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5 Commits
2a7dd0beb4
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Multiple_p
| Author | SHA1 | Date | |
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a925a28389 | ||
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3302266b4a | ||
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7954c518bd | ||
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a599160501 | ||
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8b030f46f0 |
@@ -4,9 +4,6 @@
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"roi": {
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"0": 5
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},
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"stoploss": {
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"stoploss": -1.0
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},
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"trailing": {
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"trailing_stop": false,
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"trailing_stop_positive": null,
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@@ -25,8 +22,11 @@
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"protection": {
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"drop_from_last_entry": 0.0,
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"mises_bull": 4
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},
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"stoploss": {
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"stoploss": -0.02
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2026-03-07 20:20:17.895644+00:00"
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"export_time": "2026-03-29 18:29:54.986316+00:00"
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}
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536
EmptyShort.py
536
EmptyShort.py
@@ -103,7 +103,7 @@ class EmptyShort(IStrategy):
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# trailing_only_offset_is_reached = False
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position_adjustment_enable = True
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use_custom_stoploss = True
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use_custom_stoploss = False
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#max_open_trades = 3
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@@ -373,24 +373,25 @@ class EmptyShort(IStrategy):
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last_candle_2 = dataframe.iloc[-2].squeeze()
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last_candle_3 = dataframe.iloc[-3].squeeze()
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if entry_tag == 'Range-':
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self.pairs[pair]['count_of_lost'] = 0
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if entry_tag == 'Force':
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if self.pairs[pair]['count_of_lost'] >= 1:
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self.pairs[pair]['count_of_lost'] = 0
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condition = False
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else:
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condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
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and (last_candle['sma12_deriv1_1h'] < 0.00) \
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and entry_tag != 'dist' else True
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reason = ''
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if not condition:
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reason = 'lost'
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if condition:
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if last_candle['range_pos'] > 0.05:# and self.pairs[pair]['force_stop']:
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condition = last_candle['sma5'] > last_candle['sma60']
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if not condition:
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reason = 'range'
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condition = True
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# if entry_tag == 'Range-':
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# self.pairs[pair]['count_of_lost'] = 0
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# if entry_tag == 'Force':
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# if self.pairs[pair]['count_of_lost'] >= 1:
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# self.pairs[pair]['count_of_lost'] = 0
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# condition = False
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# else:
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# condition = False if self.pairs[pair]['count_of_lost'] >= 1 \
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# and (last_candle['sma12_deriv1_1h'] < 0.00) \
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# and entry_tag != 'dist' else True
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# reason = ''
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# if not condition:
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# reason = 'lost'
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# if condition:
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# if last_candle['range_pos'] > 0.05:# and self.pairs[pair]['force_stop']:
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# condition = last_candle['sma5'] > last_candle['sma60']
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# if not condition:
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# reason = 'range'
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# if self.pairs[pair]['force_stop'] and last_candle['range_pos'] < 0.02:
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# self.pairs[pair]['force_stop'] = False
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@@ -458,7 +459,7 @@ class EmptyShort(IStrategy):
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
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action=("🟩Buy") + " " + str(minutes),
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pair=pair,
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rate=rate,
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dispo=dispo,
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@@ -471,7 +472,7 @@ class EmptyShort(IStrategy):
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self.log_trade(
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last_candle=last_candle,
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date=current_time,
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action=("🟩Buy" if allow_to_buy else "Canceled") + " " + reason,
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action=("Canceled") + " " + reason,
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pair=pair,
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rate=rate,
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dispo=dispo,
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@@ -493,7 +494,7 @@ class EmptyShort(IStrategy):
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profit = trade.calc_profit(rate)
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force = self.pairs[pair]['force_sell']
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allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force \
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or (trade.enter_tag == 'short' ) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss')
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or (trade.is_short) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss')
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minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
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@@ -561,21 +562,28 @@ class EmptyShort(IStrategy):
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dispo = round(self.wallets.get_available_stake_amount() + self.pairs[pair]['total_amount'])
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is_short = (trade.enter_tag == 'short')
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if trade.enter_tag == 'short':
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tp_price = self.pairs[pair]['take_profit']
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if trade.is_short:
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tp_price = max(0.02, self.pairs[pair]['take_profit'])
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# if current_profit < self.sell_force_sell.value \
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# and last_candle[f"close"] > last_candle[self.sell_indicator.value]:
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# self.pairs[pair]['force_sell'] = True
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# return 'sma'
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#
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if current_profit > tp_price and \
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(baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] > 0:
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(baisse > last_candle[f"range_pos"] and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] > 0: # and last_candle['sma12_deriv2_1h'] > 0.0:
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self.pairs[pair]['force_sell'] = True
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return 'B30Sht'
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return None
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# if current_profit < - 0.01 and last_candle['sma60_deriv1'] > 0 and last_candle['sma60_deriv2'] > 0:
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# self.pairs[pair]['force_sell'] = True
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# return 'StpSht'
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# if last_candle['sma12_inv_1h'] == -1:
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# return 'InvSht'
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# return None
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# self.log_trade(
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# last_candle=last_candle,
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# date=current_time,
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@@ -598,12 +606,12 @@ class EmptyShort(IStrategy):
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# and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
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# self.pairs[pair]['force_sell'] = True
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# return 'sma'
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if current_profit > 0.00 and \
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(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] <0 :
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if current_profit > 0.01:
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self.pairs[pair]['force_buy'] = True
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tp_price = max(0.02, self.pairs[pair]['take_profit'])
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if current_profit > tp_price and \
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(baisse > min(last_candle[f"range_pos"], 0.5) and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
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and last_candle['hapercent'] <0 : #and last_candle['sma12_deriv1_1h'] < 0.004:
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# if current_profit > 0.01:
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# self.pairs[pair]['force_buy'] = True
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self.pairs[pair]['force_sell'] = True
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return 'B30'
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@@ -611,9 +619,9 @@ class EmptyShort(IStrategy):
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# self.pairs[pair]['force_sell'] = True
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# return 'B50'
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if current_profit > - self.sell_force_sell.value and last_candle['has_cross_sma3_1h'] == 1:
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self.pairs[pair]['force_sell'] = True
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return 'Cross'
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# if current_profit > - self.sell_force_sell.value and last_candle['has_cross_sma3_1h'] == 1:
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# self.pairs[pair]['force_sell'] = True
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# return 'Cross'
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# if profit > max(5, expected_profit) and last_candle['sma5_deriv1_1h'] < 0 and baisse > 0.15:
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# self.pairs[pair]['force_sell'] = True
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@@ -654,41 +662,41 @@ class EmptyShort(IStrategy):
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return None
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def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1]
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profit = trade.calc_profit(current_rate)
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candle_at_buy = self.pairs[pair]['last_candle']
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# if candle_at_buy['range_pos'] > self.range_pos_stoploss.value and candle_at_buy[self.stoploss_indicator.value] < 0:
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# return self.stoploss_force.value
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# # print(f'test stop loss {self.stoploss} {last_candle["stop_buying12_1h"]}')
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# if last_candle[self.stoploss_indicator.value] and (trade.nr_of_successful_entries >= 4 or self.wallets.get_available_stake_amount() < 300): # and profit < - 30 :
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# range_min = last_candle[f"min{self.stoploss_timeperiod.value}_1h"]
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# range_max = last_candle[f"max{self.stoploss_timeperiod.value}_1h"]
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# def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
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# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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# last_candle = dataframe.iloc[-1]
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# profit = trade.calc_profit(current_rate)
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#
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# if range_max == range_min:
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# print(f'ranges={range_min}')
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# return -0.1 # sécurité
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# candle_at_buy = self.pairs[pair]['last_candle']
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#
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# range_pos = (current_rate - range_min) / (range_max - range_min)
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# # if candle_at_buy['range_pos'] > self.range_pos_stoploss.value and candle_at_buy[self.stoploss_indicator.value] < 0:
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# # return self.stoploss_force.value
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#
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# # # print(f'test stop loss {self.stoploss} {last_candle["stop_buying12_1h"]}')
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# # if last_candle[self.stoploss_indicator.value] and (trade.nr_of_successful_entries >= 4 or self.wallets.get_available_stake_amount() < 300): # and profit < - 30 :
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# # range_min = last_candle[f"min{self.stoploss_timeperiod.value}_1h"]
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# # range_max = last_candle[f"max{self.stoploss_timeperiod.value}_1h"]
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# #
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# # if range_max == range_min:
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# # print(f'ranges={range_min}')
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# # return -0.1 # sécurité
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# #
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# # range_pos = (current_rate - range_min) / (range_max - range_min)
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# #
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# # if (range_pos > 1):
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# # return -1
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# #
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# # sl_min = -0.02
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# # sl_max = -0.1 #self.stoploss
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# #
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# # dynamic_sl = (sl_min + (1 - range_pos) * (sl_max - sl_min))
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# #
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# # print(f'{current_time} Loss ranges={round(range_min,0)} {round(range_max, 0)} range_pos={round(range_pos, 3)} dynamic_sl={round(dynamic_sl, 3)} '
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# # f'profit={profit} current={current_profit} {self.stoploss_indicator.value} {self.stoploss_timeperiod.value} {last_candle[self.stoploss_indicator.value]}')
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# #
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# # return dynamic_sl
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#
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# if (range_pos > 1):
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# return -1
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#
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# sl_min = -0.02
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# sl_max = -0.1 #self.stoploss
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#
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# dynamic_sl = (sl_min + (1 - range_pos) * (sl_max - sl_min))
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#
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# print(f'{current_time} Loss ranges={round(range_min,0)} {round(range_max, 0)} range_pos={round(range_pos, 3)} dynamic_sl={round(dynamic_sl, 3)} '
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# f'profit={profit} current={current_profit} {self.stoploss_indicator.value} {self.stoploss_timeperiod.value} {last_candle[self.stoploss_indicator.value]}')
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#
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# return dynamic_sl
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return -1
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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@@ -734,6 +742,10 @@ class EmptyShort(IStrategy):
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dataframe["volume_mean"] = dataframe["volume"].rolling(20).mean()
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dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_mean"]
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dataframe['volume2'] = dataframe['volume']
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dataframe.loc[dataframe['hapercent'] < 0, 'volume2'] *= -1
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dataframe['volume_spike'] = (abs(dataframe['volume2']) > abs(dataframe['volume2'].rolling(window=20).mean() * 5)) \
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& (dataframe['volume'].rolling(window=5).max() > 1000)
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dataframe["market_state"] = 0
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@@ -751,7 +763,7 @@ class EmptyShort(IStrategy):
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informative['haclose'] = heikinashi['close']
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informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
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informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
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for timeperiod in [3, 5, 12]:
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for timeperiod in [3, 5, 12, 50, 200]:
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informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
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informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
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# informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
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@@ -763,9 +775,9 @@ class EmptyShort(IStrategy):
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informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
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informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
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for timeperiod in long_timeperiods:
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informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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# for timeperiod in long_timeperiods:
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# informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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# self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
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self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
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@@ -810,9 +822,9 @@ class EmptyShort(IStrategy):
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informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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for timeperiod in long_timeperiods:
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informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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# for timeperiod in long_timeperiods:
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# informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
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# self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
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informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
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self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
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@@ -840,10 +852,12 @@ class EmptyShort(IStrategy):
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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# ######################################################################################################
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range_min = dataframe[f"min12_1h"]
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range_max = dataframe[f"max48"]
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range_min = dataframe[f"min12_1d"]
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range_max = dataframe[f"max12_1d"]
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dataframe[f"range_pos"] = ((dataframe['mid'] - range_min) / (range_max)).rolling(5).mean()
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dataframe[f"range_pos"] = ((dataframe['mid'] - range_min) / (range_max - range_min)) #.rolling(5).mean()
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dataframe['min_max_12_1d'] = ((range_max - dataframe['close']) / range_min)
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dataframe['pct_min_max_12_1d'] = ((range_max - range_min) / range_min)
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# dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe[self.sell_sma_indicators.value], dataframe[self.sell_crossed_sma_indicators.value])
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@@ -864,6 +878,8 @@ class EmptyShort(IStrategy):
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# Compter les baisses / hausses consécutives
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dataframe = self.calculateDownAndUp(dataframe, limit=0.0001)
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dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=60).mean()
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# récupérer le dernier trade fermé
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trades = Trade.get_trades_proxy(pair=pair,is_open=False)
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if trades:
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@@ -875,297 +891,139 @@ class EmptyShort(IStrategy):
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata['pair']
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conditions = list()
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# #####################################################################################
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# CA MONTE !!
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# #####################################################################################
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1h" ] > self.start_bull_deriv1.value)
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1h"] > self.start_bull_deriv2.value)
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# conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
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# conditions.append(dataframe['sma5_deriv1_1h'] > self.buy_deriv1_sma5d.value)
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# conditions.append(dataframe['sma12_deriv1_1h'] > self.buy_deriv1_sma12d.value)
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# conditions.append(dataframe['sma36_deriv2'] > self.buy_deriv2_sma60.value)
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# conditions.append(dataframe['sma5_deriv2_1h'] > self.buy_deriv2_sma5d.value)
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# conditions.append(dataframe['sma12_deriv2_1h'] > self.buy_deriv2_sma12d.value)
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conditions.append(dataframe['hapercent'] > 0)
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# conditions.append(dataframe['percent12'] < 0.01)
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# conditions.append(dataframe['percent5'] < 0.01)
|
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# conditions.append(dataframe['max_rsi_24'] < 80)
|
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|
||||
# dynamic_rsi_threshold = 70 + 15 * np.tanh(dataframe["dist_sma200_1h"] * 5)
|
||||
# conditions.append((dataframe['max_rsi_12_1h'] < 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_1h'] > dataframe[f'sma{self.buy_longue.value}_1h'])
|
||||
# | (dataframe['sma60_inv_1h'] == -1)
|
||||
dataframe.loc[
|
||||
(
|
||||
# (dataframe['sma5_1h'] > dataframe['close'])
|
||||
# & (dataframe['sma12_deriv1_1h'] > 0)
|
||||
# & (dataframe['max_rsi_12'] > 45)
|
||||
# & (
|
||||
# (
|
||||
# (dataframe['max_rsi_24_1h'] < 45) & (dataframe['range_pos'] < 0.3)
|
||||
# | (dataframe['sma12_deriv1_1d'] > 0)
|
||||
# )
|
||||
|
||||
# if conditions:
|
||||
# dataframe.loc[
|
||||
# reduce(lambda x, y: x & y, conditions),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'bull')
|
||||
|
||||
# #####################################################################################
|
||||
# conditions = list()
|
||||
# conditions.append(dataframe['dist_sma200_1h'] < -0.05)
|
||||
# conditions.append(dataframe['sma12_inv'] == 1)
|
||||
# # conditions.append(dataframe['sma100_deriv1_1h'] > 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_1h'])
|
||||
# conditions.append(dataframe['mid_smooth12'] > dataframe['mid_smooth12'].shift(1))
|
||||
# conditions.append(dataframe['sma100_deriv1_1h'] > 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_1h"
|
||||
# },
|
||||
# "protection": {
|
||||
# "drop_from_last_entry": -0.03,
|
||||
# "b30_indicateur": "sma3",
|
||||
# "baisse": 0.31
|
||||
# },
|
||||
# #####################################################################################
|
||||
conditions = list()
|
||||
# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1h" ] < self.start_bull_deriv1.value)
|
||||
# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1h"] < self.start_bull_deriv2.value)
|
||||
# conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv1_1h" ] > self.start_bear_deriv1.value)
|
||||
# conditions.append(dataframe[f"{self.start_bear_indicator.value}_deriv2_1h"] > self.start_bear_deriv2.value)
|
||||
|
||||
# conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
|
||||
# conditions.append(dataframe['sma5_deriv1_1h'] > self.buy_deriv1_sma5d.value)
|
||||
# conditions.append(dataframe['sma12_deriv1_1h'] > self.buy_deriv1_sma12d.value)
|
||||
#
|
||||
# # conditions.append(dataframe['sma12_deriv2'] > -0.002)
|
||||
# # conditions.append(dataframe['sma5_deriv2_1h'] > 0)
|
||||
# # conditions.append(dataframe['sma12_deriv2_1h'] > -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_1h"] * 5)
|
||||
# conditions.append((dataframe['max_rsi_12_1h'] < 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_1h'] > dataframe[f'sma{self.buy_longue.value}_1h'])
|
||||
# | (dataframe['sma60_inv_1h'] == -1)
|
||||
# )
|
||||
|
||||
# if conditions:
|
||||
# dataframe.loc[
|
||||
# reduce(lambda x, y: x & y, conditions),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'bear')
|
||||
|
||||
# conditions = list()
|
||||
# conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h'])
|
||||
# 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_1h'] > 0.0)
|
||||
# conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
|
||||
# conditions.append(dataframe['sma24_deriv1_1h'] > 0.0)
|
||||
# conditions.append(dataframe['sma100_deriv1_1h'] > 0.0)
|
||||
# conditions.append(dataframe[f"range_pos"] < 0.025)
|
||||
# conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
|
||||
# # conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h'])
|
||||
# # 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['mid_regression'].shift(2) > dataframe['mid_regression'].shift(1))
|
||||
# conditions.append(dataframe['mid_regression'].shift(1) < dataframe['mid_regression'])
|
||||
conditions.append(dataframe['close'] <= dataframe['min12_1h'])
|
||||
conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
|
||||
conditions.append(dataframe['has_cross_min_6'] == 1)
|
||||
conditions.append(dataframe['down_count'] <= 5)
|
||||
conditions.append(dataframe['down_count'] <= 5)
|
||||
# conditions.append(dataframe['sma12_deriv1'] >= 0)
|
||||
# dataframe.loc[
|
||||
# reduce(lambda x, y: x & y, conditions),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'Mid')
|
||||
(dataframe['sma60_deriv1'] > 0)
|
||||
& (dataframe['sma60_deriv2'] > 0)
|
||||
# & (dataframe['sma5_deriv1_1h'] > 0)
|
||||
& (dataframe['hapercent'] > 0)
|
||||
& (dataframe['up_count'] >= 3)
|
||||
& (dataframe['up_count'] <= 7)
|
||||
# & (dataframe['open'] < dataframe['sma5_1h'] * 1.01)
|
||||
),
|
||||
['enter_long', 'enter_tag']
|
||||
] = (1, 'long')
|
||||
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# # (qtpylib.crossed_above(dataframe['sma60'], dataframe['mid_regression_1h']))
|
||||
# (dataframe['rsi_1h'] <= 30)
|
||||
# & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
|
||||
# # (dataframe['sma5_1h'] > dataframe['close'])
|
||||
# (dataframe['sma60_deriv1'] < 0)
|
||||
# & (dataframe['sma60_deriv2'] < 0)
|
||||
# & (dataframe['sma12_deriv1_1h'] > 0)
|
||||
# & (dataframe['volume_spike'] == 1)
|
||||
# & (dataframe['down_count'] >= 3)
|
||||
# & (dataframe['down_count'] < 15)
|
||||
# & (dataframe['hapercent'] > 0)
|
||||
# & (dataframe['sma60'] > dataframe['sma60'].shift(1))
|
||||
# ),
|
||||
# ['enter_long', 'enter_tag']
|
||||
# ] = (1, 'long')
|
||||
# ] = (1, 'long_spike')
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# (qtpylib.crossed_below(
|
||||
# dataframe['sma60'],
|
||||
# dataframe['mid_regression_1h'])
|
||||
# ) &
|
||||
(
|
||||
# (dataframe['close'] > dataframe['max5_1h'])
|
||||
# & (dataframe['sma12'] < dataframe['sma12'].shift(1))
|
||||
# # & (dataframe['sma120_inv'] == 1)
|
||||
# & (dataframe['close'] > dataframe['close_1h'])
|
||||
# & (dataframe['close'] > dataframe['close_1d'])
|
||||
# (dataframe['rsi_1h'] >= 65)
|
||||
# & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
|
||||
# & (dataframe['hapercent'] < 0)
|
||||
# & (dataframe['sma60'] < dataframe['sma60'].shift(1))
|
||||
(dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
|
||||
& (dataframe['close'] > dataframe['close_1d'])
|
||||
)
|
||||
# (dataframe['sma5_1h'] < dataframe['close'])
|
||||
# & (dataframe['sma12_deriv1_1h'] < -0.0005)
|
||||
# & (dataframe['max_rsi_12'] < 60)
|
||||
# & (
|
||||
# (
|
||||
# (dataframe['max_rsi_24_1h'] > 60) & (dataframe['range_pos'] > 0.8)
|
||||
# | (dataframe['sma12_deriv1_1d'] < -0.01)
|
||||
# )
|
||||
# )
|
||||
(dataframe['sma60_deriv1'] < 0)
|
||||
& (dataframe['sma60_deriv2'] < 0)
|
||||
# & (dataframe['sma5_deriv1_1h'] < 0)
|
||||
& (dataframe['hapercent'] < 0)
|
||||
# & (dataframe['pct_min_max_12_1d'] > 0.04)
|
||||
& (dataframe['down_count'] <= -3)
|
||||
& (dataframe['down_count'] >= -7)
|
||||
# & (dataframe['open'] > dataframe['sma5_1h'] * 1.01)
|
||||
),
|
||||
['enter_short', 'enter_tag']
|
||||
] = (1, 'short')
|
||||
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# (dataframe['close'] >= dataframe['max5_1h'])
|
||||
# & (dataframe['close'] >= dataframe['max12_1d'])
|
||||
# & (dataframe['sma12_1h'] <= dataframe['max12_1d'])
|
||||
# & (dataframe['mid_1h'] <= dataframe['mid_1h'].shift(61))
|
||||
# (dataframe['sma5_1h'] < dataframe['close'])
|
||||
# & (dataframe['volume_spike'] == 1)
|
||||
# & (dataframe['down_count'] <= -3)
|
||||
# & (dataframe['down_count'] > -15)
|
||||
# & (dataframe['hapercent'] < 0)
|
||||
# # & (dataframe['sma60'] < dataframe['sma60'].shift(1))
|
||||
# & (dataframe['sma60'] < dataframe['sma60'].shift(1))
|
||||
# ),
|
||||
# ['enter_short', 'enter_tag']
|
||||
# ] = (1, 'short')
|
||||
# ] = (1, 'short_spike')
|
||||
|
||||
dataframe['short'] = np.where(dataframe['enter_short'] == 1, dataframe['close'] * 0.99, np.nan)
|
||||
dataframe['long'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||||
|
||||
# no_trade = (
|
||||
# # range
|
||||
# (abs(dataframe['sma50_1h'] - dataframe['sma200_1h']) / dataframe['close'] < 0.002)
|
||||
# # momentum faible
|
||||
# | (abs(dataframe['sma12_deriv1']) < 0.0002)
|
||||
# # RSI neutre
|
||||
# | ((dataframe['rsi'] > 45) & (dataframe['rsi'] < 55))
|
||||
# # faible volatilité
|
||||
# | (dataframe['atr'] / dataframe['close'] < 0.003)
|
||||
# # milieu de range
|
||||
# | (dataframe['range_pos'].between(0.4, 0.6))
|
||||
# )
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_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)
|
||||
#
|
||||
# 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_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"
|
||||
# )
|
||||
#
|
||||
#
|
||||
# conditions.append(qtpylib.crossed_below(dataframe['mid'], dataframe['sma24']))
|
||||
# conditions.append((dataframe['range_pos'] > 0.04))
|
||||
#
|
||||
# if conditions:
|
||||
# dataframe.loc[reduce(lambda x, y: x & y, conditions), ['exit_long', 'exit_tag']] = (1, 'god')
|
||||
|
||||
# conditions = list()
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# # (qtpylib.crossed_above(
|
||||
# # dataframe['sma'],
|
||||
# # dataframe['mid_regression_1h'])
|
||||
# # )
|
||||
# (dataframe['rsi_1h'] >= 70)
|
||||
# & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
|
||||
# # (dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
|
||||
# # & (dataframe['range_pos'] > 0.5)
|
||||
# # & (dataframe['close'] > dataframe['close_1d'])
|
||||
# # & (dataframe['close'] > dataframe['close_1h'])
|
||||
#
|
||||
# (dataframe['sma12_deriv1_1h'] < -0.0005)
|
||||
# # & (dataframe['max_rsi_12'] < 70)
|
||||
# # & (dataframe['max_rsi_12_1h'] < 70)
|
||||
# # & (dataframe['range_pos'] > 0.8)
|
||||
# & (dataframe['hapercent'] < 0)
|
||||
#
|
||||
# ),
|
||||
# ['exit_long', 'exit_tag']
|
||||
# ] = (1, 'long')
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
# (qtpylib.crossed_above(
|
||||
# dataframe['sma'],
|
||||
# dataframe['mid_regression_1h'])
|
||||
# )
|
||||
(dataframe['rsi_1h'] <= 30)
|
||||
& (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
|
||||
& (dataframe['hapercent'] > 0)
|
||||
& (dataframe['sma60'] >= dataframe['sma60'].shift(1))
|
||||
),
|
||||
['exit_short', 'exit_tag']
|
||||
] = (1, 'short')
|
||||
#
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# # (qtpylib.crossed_above(
|
||||
# # dataframe['sma'],
|
||||
# # dataframe['mid_regression_1h'])
|
||||
# # )
|
||||
#
|
||||
# # (dataframe['rsi_1h'] <= 30)
|
||||
# # & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
|
||||
# # & (dataframe['hapercent'] > 0)
|
||||
# # & (dataframe['sma60'] >= dataframe['sma60'].shift(1))
|
||||
#
|
||||
# (dataframe['sma12_deriv1_1h'] > 0.0005)
|
||||
# # & (dataframe['max_rsi_12'] < 70)
|
||||
# # & (dataframe['max_rsi_12_1h'] < 70)
|
||||
# # & (dataframe['range_pos'] < 0.2)
|
||||
# & (dataframe['hapercent'] > 0)
|
||||
# ),
|
||||
# ['exit_short', 'exit_tag']
|
||||
# ] = (1, 'short')
|
||||
|
||||
return dataframe
|
||||
|
||||
@@ -1539,8 +1397,8 @@ class EmptyShort(IStrategy):
|
||||
return df
|
||||
|
||||
def calculateDownAndUp(self, dataframe, limit=0.0001):
|
||||
dataframe['down'] = dataframe['mid_regression'] <= dataframe['mid_regression'].shift(1)
|
||||
dataframe['up'] = dataframe['mid_regression'] >= dataframe['mid_regression'].shift(1)
|
||||
dataframe['down'] = dataframe['hapercent'] < 0
|
||||
dataframe['up'] = dataframe['hapercent'] > 0
|
||||
dataframe['down_count'] = - dataframe['down'].astype(int) * (
|
||||
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
|
||||
dataframe['up_count'] = dataframe['up'].astype(int) * (
|
||||
|
||||
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
|
||||
},
|
||||
"stoploss": {
|
||||
"stoploss": -1.0
|
||||
"stoploss": -0.02
|
||||
},
|
||||
"trailing": {
|
||||
"trailing_stop": false,
|
||||
|
||||
1812
FrictradeLearning.py
1812
FrictradeLearning.py
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user