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Zeus_8_3_2_B_4_2 Sell / buy BTC 01/2025 344euros

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Jérôme Delacotte
2025-11-01 18:56:12 +01:00
parent 96a0b0b2b0
commit b995936da2
2 changed files with 54 additions and 30 deletions
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37
Zeus_8_3_2_B_4_2.json
View File

@@ -2,35 +2,36 @@
"strategy_name": "Zeus_8_3_2_B_4_2", "strategy_name": "Zeus_8_3_2_B_4_2",
"params": { "params": {
"roi": { "roi": {
"0": 0.564, "0": 10
"567": 0.273,
"2814": 0.12,
"7675": 0
},
"stoploss": {
"stoploss": -1.0
}, },
"trailing": { "trailing": {
"trailing_stop": false, "trailing_stop": true,
"trailing_stop_positive": null, "trailing_stop_positive": 0.15,
"trailing_stop_positive_offset": 0.0, "trailing_stop_positive_offset": 0.2,
"trailing_only_offset_is_reached": false "trailing_only_offset_is_reached": true
}, },
"max_open_trades": { "max_open_trades": {
"max_open_trades": 80 "max_open_trades": 80
}, },
"buy": { "buy": {
"indic_5m": "mid_smooth_5", "indic_deriv_5m_buy": "sma12",
"pct_inc": 0.0022,
"deriv_5m_buy": 0.04,
"indic_5m": "sma24",
"mise_factor_buy": 0.01, "mise_factor_buy": 0.01,
"mises": 5, "mises": 2,
"pct": 0.02, "pct": 0.007
"pct_inc": 0.0028
}, },
"sell": { "sell": {
"indic_5m_sell": "sma24" "deriv_5m_sell": 0.36,
"indic_5m_sell": "sma24",
"indic_deriv_5m_sell": "sma24"
}, },
"protection": {} "protection": {},
"stoploss": {
"stoploss": -0.134
}
}, },
"ft_stratparam_v": 1, "ft_stratparam_v": 1,
"export_time": "2025-10-29 20:02:20.081550+00:00" "export_time": "2025-11-01 17:30:18.037599+00:00"
} }

47
Zeus_8_3_2_B_4_2.py
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@@ -70,9 +70,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
stakes = 40 stakes = 40
# Stoploss: # Stoploss:
stoploss = -1 # 0.256 # stoploss = -1 # 0.256
# Custom stoploss # Custom stoploss
use_custom_stoploss = True # use_custom_stoploss = False
trailing_stop = True
trailing_stop_positive = 0.15
trailing_stop_positive_offset = 0.20
trailing_only_offset_is_reached = True
# Buy hypers # Buy hypers
timeframe = '5m' timeframe = '5m'
@@ -250,14 +255,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# #
mise_factor_buy = DecimalParameter(0.01, 0.1, default=0.05, decimals=2, space='buy', optimize=True, load=True) mise_factor_buy = DecimalParameter(0.01, 0.1, default=0.05, decimals=2, space='buy', optimize=True, load=True)
indicators = {'sma5', 'sma12', 'sma24', 'sma60', 'mid_smooth_3', 'mid_smooth_5', 'mid_smooth_12', 'mid_smooth_24'} 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=True, load=True)
pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, 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=0.005, decimals=4, 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_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_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)
@@ -273,6 +281,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
indic_5m_sell = CategoricalParameter(indicators, default="sma60", space='sell') 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_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)
# 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)
@@ -401,7 +415,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
last_candle = dataframe.iloc[-1].squeeze() last_candle = dataframe.iloc[-1].squeeze()
force = self.pairs[pair]['force_sell'] force = self.pairs[pair]['force_sell']
allow_to_sell = (last_candle['hapercent'] < 0) or force or (exit_reason == 'force_exit') allow_to_sell = (last_candle['hapercent'] < 0 and trade.calc_profit(rate, amount) > 0) or force or (exit_reason == 'force_exit')
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0)) minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
@@ -1064,6 +1078,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# tr = DataFrame({'hl': high_low, 'hc': high_close, 'lc': low_close}).max(axis=1) # tr = DataFrame({'hl': high_low, 'hc': high_close, 'lc': low_close}).max(axis=1)
# dataframe['atr'] = tr.rolling(window=self.DEFAULT_PARAMS['atr_period']).mean() # dataframe['atr'] = tr.rolling(window=self.DEFAULT_PARAMS['atr_period']).mean()
dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
self.setTrends(dataframe) self.setTrends(dataframe)
return dataframe return dataframe
@@ -1131,7 +1147,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count') dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
def calculateDerivation(self, dataframe, window=12, suffixe='', timeframe='5m'): def calculateDerivation(self, dataframe, window=12, suffixe='', timeframe='5m'):
dataframe[f"mid_smooth{suffixe}"] = dataframe['mid'] dataframe[f"mid_smooth{suffixe}"] = dataframe['mid'].rolling(window).mean()
dataframe = self.calculeDerivees(dataframe, f"mid_smooth{suffixe}", timeframe=timeframe, ema_period=window) dataframe = self.calculeDerivees(dataframe, f"mid_smooth{suffixe}", timeframe=timeframe, ema_period=window)
return dataframe return dataframe
@@ -1322,9 +1338,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe.loc[ 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(2) >= dataframe[f"{self.indic_5m.value}"].shift(1))
& (dataframe[f"{self.indic_5m.value}"].shift(1) <= dataframe['sma5']) & (dataframe[f"{self.indic_5m.value}"].shift(1) <= dataframe[f"{self.indic_5m.value}"])
& (dataframe[f"sma60_deriv1"] >= -0.2) & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
), ['enter_long', 'enter_tag']] = (1, 'sma5') # & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m.value}")
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)
@@ -1488,8 +1506,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
( (
(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(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_5m_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_sell.value}"])
& (dataframe[f"sma60_deriv1"] > 0.3) & (dataframe[f"{self.indic_deriv_5m_sell.value}_deriv1"] < self.deriv_5m_sell.value)
), ['exit_long', 'exit_tag']] = (1, 'sma5') ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_sell.value}")
# dataframe.loc[
# (
# (dataframe[f"{self.indic_percent_sell.value}"] < self.percent_5m_sell.value)
# ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_percent_sell.value}")
return dataframe return dataframe
@@ -1559,7 +1582,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
if not self.should_enter_trade(pair, last_candle, current_time): if not self.should_enter_trade(pair, last_candle, current_time):
return None return None
condition = (last_candle['enter_long'] and last_candle['sma5_deriv1_1h'] > 0) or (last_candle['percent3'] < -0.03 and last_candle['percent'] > 0) condition = (last_candle['enter_long'] and last_candle['sma5_deriv1'] > 0 and last_candle['hapercent'] > 0) or (last_candle['percent3'] < -0.03 and last_candle['percent'] > 0)
# if (self.getShortName(pair) != 'BTC' and count_of_buys > 3): # if (self.getShortName(pair) != 'BTC' and count_of_buys > 3):
# condition = before_last_candle_24['mid_smooth_3_1h'] > before_last_candle_12['mid_smooth_3_1h'] and before_last_candle_12['mid_smooth_3_1h'] < last_candle['mid_smooth_3_1h'] #and last_candle['mid_smooth_3_deriv1_1h'] < -1.5 # condition = before_last_candle_24['mid_smooth_3_1h'] > before_last_candle_12['mid_smooth_3_1h'] and before_last_candle_12['mid_smooth_3_1h'] < last_candle['mid_smooth_3_1h'] #and last_candle['mid_smooth_3_deriv1_1h'] < -1.5