EMPTY pas tant que ça
This commit is contained in:
Jérôme Delacotte
50
Empty.json
Normal file
50
Empty.json
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@@ -0,0 +1,50 @@
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{
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"strategy_name": "Empty",
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"params": {
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"roi": {
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"0": 10
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},
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"max_open_trades": {
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"max_open_trades": 20
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},
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"buy": {
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"buy_crossed_indicator0": "sma24_deriv2",
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"buy_crossed_indicator1": "sma48_deriv2",
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"buy_crossed_indicator2": "sma60_deriv1",
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"buy_indicator0": "sma12_deriv1",
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"buy_indicator1": "sma3_score",
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"buy_indicator2": "sma5_deriv2",
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"buy_operator0": ">",
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"buy_operator1": "D",
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"buy_operator2": "D",
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"buy_real_num0": -0.7,
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"buy_real_num1": 1.0,
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"buy_real_num2": -0.4
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},
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"sell": {
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"sell_crossed_indicator0": "sma24_deriv2",
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"sell_crossed_indicator1": "sma48_score",
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"sell_crossed_indicator2": "sma24_score",
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"sell_indicator0": "sma5_deriv2",
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"sell_indicator1": "sma60_score",
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"sell_indicator2": "sma60_score",
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"sell_operator0": "<",
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"sell_operator1": ">",
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"sell_operator2": "CB",
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"sell_real_num0": 0.7,
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"sell_real_num1": 0.5,
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"sell_real_num2": 0.9
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},
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"stoploss": {
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"stoploss": -1
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},
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"trailing": {
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"trailing_stop": false,
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"trailing_stop_positive": 0.055,
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"trailing_stop_positive_offset": 0.106,
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"trailing_only_offset_is_reached": false
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2026-02-19 19:14:20.539321+00:00"
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}
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804
Empty.py
804
Empty.py
@@ -2,18 +2,238 @@
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# isort: skip_file
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# isort: skip_file
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# --- Do not remove these libs ---
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# --- Do not remove these libs ---
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from datetime import datetime
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from datetime import datetime
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from freqtrade.persistence import Trade
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import numpy as np # noqa
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import numpy as np # noqa
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import pandas as pd # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from pandas import DataFrame
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from datetime import timezone, timedelta
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from datetime import timedelta, datetime
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from freqtrade.strategy.interface import IStrategy
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from freqtrade.strategy.interface import IStrategy
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from freqtrade.persistence import Trade
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from typing import Optional, Union, Tuple
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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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# --------------------------------
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# --------------------------------
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# Add your lib to import here
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# Add your lib to import here
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import talib.abstract as ta
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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 freqtrade.vendor.qtpylib.indicators as qtpylib
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from functools import reduce
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from random import shuffle
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timeperiods = [3, 5, 12, 24, 48, 60]
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god_genes_with_timeperiod = list()
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for timeperiod in timeperiods:
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# god_genes_with_timeperiod.append(f'max{timeperiod}')
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# god_genes_with_timeperiod.append(f'min{timeperiod}')
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# god_genes_with_timeperiod.append(f"percent{timeperiod}")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_deriv1")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_deriv2")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_score")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_up")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_down")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_change_up")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_change_down")
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operators = [
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"D", # Disabled gene
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">", # Indicator, bigger than cross indicator
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"<", # Indicator, smaller than cross indicator
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"=", # Indicator, equal with cross indicator
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"C", # Indicator, crossed the cross indicator
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"CA", # Indicator, crossed above the cross indicator
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"CB", # Indicator, crossed below the cross indicator
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# ">R", # Normalized indicator, bigger than real number
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# "=R", # Normalized indicator, equal with real number
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# "<R", # Normalized indicator, smaller than real number
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# "/>R", # Normalized indicator devided to cross indicator, bigger than real number
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# "/=R", # Normalized indicator devided to cross indicator, equal with real number
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# "/<R", # Normalized indicator devided to cross indicator, smaller than real number
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# "UT", # Indicator, is in UpTrend status
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# "DT", # Indicator, is in DownTrend status
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# "OT", # Indicator, is in Off trend status(RANGE)
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# "CUT", # Indicator, Entered to UpTrend status
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# "CDT", # Indicator, Entered to DownTrend status
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# "COT" # Indicator, Entered to Off trend status(RANGE)
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]
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# number of candles to check up,don,off trend.
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TREND_CHECK_CANDLES = 4
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DECIMALS = 1
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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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def gene_calculator(dataframe, indicator):
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# print(indicator)
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# Cuz Timeperiods not effect calculating CDL patterns recognations
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if 'CDL' in indicator:
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splited_indicator = indicator.split('-')
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splited_indicator[1] = "0"
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new_indicator = "-".join(splited_indicator)
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# print(indicator, new_indicator)
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indicator = new_indicator
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gene = indicator.split("-")
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gene_name = gene[0]
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gene_len = len(gene)
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# print(f"GENE {gene_name} {gene_len} {indicator}")
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if gene_name in dataframe.keys():
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# print(f"{indicator}, calculated befoure")
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# print(len(dataframe.keys()))
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return dataframe[gene_name]
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if indicator in dataframe.keys():
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# print(f"{indicator}, calculated befoure")
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# print(len(dataframe.keys()))
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return dataframe[indicator]
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else:
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result = None
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# For Pattern Recognations
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if gene_len == 1:
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# print('gene_len == 1\t', indicator)
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result = getattr(talib, gene_name)(
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dataframe
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)
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return normalize(result)
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elif gene_len == 2:
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# print('gene_len == 2\t', indicator)
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gene_timeperiod = int(gene[1])
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result = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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)
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return normalize(result)
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# For
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elif gene_len == 3:
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# print('gene_len == 3\t', indicator)
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gene_timeperiod = int(gene[2])
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gene_index = int(gene[1])
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result = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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).iloc[:, gene_index]
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return normalize(result)
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# For trend operators(MA-5-SMA-4)
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elif gene_len == 4:
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# print('gene_len == 4\t', indicator)
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gene_timeperiod = int(gene[1])
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sharp_indicator = f'{gene_name}-{gene_timeperiod}'
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dataframe[sharp_indicator] = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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)
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return normalize(talib.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
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# For trend operators(STOCH-0-4-SMA-4)
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elif gene_len == 5:
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# print('gene_len == 5\t', indicator)
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gene_timeperiod = int(gene[2])
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gene_index = int(gene[1])
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sharp_indicator = f'{gene_name}-{gene_index}-{gene_timeperiod}'
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dataframe[sharp_indicator] = getattr(talib, gene_name)(
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dataframe,
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timeperiod=gene_timeperiod,
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).iloc[:, gene_index]
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return normalize(talib.SMA(dataframe[sharp_indicator].fillna(0), TREND_CHECK_CANDLES))
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def condition_generator(dataframe, operator, indicator, crossed_indicator, real_num):
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condition = (dataframe['volume'] > 10)
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# TODO : it ill callculated in populate indicators.
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pd.set_option('display.max_rows', None)
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pd.set_option('display.max_columns', None)
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pd.set_option("display.width", 200)
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# print(f"{indicator} {crossed_indicator} {real_num}")
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dataframe[indicator] = gene_calculator(dataframe, indicator)
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dataframe[crossed_indicator] = gene_calculator(dataframe, crossed_indicator)
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indicator_trend_sma = f"{indicator}-SMA-{TREND_CHECK_CANDLES}"
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if operator in ["UT", "DT", "OT", "CUT", "CDT", "COT"]:
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dataframe[indicator_trend_sma] = gene_calculator(dataframe, indicator_trend_sma)
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if operator == ">":
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condition = (dataframe[indicator] > dataframe[crossed_indicator])
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elif operator == "=":
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condition = (np.isclose(dataframe[indicator], dataframe[crossed_indicator]))
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elif operator == "<":
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condition = (dataframe[indicator] < dataframe[crossed_indicator])
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elif operator == "C":
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condition = (
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(qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator])) |
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(qtpylib.crossed_above(
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dataframe[indicator], dataframe[crossed_indicator]))
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)
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elif operator == "CA":
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condition = (qtpylib.crossed_above(dataframe[indicator], dataframe[crossed_indicator]))
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elif operator == "CB":
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condition = (qtpylib.crossed_below(dataframe[indicator], dataframe[crossed_indicator]))
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elif operator == ">R":
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condition = (dataframe[indicator] > real_num)
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elif operator == "=R":
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condition = (np.isclose(dataframe[indicator], real_num))
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elif operator == "<R":
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condition = (dataframe[indicator] < real_num)
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elif operator == "/>R":
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condition = (dataframe[indicator].div(dataframe[crossed_indicator]) > real_num)
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elif operator == "/=R":
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condition = (np.isclose(dataframe[indicator].div(dataframe[crossed_indicator]), real_num))
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elif operator == "/<R":
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condition = (dataframe[indicator].div(dataframe[crossed_indicator]) < real_num)
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elif operator == "UT":
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condition = (dataframe[indicator] > dataframe[indicator_trend_sma])
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elif operator == "DT":
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condition = (dataframe[indicator] < dataframe[indicator_trend_sma])
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elif operator == "OT":
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condition = (np.isclose(dataframe[indicator], dataframe[indicator_trend_sma]))
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elif operator == "CUT":
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condition = (
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(
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qtpylib.crossed_above(dataframe[indicator],dataframe[indicator_trend_sma])
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) & (
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dataframe[indicator] > dataframe[indicator_trend_sma]
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)
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)
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elif operator == "CDT":
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condition = (
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(
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qtpylib.crossed_below(dataframe[indicator], dataframe[indicator_trend_sma])
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) &
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(
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dataframe[indicator] < dataframe[indicator_trend_sma]
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)
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)
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elif operator == "COT":
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condition = (
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(
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(
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qtpylib.crossed_below(dataframe[indicator], dataframe[indicator_trend_sma])
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) |
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(
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qtpylib.crossed_above(dataframe[indicator], dataframe[indicator_trend_sma])
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)
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) &
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(
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np.isclose(dataframe[indicator], dataframe[indicator_trend_sma])
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)
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)
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return condition, dataframe
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# #########################################################################################################################
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# This class is a sample. Feel free to customize it.
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# This class is a sample. Feel free to customize it.
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class Empty(IStrategy):
|
class Empty(IStrategy):
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@@ -30,14 +250,18 @@ class Empty(IStrategy):
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# Stoploss:
|
# Stoploss:
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stoploss = -1
|
stoploss = -1
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trailing_stop = True
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trailing_stop = True
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trailing_stop_positive = 0.001
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trailing_stop_positive = 0.15
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trailing_stop_positive_offset = 0.0175 #0.015
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trailing_stop_positive_offset = 0.20
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trailing_only_offset_is_reached = True
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trailing_only_offset_is_reached = True
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position_adjustment_enable = True
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use_custom_stoploss = True
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#max_open_trades = 3
|
#max_open_trades = 3
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# Optimal ticker interval for the strategy.
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# Optimal ticker interval for the strategy.
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timeframe = '5m'
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timeframe = '1h'
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# Run "populate_indicators()" only for new candle.
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = False
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process_only_new_candles = False
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@@ -50,74 +274,546 @@ class Empty(IStrategy):
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# Number of candles the strategy requires before producing valid signals
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 30
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startup_candle_count: int = 30
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plot_config = {
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pairs = {
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# Main plot indicators (Moving averages, ...)
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pair: {
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'main_plot': {
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'first_amount': 0,
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'bb_lowerband': {'color': 'white'},
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"first_buy": 0,
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'bb_upperband': {'color': 'white'},
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"last_buy": 0.0,
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},
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"last_min": 999999999999999.5,
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'subplots': {
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"last_max": 0,
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# Subplots - each dict defines one additional plot
|
"trade_info": {},
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"BB": {
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"max_touch": 0.0,
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'bb_width': {'color': 'white'},
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"last_sell": 0.0,
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},
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'count_of_buys': 0,
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"Aaron": {
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'current_profit': 0,
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'aroonup': {'color': 'blue'},
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'expected_profit': 0,
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'aroondown': {'color': 'red'}
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'previous_profit': 0,
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}
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"last_candle": {},
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"last_count_of_buys": 0,
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'base_stake_amount': 0,
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'stop_buy': False,
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'last_date': 0,
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'stop': False,
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'max_profit': 0,
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'total_amount': 0,
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'has_gain': 0,
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'force_sell': False,
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'force_buy': False,
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'current_trade': None,
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'last_trade': None
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}
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}
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|
for pair in ["BTC/USDC", "BTC/USDT"]
|
||||||
}
|
}
|
||||||
|
|
||||||
|
plot_config = {
|
||||||
|
"main_plot": {
|
||||||
|
"sma5": {
|
||||||
|
"color": "#7aa90b"
|
||||||
|
},
|
||||||
|
"min24": {
|
||||||
|
"color": "#121acd"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"subplots": {
|
||||||
|
"Inv": {
|
||||||
|
"sma5_inv": {
|
||||||
|
"color": "#aef878",
|
||||||
|
"type": "line"
|
||||||
|
},
|
||||||
|
"zero": {
|
||||||
|
"color": "#fdba52"
|
||||||
|
},
|
||||||
|
"sma24_score": {
|
||||||
|
"color": "#f1f5b0"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"drv": {
|
||||||
|
"sma5_deriv1": {
|
||||||
|
"color": "#96eebb"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"options": {
|
||||||
|
"markAreaZIndex": 1
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# Buy Hyperoptable Parameters/Spaces.
|
||||||
|
buy_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="ADD-20", space='buy')
|
||||||
|
buy_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="ASIN-6", space='buy')
|
||||||
|
buy_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLEVENINGSTAR-50", space='buy')
|
||||||
|
|
||||||
|
buy_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="SMA-100", space='buy')
|
||||||
|
buy_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="WILLR-50", space='buy')
|
||||||
|
buy_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLHANGINGMAN-20", space='buy')
|
||||||
|
|
||||||
|
buy_operator0 = CategoricalParameter(operators, default="/<R", space='buy')
|
||||||
|
buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
|
||||||
|
buy_operator2 = CategoricalParameter(operators, default="CB", space='buy')
|
||||||
|
|
||||||
|
buy_real_num0 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='buy')
|
||||||
|
buy_real_num1 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='buy')
|
||||||
|
buy_real_num2 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='buy')
|
||||||
|
|
||||||
|
# Sell Hyperoptable Parameters/Spaces.
|
||||||
|
sell_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="CDLSHOOTINGSTAR-150", space='sell')
|
||||||
|
sell_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="MAMA-1-100", space='sell')
|
||||||
|
sell_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDLMATHOLD-6", space='sell')
|
||||||
|
|
||||||
|
sell_indicator0 = CategoricalParameter(god_genes_with_timeperiod, default="CDLUPSIDEGAP2CROWS-5", space='sell')
|
||||||
|
sell_indicator1 = CategoricalParameter(god_genes_with_timeperiod, default="CDLHARAMICROSS-150", space='sell')
|
||||||
|
sell_indicator2 = CategoricalParameter(god_genes_with_timeperiod, default="CDL2CROWS-5", space='sell')
|
||||||
|
|
||||||
|
sell_operator0 = CategoricalParameter(operators, default="<R", space='sell')
|
||||||
|
sell_operator1 = CategoricalParameter(operators, default="D", space='sell')
|
||||||
|
sell_operator2 = CategoricalParameter(operators, default="/>R", space='sell')
|
||||||
|
|
||||||
|
sell_real_num0 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='sell')
|
||||||
|
sell_real_num1 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='sell')
|
||||||
|
sell_real_num2 = DecimalParameter(-1, 1, decimals=DECIMALS, default=0, space='sell')
|
||||||
|
|
||||||
|
|
||||||
|
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
||||||
|
proposed_stake: float, min_stake: float, max_stake: float,
|
||||||
|
**kwargs) -> float:
|
||||||
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||||
|
last_candle = dataframe.iloc[-1].squeeze()
|
||||||
|
return self.adjust_stake_amount(pair, last_candle)
|
||||||
|
|
||||||
|
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
|
||||||
|
if (self.pairs[pair]['first_amount'] > 0):
|
||||||
|
amount = min(self.wallets.get_available_stake_amount(), self.pairs[pair]['first_amount'])
|
||||||
|
else:
|
||||||
|
amount = self.wallets.get_available_stake_amount() / 4
|
||||||
|
return min(amount, self.wallets.get_available_stake_amount())
|
||||||
|
|
||||||
|
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||||||
|
current_rate: float, current_profit: float, min_stake: float,
|
||||||
|
max_stake: float, **kwargs):
|
||||||
|
# ne rien faire si ordre deja en cours
|
||||||
|
if trade.has_open_orders:
|
||||||
|
# print("skip open orders")
|
||||||
|
return None
|
||||||
|
if (self.wallets.get_available_stake_amount() < 10): # or trade.stake_amount >= max_stake:
|
||||||
|
return 0
|
||||||
|
#
|
||||||
|
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
|
||||||
|
last_candle = dataframe.iloc[-1].squeeze()
|
||||||
|
before_last_candle = dataframe.iloc[-2].squeeze()
|
||||||
|
before_last_candle_12 = dataframe.iloc[-13].squeeze()
|
||||||
|
before_last_candle_24 = dataframe.iloc[-25].squeeze()
|
||||||
|
last_candle_3 = dataframe.iloc[-4].squeeze()
|
||||||
|
last_candle_previous_1h = dataframe.iloc[-13].squeeze()
|
||||||
|
# prépare les données
|
||||||
|
current_time = current_time.astimezone(timezone.utc)
|
||||||
|
open_date = trade.open_date.astimezone(timezone.utc)
|
||||||
|
dispo = round(self.wallets.get_available_stake_amount())
|
||||||
|
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
|
||||||
|
days_since_first_buy = (current_time - trade.open_date_utc).days
|
||||||
|
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
|
||||||
|
count_of_buys = trade.nr_of_successful_entries
|
||||||
|
current_time_utc = current_time.astimezone(timezone.utc)
|
||||||
|
open_date = trade.open_date.astimezone(timezone.utc)
|
||||||
|
days_since_open = (current_time_utc - open_date).days
|
||||||
|
pair = trade.pair
|
||||||
|
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
|
||||||
|
# last_lost = self.getLastLost(last_candle, pair)
|
||||||
|
# pct_first = 0
|
||||||
|
stake_amount = self.adjust_stake_amount(pair, last_candle)
|
||||||
|
# if (last_candle['enter_long'] == 1 and current_profit < -0.05 and stake_amount > 10) :
|
||||||
|
#
|
||||||
|
# print(f"adjust {current_time} {stake_amount}")
|
||||||
|
# print(f"adjust {pair} {current_time} dispo={dispo} amount={stake_amount} rate={current_rate}")
|
||||||
|
# return stake_amount
|
||||||
|
|
||||||
|
if last_candle['enter_long'] != 1:
|
||||||
|
return None
|
||||||
|
|
||||||
|
filled_buys = [
|
||||||
|
o for o in trade.orders
|
||||||
|
if o.status == "closed" and o.ft_order_side == "buy"
|
||||||
|
]
|
||||||
|
|
||||||
|
if not filled_buys:
|
||||||
|
return None
|
||||||
|
|
||||||
|
last_buy = max(filled_buys, key=lambda o: o.order_date)
|
||||||
|
last_entry_price = last_buy.price
|
||||||
|
|
||||||
|
drop_from_last_entry = (current_rate - last_entry_price) / last_entry_price
|
||||||
|
|
||||||
|
if drop_from_last_entry <= -0.05:
|
||||||
|
# stake_amount = trade.stake_amount
|
||||||
|
print(f"adjust {current_time} {stake_amount}")
|
||||||
|
print(f"adjust {pair} {current_time} dispo={dispo} amount={stake_amount} rate={current_rate}")
|
||||||
|
return stake_amount
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
|
||||||
|
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
|
||||||
|
|
||||||
|
minutes = 0
|
||||||
|
if self.pairs[pair]['last_date'] != 0:
|
||||||
|
minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
|
||||||
|
|
||||||
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||||
|
last_candle = dataframe.iloc[-1].squeeze()
|
||||||
|
last_candle_2 = dataframe.iloc[-2].squeeze()
|
||||||
|
last_candle_3 = dataframe.iloc[-3].squeeze()
|
||||||
|
|
||||||
|
condition = True #(last_candle[f"{indic_5m}_deriv1"] >= indic_deriv1_5m) and (last_candle[f"{indic_5m}_deriv2"] >= indic_deriv2_5m)
|
||||||
|
|
||||||
|
# self.should_enter_trade(pair, last_candle, current_time)
|
||||||
|
allow_to_buy = (condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
|
||||||
|
|
||||||
|
# force = self.pairs[pair]['force_buy']
|
||||||
|
# if self.pairs[pair]['force_buy']:
|
||||||
|
# self.pairs[pair]['force_buy'] = False
|
||||||
|
# allow_to_buy = True
|
||||||
|
# else:
|
||||||
|
# if not self.should_enter_trade(pair, last_candle, current_time):
|
||||||
|
# allow_to_buy = False
|
||||||
|
|
||||||
|
if allow_to_buy:
|
||||||
|
self.pairs[pair]['first_buy'] = rate
|
||||||
|
self.pairs[pair]['last_buy'] = rate
|
||||||
|
self.pairs[pair]['max_touch'] = last_candle['close']
|
||||||
|
self.pairs[pair]['last_candle'] = last_candle
|
||||||
|
self.pairs[pair]['count_of_buys'] = 1
|
||||||
|
self.pairs[pair]['current_profit'] = 0
|
||||||
|
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
|
||||||
|
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
|
||||||
|
self.pairs[pair]['last_date'] = current_time
|
||||||
|
|
||||||
|
dispo = round(self.wallets.get_available_stake_amount())
|
||||||
|
# self.printLineLog()
|
||||||
|
|
||||||
|
stake_amount = self.adjust_stake_amount(pair, last_candle)
|
||||||
|
self.pairs[pair]['first_amount'] = stake_amount
|
||||||
|
self.pairs[pair]['total_amount'] = stake_amount
|
||||||
|
print(f"Buy {pair} {current_time} {entry_tag} dispo={dispo} amount={amount} rate={rate} rate={rate}")
|
||||||
|
|
||||||
|
# self.log_trade(
|
||||||
|
# last_candle=last_candle,
|
||||||
|
# date=current_time,
|
||||||
|
# action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
|
||||||
|
# pair=pair,
|
||||||
|
# rate=rate,
|
||||||
|
# dispo=dispo,
|
||||||
|
# profit=0,
|
||||||
|
# trade_type=entry_tag,
|
||||||
|
# buys=1,
|
||||||
|
# stake=round(stake_amount, 2)
|
||||||
|
# )
|
||||||
|
|
||||||
|
return allow_to_buy
|
||||||
|
|
||||||
|
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
||||||
|
time_in_force: str,
|
||||||
|
exit_reason: str, current_time, **kwargs, ) -> bool:
|
||||||
|
|
||||||
|
# allow_to_sell = (minutes > 30)
|
||||||
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||||
|
last_candle = dataframe.iloc[-1].squeeze()
|
||||||
|
|
||||||
|
profit = trade.calc_profit(rate)
|
||||||
|
force = self.pairs[pair]['force_sell']
|
||||||
|
allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
|
||||||
|
|
||||||
|
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
|
||||||
|
|
||||||
|
if allow_to_sell:
|
||||||
|
self.pairs[pair]['last_sell'] = rate
|
||||||
|
self.pairs[pair]['last_candle'] = last_candle
|
||||||
|
self.pairs[pair]['max_profit'] = 0
|
||||||
|
profit = trade.calc_profit(rate)
|
||||||
|
self.pairs[pair]['previous_profit'] = profit
|
||||||
|
dispo = round(self.wallets.get_available_stake_amount())
|
||||||
|
print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate} profit={profit}")
|
||||||
|
# self.log_trade(
|
||||||
|
# last_candle=last_candle,
|
||||||
|
# date=current_time,
|
||||||
|
# action="🟥Sell " + str(minutes),
|
||||||
|
# pair=pair,
|
||||||
|
# trade_type=exit_reason,
|
||||||
|
# rate=last_candle['close'],
|
||||||
|
# dispo=dispo,
|
||||||
|
# profit=round(profit, 2)
|
||||||
|
# )
|
||||||
|
self.pairs[pair]['force_sell'] = False
|
||||||
|
self.pairs[pair]['has_gain'] = 0
|
||||||
|
self.pairs[pair]['current_profit'] = 0
|
||||||
|
self.pairs[pair]['total_amount'] = 0
|
||||||
|
self.pairs[pair]['count_of_buys'] = 0
|
||||||
|
self.pairs[pair]['max_touch'] = 0
|
||||||
|
self.pairs[pair]['last_buy'] = 0
|
||||||
|
self.pairs[pair]['last_date'] = current_time
|
||||||
|
self.pairs[pair]['last_trade'] = self.pairs[pair]['current_trade']
|
||||||
|
self.pairs[pair]['current_trade'] = None
|
||||||
|
# else:
|
||||||
|
# print(f"STOP triggered for {pair} ({exit_reason}) but condition blocked", "warning")
|
||||||
|
return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss') | force
|
||||||
|
|
||||||
|
def custom_exit(self, pair, trade, current_time,
|
||||||
|
current_rate, current_profit, **kwargs):
|
||||||
|
|
||||||
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||||
|
last_row = dataframe.iloc[-1]
|
||||||
|
self.pairs[pair]['current_trade'] = trade
|
||||||
|
momentum = last_row["sma24_score"]
|
||||||
|
|
||||||
|
# Si momentum fort → on laisse courir
|
||||||
|
if momentum > 1:
|
||||||
|
return None
|
||||||
|
|
||||||
|
# Si momentum faiblit → on prend profit plus tôt
|
||||||
|
if momentum < 0 and current_profit > 0.02:
|
||||||
|
return "momentum_drop"
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def custom_stoploss(self, pair, trade, current_time,
|
||||||
|
current_rate, current_profit, **kwargs):
|
||||||
|
|
||||||
|
if current_profit < - 0.1 and self.wallets.get_available_stake_amount():
|
||||||
|
return -0.1
|
||||||
|
|
||||||
|
return -1
|
||||||
|
|
||||||
def informative_pairs(self):
|
def informative_pairs(self):
|
||||||
|
|
||||||
return []
|
return []
|
||||||
|
|
||||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||||
|
|
||||||
# MACD
|
dataframe = dataframe.copy()
|
||||||
#macd = ta.MACD(dataframe)
|
heikinashi = qtpylib.heikinashi(dataframe)
|
||||||
#dataframe['macd'] = macd['macd']
|
dataframe['haopen'] = heikinashi['open']
|
||||||
#dataframe['macdsignal'] = macd['macdsignal']
|
dataframe['haclose'] = heikinashi['close']
|
||||||
#dataframe['macdhist'] = macd['macdhist']
|
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
|
||||||
|
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
|
||||||
|
dataframe['zero'] = 0;
|
||||||
|
|
||||||
# RSI
|
for timeperiod in timeperiods:
|
||||||
#dataframe['rsi'] = ta.RSI(dataframe)
|
dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
|
||||||
|
dataframe[f'min{timeperiod}'] = talib.MIN(dataframe['close'], timeperiod=timeperiod)
|
||||||
|
dataframe[f"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod)
|
||||||
|
dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
|
||||||
|
self.calculeDerivees(dataframe, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
|
||||||
|
|
||||||
# Bollinger Bands
|
# ######################################################################################################
|
||||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
dataframe['stop_buying_deb'] = (dataframe['sma60_trend_change_down'] == 1)
|
||||||
dataframe['bb_lowerband'] = bollinger['lower']
|
dataframe['stop_buying_end'] = (dataframe['sma60_trend_change_up'] == 1)
|
||||||
dataframe['bb_middleband'] = bollinger['mid']
|
latched = np.zeros(len(dataframe), dtype=bool)
|
||||||
dataframe['bb_upperband'] = bollinger['upper']
|
|
||||||
dataframe["bb_percent"] = (
|
for i in range(1, len(dataframe)):
|
||||||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
if dataframe['stop_buying_deb'].iloc[i]:
|
||||||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
latched[i] = True
|
||||||
)
|
elif dataframe['stop_buying_end'].iloc[i]:
|
||||||
dataframe["bb_width"] = (
|
latched[i] = False
|
||||||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
|
else:
|
||||||
)
|
latched[i] = latched[i - 1]
|
||||||
|
dataframe['stop_buying'] = latched
|
||||||
|
|
||||||
return dataframe
|
return dataframe
|
||||||
|
|
||||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||||
|
|
||||||
dataframe.loc[
|
# dataframe.loc[
|
||||||
(
|
# (
|
||||||
# (
|
# (dataframe['sma24_score'].shift(2) <= dataframe['zero'])
|
||||||
# (dataframe['close'] < dataframe['bb_lowerband'])
|
# & (dataframe['sma5'].shift(1) <= dataframe['sma5'])
|
||||||
# & (dataframe['bb_width'] >= 0.065)
|
# & (dataframe['sma5_inv'] == -1)
|
||||||
# #& (dataframe['rsi'] < 45)
|
# & (dataframe['min24'].shift(3) == dataframe['min24'])
|
||||||
# & (dataframe['volume'] > 0)
|
# ),
|
||||||
# )
|
# 'buy'] = 1
|
||||||
),
|
|
||||||
'buy'] = 1
|
conditions = list()
|
||||||
|
|
||||||
|
# print(dataframe.columns)
|
||||||
|
|
||||||
|
buy_indicator = self.buy_indicator0.value
|
||||||
|
buy_crossed_indicator = self.buy_crossed_indicator0.value
|
||||||
|
buy_operator = self.buy_operator0.value
|
||||||
|
buy_real_num = self.buy_real_num0.value
|
||||||
|
condition, dataframe = condition_generator(
|
||||||
|
dataframe,
|
||||||
|
buy_operator,
|
||||||
|
buy_indicator,
|
||||||
|
buy_crossed_indicator,
|
||||||
|
buy_real_num
|
||||||
|
)
|
||||||
|
conditions.append(condition)
|
||||||
|
# backup
|
||||||
|
buy_indicator = self.buy_indicator1.value
|
||||||
|
buy_crossed_indicator = self.buy_crossed_indicator1.value
|
||||||
|
buy_operator = self.buy_operator1.value
|
||||||
|
buy_real_num = self.buy_real_num1.value
|
||||||
|
|
||||||
|
condition, dataframe = condition_generator(
|
||||||
|
dataframe,
|
||||||
|
buy_operator,
|
||||||
|
buy_indicator,
|
||||||
|
buy_crossed_indicator,
|
||||||
|
buy_real_num
|
||||||
|
)
|
||||||
|
conditions.append(condition)
|
||||||
|
|
||||||
|
buy_indicator = self.buy_indicator2.value
|
||||||
|
buy_crossed_indicator = self.buy_crossed_indicator2.value
|
||||||
|
buy_operator = self.buy_operator2.value
|
||||||
|
buy_real_num = self.buy_real_num2.value
|
||||||
|
condition, dataframe = condition_generator(
|
||||||
|
dataframe,
|
||||||
|
buy_operator,
|
||||||
|
buy_indicator,
|
||||||
|
buy_crossed_indicator,
|
||||||
|
buy_real_num
|
||||||
|
)
|
||||||
|
conditions.append(condition)
|
||||||
|
conditions.append((dataframe['stop_buying'] == False))
|
||||||
|
|
||||||
|
# conditions.append((dataframe['min60'].shift(5) == dataframe['min60']))
|
||||||
|
# conditions.append((dataframe['low'] < dataframe['min60']))
|
||||||
|
|
||||||
|
print(f"BUY indicators tested \n"
|
||||||
|
f"{self.buy_indicator0.value} {self.buy_crossed_indicator0.value} {self.buy_operator0.value} {self.buy_real_num0.value} \n"
|
||||||
|
f"{self.buy_indicator1.value} {self.buy_crossed_indicator1.value} {self.buy_operator1.value} {self.buy_real_num1.value} \n"
|
||||||
|
f"{self.buy_indicator2.value} {self.buy_crossed_indicator2.value} {self.buy_operator2.value} {self.buy_real_num2.value} \n"
|
||||||
|
)
|
||||||
|
|
||||||
|
if conditions:
|
||||||
|
dataframe.loc[
|
||||||
|
reduce(lambda x, y: x & y, conditions),
|
||||||
|
['enter_long', 'enter_tag']
|
||||||
|
] = (1, 'god')
|
||||||
|
|
||||||
return dataframe
|
return dataframe
|
||||||
|
|
||||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||||
|
conditions = list()
|
||||||
|
# TODO: Its not dry code!
|
||||||
|
sell_indicator = self.sell_indicator0.value
|
||||||
|
sell_crossed_indicator = self.sell_crossed_indicator0.value
|
||||||
|
sell_operator = self.sell_operator0.value
|
||||||
|
sell_real_num = self.sell_real_num0.value
|
||||||
|
condition, dataframe = condition_generator(
|
||||||
|
dataframe,
|
||||||
|
sell_operator,
|
||||||
|
sell_indicator,
|
||||||
|
sell_crossed_indicator,
|
||||||
|
sell_real_num
|
||||||
|
)
|
||||||
|
conditions.append(condition)
|
||||||
|
|
||||||
dataframe.loc[
|
sell_indicator = self.sell_indicator1.value
|
||||||
(
|
sell_crossed_indicator = self.sell_crossed_indicator1.value
|
||||||
|
sell_operator = self.sell_operator1.value
|
||||||
|
sell_real_num = self.sell_real_num1.value
|
||||||
|
condition, dataframe = condition_generator(
|
||||||
|
dataframe,
|
||||||
|
sell_operator,
|
||||||
|
sell_indicator,
|
||||||
|
sell_crossed_indicator,
|
||||||
|
sell_real_num
|
||||||
|
)
|
||||||
|
conditions.append(condition)
|
||||||
|
|
||||||
),
|
sell_indicator = self.sell_indicator2.value
|
||||||
'sell'] = 1
|
sell_crossed_indicator = self.sell_crossed_indicator2.value
|
||||||
|
sell_operator = self.sell_operator2.value
|
||||||
|
sell_real_num = self.sell_real_num2.value
|
||||||
|
condition, dataframe = condition_generator(
|
||||||
|
dataframe,
|
||||||
|
sell_operator,
|
||||||
|
sell_indicator,
|
||||||
|
sell_crossed_indicator,
|
||||||
|
sell_real_num
|
||||||
|
)
|
||||||
|
conditions.append(condition)
|
||||||
|
|
||||||
|
|
||||||
|
print(f"SELL indicators tested \n"
|
||||||
|
f"{self.sell_indicator0.value} {self.sell_crossed_indicator0.value} {self.sell_operator0.value} {self.sell_real_num0.value} \n"
|
||||||
|
f"{self.sell_indicator1.value} {self.sell_crossed_indicator1.value} {self.sell_operator1.value} {self.sell_real_num1.value} \n"
|
||||||
|
f"{self.sell_indicator2.value} {self.sell_crossed_indicator2.value} {self.sell_operator2.value} {self.sell_real_num2.value} \n"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if conditions:
|
||||||
|
dataframe.loc[reduce(lambda x, y: x & y, conditions), ['exit_long', 'exit_tag']] = (1, 'god')
|
||||||
return dataframe
|
return dataframe
|
||||||
|
|
||||||
|
def calculeDerivees(
|
||||||
|
self,
|
||||||
|
dataframe: pd.DataFrame,
|
||||||
|
name: str,
|
||||||
|
suffixe: str = '',
|
||||||
|
window: int = 100,
|
||||||
|
coef: float = 0.15,
|
||||||
|
ema_period: int = 10,
|
||||||
|
verbose: bool = True,
|
||||||
|
timeframe: str = '5m'
|
||||||
|
) -> pd.DataFrame:
|
||||||
|
"""
|
||||||
|
Calcule deriv1/deriv2 (relative simple), applique EMA, calcule tendency
|
||||||
|
avec epsilon adaptatif basé sur rolling percentiles.
|
||||||
|
"""
|
||||||
|
d1_col = f"{name}{suffixe}_deriv1"
|
||||||
|
d2_col = f"{name}{suffixe}_deriv2"
|
||||||
|
tendency_col = f"{name}{suffixe}_state"
|
||||||
|
|
||||||
|
series = dataframe[f"{name}{suffixe}"]
|
||||||
|
d1 = series.diff()
|
||||||
|
d2 = d1.diff()
|
||||||
|
cond_bas = (d1.rolling(3).mean() > d1.rolling(10).mean())
|
||||||
|
cond_haut = (d1.rolling(3).mean() < d1.rolling(10).mean())
|
||||||
|
|
||||||
|
|
||||||
|
dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3)
|
||||||
|
dataframe[d2_col] = (dataframe[d1_col] - dataframe[d1_col].shift(1))
|
||||||
|
|
||||||
|
dataframe[f"{name}{suffixe}_inv"] = np.where(cond_bas, -1, np.where(cond_haut, 1, 0))
|
||||||
|
|
||||||
|
short = d1.rolling(3).mean()
|
||||||
|
long = d1.rolling(10).mean()
|
||||||
|
|
||||||
|
spread = short - long
|
||||||
|
zscore = (spread - spread.rolling(ema_period).mean()) / spread.rolling(ema_period).std()
|
||||||
|
|
||||||
|
dataframe[f"{name}{suffixe}_score"] = zscore
|
||||||
|
|
||||||
|
# ####################################################################
|
||||||
|
# Calcul de la pente lissée
|
||||||
|
d1 = series.diff()
|
||||||
|
d1_smooth = d1.rolling(5).mean()
|
||||||
|
# Normalisation
|
||||||
|
z = (d1_smooth - d1_smooth.rolling(ema_period).mean()) / d1_smooth.rolling(ema_period).std()
|
||||||
|
|
||||||
|
dataframe[f"{name}{suffixe}_trend_up"] = (
|
||||||
|
(d1_smooth.shift(1) < 0) &
|
||||||
|
(d1_smooth > 0) &
|
||||||
|
(z > 1.0)
|
||||||
|
)
|
||||||
|
|
||||||
|
dataframe[f"{name}{suffixe}_trend_down"] = (
|
||||||
|
(d1_smooth.shift(1) > 0) &
|
||||||
|
(d1_smooth < 0) &
|
||||||
|
(z < -1.0)
|
||||||
|
)
|
||||||
|
|
||||||
|
momentum_short = d1.rolling(int(ema_period / 2)).mean()
|
||||||
|
momentum_long = d1.rolling(ema_period * 2).mean()
|
||||||
|
|
||||||
|
dataframe[f"{name}{suffixe}_trend_change_up"] = (
|
||||||
|
(momentum_short.shift(1) < momentum_long.shift(1)) &
|
||||||
|
(momentum_short > momentum_long)
|
||||||
|
)
|
||||||
|
|
||||||
|
dataframe[f"{name}{suffixe}_trend_change_down"] = (
|
||||||
|
(momentum_short.shift(1) > momentum_long.shift(1)) &
|
||||||
|
(momentum_short < momentum_long)
|
||||||
|
)
|
||||||
|
|
||||||
|
return dataframe
|
||||||
Reference in New Issue
Block a user