_asks)
|
||
|
||
# Convertir les ordres de vente en numpy array pour faciliter le traitement
|
||
asks_array = np.array(filtered_ask_df, dtype=float)
|
||
|
||
# Détecter les résistances : on peut définir qu'une résistance est un niveau de prix où la quantité est élevée
|
||
# Ex: seuil de résistance à partir des 10% des plus grosses quantités
|
||
resistance_threshold = np.percentile(asks_array[:, 1], 90)
|
||
resistances = asks_array[asks_array[:, 1] >= resistance_threshold]
|
||
|
||
# Afficher les résistances trouvées
|
||
# print(f"{pair} Niveaux de résistance détectés:")
|
||
# for resistance in resistances:
|
||
# print(f"{pair} Prix: {resistance[0]}, Quantité: {resistance[1]}")
|
||
|
||
# Exemple : somme des quantités sur des plages de prix
|
||
# Intervalles de 10 USDT
|
||
step = last_candle['close'] / 100
|
||
price_intervals = np.arange(asks_array[:, 0].min(), asks_array[:, 0].max(), step=step)
|
||
|
||
for start_price in price_intervals:
|
||
end_price = start_price + step
|
||
mask = (asks_array[:, 0] >= start_price) & (asks_array[:, 0] < end_price)
|
||
volume_in_range = asks_array[mask, 1].sum()
|
||
amount = volume_in_range * end_price
|
||
print(
|
||
f"Prix entre {start_price:.6f} et {end_price:.6f}: Volume total = {volume_in_range:.2f} amount={amount:.2f}")
|
||
|
||
# Trier les asks par quantité en ordre décroissant
|
||
asks_sorted = asks_array[asks_array[:, 1].argsort()][::-1]
|
||
|
||
# Sélectionner les trois plus gros resistances
|
||
top_3_resistances = asks_sorted[:3]
|
||
|
||
# Afficher les trois plus gros resistances
|
||
print("Les trois plus grosses resistances détectées : ")
|
||
self.updateLastValue(dataframe, 'r3', top_3_resistances[0][0])
|
||
self.updateLastValue(dataframe, 'r2', top_3_resistances[1][0])
|
||
self.updateLastValue(dataframe, 'r1', top_3_resistances[2][0])
|
||
self.updateLastValue(dataframe, 'r_min',
|
||
min(top_3_resistances[0][0], top_3_resistances[1][0], top_3_resistances[2][0]))
|
||
for resistance in top_3_resistances:
|
||
print(f"{pair} Prix: {resistance[0]}, Quantité: {resistance[1]}")
|
||
|
||
def calculateSupport(self, pair, bids, dataframe: DataFrame):
|
||
last_candle = dataframe.iloc[-1].squeeze()
|
||
|
||
# Convert to pandas DataFrame to apply moving average
|
||
current_price = float(last_candle['close'])
|
||
lower_bound = current_price * 0.95
|
||
upper_bound = current_price * 1.05
|
||
bid_prices = [float(bid[0]) for bid in bids]
|
||
bid_volumes = [float(bid[1]) for bid in bids]
|
||
bid_df = pd.DataFrame({'price': bid_prices, 'volume': bid_volumes})
|
||
filtered_bid_df = bid_df[(bid_df['price'] >= lower_bound) & (bid_df['price'] <= upper_bound)]
|
||
# Trier le DataFrame sur la colonne 'volume' en ordre décroissant
|
||
sorted_bid_df = filtered_bid_df.sort_values(by='volume', ascending=False)
|
||
|
||
# Ne garder que les 3 premières lignes (les 3 plus gros volumes)
|
||
top_3_bids = sorted_bid_df.head(3)
|
||
print(top_3_bids)
|
||
|
||
# Convertir les ordres d'achat en numpy array pour faciliter le traitement
|
||
bids_array = np.array(filtered_bid_df, dtype=float)
|
||
|
||
# Détecter les supports : on peut définir qu'un support est un niveau de prix où la quantité est élevée
|
||
# Ex: seuil de support à partir des 10% des plus grosses quantités
|
||
support_threshold = np.percentile(bids_array[:, 1], 90)
|
||
supports = bids_array[bids_array[:, 1] >= support_threshold]
|
||
|
||
# Afficher les supports trouvés
|
||
# print(f"{pair} Niveaux de support détectés:")
|
||
# for support in supports:
|
||
# print(f"{pair} Prix: {support[0]}, Quantité: {support[1]}")
|
||
|
||
step = last_candle['close'] / 100
|
||
# Exemple : somme des quantités sur des plages de prix pour les bids
|
||
price_intervals = np.arange(bids_array[:, 0].min(), bids_array[:, 0].max(), step=step) # Intervalles de 10 USDT
|
||
|
||
for start_price in price_intervals:
|
||
end_price = start_price + step
|
||
mask = (bids_array[:, 0] >= start_price) & (bids_array[:, 0] < end_price)
|
||
volume_in_range = bids_array[mask, 1].sum()
|
||
amount = volume_in_range * end_price
|
||
print(
|
||
f"Prix entre {start_price:.6f} et {end_price:.6f}: Volume total = {volume_in_range:.2f} amount={amount:.2f}")
|
||
|
||
# Trier les bids par quantité en ordre décroissant
|
||
bids_sorted = bids_array[bids_array[:, 1].argsort()][::-1]
|
||
|
||
# Sélectionner les trois plus gros supports
|
||
top_3_supports = bids_sorted[:3]
|
||
|
||
# Afficher les trois plus gros supports
|
||
print("Les trois plus gros supports détectés:")
|
||
|
||
self.updateLastValue(dataframe, 's1', top_3_supports[0][0])
|
||
self.updateLastValue(dataframe, 's2', top_3_supports[1][0])
|
||
self.updateLastValue(dataframe, 's3', top_3_supports[2][0])
|
||
self.updateLastValue(dataframe, 's_min', max(top_3_supports[0][0], top_3_supports[1][0], top_3_supports[2][0]))
|
||
|
||
for support in top_3_supports:
|
||
print(f"{pair} Prix: {support[0]}, Quantité: {support[1]}")
|
||
|
||
def updateLastValue(self, df: DataFrame, col, value):
|
||
if col in df.columns:
|
||
print(f"update last col {col} {value}")
|
||
df.iloc[-1, df.columns.get_loc(col)] = value
|
||
else:
|
||
print(f"update all col {col} {value}")
|
||
df[col] = value
|
||
|
||
def smooth_series(self, series, alpha_low=0.1, alpha_high=0.5, threshold=0.2):
|
||
"""
|
||
Applique un lissage adaptatif sur une série Pandas.
|
||
- alpha_low : lissage fort (forte inertie, pour petites variations)
|
||
- alpha_high : lissage faible (rapide réaction, pour grandes variations)
|
||
- threshold : variation (%) à partir de laquelle on considère le mouvement significatif
|
||
"""
|
||
smoothed = [series.iloc[0]]
|
||
for i in range(1, len(series)):
|
||
prev = smoothed[-1]
|
||
current = series.iloc[i]
|
||
variation = abs(current - prev) / prev * 100 if prev != 0 else 0
|
||
|
||
alpha = alpha_high if variation > threshold else alpha_low
|
||
new_val = prev + alpha * (current - prev)
|
||
smoothed.append(new_val)
|
||
|
||
return pd.Series(smoothed, index=series.index)
|
||
|
||
# def update_last_record(self, dataframe: DataFrame, new_data):
|
||
# # Vérifiez si de nouvelles données ont été reçues
|
||
# if new_data is not None:
|
||
# # Ne mettez à jour que la dernière ligne du dataframe
|
||
# last_index = dataframe.index[-1] # Sélectionne le dernier enregistrement
|
||
# dataframe.loc[last_index] = new_data # Met à jour le dernier enregistrement avec les nouvelles données
|
||
# return dataframe
|
||
|
||
def calculateSMA(self, nb, asks, bids):
|
||
# Prepare data for plotting
|
||
bid_prices = [float(bid[0]) for bid in bids]
|
||
bid_volumes = [float(bid[1]) for bid in bids]
|
||
|
||
ask_prices = [float(ask[0]) for ask in asks]
|
||
ask_volumes = [float(ask[1]) for ask in asks]
|
||
|
||
# Convert to pandas DataFrame to apply moving average
|
||
bid_df = pd.DataFrame({'price': bid_prices, 'volume': bid_volumes})
|
||
ask_df = pd.DataFrame({'price': ask_prices, 'volume': ask_volumes})
|
||
|
||
# Apply a rolling window to calculate a 10-value simple moving average (SMA)
|
||
bid_df['volume_sma'] = bid_df['volume'].rolling(window=nb).mean()
|
||
ask_df['volume_sma'] = ask_df['volume'].rolling(window=nb).mean()
|
||
|
||
# Pour bid_df
|
||
bid_df = bid_df.dropna(subset=['volume_sma'])
|
||
bids_with_sma = list(zip(bid_df['price'], bid_df['volume_sma']))
|
||
|
||
# Pour ask_df
|
||
ask_df = ask_df.dropna(subset=['volume_sma'])
|
||
asks_with_sma = list(zip(ask_df['price'], ask_df['volume_sma']))
|
||
|
||
# print(bids_with_sma)
|
||
# print(asks_with_sma)
|
||
|
||
return asks_with_sma, bids_with_sma
|
||
|
||
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 apply_regression_derivatives(self,
|
||
dataframe: DataFrame,
|
||
column: str = 'close',
|
||
window: int = 50,
|
||
degree: int = 3,
|
||
future_offset: int = 10 # projection à n bougies après
|
||
) -> DataFrame:
|
||
df = dataframe.copy()
|
||
|
||
regression_fit = []
|
||
deriv1 = []
|
||
deriv2 = []
|
||
regression_future_fit = []
|
||
regression_future_deriv1 = []
|
||
regression_future_deriv2 = []
|
||
|
||
for i in range(len(df)):
|
||
if i < window or i + future_offset >= len(df):
|
||
regression_fit.append(np.nan)
|
||
deriv1.append(np.nan)
|
||
deriv2.append(np.nan)
|
||
regression_future_fit.append(np.nan)
|
||
regression_future_deriv1.append(np.nan)
|
||
regression_future_deriv2.append(np.nan)
|
||
continue
|
||
|
||
y = df[column].iloc[i - window:i].values
|
||
x = np.arange(window)
|
||
|
||
coeffs = np.polyfit(x, y, degree)
|
||
poly = np.poly1d(coeffs)
|
||
|
||
x_now = window - 1
|
||
x_future = x_now + future_offset
|
||
|
||
regression_fit.append(poly(x_now))
|
||
deriv1.append(np.polyder(poly, 1)(x_now))
|
||
deriv2.append(np.polyder(poly, 2)(x_now))
|
||
|
||
regression_future_fit.append(poly(x_future))
|
||
regression_future_deriv1.append(np.polyder(poly, 1)(x_future))
|
||
regression_future_deriv2.append(np.polyder(poly, 2)(x_future))
|
||
|
||
df['regression_fit'] = regression_fit
|
||
df['regression_deriv1'] = deriv1
|
||
df['regression_deriv2'] = deriv2
|
||
|
||
df['regression_future_fit'] = regression_future_fit
|
||
df['regression_future_deriv1'] = regression_future_deriv1
|
||
df['regression_future_deriv2'] = regression_future_deriv2
|
||
|
||
return df
|
||
|
||
def test_signal_success(self, df, condition, percent=0.03, window_size=36):
|
||
"""
|
||
df : DataFrame avec colonnes ['close', 'high', ...]
|
||
percent : hausse recherchée (ex: 0.03 pour +3%)
|
||
window_size : nombre de bougies (ex: 36 pour 3h en 5m)
|
||
"""
|
||
# Exemple condition : RSI < 30 et EMA20 > SMA50
|
||
hits = 0
|
||
total = 0
|
||
|
||
for idx in df[condition].index:
|
||
price_now = df.loc[idx, 'close']
|
||
idx_pos = df.index.get_loc(idx)
|
||
|
||
# Fenêtre de h heures
|
||
future_idx = df.index[idx_pos + 1: idx_pos + 1 + window_size]
|
||
if len(future_idx) < window_size:
|
||
continue
|
||
|
||
future_highs = df.loc[future_idx, 'close']
|
||
if (future_highs >= price_now * (1 + percent)).any():
|
||
# print(f"{price_now} ==> {df.loc[future_idx]['close']}")
|
||
hits += 1
|
||
total += 1
|
||
|
||
prob = hits / total if total > 0 else 0
|
||
print(f"✅ {hits}/{total} hausses >= {percent*100:.1f}% dans {window_size} bougies → probabilité : {prob:.2%}")
|
||
return prob
|
||
|
||
|