1130 lines
53 KiB
Python
1130 lines
53 KiB
Python
# Zeus Strategy: First Generation of GodStra Strategy with maximum
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# AVG/MID profit in USDT
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
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# --- Do not remove these libs ---
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from datetime import timedelta, datetime
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from freqtrade.persistence import Trade
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from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
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IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
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import pandas as pd
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import numpy as np
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from pandas import DataFrame
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from typing import Optional, Union, Tuple
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from scipy.special import binom
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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_11(IStrategy):
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levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
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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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"min200": {
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"color": "#86c932"
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},
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"max50": {
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"color": "white"
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},
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"max200": {
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"color": "yellow"
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},
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"bb_lowerband": {
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"color": "#da59a6"},
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"bb_upperband": {
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"color": "#da59a6",
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}
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},
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"subplots": {
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"Rsi": {
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"rsi": {
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"color": "pink"
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}
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},
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"Percent": {
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"max_min": {
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"color": "#74effc"
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}
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}
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}
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}
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# 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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profit_b_no_change = BooleanParameter(default=True, space="sell")
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profit_b_quick_lost = BooleanParameter(default=True, space="sell")
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profit_b_sma5 = BooleanParameter(default=True, space="sell")
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profit_b_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_sma20 = BooleanParameter(default=True, space="sell")
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profit_b_quick_gain = BooleanParameter(default=True, space="sell")
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profit_b_quick_gain_3 = BooleanParameter(default=True, space="sell")
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profit_b_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_very_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_over_rsi = BooleanParameter(default=True, space="sell")
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profit_b_short_loss = BooleanParameter(default=True, space="sell")
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sell_b_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_candels = IntParameter(0, 48, default=12, space='sell')
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sell_b_too_old_day = IntParameter(0, 10, default=300, space='sell')
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sell_b_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell')
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sell_b_profit_percent12 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell')
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sell_b_RSI = IntParameter(70, 98, default=88, space='sell')
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sell_b_RSI2 = IntParameter(70, 98, default=88, space='sell')
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sell_b_RSI3 = IntParameter(70, 98, default=80, space='sell')
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sell_b_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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# sell_b_expected_profit = DecimalParameter(0, 0.01, decimals=3, default=0.01, space='sell')
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profit_h_no_change = BooleanParameter(default=True, space="sell")
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profit_h_quick_lost = BooleanParameter(default=True, space="sell")
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profit_h_sma5 = BooleanParameter(default=True, space="sell")
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profit_h_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_sma20 = BooleanParameter(default=True, space="sell")
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profit_h_quick_gain = BooleanParameter(default=True, space="sell")
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profit_h_quick_gain_3 = BooleanParameter(default=True, space="sell")
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profit_h_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_very_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_over_rsi = BooleanParameter(default=True, space="sell")
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profit_h_short_loss = BooleanParameter(default=True, space="sell")
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sell_h_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_candels = IntParameter(0, 48, default=12, space='sell')
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sell_h_too_old_day = IntParameter(0, 10, default=300, space='sell')
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sell_h_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell')
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sell_h_profit_percent12 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell')
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sell_h_RSI = IntParameter(70, 98, default=88, space='sell')
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sell_h_RSI2 = IntParameter(70, 98, default=88, space='sell')
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sell_h_RSI3 = IntParameter(70, 98, default=80, space='sell')
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sell_h_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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protection_percent_buy_lost = IntParameter(1, 10, default=5, space='protection')
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# protection_nb_buy_lost = IntParameter(1, 2, default=2, space='protection')
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protection_fibo = IntParameter(1, 10, default=2, space='protection')
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# trailing stoploss hyperopt parameters
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# hard stoploss profit
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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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# pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=False, load=True)
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# # profit threshold 1, trigger point, SL_1 is used
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# pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=True, load=True)
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# pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=True, load=True)
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#
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# # profit threshold 2, SL_2 is used
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# pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=True, load=True)
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# pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, 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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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_12 = dataframe.iloc[-13].squeeze()
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# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
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allow_to_buy = True # (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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print(f"BUY {pair} {entry_tag} {current_time} allow_to_buy={allow_to_buy} dispo={dispo}")
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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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if allow_to_sell:
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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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else:
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print('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
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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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# self.analyze_conditions(pair, dataframe)
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# print("---------------" + pair + "----------------")
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expected_profit = self.expectedProfit(pair, last_candle)
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# Calcul du prix cible basé sur l'ATR
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atr_take_profit = trade.open_rate + (last_candle['atr'] * 2) # Prendre profit à 2x l'ATR
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# print(f"{pair} Custom exit atr_take_profit={atr_take_profit:.4f}")
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# if current_rate >= atr_take_profit:
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# return 'sell_atr_take_profit'
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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 'min_max200'
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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'
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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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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Add all ta features
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pair = metadata['pair']
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['haopen'] = heikinashi['open']
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dataframe['haclose'] = heikinashi['close']
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dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
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dataframe['close_02'] = dataframe['haclose'] * 1.02
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dataframe['pct_change'] = dataframe['close'].pct_change(5)
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dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200)
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dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
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dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50)
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dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
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dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
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dataframe['max144'] = talib.MAX(dataframe['close'], timeperiod=144)
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dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
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dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
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dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200']
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dataframe['max200_diff'] = (dataframe['max200'] - dataframe['close']) / dataframe['close']
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dataframe['max50_diff'] = (dataframe['max50'] - dataframe['close']) / dataframe['close']
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dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
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dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
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dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
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dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
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dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
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dataframe["percent5"] = (dataframe["close"] - dataframe["open"].shift(5)) / dataframe["open"].shift(5)
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dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
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dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
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dataframe["percent48"] = (dataframe["close"] - dataframe["open"].shift(48)) / dataframe["open"].shift(48)
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dataframe["percent_max_144"] = (dataframe["close"] - dataframe["max144"]) / dataframe["close"]
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dataframe['sma10_s2'] = dataframe['sma10'].shift(1)
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dataframe['sma20_s2'] = dataframe['sma20'].shift(1)
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dataframe['percent12_s2'] = dataframe['percent12'].shift(1)
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dataframe['sma5_s5'] = dataframe['sma5'].shift(4)
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dataframe['sma10_s5'] = dataframe['sma10'].shift(4)
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dataframe['sma20_s5'] = dataframe['sma20'].shift(4)
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# print(metadata['pair'])
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dataframe['rsi'] = talib.RSI(dataframe['close'], length=14)
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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# Normalization
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dataframe['average_line'] = dataframe['close'].mean()
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dataframe['average_line_50'] = talib.MIDPOINT(dataframe['close'], timeperiod=50)
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dataframe['average_line_288'] = talib.MIDPOINT(dataframe['close'], timeperiod=288)
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dataframe['average_line_288_098'] = dataframe['average_line_288'] * 0.98
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dataframe['average_line_288_099'] = dataframe['average_line_288'] * 0.99
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# Sort the close prices to find the 4 lowest values
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sorted_close_prices = dataframe['close'].tail(576).sort_values()
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lowest_4 = sorted_close_prices.head(20)
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dataframe['lowest_4_average'] = lowest_4.mean()
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# Propagate this mean value across the entire dataframe
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# dataframe['lowest_4_average'] = dataframe['lowest_4_average'].iloc[0]
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# # Sort the close prices to find the 4 highest values
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sorted_close_prices = dataframe['close'].tail(288).sort_values(ascending=False)
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highest_4 = sorted_close_prices.head(20)
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# # Calculate the mean of the 4 highest values
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dataframe['highest_4_average'] = highest_4.mean()
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# # Propagate this mean value across the entire dataframe
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# dataframe['highest_4_average'] = dataframe['highest_4_average'].iloc[0]
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dataframe['volatility'] = talib.STDDEV(dataframe['close'], timeperiod=144) / dataframe['close']
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dataframe['atr'] = talib.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=144) / \
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dataframe['close']
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# dataframe['pct_average'] = (dataframe['highest_4_average'] - dataframe['close']) / dataframe['lowest_4_average']
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# dataframe['highest_4_average_1'] = dataframe['highest_4_average'] * 0.99
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# dataframe['highest_4_average_2'] = dataframe['highest_4_average'] * 0.98
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# dataframe['highest_4_average_3'] = dataframe['highest_4_average'] * 0.97
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# dataframe['highest_4_average_4'] = dataframe['highest_4_average'] * 0.96
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# dataframe['highest_4_average_5'] = dataframe['highest_4_average'] * 0.95
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# Compter les baisses consécutives
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dataframe['down'] = dataframe['hapercent'] <= 0.001
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dataframe['up'] = dataframe['hapercent'] >= -0.001
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dataframe['down_count'] = - dataframe['down'].astype(int) * (
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dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
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dataframe['up_count'] = dataframe['up'].astype(int) * (
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dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
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dataframe['down_tag'] = (dataframe['down_count'] < -7)
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dataframe['up_tag'] = (dataframe['up_count'] > 7)
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# Créer une colonne vide
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dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
|
|
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
|
|
|
|
# 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")
|
|
informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
|
|
informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
|
|
|
################### INFORMATIVE 1d
|
|
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
|
sorted_close_prices = informative['close'].tail(365).sort_values()
|
|
lowest_4 = sorted_close_prices.head(4)
|
|
informative['lowest_4'] = lowest_4.mean()
|
|
|
|
sorted_close_prices = informative['close'].tail(365).sort_values(ascending=False)
|
|
highest_4 = sorted_close_prices.head(4)
|
|
informative['highest_4'] = highest_4.mean()
|
|
|
|
last_14_days = informative.tail(14)
|
|
|
|
# Récupérer le minimum et le maximum de la colonne 'close' des 14 derniers jours
|
|
min_14_days = last_14_days['close'].min()
|
|
max_14_days = last_14_days['close'].max()
|
|
informative['lowest'] = min_14_days
|
|
informative['highest'] = max_14_days
|
|
informative['pct_min_max'] = (max_14_days - min_14_days) / min_14_days
|
|
informative['mid_min_max'] = min_14_days + (max_14_days - min_14_days) / 2
|
|
informative['middle'] = informative['lowest_4'] + (informative['highest_4'] - informative['lowest_4']) / 2
|
|
informative['mid_min_max_0.98'] = informative['mid_min_max'] * 0.98
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
|
|
|
dataframe['count_buys'] = 0
|
|
|
|
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['amount'] = 0
|
|
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')
|
|
dataframe['count_buys'] = len(filled_buys)
|
|
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)
|
|
dataframe['buy_level'] = dataframe['max50'] * 0.99 #(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)
|
|
|
|
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['buy_level'] # self.get_buy_level(pair, dataframe)
|
|
|
|
dataframe.loc[
|
|
(
|
|
#(dataframe['hapercent'] > 0)
|
|
(dataframe['down_count'].shift(1) < - 6)
|
|
& (dataframe['down_count'] == 0)
|
|
& (dataframe['down_pct'].shift(1) <= -0.5)
|
|
), ['enter_long', 'enter_tag']] = (1, 'buy_hapercent')
|
|
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
|
|
|
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:
|
|
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()
|
|
# prépare les données
|
|
count_of_buys = trade.nr_of_successful_entries
|
|
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):
|
|
return None
|
|
pair = trade.pair
|
|
if pair not in ('BTC/USDC', 'DOGE/USDC', 'ETH/USDC'):
|
|
return None
|
|
max_buys = 20
|
|
|
|
filled_buys = trade.select_filled_orders('buy')
|
|
count_of_buys = len(filled_buys)
|
|
if count_of_buys >= max_buys:
|
|
return None
|
|
|
|
# if 'buy' in last_candle:
|
|
# condition = (last_candle['buy'] == 1)
|
|
# else:
|
|
# condition = False
|
|
# self.protection_nb_buy_lost.value
|
|
limit = last_candle['limit']
|
|
stake_amount = min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
|
|
|
|
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
|
|
|
|
# if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
|
|
if ((last_candle['enter_long'] == 1) or last_candle['percent48'] < - 0.03) \
|
|
and ( hours > 48 or (current_profit < -0.015 * count_of_buys)):
|
|
try:
|
|
|
|
# This then calculates current safety order size
|
|
# stake_amount = stake_amount * pow(1.5, count_of_buys)
|
|
print(
|
|
f"Adjust {current_time} price={trade.pair} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
|
|
|
|
return stake_amount
|
|
except Exception as exception:
|
|
print(exception)
|
|
return None
|
|
return None
|
|
|
|
def adjust_stake_amount(self, pair: str, dataframe: DataFrame):
|
|
# Calculer le minimum des 14 derniers jours
|
|
current_price = dataframe['close']
|
|
|
|
# trade = self.getTrade(pair)
|
|
# if trade:
|
|
# current_price = trade.open_rate
|
|
base_stake_amount = self.config['stake_amount'] #.get('stake_amount', 50) # Montant de base configuré
|
|
|
|
# Calculer le max des 14 derniers jours
|
|
min_14_days_4 = dataframe['lowest_4_1d']
|
|
max_14_days_4 = dataframe['highest_4_1d']
|
|
percent_4 = 1 - (current_price - min_14_days_4) / (max_14_days_4 - min_14_days_4)
|
|
factor_4 = 1 / ((current_price - min_14_days_4) / (max_14_days_4 - min_14_days_4))
|
|
max_min_4 = max_14_days_4 / min_14_days_4
|
|
|
|
# min_14_days = dataframe['lowest_1d']
|
|
# max_14_days = dataframe['highest_1d']
|
|
# percent = 1 - (current_price - min_14_days) / (max_14_days - min_14_days)
|
|
# factor = 1 / ((current_price - min_14_days) / (max_14_days - min_14_days))
|
|
# max_min = max_14_days / min_14_days
|
|
# Stack amount ajusté price=2473.47 min_max=0.15058074985054215 percent=0.8379141364642171 amount=20.0
|
|
|
|
adjusted_stake_amount = max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
|
# if pair in ('BTC/USDT', 'ETH/USDT'):
|
|
# if percent_4 > 0.5:
|
|
# adjusted_stake_amount = 300
|
|
|
|
# adjusted_stake_amount_2 = max(base_stake_amount / 2.5, min(75, base_stake_amount * percent))
|
|
|
|
# print(
|
|
# f"Stack amount ajusté price={current_price} max_min={max_min_4:.4f} min_14={min_14_days_4:.4f} max_14={max_14_days_4:.4f} factor={factor_4:.4f} percent={percent_4:.4f} amount={adjusted_stake_amount:.4f}")
|
|
# print(f"Stack amount ajusté price={current_price} max_min={max_min:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} factor={factor:.4f} percent={percent:.4f} amount={adjusted_stake_amount_2:.4f}")
|
|
|
|
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, dataframe: DataFrame):
|
|
|
|
current_price = dataframe['last_price'] # dataframe['close']
|
|
|
|
# trade = self.getTrade(pair)
|
|
# if trade:
|
|
# current_price = trade.open_rate
|
|
|
|
# Calculer le max des 14 derniers jours
|
|
min_14_days = dataframe['lowest_1d']
|
|
max_14_days = dataframe['highest_1d']
|
|
percent = (max_14_days - current_price) / (min_14_days)
|
|
|
|
min_max = dataframe['pct_min_max_1d'] # (max_14_days - min_14_days) / min_14_days
|
|
expected_profit = min(0.1, max(0.01, dataframe['min_max200'] * 0.5))
|
|
|
|
# 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 analyze_conditions(self, pair: str, row: DataFrame):
|
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
|
|
|
if dataframe is None or dataframe.empty:
|
|
return
|
|
if row is None or row.empty:
|
|
return
|
|
# Créer un tableau pour stocker les résultats de l'analyse
|
|
results = []
|
|
# row = dataframe.iloc[-1].squeeze()
|
|
# result = {'triggered': False, 'conditions_failed': []}
|
|
try:
|
|
buy_level = row['buy_level']
|
|
except Exception as exception:
|
|
print(exception)
|
|
return None
|
|
# Première condition : 'buy_fractal'
|
|
print('------------------------------------------------')
|
|
print('Test buy fractal ' + pair + ' buy_level=' + str(buy_level))
|
|
if not (
|
|
(row['close'] <= (row['min200'] * 1.002)) and
|
|
(row['percent_max_144'] <= -0.012) and
|
|
(row['haopen'] < buy_level) and
|
|
(row['open'] < row['average_line_288']) and
|
|
(dataframe['min50'].shift(3).iloc[-1] == row['min50'])
|
|
):
|
|
failed_conditions = []
|
|
if row['close'] > (row['min200'] * 1.002):
|
|
print('close > min200 * 1.002')
|
|
if row['percent_max_144'] > -0.012:
|
|
print('percent_max_144 > -0.012')
|
|
if row['haopen'] >= buy_level:
|
|
print('haopen >= buy_level')
|
|
if row['open'] >= row['average_line_288']:
|
|
print('open >= average_line_288')
|
|
if dataframe['min50'].shift(3).iloc[-1] != row['min50']:
|
|
print('min50.shift(3) != min50')
|
|
# result['conditions_failed'].append({'buy_fractal': failed_conditions})
|
|
print('------------------------------------------------')
|
|
print('Test buy_max_diff_015 ' + pair + ' buy_level=' + str(buy_level))
|
|
# Deuxième condition : 'buy_max_diff_015'
|
|
if not (
|
|
(dataframe['max200_diff'].shift(4).iloc[-1] >= 0.015) and
|
|
(row['close'] <= row['lowest_4_average'] * 1.002) and
|
|
(row['close'] <= row['min200'] * 1.002) and
|
|
(dataframe['max50_diff'].shift(4).iloc[-1] >= 0.01) and
|
|
(row['haclose'] < row['bb_middleband']) and
|
|
(row['close'] < buy_level) and
|
|
(row['open'] < row['average_line_288']) and
|
|
(dataframe['min50'].shift(3).iloc[-1] == row['min50'])
|
|
):
|
|
if dataframe['max200_diff'].shift(4).iloc[-1] < 0.015:
|
|
print('max200_diff.shift(4) < 0.015')
|
|
if row['close'] > row['lowest_4_average'] * 1.002:
|
|
print('close > lowest_4_average * 1.002')
|
|
if row['close'] > row['min200'] * 1.002:
|
|
print('close > min200 * 1.002')
|
|
if dataframe['max50_diff'].shift(4).iloc[-1] < 0.01:
|
|
print('max50_diff.shift(4) < 0.01')
|
|
if row['haclose'] >= row['bb_middleband']:
|
|
print('haclose >= bb_middleband')
|
|
if row['close'] >= buy_level:
|
|
print('close >= buy_level')
|
|
if row['open'] >= row['average_line_288']:
|
|
print('open >= average_line_288')
|
|
if dataframe['min50'].shift(3).iloc[-1] != row['min50']:
|
|
print('min50.shift(3) != min50')
|
|
print('------------------------------------------------')
|
|
print('Test buy_min_max_200 ' + pair + ' buy_level=' + str(buy_level))
|
|
if not (
|
|
(row['close'] <= row['min200'] * 1.002)
|
|
and (row['min_max200'] > 0.015)
|
|
and (row['haopen'] < buy_level)
|
|
and (row['open'] < row['average_line_288'])
|
|
):
|
|
if row['close'] > row['min200'] * 1.002:
|
|
print('close > row[min200] * 1.002')
|
|
if row['min_max200'] <= 0.015:
|
|
print('row[min_max200] <= 0.015')
|
|
if row['haopen'] < buy_level:
|
|
print('row[haopen] < buy_level')
|
|
if row['open'] < row['average_line_288']:
|
|
print('row[open] >= row[average_line_288]')
|
|
print('------------------------------------------------')
|
|
|
|
# Ajouter le résultat à la liste des résultats
|
|
# results.append(result)
|
|
|
|
# print(result)
|
|
|
|
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 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
|
|
|
|
|