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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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169
Zeus_8_3_2_B_4_2.py
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@@ -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'}
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_inc = DecimalParameter(0.0001, 0.003, default=0.005, decimals=4, 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=True, load=True)
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_deriv2_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_1h = CategoricalParameter(indicators, default="sma60", space='buy')
# 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_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_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_deriv2_1d_p_start = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
# 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_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_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_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_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_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)
@@ -515,26 +515,26 @@ class Zeus_8_3_2_B_4_2(IStrategy):
pair_name = self.getShortName(pair)
# if (current_profit > expected_profit) and last_candle['can_sell']:
# return 'Can_' + pair_name + '_' + str(count_of_buys)
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 (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.log_trade(
last_candle=last_candle,
date=current_time,
action="🔴STOP",
dispo=dispo,
pair=pair,
rate=last_candle['close'],
trade_type='',
profit=self.pairs[pair]['current_profit'],
buys=self.pairs[pair]['count_of_buys'],
stake=0
)
return "MAX_RSI"
return None
# 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 (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.log_trade(
# last_candle=last_candle,
# date=current_time,
# action="🔴STOP",
# dispo=dispo,
# pair=pair,
# rate=last_candle['close'],
# trade_type='',
# profit=self.pairs[pair]['current_profit'],
# buys=self.pairs[pair]['count_of_buys'],
# stake=0
# )
# return "MAX_RSI"
#
# return None
# 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:
@@ -957,6 +957,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
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'):
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)
if timeframe == '1h' and verbose and self.dp.runmode.value in ('backtest'):
print("##################")
print(f"# STAT {timeframe} {name}{suffixe}")
print("##################")
self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent'], indic_1=f"{name}{suffixe}_deriv1", indic_2=f"{name}{suffixe}_deriv2")
# if timeframe == '1h' and verbose and self.dp.runmode.value in ('backtest'):
# print("##################")
# print(f"# STAT {timeframe} {name}{suffixe}")
# print("##################")
# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent'], indic_1=f"{name}{suffixe}_deriv1", indic_2=f"{name}{suffixe}_deriv2")
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)
# ), ['enter_long', 'enter_tag']] = (1, 'smth')
dataframe.loc[
(
(dataframe['sma24_deriv2'].shift(1) < 0)
& (dataframe['sma24_deriv2'] > 0)
& ((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"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
), ['enter_long', 'enter_tag']] = (1, 'invert')
dataframe.loc[
(
(dataframe['sma24_deriv1'] > 0)
& (dataframe['sma60_deriv1'].shift(1) < 0)
& (dataframe['sma60_deriv1'] > 0)
& ((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"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
), ['enter_long', 'enter_tag']] = (1, 'raise')
# dataframe.loc[
# (
# (dataframe['sma24_deriv2'].shift(1) < 0)
# & (dataframe['sma24_deriv2'] > 0)
# & ((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"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'invert')
#
# dataframe.loc[
# (
# (dataframe['sma24_deriv1'] > 0)
# & (dataframe['sma60_deriv1'].shift(1) < 0)
# & (dataframe['sma60_deriv1'] > 0)
# & ((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"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'raise')
#
# dataframe.loc[
# (
# (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"{indic_1h}_deriv1"] >= self.indic_deriv1_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')
#
# 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['mid_smooth_3_1h'].shift(24) >= dataframe['mid_smooth_3_1h'].shift(12))
& (dataframe['mid_smooth_3_1h'].shift(12) <= dataframe['mid_smooth_3_1h'])
& ((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"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
), ['enter_long', 'enter_tag']] = (1, 'smth3_inv')
(dataframe['percent3'] < -0.03)
& (dataframe['percent'] > 0)
), ['enter_long', 'enter_tag']] = (1, 'pct3')
dataframe.loc[
(
(dataframe[f"{self.indic_5m.value}"].shift(2) >= dataframe[f"{self.indic_5m.value}"].shift(1))
& (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)
@@ -1464,6 +1482,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# (dataframe['mid_smooth_deriv1'] == 0)
# & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
# ), ['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
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
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()),
self.adjust_stake_amount(pair,
last_candle) - 10 * last_lost / self.mise_factor_buy.value) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
self.adjust_stake_amount(pair, last_candle) * abs(last_lost / self.mise_factor_buy.value))
if self.wallets.get_available_stake_amount() > stake_amount:
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
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)
if True: # not pair in ('BTC/USDT', 'BTC/USDC'):