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Zeus_8_3_2_B_4_2 Test slope_inf et sup

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This commit is contained in:
Jérôme Delacotte
2025-11-01 22:50:56 +01:00
parent b995936da2
commit 6757f4d432
2 changed files with 73 additions and 34 deletions
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34
Zeus_8_3_2_B_4_2.json
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@@ -4,6 +4,9 @@
"roi": {
"0": 10
},
"stoploss": {
"stoploss": -1.0
},
"trailing": {
"trailing_stop": true,
"trailing_stop_positive": 0.15,
@@ -14,24 +17,27 @@
"max_open_trades": 80
},
"buy": {
"indic_deriv_5m_buy": "sma12",
"indic_deriv_5m_slop_sup_buy": "sma12",
"indic_deriv_5m_slop_sup_inf": "sma12",
"mises": 10,
"pct": 0.012,
"pct_inc": 0.0022,
"deriv_5m_buy": 0.04,
"indic_5m": "sma24",
"mise_factor_buy": 0.01,
"mises": 2,
"pct": 0.007
"deriv_5m_slope_sup_buy": -0.02,
"deriv_5m_slope_sup_inf": 0.27,
"indic_5m_slope_inf": "sma12",
"indic_5m_slope_sup": "sma60",
"mise_factor_buy": 0.01
},
"sell": {
"deriv_5m_sell": 0.36,
"indic_5m_sell": "sma24",
"indic_deriv_5m_sell": "sma24"
"deriv_5m_slope_inf_sell": -0.08,
"deriv_5m_slope_sup_sell": 0.14,
"indic_5m_slope_inf_sell": "sma24",
"indic_5m_slope_sup_sell": "sma24",
"indic_deriv_5m_slope_inf_sell": "sma5",
"indic_deriv_5m_slope_sup_sell": "sma60"
},
"protection": {},
"stoploss": {
"stoploss": -0.134
}
"protection": {}
},
"ft_stratparam_v": 1,
"export_time": "2025-11-01 17:30:18.037599+00:00"
"export_time": "2025-11-01 21:44:32.435062+00:00"
}

73
Zeus_8_3_2_B_4_2.py
View File

@@ -258,14 +258,20 @@ class Zeus_8_3_2_B_4_2(IStrategy):
indicators = {'sma5', 'sma12', 'sma24', 'sma60'}
indicators_percent = {'percent', 'percent3', 'percent12', 'percent24', 'percent_1h', 'percent3_1h', 'percent12_1h', 'percent24_1h'}
mises = IntParameter(1, 50, default=10, space='buy', optimize=True, load=True)
mises = IntParameter(1, 50, default=10, space='buy', optimize=False, load=False)
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=0.0022, decimals=4, space='buy', optimize=False, load=False)
indic_5m_slope_sup = CategoricalParameter(indicators, default="sma60", space='buy')
indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=False, load=False)
deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
indic_5m_slope_inf = CategoricalParameter(indicators, default="sma60", space='buy')
indic_deriv_5m_slop_sup_inf = CategoricalParameter(indicators, default="sma12", space='buy', optimize=False, load=False)
deriv_5m_slope_sup_inf = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
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=0.0022, decimals=4, space='buy', optimize=False, load=True)
indic_5m = CategoricalParameter(indicators, default="sma60", space='buy')
indic_deriv_5m_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=False, load=False)
deriv_5m_buy = DecimalParameter(-0.1, 0.5, 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)
@@ -280,9 +286,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# 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_deriv_5m_sell = CategoricalParameter(indicators, default="sma60", space='sell')
deriv_5m_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
indic_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
indic_deriv_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
deriv_5m_slope_sup_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
indic_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
indic_deriv_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
deriv_5m_slope_inf_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
# indic_percent_sell = CategoricalParameter(indicators_percent, default="sma60", space='sell')
# percent_5m_sell = DecimalParameter(-0.1, -0.0, default=0, decimals=2, space='sell', optimize=True, load=True)
@@ -1337,12 +1348,23 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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[f"{self.indic_5m.value}"])
& (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
(dataframe[f"{self.indic_5m_slope_sup.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_sup.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_sup.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_sup.value}"])
& (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m.value}")
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_sup.value}_sup")
dataframe.loc[
(
(dataframe[f"{self.indic_5m_slope_inf.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_inf.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_inf.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_inf.value}"])
& (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_inf.value}_inf")
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
@@ -1504,10 +1526,19 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# {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"{self.indic_deriv_5m_sell.value}_deriv1"] < self.deriv_5m_sell.value)
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_sell.value}")
(dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_sup_sell.value}"])
& (dataframe[f"{self.indic_deriv_5m_slope_sup_sell.value}_deriv1"] < self.deriv_5m_slope_sup_sell.value)
& (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_sup_sell.value}_sup")
dataframe.loc[
(
(dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_inf_sell.value}"])
& (dataframe[f"{self.indic_deriv_5m_slope_inf_sell.value}_deriv1"] < self.deriv_5m_slope_inf_sell.value)
& (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_inf_sell.value}_inf")
# dataframe.loc[
# (
@@ -1757,8 +1788,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# dist_max = self.getDistMax(last_candle, pair)
# factor = self.multi_step_interpolate(dist_max, self.thresholds, self.factors)
factor = 1 #65 / min(65, last_candle['rsi_1d'])
# if last_candle['sma60_deriv1'] > 0 and last_candle['sma24_deriv1_1h'] > 0:
# factor = 1.5
if last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']:
factor = 2
adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
else:
@@ -2351,6 +2382,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
limit = 3
# return last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']
if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == 'BR1':
dispo = round(self.wallets.get_available_stake_amount())
self.pairs[pair]['stop'] = False
@@ -2552,7 +2585,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# df["slope_smooth"] = savgol_filter(df["slope_smooth"], window_length=21, polyorder=3)
# --- normalisation relative ---
df['slope_norm'] = df['slope_smooth'] / df['close']
df['slope_norm'] = 10000 * df['slope_smooth'] / df['close']
# df['slope_norm'].fillna(0, inplace=True)
df['slope_norm'] = df['slope_norm'].fillna(0)