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Zeus_8_3_2_B_4_2 test Sell

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Jérôme Delacotte
2025-10-29 21:05:06 +01:00
parent d5e4c93947
commit 96a0b0b2b0
2 changed files with 110 additions and 92 deletions
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33
Zeus_8_3_2_B_4_2.json
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@@ -2,15 +2,18 @@
"strategy_name": "Zeus_8_3_2_B_4_2", "strategy_name": "Zeus_8_3_2_B_4_2",
"params": { "params": {
"roi": { "roi": {
"0": 10 "0": 0.564,
"567": 0.273,
"2814": 0.12,
"7675": 0
}, },
"stoploss": { "stoploss": {
"stoploss": -1.0 "stoploss": -1.0
}, },
"trailing": { "trailing": {
"trailing_stop": false, "trailing_stop": false,
"trailing_stop_positive": 0.254, "trailing_stop_positive": null,
"trailing_stop_positive_offset": 0.323, "trailing_stop_positive_offset": 0.0,
"trailing_only_offset_is_reached": false "trailing_only_offset_is_reached": false
}, },
"max_open_trades": { "max_open_trades": {
@@ -18,26 +21,16 @@
}, },
"buy": { "buy": {
"indic_5m": "mid_smooth_5", "indic_5m": "mid_smooth_5",
"indic_deriv1_5m": -1.52, "mise_factor_buy": 0.01,
"indic_deriv2_5m": -0.15, "mises": 5,
"mise_factor_buy": 0.1, "pct": 0.02,
"mises": 10, "pct_inc": 0.0028
"pct": 0.012,
"pct_inc": 0.005
}, },
"sell": { "sell": {
"indic_5m_sell": "mid_smooth_24", "indic_5m_sell": "sma24"
"indic_deriv1_5m_sell": -1.93,
"indic_deriv2_5m_sell": -1.4
}, },
"protection": { "protection": {}
"indic_1d_p": "mid_smooth_24",
"indic_deriv1_1d_p_start": -1.9,
"indic_deriv1_1d_p_stop": 1.1,
"indic_deriv2_1d_p_start": 3.7,
"indic_deriv2_1d_p_stop": -0.9
}
}, },
"ft_stratparam_v": 1, "ft_stratparam_v": 1,
"export_time": "2025-10-26 16:32:08.937921+00:00" "export_time": "2025-10-29 20:02:20.081550+00:00"
} }

169
Zeus_8_3_2_B_4_2.py
View File

@@ -252,29 +252,29 @@ class Zeus_8_3_2_B_4_2(IStrategy):
indicators = {'sma5', 'sma12', 'sma24', 'sma60', 'mid_smooth_3', 'mid_smooth_5', 'mid_smooth_12', 'mid_smooth_24'} indicators = {'sma5', 'sma12', 'sma24', 'sma60', 'mid_smooth_3', 'mid_smooth_5', 'mid_smooth_12', 'mid_smooth_24'}
mises = IntParameter(5, 50, default=10, space='buy', optimize=False, load=False) mises = IntParameter(1, 50, default=10, space='buy', optimize=True, load=True)
pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=False, load=False) pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=True, load=True)
pct_inc = DecimalParameter(0.0001, 0.003, default=0.005, decimals=4, space='buy', optimize=False, load=False) pct_inc = DecimalParameter(0.0001, 0.003, default=0.005, decimals=4, space='buy', optimize=True, load=True)
indic_5m = CategoricalParameter(indicators, default="sma60", space='buy') indic_5m = CategoricalParameter(indicators, default="sma60", space='buy')
indic_deriv1_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True) # indic_deriv1_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
indic_deriv2_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True) # indic_deriv2_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
# indic_1h = CategoricalParameter(indicators, default="sma60", space='buy') # indic_1h = CategoricalParameter(indicators, default="sma60", space='buy')
# indic_deriv1_1h = DecimalParameter(-5, 5, default=0, decimals=1, space='buy', optimize=True, load=True) # indic_deriv1_1h = DecimalParameter(-5, 5, default=0, decimals=1, space='buy', optimize=True, load=True)
# indic_deriv2_1h = DecimalParameter(-10, 10, default=0, decimals=1, space='buy', optimize=True, load=True) # indic_deriv2_1h = DecimalParameter(-10, 10, default=0, decimals=1, space='buy', optimize=True, load=True)
indic_1d_p = CategoricalParameter(indicators, default="sma60", space='protection') # indic_1d_p = CategoricalParameter(indicators, default="sma60", space='protection')
indic_deriv1_1d_p_stop = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True) # indic_deriv1_1d_p_stop = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
indic_deriv2_1d_p_stop = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True) # indic_deriv2_1d_p_stop = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
indic_deriv1_1d_p_start = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True) # indic_deriv1_1d_p_start = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
indic_deriv2_1d_p_start = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True) # indic_deriv2_1d_p_start = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
indic_5m_sell = CategoricalParameter(indicators, default="sma60", space='sell') indic_5m_sell = CategoricalParameter(indicators, default="sma60", space='sell')
indic_deriv1_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True) # indic_deriv1_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
indic_deriv2_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True) # indic_deriv2_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
# indic_deriv1_1h_sell = DecimalParameter(-5, 5, default=0, decimals=1, space='sell', optimize=True, load=True) # indic_deriv1_1h_sell = DecimalParameter(-5, 5, default=0, decimals=1, space='sell', optimize=True, load=True)
# indic_deriv2_1h_sell = DecimalParameter(-10, 10, default=0, decimals=1, space='sell', optimize=True, load=True) # indic_deriv2_1h_sell = DecimalParameter(-10, 10, default=0, decimals=1, space='sell', optimize=True, load=True)
@@ -515,26 +515,26 @@ class Zeus_8_3_2_B_4_2(IStrategy):
pair_name = self.getShortName(pair) pair_name = self.getShortName(pair)
# if (current_profit > expected_profit) and last_candle['can_sell']: # if (current_profit > expected_profit) and last_candle['can_sell']:
# return 'Can_' + pair_name + '_' + str(count_of_buys) # return 'Can_' + pair_name + '_' + str(count_of_buys)
trend = last_candle['trend_class_1d'] # trend = last_candle['trend_class_1d']
if (trend == "BR1" or trend == "BR2") and self.pairs[pair]['has_gain'] == 0: # and (last_candle[f"{indic_5m_sell}_deriv1"] <= indic_deriv1_5m_sell and last_candle[f"{indic_5m_sell}_deriv2"] <= indic_deriv2_5m_sell): # if (trend == "BR1" or trend == "BR2") and self.pairs[pair]['has_gain'] == 0: # and (last_candle[f"{indic_5m_sell}_deriv1"] <= indic_deriv1_5m_sell and last_candle[f"{indic_5m_sell}_deriv2"] <= indic_deriv2_5m_sell):
#
if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 'BU1' and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0): # if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 'BU1' and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0):
self.pairs[pair]['stop'] = True # self.pairs[pair]['stop'] = True
self.log_trade( # self.log_trade(
last_candle=last_candle, # last_candle=last_candle,
date=current_time, # date=current_time,
action="🔴STOP", # action="🔴STOP",
dispo=dispo, # dispo=dispo,
pair=pair, # pair=pair,
rate=last_candle['close'], # rate=last_candle['close'],
trade_type='', # trade_type='',
profit=self.pairs[pair]['current_profit'], # profit=self.pairs[pair]['current_profit'],
buys=self.pairs[pair]['count_of_buys'], # buys=self.pairs[pair]['count_of_buys'],
stake=0 # stake=0
) # )
return "MAX_RSI" # return "MAX_RSI"
#
return None # return None
# if (trend == "BR1" or trend == "BR2") and last_candle[f"{self.indic_5m_sell.value}_deriv1"] <= self.indic_deriv1_5m_sell.value \ # if (trend == "BR1" or trend == "BR2") and last_candle[f"{self.indic_5m_sell.value}_deriv1"] <= self.indic_deriv1_5m_sell.value \
# and last_candle[f"{self.indic_5m_sell.value}_deriv2"] <= self.indic_deriv2_5m_sell.value: # and last_candle[f"{self.indic_5m_sell.value}_deriv2"] <= self.indic_deriv2_5m_sell.value:
@@ -957,6 +957,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"] dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3) dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
if self.dp.runmode.value in ('backtest'): if self.dp.runmode.value in ('backtest'):
dataframe['futur_percent'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close'] dataframe['futur_percent'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
@@ -1229,11 +1231,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe[tendency_col] = dataframe.apply(tag_by_derivatives, axis=1) dataframe[tendency_col] = dataframe.apply(tag_by_derivatives, axis=1)
if timeframe == '1h' and verbose and self.dp.runmode.value in ('backtest'): # if timeframe == '1h' and verbose and self.dp.runmode.value in ('backtest'):
print("##################") # print("##################")
print(f"# STAT {timeframe} {name}{suffixe}") # print(f"# STAT {timeframe} {name}{suffixe}")
print("##################") # print("##################")
self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent'], indic_1=f"{name}{suffixe}_deriv1", indic_2=f"{name}{suffixe}_deriv2") # self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent'], indic_1=f"{name}{suffixe}_deriv1", indic_2=f"{name}{suffixe}_deriv2")
return dataframe return dataframe
@@ -1263,29 +1265,31 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value) # # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# ), ['enter_long', 'enter_tag']] = (1, 'smth') # ), ['enter_long', 'enter_tag']] = (1, 'smth')
dataframe.loc[ # dataframe.loc[
( # (
(dataframe['sma24_deriv2'].shift(1) < 0) # (dataframe['sma24_deriv2'].shift(1) < 0)
& (dataframe['sma24_deriv2'] > 0) # & (dataframe['sma24_deriv2'] > 0)
& ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d'])) # & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
& (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value) # & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
& (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value) # & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value) # # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value) # # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
), ['enter_long', 'enter_tag']] = (1, 'invert') # & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'invert')
dataframe.loc[ #
( # dataframe.loc[
(dataframe['sma24_deriv1'] > 0) # (
& (dataframe['sma60_deriv1'].shift(1) < 0) # (dataframe['sma24_deriv1'] > 0)
& (dataframe['sma60_deriv1'] > 0) # & (dataframe['sma60_deriv1'].shift(1) < 0)
& ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d'])) # & (dataframe['sma60_deriv1'] > 0)
& (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value) # & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
& (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value) # & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value) # & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value) # # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
), ['enter_long', 'enter_tag']] = (1, 'raise') # # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'raise')
#
# dataframe.loc[ # dataframe.loc[
# ( # (
# (dataframe['sma60_deriv1'].shift(1) < 0) # (dataframe['sma60_deriv1'].shift(1) < 0)
@@ -1295,18 +1299,32 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value) # & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value) # # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value) # # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'stg_inv') # ), ['enter_long', 'enter_tag']] = (1, 'stg_inv')
#
# dataframe.loc[
# (
# (dataframe['mid_smooth_24'].shift(24) >= dataframe['mid_smooth_24'].shift(12))
# & (dataframe['mid_smooth_24'].shift(12) <= dataframe['mid_smooth_24'])
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'smth3_inv')
dataframe.loc[ dataframe.loc[
( (
(dataframe['mid_smooth_3_1h'].shift(24) >= dataframe['mid_smooth_3_1h'].shift(12)) (dataframe['percent3'] < -0.03)
& (dataframe['mid_smooth_3_1h'].shift(12) <= dataframe['mid_smooth_3_1h']) & (dataframe['percent'] > 0)
& ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d'])) ), ['enter_long', 'enter_tag']] = (1, 'pct3')
& (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
& (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value) dataframe.loc[
# & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value) (
# & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value) (dataframe[f"{self.indic_5m.value}"].shift(2) >= dataframe[f"{self.indic_5m.value}"].shift(1))
), ['enter_long', 'enter_tag']] = (1, 'smth3_inv') & (dataframe[f"{self.indic_5m.value}"].shift(1) <= dataframe['sma5'])
& (dataframe[f"sma60_deriv1"] >= -0.2)
), ['enter_long', 'enter_tag']] = (1, 'sma5')
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan) dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
@@ -1464,6 +1482,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# (dataframe['mid_smooth_deriv1'] == 0) # (dataframe['mid_smooth_deriv1'] == 0)
# & (dataframe['mid_smooth_deriv1'].shift(1) > 0) # & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
# ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h') # ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
# {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}
dataframe.loc[
(
(dataframe[f"{self.indic_5m_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_sell.value}"].shift(1))
& (dataframe[f"{self.indic_5m_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_sell.value}"])
& (dataframe[f"sma60_deriv1"] > 0.3)
), ['exit_long', 'exit_tag']] = (1, 'sma5')
return dataframe return dataframe
def adjust_trade_position(self, trade: Trade, current_time: datetime, def adjust_trade_position(self, trade: Trade, current_time: datetime,
@@ -1562,10 +1589,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# return None # return None
max_amount = self.config.get('stake_amount') * 2.5 max_amount = self.config.get('stake_amount') * 2.5
# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()), stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
self.adjust_stake_amount(pair, self.adjust_stake_amount(pair, last_candle) * abs(last_lost / self.mise_factor_buy.value))
last_candle) - 10 * last_lost / self.mise_factor_buy.value) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
if self.wallets.get_available_stake_amount() > stake_amount: if self.wallets.get_available_stake_amount() > stake_amount:
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48' trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
@@ -1699,7 +1724,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# Calculer le minimum des 14 derniers jours # Calculer le minimum des 14 derniers jours
nb_pairs = len(self.dp.current_whitelist()) nb_pairs = len(self.dp.current_whitelist())
base_stake_amount = self.config.get('stake_amount') #/ (self.mises.value * nb_pairs) # Montant de base configuré base_stake_amount = self.config.get('stake_amount') / (self.mises.value) # * nb_pairs) # Montant de base configuré
# pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2) # pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2)
if True: # not pair in ('BTC/USDT', 'BTC/USDC'): if True: # not pair in ('BTC/USDT', 'BTC/USDC'):