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18 Commits
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Multiple_p
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f5bf4a4ac1 |
@@ -1,30 +0,0 @@
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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from math import sqrt
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class CustomPerformanceLoss(IHyperOptLoss):
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@staticmethod
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def hyperopt_loss_function(results, trade_count, *args, **kwargs):
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total_profit = results['profit_total_abs']
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max_dd = results['max_drawdown']
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profit_factor = results['profit_factor']
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sqn = results['sqn']
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# Sécurité minimale
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if trade_count < 30:
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return 1000
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# Éviter division par zéro
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if max_dd == 0:
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max_dd = 0.0001
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# Score principal
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performance_score = (
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(profit_factor * sqn * total_profit)
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/ (max_dd ** 1.5)
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)
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# On retourne l'inverse car hyperopt minimise
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return -performance_score
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36
Empty.json
36
Empty.json
@@ -2,7 +2,7 @@
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"strategy_name": "Empty",
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"strategy_name": "Empty",
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"params": {
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"params": {
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"roi": {
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"roi": {
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"0": 10
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"0": 5
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},
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},
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"stoploss": {
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"stoploss": {
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"stoploss": -1.0
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"stoploss": -1.0
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@@ -17,23 +17,33 @@
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"max_open_trades": 1
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"max_open_trades": 1
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},
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},
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"buy": {
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"buy": {
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"buy_deriv1_sma12d": -0.05,
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"buy_deriv1_sma12d": -0.04,
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"buy_deriv1_sma5d": -0.03,
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"buy_deriv1_sma5d": -0.02,
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"buy_deriv1_sma60": 0.0,
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"buy_deriv1_sma60": 0.001,
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"buy_deriv2_sma12d": 0.0,
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"buy_deriv2_sma12d": -0.02,
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"buy_deriv2_sma5d": -0.07,
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"buy_deriv2_sma5d": 0.0,
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"buy_deriv2_sma60": -0.003,
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"buy_deriv2_sma60": -0.001,
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"buy_longue": 200
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"buy_longue": 160,
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"buy_longue_derive": "sma80_deriv1_1d",
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"start_bear_deriv1": 0.003,
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"start_bear_deriv2": 0.003,
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"start_bear_indicator": "sma24",
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"start_bull_deriv1": -0.005,
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"start_bull_deriv2": 0.0,
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"start_bull_indicator": "sma3"
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},
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},
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"protection": {
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"protection": {
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"drop_from_last_entry": 0.0
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"drop_from_last_entry": -0.03,
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"mises": 1
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},
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},
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"sell": {
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"sell": {
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"sell_crossed_sma_indicators": "sma60",
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"b30_indicateur": "sma36",
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"sell_score_indicator": "sma60_score",
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"baisse": 0.25,
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"sell_sma_indicators": "sma48_1d"
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"sell_force_sell": -0.169,
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"sell_indicator": "sma3",
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"sell_score_indicator": "sma3_score"
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}
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}
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},
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},
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"ft_stratparam_v": 1,
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"ft_stratparam_v": 1,
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"export_time": "2026-02-26 20:23:41.717021+00:00"
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"export_time": "2026-03-07 11:58:51.712499+00:00"
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}
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}
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629
Empty.py
629
Empty.py
@@ -39,19 +39,24 @@ timeperiods = [3, 5, 12, 24, 36, 48, 60]
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long_timeperiods = [80, 100, 120, 140, 160, 180, 200]
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long_timeperiods = [80, 100, 120, 140, 160, 180, 200]
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sma_indicators = list()
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sma_indicators = list()
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sma_indicators_h = list()
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sma_indicators_d = list()
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score_indicators = list()
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score_indicators = list()
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stop_buying_indicators = list()
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stop_buying_indicators = list()
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god_genes_with_timeperiod = list()
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sma_deriv1_indicators = list()
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for timeperiod in timeperiods:
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for timeperiod in timeperiods:
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sma_indicators.append(f"sma{timeperiod}")
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sma_indicators.append(f"sma{timeperiod}")
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sma_indicators_h.append(f"sma{timeperiod}")
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sma_indicators_d.append(f"sma{timeperiod}")
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sma_indicators.append(f"sma{timeperiod}_1d")
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sma_indicators.append(f"sma{timeperiod}_1d")
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# god_genes_with_timeperiod.append(f'max{timeperiod}')
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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'min{timeperiod}')
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# god_genes_with_timeperiod.append(f"percent{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}")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_deriv1")
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# sma_deriv1_indicators.append(f"sma{timeperiod}_deriv1")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_deriv2")
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sma_deriv1_indicators.append(f"sma{timeperiod}_deriv1")
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god_genes_with_timeperiod.append(f"sma{timeperiod}_score")
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# sma_deriv1_indicators.append(f"sma{timeperiod}_deriv2")
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# sma_deriv1_indicators.append(f"sma{timeperiod}_score")
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# stoploss_indicators.append(f"stop_buying{timeperiod}")
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# stoploss_indicators.append(f"stop_buying{timeperiod}")
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stop_buying_indicators.append(f"stop_buying{timeperiod}_1d")
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stop_buying_indicators.append(f"stop_buying{timeperiod}_1d")
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@@ -63,29 +68,10 @@ for timeperiod in timeperiods:
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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_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_up")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_change_down")
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# god_genes_with_timeperiod.append(f"sma{timeperiod}_trend_change_down")
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for timeperiod in long_timeperiods:
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sma_deriv1_indicators.append(f"sma{timeperiod}_deriv1_1d")
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sma_indicators_d.append(f"sma{timeperiod}")
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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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# number of candles to check up,don,off trend.
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TREND_CHECK_CANDLES = 4
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TREND_CHECK_CANDLES = 4
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DECIMALS = 1
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DECIMALS = 1
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@@ -94,169 +80,6 @@ def normalize(df):
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df = (df-df.min())/(df.max()-df.min())
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df = (df-df.min())/(df.max()-df.min())
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return df
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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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) & (
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dataframe[indicator] > dataframe[indicator_trend_sma]
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|
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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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(
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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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|
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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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|
||||||
(
|
|
||||||
(
|
|
||||||
qtpylib.crossed_below(dataframe[indicator], dataframe[indicator_trend_sma])
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|
||||||
) |
|
|
||||||
(
|
|
||||||
qtpylib.crossed_above(dataframe[indicator], dataframe[indicator_trend_sma])
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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.
|
# This class is a sample. Feel free to customize it.
|
||||||
class Empty(IStrategy):
|
class Empty(IStrategy):
|
||||||
|
|
||||||
@@ -364,6 +187,14 @@ class Empty(IStrategy):
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
start_bull_indicator = CategoricalParameter(sma_indicators_d, default='sma100', space='buy', optimize=True, load=True)
|
||||||
|
start_bull_deriv1 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
|
||||||
|
start_bull_deriv2 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
|
||||||
|
|
||||||
|
start_bear_indicator = CategoricalParameter(sma_indicators_d, default='sma100', space='buy', optimize=True, load=True)
|
||||||
|
start_bear_deriv1 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
|
||||||
|
start_bear_deriv2 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=True, load=True)
|
||||||
|
|
||||||
buy_deriv1_sma60 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=False, load=True)
|
buy_deriv1_sma60 = DecimalParameter(-0.005, 0.005, decimals=3, default=0, space='buy', optimize=False, load=True)
|
||||||
buy_deriv1_sma5d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
buy_deriv1_sma5d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
||||||
buy_deriv1_sma12d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
buy_deriv1_sma12d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
||||||
@@ -372,48 +203,28 @@ class Empty(IStrategy):
|
|||||||
buy_deriv2_sma5d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
buy_deriv2_sma5d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
||||||
buy_deriv2_sma12d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
buy_deriv2_sma12d = DecimalParameter(-0.07, 0.07, decimals=2, default=0, space='buy', optimize=False, load=True)
|
||||||
|
|
||||||
buy_longue = CategoricalParameter(long_timeperiods, default=120, space='buy')
|
buy_longue = CategoricalParameter(long_timeperiods, default=120, space='buy', optimize=False, load=True)
|
||||||
|
buy_longue_derive = CategoricalParameter(sma_deriv1_indicators, default="sma60_deriv1_1d", space='buy', optimize=False, load=True)
|
||||||
# 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')
|
|
||||||
|
|
||||||
sell_score_indicator = CategoricalParameter(score_indicators, default="sma24_score", space='sell')
|
sell_score_indicator = CategoricalParameter(score_indicators, default="sma24_score", space='sell')
|
||||||
|
|
||||||
sell_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
|
# sell_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
|
||||||
sell_crossed_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
|
# sell_crossed_sma_indicators = CategoricalParameter(sma_indicators, default="sma24_score", space='sell')
|
||||||
|
|
||||||
|
drop_from_last_entry = DecimalParameter(-0.1, 0, decimals=2, default=-0.025, space='protection', optimize=False, load=True)
|
||||||
|
|
||||||
|
# baisses = list()
|
||||||
|
# for i in range(0, 0.5, 0.05):
|
||||||
|
# baisses.append(i)
|
||||||
|
mises = IntParameter(1, 10, default=1, space='protection')
|
||||||
|
sell_force_sell = DecimalParameter(-0.2, 0, decimals=3, default=-0.02, space='sell')
|
||||||
|
sell_indicator = CategoricalParameter(sma_indicators, default="sma36", space='sell', optimize=True, load=True)
|
||||||
|
|
||||||
|
baisse = DecimalParameter(0.1, 0.5, decimals=2, default=0.3, space='sell', optimize=True, load=True)
|
||||||
|
b30_indicateur = CategoricalParameter(sma_indicators_h, default="sma36", space='sell', optimize=True, load=True)
|
||||||
|
|
||||||
|
# lost_indicator = CategoricalParameter(sma_deriv1_indicators, default="sma5_deriv1", space='protection')
|
||||||
|
|
||||||
drop_from_last_entry = DecimalParameter(-0.1, 0, decimals=2, default=-0.025, space='protection')
|
|
||||||
# range_pos_stoploss = DecimalParameter(0, 0.1, decimals=2, default=0.05, space='protection')
|
# range_pos_stoploss = DecimalParameter(0, 0.1, decimals=2, default=0.05, space='protection')
|
||||||
# stoploss_force = DecimalParameter(-0.2, 0, decimals=2, default=-0.05, space='protection')
|
# stoploss_force = DecimalParameter(-0.2, 0, decimals=2, default=-0.05, space='protection')
|
||||||
|
|
||||||
@@ -438,12 +249,23 @@ class Empty(IStrategy):
|
|||||||
#
|
#
|
||||||
# range_pos = (last_candle['close'] - range_min) / (range_max - range_min)
|
# range_pos = (last_candle['close'] - range_min) / (range_max - range_min)
|
||||||
|
|
||||||
range_pos = last_candle[f"range_pos"]
|
# range_pos = last_candle[f"range_pos"]
|
||||||
sl_min = self.wallets.get_available_stake_amount() / 6
|
# sl_min = self.wallets.get_available_stake_amount() / 6
|
||||||
sl_max = self.wallets.get_available_stake_amount() / 2
|
# sl_max = self.wallets.get_available_stake_amount() / 2
|
||||||
|
#
|
||||||
|
# amount = sl_min + (1 - range_pos) * (sl_max - sl_min)
|
||||||
|
if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
|
||||||
|
amount = self.wallets.get_available_stake_amount() / 5
|
||||||
|
else:
|
||||||
|
amount = self.wallets.get_available_stake_amount() / self.mises.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
|
||||||
|
|
||||||
amount = sl_min + (1 - range_pos) * (sl_max - sl_min)
|
# factor = 1
|
||||||
amount = self.wallets.get_available_stake_amount()
|
#
|
||||||
|
# if self.pairs[pair]['last_trade'] is not None \
|
||||||
|
# and self.pairs[pair]['last_trade'].close_profit is not None \
|
||||||
|
# and self.pairs[pair]['last_trade'].close_profit > 0.01:
|
||||||
|
# factor = self.pairs[pair]['last_trade'].close_profit / 0.01
|
||||||
|
# amount = amount / factor
|
||||||
|
|
||||||
return min(amount, self.wallets.get_available_stake_amount())
|
return min(amount, self.wallets.get_available_stake_amount())
|
||||||
|
|
||||||
@@ -546,10 +368,16 @@ class Empty(IStrategy):
|
|||||||
last_candle_2 = dataframe.iloc[-2].squeeze()
|
last_candle_2 = dataframe.iloc[-2].squeeze()
|
||||||
last_candle_3 = dataframe.iloc[-3].squeeze()
|
last_candle_3 = dataframe.iloc[-3].squeeze()
|
||||||
|
|
||||||
|
if entry_tag == 'Range-':
|
||||||
|
self.pairs[pair]['count_of_lost'] = 0
|
||||||
|
if entry_tag == 'Force':
|
||||||
|
if self.pairs[pair]['count_of_lost'] >= 1:
|
||||||
|
self.pairs[pair]['count_of_lost'] = 0
|
||||||
|
condition = False
|
||||||
|
else:
|
||||||
condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
|
condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
|
||||||
and (last_candle['sma12_deriv1_1d'] < 0.001 \
|
and (last_candle['sma12_deriv1_1d'] < 0.001 or last_candle['sma12_deriv2_1d'] < 0.001) \
|
||||||
or last_candle['sma12_deriv2_1d'] < 0.001) else True
|
and entry_tag != 'dist' else True
|
||||||
|
|
||||||
reason = ''
|
reason = ''
|
||||||
if not condition:
|
if not condition:
|
||||||
reason = 'lost'
|
reason = 'lost'
|
||||||
@@ -619,19 +447,19 @@ class Empty(IStrategy):
|
|||||||
buys=1,
|
buys=1,
|
||||||
stake=round(stake_amount, 2)
|
stake=round(stake_amount, 2)
|
||||||
)
|
)
|
||||||
else:
|
# else:
|
||||||
self.log_trade(
|
# self.log_trade(
|
||||||
last_candle=last_candle,
|
# last_candle=last_candle,
|
||||||
date=current_time,
|
# date=current_time,
|
||||||
action=("🟩Buy" if allow_to_buy else "Canceled") + " " + reason,
|
# action=("🟩Buy" if allow_to_buy else "Canceled") + " " + reason,
|
||||||
pair=pair,
|
# pair=pair,
|
||||||
rate=rate,
|
# rate=rate,
|
||||||
dispo=dispo,
|
# dispo=dispo,
|
||||||
profit=0,
|
# profit=0,
|
||||||
trade_type=entry_tag,
|
# trade_type=entry_tag,
|
||||||
buys=1,
|
# buys=1,
|
||||||
stake=0
|
# stake=0
|
||||||
)
|
# )
|
||||||
return allow_to_buy
|
return allow_to_buy
|
||||||
|
|
||||||
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
|
||||||
@@ -701,6 +529,8 @@ class Empty(IStrategy):
|
|||||||
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
|
||||||
max_profit = self.pairs[pair]['max_profit']
|
max_profit = self.pairs[pair]['max_profit']
|
||||||
|
|
||||||
|
# baisse_min = last_candle['close'] - last_candle['min12']
|
||||||
|
|
||||||
baisse = 0
|
baisse = 0
|
||||||
if profit > 0:
|
if profit > 0:
|
||||||
baisse = 1 - (profit / max_profit)
|
baisse = 1 - (profit / max_profit)
|
||||||
@@ -722,22 +552,26 @@ class Empty(IStrategy):
|
|||||||
# buys=count_of_buys,
|
# buys=count_of_buys,
|
||||||
# stake=0
|
# stake=0
|
||||||
# )
|
# )
|
||||||
|
if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
|
||||||
if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60']:
|
if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
|
||||||
self.pairs[pair]['force_sell'] = True
|
self.pairs[pair]['force_sell'] = True
|
||||||
return 'sma60'
|
return 'smaBF'
|
||||||
|
else:
|
||||||
|
if current_profit < self.sell_force_sell.value \
|
||||||
|
and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
|
||||||
|
self.pairs[pair]['force_sell'] = True
|
||||||
|
return 'sma'
|
||||||
|
|
||||||
cross = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
|
if profit > max(5, expected_profit) and \
|
||||||
|
(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
|
||||||
# if profit > 0 and cross.iloc[-1]:
|
and last_candle['hapercent'] <0 :
|
||||||
# self.pairs[pair]['force_sell'] = True
|
|
||||||
# return 'Cross'
|
|
||||||
|
|
||||||
if profit > max(5, expected_profit) and baisse > 0.30 and last_candle[f"close"] <= last_candle['sma36']:
|
|
||||||
# and last_candle['percent3'] < 0 and last_candle['percent5'] < 0:
|
|
||||||
self.pairs[pair]['force_sell'] = True
|
self.pairs[pair]['force_sell'] = True
|
||||||
return 'B30'
|
return 'B30'
|
||||||
|
|
||||||
|
# if profit > max(5, expected_profit) and last_candle['sma5_deriv1_1d'] < 0 and baisse > 0.15:
|
||||||
|
# self.pairs[pair]['force_sell'] = True
|
||||||
|
# return 'Drv5d'
|
||||||
|
|
||||||
# if last_candle['range_pos'] > 0.05 and current_profit < - last_candle['range_pos'] /4 : #last_candle['cross_sma60']:
|
# if last_candle['range_pos'] > 0.05 and current_profit < - last_candle['range_pos'] /4 : #last_candle['cross_sma60']:
|
||||||
# self.pairs[pair]['force_sell'] = True
|
# self.pairs[pair]['force_sell'] = True
|
||||||
# return 'Range'
|
# return 'Range'
|
||||||
@@ -827,7 +661,9 @@ class Empty(IStrategy):
|
|||||||
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
|
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
|
||||||
dataframe['zero'] = 0
|
dataframe['zero'] = 0
|
||||||
|
|
||||||
|
dataframe[f"percent"] = dataframe['close'].pct_change()
|
||||||
for timeperiod in timeperiods:
|
for timeperiod in timeperiods:
|
||||||
|
dataframe[f"mid_smooth{timeperiod}"] = dataframe['mid'].rolling(timeperiod).mean()
|
||||||
dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
|
dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
|
||||||
dataframe[f'min{timeperiod}'] = talib.MIN(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"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod)
|
||||||
@@ -853,11 +689,27 @@ class Empty(IStrategy):
|
|||||||
|
|
||||||
for timeperiod in long_timeperiods:
|
for timeperiod in long_timeperiods:
|
||||||
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
|
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
|
||||||
|
self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
|
||||||
|
|
||||||
informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
|
informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
|
||||||
self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
|
self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
|
||||||
informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
|
informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
|
||||||
informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
|
informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
|
||||||
|
|
||||||
|
informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
|
||||||
|
informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
|
||||||
|
|
||||||
|
latched = np.zeros(len(informative), dtype=bool)
|
||||||
|
|
||||||
|
for i in range(1, len(informative)):
|
||||||
|
if informative['stop_buying_deb'].iloc[i]:
|
||||||
|
latched[i] = True
|
||||||
|
elif informative['stop_buying_end'].iloc[i]:
|
||||||
|
latched[i] = False
|
||||||
|
else:
|
||||||
|
latched[i] = latched[i - 1]
|
||||||
|
informative['stop_buying'] = latched
|
||||||
|
|
||||||
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||||||
# ######################################################################################################
|
# ######################################################################################################
|
||||||
|
|
||||||
@@ -868,7 +720,8 @@ class Empty(IStrategy):
|
|||||||
|
|
||||||
# dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
|
# dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
|
||||||
|
|
||||||
dataframe[f'stop_buying'] = qtpylib.crossed_below(dataframe[f"sma12"], dataframe['sma48'])
|
dataframe[f'has_cross_min'] = qtpylib.crossed_above(dataframe[f"close"], dataframe['min60_1d'])
|
||||||
|
dataframe[f'has_cross_min_6'] = (dataframe['has_cross_min'].rolling(6).max() == 1)
|
||||||
|
|
||||||
dataframe['atr'] = talib.ATR(dataframe)
|
dataframe['atr'] = talib.ATR(dataframe)
|
||||||
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
|
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
|
||||||
@@ -891,62 +744,13 @@ class Empty(IStrategy):
|
|||||||
|
|
||||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||||
|
|
||||||
# dataframe.loc[
|
|
||||||
# (
|
|
||||||
# (dataframe['sma24_score'].shift(2) <= dataframe['zero'])
|
|
||||||
# & (dataframe['sma5'].shift(1) <= dataframe['sma5'])
|
|
||||||
# & (dataframe['sma5_inv'] == -1)
|
|
||||||
# & (dataframe['min24'].shift(3) == dataframe['min24'])
|
|
||||||
# ),
|
|
||||||
# 'buy'] = 1
|
|
||||||
|
|
||||||
conditions = list()
|
conditions = list()
|
||||||
|
|
||||||
# # print(dataframe.columns)
|
# #####################################################################################
|
||||||
#
|
# CA MONTE !!
|
||||||
# buy_indicator = self.buy_indicator0.value
|
# #####################################################################################
|
||||||
# buy_crossed_indicator = self.buy_crossed_indicator0.value
|
conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1d" ] > self.start_bull_deriv1.value)
|
||||||
# buy_operator = self.buy_operator0.value
|
conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1d"] > self.start_bull_deriv2.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[self.stop_buying_indicator.value] == False) | (dataframe['range_pos'] < 0))
|
|
||||||
|
|
||||||
#conditions.append((dataframe[self.stop_buying_indicator.value] == False) | (dataframe['range_pos'] < 0))
|
|
||||||
|
|
||||||
conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
|
conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
|
||||||
conditions.append(dataframe['sma5_deriv1_1d'] > self.buy_deriv1_sma5d.value)
|
conditions.append(dataframe['sma5_deriv1_1d'] > self.buy_deriv1_sma5d.value)
|
||||||
conditions.append(dataframe['sma12_deriv1_1d'] > self.buy_deriv1_sma12d.value)
|
conditions.append(dataframe['sma12_deriv1_1d'] > self.buy_deriv1_sma12d.value)
|
||||||
@@ -962,11 +766,6 @@ class Empty(IStrategy):
|
|||||||
|
|
||||||
dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1d"] * 5)
|
dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1d"] * 5)
|
||||||
conditions.append((dataframe['max_rsi_12_1d'] < dynamic_rsi_threshold))
|
conditions.append((dataframe['max_rsi_12_1d'] < dynamic_rsi_threshold))
|
||||||
# | (
|
|
||||||
# (dataframe['sma5_deriv1'] > 0) & (dataframe['sma12_deriv1'] > 0) & (dataframe['sma24_deriv1'] > 0) & (
|
|
||||||
# dataframe['sma48_deriv1'] > 0) & (dataframe['sma60_deriv1'] > 0) & (dataframe['sma5_deriv1_1d'] > 0))
|
|
||||||
# )
|
|
||||||
|
|
||||||
conditions.append(dataframe[f"close"] > dataframe['sma60'])
|
conditions.append(dataframe[f"close"] > dataframe['sma60'])
|
||||||
conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
|
conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
|
||||||
|
|
||||||
@@ -975,17 +774,131 @@ class Empty(IStrategy):
|
|||||||
| (dataframe['sma60_inv_1d'] == -1)
|
| (dataframe['sma60_inv_1d'] == -1)
|
||||||
)
|
)
|
||||||
|
|
||||||
# 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:
|
if conditions:
|
||||||
dataframe.loc[
|
dataframe.loc[
|
||||||
reduce(lambda x, y: x & y, conditions),
|
reduce(lambda x, y: x & y, conditions),
|
||||||
['enter_long', 'enter_tag']
|
['enter_long', 'enter_tag']
|
||||||
] = (1, 'god')
|
] = (1, 'bull')
|
||||||
|
|
||||||
|
# #####################################################################################
|
||||||
|
# conditions = list()
|
||||||
|
# conditions.append(dataframe['dist_sma200_1d'] < -0.05)
|
||||||
|
# conditions.append(dataframe['sma12_inv'] == 1)
|
||||||
|
# # conditions.append(dataframe['sma100_deriv1_1d'] > 0)
|
||||||
|
# if conditions:
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'dist')
|
||||||
|
|
||||||
|
# #####################################################################################
|
||||||
|
conditions = list()
|
||||||
|
conditions.append(dataframe['close'] < dataframe['sma100_1d'])
|
||||||
|
conditions.append(dataframe['mid_smooth12'] > dataframe['mid_smooth12'].shift(1))
|
||||||
|
conditions.append(dataframe['sma100_deriv1_1d'] > 0)
|
||||||
|
conditions.append(dataframe[f"range_pos"] < 0.01)
|
||||||
|
# if conditions:
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'dist')
|
||||||
|
|
||||||
|
# #####################################################################################
|
||||||
|
# CA BAISSE !!
|
||||||
|
# "buy": {
|
||||||
|
# "buy_deriv1_sma12d": 0.0,
|
||||||
|
# "buy_deriv1_sma5d": 0.0,
|
||||||
|
# "buy_deriv1_sma60": 0.003,
|
||||||
|
# "buy_deriv2_sma12d": -0.03,
|
||||||
|
# "buy_deriv2_sma5d": 0.0,
|
||||||
|
# "buy_deriv2_sma60": -0.002,
|
||||||
|
# "buy_longue": 200,
|
||||||
|
# "buy_longue_derive": "sma160_deriv1_1d"
|
||||||
|
# },
|
||||||
|
# "protection": {
|
||||||
|
# "drop_from_last_entry": -0.03,
|
||||||
|
# "b30_indicateur": "sma3",
|
||||||
|
# "baisse": 0.31
|
||||||
|
# },
|
||||||
|
# #####################################################################################
|
||||||
|
conditions = list()
|
||||||
|
conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv1_1d" ] < self.start_bear_deriv1.value)
|
||||||
|
conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv2_1d"] < self.start_bear_deriv2.value)
|
||||||
|
|
||||||
|
conditions.append(dataframe['sma12_deriv1'] > 0.003)
|
||||||
|
conditions.append(dataframe['sma5_deriv1_1d'] > 0.0)
|
||||||
|
conditions.append(dataframe['sma12_deriv1_1d'] > 0.0)
|
||||||
|
|
||||||
|
conditions.append(dataframe['sma12_deriv2'] > -0.002)
|
||||||
|
conditions.append(dataframe['sma5_deriv2_1d'] > 0)
|
||||||
|
conditions.append(dataframe['sma12_deriv2_1d'] > -0.03)
|
||||||
|
|
||||||
|
conditions.append(dataframe['hapercent'] > 0)
|
||||||
|
# conditions.append(dataframe['percent12'] < 0.01)
|
||||||
|
# conditions.append(dataframe['percent5'] < 0.01)
|
||||||
|
conditions.append(dataframe['max_rsi_24'] < 80)
|
||||||
|
|
||||||
|
dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1d"] * 5)
|
||||||
|
conditions.append((dataframe['max_rsi_12_1d'] < dynamic_rsi_threshold))
|
||||||
|
conditions.append(dataframe[f"close"] > dataframe['sma60'])
|
||||||
|
conditions.append(((dataframe[f"range_pos"] < 0.05) ) | ((dataframe['sma12_deriv1'] > 0) & (dataframe['sma12_deriv2'] > 0)))
|
||||||
|
|
||||||
|
conditions.append(
|
||||||
|
(dataframe['close_1d'] > dataframe[f'sma{self.buy_longue.value}_1d'])
|
||||||
|
| (dataframe['sma60_inv_1d'] == -1)
|
||||||
|
)
|
||||||
|
|
||||||
|
# if conditions:
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'bear')
|
||||||
|
|
||||||
|
# conditions = list()
|
||||||
|
# conditions.append(dataframe['close_1d'] < dataframe[f'sma{self.buy_longue.value}_1d'])
|
||||||
|
# conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
|
||||||
|
# conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
|
||||||
|
# conditions.append(dataframe['hapercent'] > 0)
|
||||||
|
# conditions.append(dataframe['percent5'] < 0.01)
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'cross_min')
|
||||||
|
|
||||||
|
conditions = list()
|
||||||
|
conditions.append(dataframe['sma12_deriv1'] > 0.00)
|
||||||
|
conditions.append(dataframe['sma60_deriv1'] > 0.0)
|
||||||
|
conditions.append(dataframe['sma5_deriv1_1d'] > 0.0)
|
||||||
|
conditions.append(dataframe['sma12_deriv1_1d'] > 0.0)
|
||||||
|
conditions.append(dataframe['sma24_deriv1_1d'] > 0.0)
|
||||||
|
conditions.append(dataframe['sma100_deriv1_1d'] > 0.0)
|
||||||
|
conditions.append(dataframe[f"range_pos"] < 0.025)
|
||||||
|
# conditions.append(dataframe['sma12_deriv1_1d'] > 0.0)
|
||||||
|
# # conditions.append(dataframe['close_1d'] < dataframe[f'sma{self.buy_longue.value}_1d'])
|
||||||
|
# # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
|
||||||
|
# # conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
|
||||||
|
# conditions.append(dataframe['min12'] == dataframe['min12'].shift(3))
|
||||||
|
# conditions.append((dataframe['percent24'] < -0.025) | (dataframe['percent12'] < -0.025))
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'Rise')
|
||||||
|
|
||||||
|
# conditions = list()
|
||||||
|
# conditions.append(dataframe['has_cross_min_6'] == True)
|
||||||
|
# conditions.append(dataframe['min36'] == dataframe['min36'].shift(3))
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'Force')
|
||||||
|
|
||||||
|
# conditions = list()
|
||||||
|
# conditions.append(dataframe['range_pos'] < -0.03)
|
||||||
|
# conditions.append(dataframe['min36'] == dataframe['min36'].shift(3))
|
||||||
|
# dataframe.loc[
|
||||||
|
# reduce(lambda x, y: x & y, conditions),
|
||||||
|
# ['enter_long', 'enter_tag']
|
||||||
|
# ] = (1, 'Range-')
|
||||||
|
|
||||||
return dataframe
|
return dataframe
|
||||||
|
|
||||||
@@ -1087,35 +1000,35 @@ class Empty(IStrategy):
|
|||||||
|
|
||||||
# ####################################################################
|
# ####################################################################
|
||||||
# Calcul de la pente lissée
|
# Calcul de la pente lissée
|
||||||
d1 = series.diff()
|
# d1 = series.diff()
|
||||||
d1_smooth = d1.rolling(5).mean()
|
# d1_smooth = d1.rolling(5).mean()
|
||||||
# Normalisation
|
# # Normalisation
|
||||||
z = (d1_smooth - d1_smooth.rolling(ema_period).mean()) / d1_smooth.rolling(ema_period).std()
|
# z = (d1_smooth - d1_smooth.rolling(ema_period).mean()) / d1_smooth.rolling(ema_period).std()
|
||||||
|
|
||||||
dataframe[f"{name}{suffixe}_trend_up"] = (
|
# dataframe[f"{name}{suffixe}_trend_up"] = (
|
||||||
(d1_smooth.shift(1) < 0) &
|
# (d1_smooth.shift(1) < 0) &
|
||||||
(d1_smooth > 0) &
|
# (d1_smooth > 0) &
|
||||||
(z > 1.0)
|
# (z > 1.0)
|
||||||
)
|
# )
|
||||||
|
#
|
||||||
dataframe[f"{name}{suffixe}_trend_down"] = (
|
# dataframe[f"{name}{suffixe}_trend_down"] = (
|
||||||
(d1_smooth.shift(1) > 0) &
|
# (d1_smooth.shift(1) > 0) &
|
||||||
(d1_smooth < 0) &
|
# (d1_smooth < 0) &
|
||||||
(z < -1.0)
|
# (z < -1.0)
|
||||||
)
|
# )
|
||||||
|
#
|
||||||
momentum_short = d1.rolling(int(ema_period / 2)).mean()
|
# momentum_short = d1.rolling(int(ema_period / 2)).mean()
|
||||||
momentum_long = d1.rolling(ema_period * 2).mean()
|
# momentum_long = d1.rolling(ema_period * 2).mean()
|
||||||
|
#
|
||||||
dataframe[f"{name}{suffixe}_trend_change_up"] = (
|
# dataframe[f"{name}{suffixe}_trend_change_up"] = (
|
||||||
(momentum_short.shift(1) < momentum_long.shift(1)) &
|
# (momentum_short.shift(1) < momentum_long.shift(1)) &
|
||||||
(momentum_short > momentum_long)
|
# (momentum_short > momentum_long)
|
||||||
)
|
# )
|
||||||
|
#
|
||||||
dataframe[f"{name}{suffixe}_trend_change_down"] = (
|
# dataframe[f"{name}{suffixe}_trend_change_down"] = (
|
||||||
(momentum_short.shift(1) > momentum_long.shift(1)) &
|
# (momentum_short.shift(1) > momentum_long.shift(1)) &
|
||||||
(momentum_short < momentum_long)
|
# (momentum_short < momentum_long)
|
||||||
)
|
# )
|
||||||
|
|
||||||
return dataframe
|
return dataframe
|
||||||
|
|
||||||
@@ -1291,3 +1204,11 @@ class Empty(IStrategy):
|
|||||||
|
|
||||||
self.pairs[pair]['expected_profit'] = expected_profit
|
self.pairs[pair]['expected_profit'] = expected_profit
|
||||||
return expected_profit
|
return expected_profit
|
||||||
|
|
||||||
|
def getLastClosedTrade(self):
|
||||||
|
# récupérer le dernier trade fermé
|
||||||
|
trades = Trade.get_trades_proxy(pair=pair,is_open=False)
|
||||||
|
if trades:
|
||||||
|
last_trade = trades[-1]
|
||||||
|
self.pairs[pair]['last_profit'] = last_trade.close_profit # ex: 0.12 = +12%
|
||||||
|
self.pairs[pair]['last_trade'] = last_trade
|
||||||
32
Empty5m.json
Normal file
32
Empty5m.json
Normal file
@@ -0,0 +1,32 @@
|
|||||||
|
{
|
||||||
|
"strategy_name": "Empty5m",
|
||||||
|
"params": {
|
||||||
|
"roi": {
|
||||||
|
"0": 5
|
||||||
|
},
|
||||||
|
"stoploss": {
|
||||||
|
"stoploss": -1.0
|
||||||
|
},
|
||||||
|
"trailing": {
|
||||||
|
"trailing_stop": false,
|
||||||
|
"trailing_stop_positive": null,
|
||||||
|
"trailing_stop_positive_offset": 0.0,
|
||||||
|
"trailing_only_offset_is_reached": false
|
||||||
|
},
|
||||||
|
"max_open_trades": {
|
||||||
|
"max_open_trades": 20
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"b30_indicateur": "sma36",
|
||||||
|
"baisse": 0.3,
|
||||||
|
"sell_force_sell": -0.03,
|
||||||
|
"sell_indicator": "sma36"
|
||||||
|
},
|
||||||
|
"protection": {
|
||||||
|
"drop_from_last_entry": -0.01,
|
||||||
|
"mises_bull": 4
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"ft_stratparam_v": 1,
|
||||||
|
"export_time": "2026-03-07 20:20:17.895644+00:00"
|
||||||
|
}
|
||||||
1452
Empty5m.py
Normal file
1452
Empty5m.py
Normal file
File diff suppressed because it is too large
Load Diff
1218
EmptyAI.py
Normal file
1218
EmptyAI.py
Normal file
File diff suppressed because it is too large
Load Diff
32
EmptyShort.json
Normal file
32
EmptyShort.json
Normal file
@@ -0,0 +1,32 @@
|
|||||||
|
{
|
||||||
|
"strategy_name": "EmptyShort",
|
||||||
|
"params": {
|
||||||
|
"roi": {
|
||||||
|
"0": 5
|
||||||
|
},
|
||||||
|
"trailing": {
|
||||||
|
"trailing_stop": false,
|
||||||
|
"trailing_stop_positive": null,
|
||||||
|
"trailing_stop_positive_offset": 0.0,
|
||||||
|
"trailing_only_offset_is_reached": false
|
||||||
|
},
|
||||||
|
"max_open_trades": {
|
||||||
|
"max_open_trades": 20
|
||||||
|
},
|
||||||
|
"sell": {
|
||||||
|
"b30_indicateur": "sma24",
|
||||||
|
"baisse": 0.2,
|
||||||
|
"sell_force_sell": -0.02,
|
||||||
|
"sell_indicator": "sma36"
|
||||||
|
},
|
||||||
|
"protection": {
|
||||||
|
"drop_from_last_entry": 0.0,
|
||||||
|
"mises_bull": 4
|
||||||
|
},
|
||||||
|
"stoploss": {
|
||||||
|
"stoploss": -0.02
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"ft_stratparam_v": 1,
|
||||||
|
"export_time": "2026-03-29 18:29:54.986316+00:00"
|
||||||
|
}
|
||||||
1419
EmptyShort.py
Normal file
1419
EmptyShort.py
Normal file
File diff suppressed because it is too large
Load Diff
188
EmptyShort.txt
Normal file
188
EmptyShort.txt
Normal file
@@ -0,0 +1,188 @@
|
|||||||
|
BACKTESTING REPORT
|
||||||
|
┏━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||||
|
┡━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ BTC/USDT:USDT │ 54 │ 0.75 │ 422.136 │ 42.21 │ 1 day, 5:07:00 │ 25 0 29 46.3 │
|
||||||
|
│ TOTAL │ 54 │ 0.75 │ 422.136 │ 42.21 │ 1 day, 5:07:00 │ 25 0 29 46.3 │
|
||||||
|
└───────────────┴────────┴──────────────┴─────────────────┴──────────────┴────────────────┴────────────────────────┘
|
||||||
|
LEFT OPEN TRADES REPORT
|
||||||
|
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||||
|
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ TOTAL │ 0 │ 0.0 │ 0.000 │ 0.0 │ 0:00 │ 0 0 0 0 │
|
||||||
|
└───────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
|
||||||
|
ENTER TAG STATS
|
||||||
|
┏━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||||
|
┡━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ short │ 28 │ 0.72 │ 211.154 │ 21.12 │ 19:27:00 │ 13 0 15 46.4 │
|
||||||
|
│ short_spike │ 25 │ 0.65 │ 174.806 │ 17.48 │ 1 day, 10:54:00 │ 11 0 14 44.0 │
|
||||||
|
│ long │ 1 │ 3.75 │ 36.176 │ 3.62 │ 6 days, 10:57:00 │ 1 0 0 100 │
|
||||||
|
│ TOTAL │ 54 │ 0.75 │ 422.136 │ 42.21 │ 1 day, 5:07:00 │ 25 0 29 46.3 │
|
||||||
|
└─────────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||||
|
EXIT REASON STATS
|
||||||
|
┏━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||||
|
┡━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ B30Sht │ 24 │ 4.06 │ 1155.077 │ 115.51 │ 1 day, 10:30:00 │ 24 0 0 100 │
|
||||||
|
│ B30 │ 1 │ 3.75 │ 36.176 │ 3.62 │ 6 days, 10:57:00 │ 1 0 0 100 │
|
||||||
|
│ stop_loss │ 29 │ -2.1 │ -769.117 │ -76.91 │ 20:19:00 │ 0 0 29 0 │
|
||||||
|
│ TOTAL │ 54 │ 0.75 │ 422.136 │ 42.21 │ 1 day, 5:07:00 │ 25 0 29 46.3 │
|
||||||
|
└─────────────┴───────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||||
|
MIXED TAG STATS
|
||||||
|
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃
|
||||||
|
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ short │ B30Sht │ 13 │ 3.98 │ 601.641 │ 60.16 │ 1 day, 9:46:00 │ 13 0 0 100 │
|
||||||
|
│ short_spike │ B30Sht │ 11 │ 4.16 │ 553.436 │ 55.34 │ 1 day, 11:22:00 │ 11 0 0 100 │
|
||||||
|
│ long │ B30 │ 1 │ 3.75 │ 36.176 │ 3.62 │ 6 days, 10:57:00 │ 1 0 0 100 │
|
||||||
|
│ short_spike │ stop_loss │ 14 │ -2.1 │ -378.630 │ -37.86 │ 1 day, 10:32:00 │ 0 0 14 0 │
|
||||||
|
│ short │ stop_loss │ 15 │ -2.1 │ -390.487 │ -39.05 │ 7:03:00 │ 0 0 15 0 │
|
||||||
|
│ TOTAL │ │ 54 │ 0.75 │ 422.136 │ 42.21 │ 1 day, 5:07:00 │ 25 0 29 46.3 │
|
||||||
|
└─────────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
|
||||||
|
DAY BREAKDOWN
|
||||||
|
┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Day ┃ Trades ┃ Tot Profit USDT ┃ Profit Factor ┃ Win Draw Loss Win% ┃
|
||||||
|
┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ 07/01/2026 │ 1 │ 36.176 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 08/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 09/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 10/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 11/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 12/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 13/01/2026 │ 1 │ -20.15 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 14/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 15/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 16/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 17/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 18/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 19/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 20/01/2026 │ 1 │ 49.664 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 21/01/2026 │ 1 │ 20.963 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 22/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 23/01/2026 │ 1 │ -22.227 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 24/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 25/01/2026 │ 1 │ 32.61 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 26/01/2026 │ 1 │ -21.707 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 27/01/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 28/01/2026 │ 1 │ -21.939 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 29/01/2026 │ 1 │ 50.715 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 30/01/2026 │ 1 │ -22.455 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 31/01/2026 │ 1 │ 59.691 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 01/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 02/02/2026 │ 3 │ -7.976 │ 0.83 │ 1 0 2 33.3 │
|
||||||
|
│ 03/02/2026 │ 1 │ 22.032 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 04/02/2026 │ 1 │ 33.959 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 05/02/2026 │ 1 │ 39.293 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 06/02/2026 │ 4 │ 74.327 │ 1.89 │ 1 0 3 25.0 │
|
||||||
|
│ 07/02/2026 │ 2 │ 12.485 │ 1.46 │ 1 0 1 50.0 │
|
||||||
|
│ 08/02/2026 │ 2 │ -54.289 │ 0.0 │ 0 0 2 0 │
|
||||||
|
│ 09/02/2026 │ 2 │ -1.939 │ 0.93 │ 1 0 1 50.0 │
|
||||||
|
│ 10/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 11/02/2026 │ 4 │ 20.391 │ 1.38 │ 2 0 2 50.0 │
|
||||||
|
│ 12/02/2026 │ 1 │ 35.171 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 13/02/2026 │ 1 │ -26.178 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 14/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 15/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 16/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 17/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 18/02/2026 │ 1 │ 36.046 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 19/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 20/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 21/02/2026 │ 1 │ -26.642 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 22/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 23/02/2026 │ 1 │ 57.25 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 24/02/2026 │ 2 │ 7.103 │ 1.25 │ 1 0 1 50.0 │
|
||||||
|
│ 25/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 26/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 27/02/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 28/02/2026 │ 1 │ 87.891 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 01/03/2026 │ 1 │ -29.21 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 02/03/2026 │ 1 │ -29.332 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 03/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 04/03/2026 │ 2 │ -57.218 │ 0.0 │ 0 0 2 0 │
|
||||||
|
│ 05/03/2026 │ 1 │ -27.178 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 06/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 07/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 08/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 09/03/2026 │ 1 │ 104.735 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 10/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 11/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 12/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 13/03/2026 │ 1 │ -30.23 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 14/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 15/03/2026 │ 1 │ -28.727 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 16/03/2026 │ 1 │ -27.688 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 17/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 18/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 19/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 20/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 21/03/2026 │ 1 │ 59.772 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 22/03/2026 │ 0 │ 0 │ 0.0 │ 0 0 0 0 │
|
||||||
|
│ 23/03/2026 │ 1 │ -29.287 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 24/03/2026 │ 1 │ 29.577 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 25/03/2026 │ 1 │ -27.872 │ 0.0 │ 0 0 1 0 │
|
||||||
|
│ 26/03/2026 │ 1 │ 36.754 │ 0.0 │ 1 0 0 100 │
|
||||||
|
│ 27/03/2026 │ 1 │ 27.777 │ 0.0 │ 1 0 0 100 │
|
||||||
|
└────────────┴────────┴─────────────────┴───────────────┴────────────────────────┘
|
||||||
|
SUMMARY METRICS
|
||||||
|
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Metric ┃ Value ┃
|
||||||
|
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ Backtesting from │ 2026-01-01 00:00:00 │
|
||||||
|
│ Backtesting to │ 2026-03-27 15:43:00 │
|
||||||
|
│ Trading Mode │ Isolated Futures │
|
||||||
|
│ Max open trades │ 1 │
|
||||||
|
│ │ │
|
||||||
|
│ Total/Daily Avg Trades │ 54 / 0.64 │
|
||||||
|
│ Starting balance │ 1000 USDT │
|
||||||
|
│ Final balance │ 1422.136 USDT │
|
||||||
|
│ Absolute profit │ 422.136 USDT │
|
||||||
|
│ Total profit % │ 42.21% │
|
||||||
|
│ CAGR % │ 353.68% │
|
||||||
|
│ Sortino │ 36.18 │
|
||||||
|
│ Sharpe │ 2.25 │
|
||||||
|
│ Calmar │ 96.27 │
|
||||||
|
│ SQN │ 1.35 │
|
||||||
|
│ Profit factor │ 1.55 │
|
||||||
|
│ Expectancy (Ratio) │ 7.82 (0.29) │
|
||||||
|
│ Avg. daily profit │ 4.966 USDT │
|
||||||
|
│ Avg. stake amount │ 1224.501 USDT │
|
||||||
|
│ Total trade volume │ 131900.967 USDT │
|
||||||
|
│ │ │
|
||||||
|
│ Long / Short trades │ 1 / 53 │
|
||||||
|
│ Long / Short profit % │ 3.62% / 38.60% │
|
||||||
|
│ Long / Short profit USDT │ 36.176 / 385.960 │
|
||||||
|
│ │ │
|
||||||
|
│ Best Pair │ BTC/USDT:USDT 42.21% │
|
||||||
|
│ Worst Pair │ BTC/USDT:USDT 42.21% │
|
||||||
|
│ Best trade │ BTC/USDT:USDT 13.07% │
|
||||||
|
│ Worst trade │ BTC/USDT:USDT -2.14% │
|
||||||
|
│ Best day │ 104.735 USDT │
|
||||||
|
│ Worst day │ -57.218 USDT │
|
||||||
|
│ Days win/draw/lose │ 22 / 39 / 19 │
|
||||||
|
│ Min/Max/Avg. Duration Winners │ 0d 00:58 / 6d 10:57 / 1d 15:19 │
|
||||||
|
│ Min/Max/Avg. Duration Losers │ 0d 00:18 / 5d 19:41 / 0d 20:19 │
|
||||||
|
│ Max Consecutive Wins / Loss │ 4 / 5 │
|
||||||
|
│ Rejected Entry signals │ 0 │
|
||||||
|
│ Entry/Exit Timeouts │ 0 / 0 │
|
||||||
|
│ │ │
|
||||||
|
│ Min balance │ 1016.026 USDT │
|
||||||
|
│ Max balance │ 1450.263 USDT │
|
||||||
|
│ Max % of account underwater │ 9.86% │
|
||||||
|
│ Absolute drawdown │ 142.938 USDT (9.86%) │
|
||||||
|
│ Drawdown duration │ 4 days 23:26:00 │
|
||||||
|
│ Profit at drawdown start │ 450.263 USDT │
|
||||||
|
│ Profit at drawdown end │ 307.325 USDT │
|
||||||
|
│ Drawdown start │ 2026-02-28 11:00:00 │
|
||||||
|
│ Drawdown end │ 2026-03-05 10:26:00 │
|
||||||
|
│ Market change │ -24.27% │
|
||||||
|
└───────────────────────────────┴────────────────────────────────┘
|
||||||
|
|
||||||
|
Backtested 2026-01-01 00:00:00 -> 2026-03-27 15:43:00 | Max open trades : 1
|
||||||
|
STRATEGY SUMMARY
|
||||||
|
┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
|
||||||
|
┃ Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃ Win Draw Loss Win% ┃ Drawdown ┃
|
||||||
|
┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
|
||||||
|
│ EmptyShort │ 54 │ 0.75 │ 422.136 │ 42.21 │ 1 day, 5:07:00 │ 25 0 29 46.3 │ 142.938 USDT 9.86% │
|
||||||
|
└────────────┴────────┴──────────────┴─────────────────┴──────────────┴────────────────┴────────────────────────┴─────────────────────┘
|
||||||
@@ -5,7 +5,7 @@
|
|||||||
"0": 10
|
"0": 10
|
||||||
},
|
},
|
||||||
"stoploss": {
|
"stoploss": {
|
||||||
"stoploss": -1.0
|
"stoploss": -0.02
|
||||||
},
|
},
|
||||||
"trailing": {
|
"trailing": {
|
||||||
"trailing_stop": false,
|
"trailing_stop": false,
|
||||||
|
|||||||
1933
FrictradeLearning.py
1933
FrictradeLearning.py
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user