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Zeus_8_3_2_B_4_2 test RSI exit

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Jérôme Delacotte
2025-10-21 11:51:21 +02:00
parent 4c0692426e
commit 074205cc09
1 changed files with 91 additions and 106 deletions
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Zeus_8_3_2_B_4_2.py
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@@ -104,19 +104,23 @@ class Zeus_8_3_2_B_4_2(IStrategy):
},
"subplots": {
"Rsi": {
"rsi_1h": {
"color": "red"
},
"rsi_1d": {
"max_rsi_24": {
"color": "blue"
}
# "rsi_1h": {
# "color": "red"
# },
# "rsi_1d": {
# "color": "blue"
# }
},
"Rsi_deriv1": {
"rsi_deriv1_1h": {
"color": "red"
},
"rsi_deriv1_1d": {
"color": "blue"
},
# "rsi_deriv1_1h": {
# "color": "red"
# },
# "rsi_deriv1_1d": {
# "color": "blue"
# },
"sma60_deriv1": {
"color": "green"
}
@@ -131,34 +135,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
"sma60_deriv2": {
"color": "green"
}
},
# "Down": {
# "down_count_1h": {
# "color": "green"
# },
# "up_count_1h": {
# "color": "blue"
# }
# },
# "Diff": {
# "sma10_deriv1": {
# "color": "#74effc"
# }
# },
# "smooth": {
# 'sma60_deriv1': {
# "color": "blue"
# },
# # 'mid_smooth_1h_deriv1': {
# # "color": "red"
# # },
# 'sma5_deriv2_1h': {
# "color": "pink"
# },
# # 'mid_smooth_1h_deriv2': {
# # "color": "#da59a6"
# # }
# }
}
}
}
@@ -224,7 +200,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# optimize=True, load=True)
sma5_deriv1_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection',
optimize=True, load=True)
sma5_deriv2_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection', optimize=True,
sma5_deriv2_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection',
optimize=True,
load=True)
mise_factor_buy = DecimalParameter(0.01, 0.2, default=0.05, decimals=2, space='buy', optimize=True, load=True)
@@ -320,7 +297,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# val = self.getProbaHausse144(last_candle)
# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
allow_to_buy = not self.pairs[pair]['stop'] # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
allow_to_buy = not self.pairs[pair][
'stop'] # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
# if allow_to_buy:
# poly_func, x_future, y_future, count = self.polynomial_forecast(
@@ -457,7 +435,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
max_profit = self.pairs[pair]['max_profit']
baisse = 0
if profit > 0:
baisse = max_profit - profit
baisse = 1 - (profit / max_profit)
mx = max_profit / 5
self.pairs[pair]['count_of_buys'] = count_of_buys
self.pairs[pair]['current_profit'] = profit
@@ -500,31 +478,27 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# self.pairs[pair]['force_sell'] = False
# return 'Force' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
# if profit > - 2 and last_candle['rsi_1h'] < 50 \
# and (last_candle['sma20'] - before_last_candle['sma20'] < 0) \
# and (last_candle['rsi_deriv1_1h'] < -4 and last_candle['rsi_deriv2_1h'] < -4) :
# self.pairs[pair]['force_sell'] = False
# return 'stoploss_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
if profit > 0.5 * count_of_buys and baisse > 0.15:
self.pairs[pair]['force_sell'] = False
return str(count_of_buys) + '_' + 'Bas_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
factor = 1
if (self.getShortName(pair) == 'BTC'):
factor = 0.5
if baisse > 2 and baisse > factor * self.pairs[pair]['total_amount'] / 100:
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
#
# # if 1 <= count_of_buys <= 3:
# if last_candle['rsi_1h'] < 50 \
# and last_candle['mid_smooth_1h_deriv1'] < 0 and profit > expected_profit \
# and (last_candle['sma20'] - before_last_candle['sma20'] < 0) :
# if baisse > 2 and baisse > factor * self.pairs[pair]['total_amount'] / 100:
# self.pairs[pair]['force_sell'] = False
# return 'Drv3_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
if last_candle['mid_smooth_1h_deriv1'] < 0 and last_candle['rsi_1h'] < 50 and profit > expected_profit:
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
# return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
#
# if 1 <= count_of_buys <= 3:
if last_candle['max_rsi_24'] > 75 and profit > expected_profit and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0:
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
return 'Drv3_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
return str(count_of_buys) + '_' + 'Rsi_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
# if last_candle['mid_smooth_1h_deriv1'] < 0 and profit > expected_profit:
# self.pairs[pair]['force_sell'] = False
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
# return str(count_of_buys) + '_' + 'Drv3_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
# if 4 <= count_of_buys <= 6:
# if ((before_last_candle_2['mid_smooth_12_deriv1'] <= before_last_candle['mid_smooth_12_deriv1'])
@@ -592,12 +566,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
if self.columns_logged % 10 == 0:
self.printLog(
f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
f"Tdc|{'val':>6}|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|"
f"Tdc|{'rsi':>6}|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|"
)
self.printLineLog()
df = pd.DataFrame.from_dict(self.pairs, orient='index')
colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index', 'current_trade',
'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index', #'current_trade',
'trade_info', 'last_date', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
@@ -663,8 +637,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
color_smooth_1h = GREEN if last_candle['mid_smooth_1h_deriv1'] > 0 else RED
color_smooth2_1h = GREEN if last_candle['mid_smooth_1h_deriv2'] > 0 else RED
last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(self.pairs[pair]['last_max'],3)
last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(self.pairs[pair]['last_min'], 3)
last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(
self.pairs[pair]['last_max'], 3)
last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(
self.pairs[pair]['last_min'], 3)
profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
@@ -681,7 +657,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
f"|{last_candle['tendency_12'] or '-':>3}|" # {last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
# f"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
# f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
f"{round(val, 1) or '-' :>6}|"
f"{round(last_candle['max_rsi_24'], 1) or '-' :>6}|"
f"{dist_max:>7}|{color_sma20}{round(last_candle['sma20_deriv1_1d'], 2):>5}{RESET}"
f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1d'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1d'], 2):>5}{RESET}"
f"|{color_sma5_1h}{round(last_candle['sma60_deriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma60_deriv2'], 2):>5}{RESET}"
@@ -771,7 +747,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# print(metadata['pair'])
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
dataframe['max_rsi_12'] = talib.MAX(dataframe['rsi'], timeperiod=12)
dataframe['max_rsi_24'] = talib.MAX(dataframe['rsi'], timeperiod=24)
self.calculeDerivees(dataframe, 'rsi', horizon=12)
dataframe['max48'] = talib.MAX(dataframe['close'], timeperiod=48)
@@ -895,13 +871,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# self.calculateStats(informative, 'sma5_deriv1', 'futur_percent_3')
if self.dp.runmode.value in ('backtest'):
informative['futur_percent'] = 100 * (informative['close'].shift(-1) - informative['close']) / informative['close']
informative['futur_percent'] = 100 * (informative['close'].shift(-1) - informative['close']) / informative[
'close']
# informative['futur_percent_3d'] = 100 * (informative['close'].shift(-3) - informative['close']) / informative['close']
#
# self.calculateProbabilite2Index(informative, ['futur_percent_1d'], 'rsi_deriv1', 'rsi')
# # self.calculateProbabilite2Index(dataframe, ['futur_percent_3d'], 'rsi_deriv1', 'sma5')
# informative['close_smooth'] = self.conditional_smoothing(informative['mid'].dropna(), threshold=0.0015).dropna()
# informative['smooth'], informative['deriv1'], informative['deriv2'] = self.smooth_and_derivatives(informative['close_smooth'])
# informative['deriv1'] = 100 * informative['deriv1'] / informative['mid']
@@ -1064,7 +1040,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
return dataframe
def calculeDerivees(self, dataframe, indic, factor_1=100, factor_2=10, horizon=5):
dataframe[f"{indic}_deriv1"] = (factor_1 * dataframe[f"{indic}"].diff() / dataframe[f"{indic}"]).rolling(horizon).mean()
dataframe[f"{indic}_deriv1"] = (factor_1 * dataframe[f"{indic}"].diff() / dataframe[f"{indic}"]).rolling(
horizon).mean()
dataframe[f"{indic}_deriv2"] = (factor_2 * dataframe[f"{indic}_deriv1"].diff()).rolling(horizon).mean()
def calculateDownAndUp(self, dataframe, limit=0.0001):
@@ -1160,7 +1137,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# (valid_inversion & inversion_last5 )
(dataframe['mid_smooth_3'].shift(1) < dataframe['mid_smooth_3'])
& (dataframe['hapercent'] > 0)
& (dataframe['max_rsi_12'] < 70)
& (dataframe['max_rsi_24'] < 70)
& (dataframe['open'] <= dataframe['bb_middleband'])
# valid_inversion
@@ -1173,8 +1150,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# & (dataframe['close'] * factor < dataframe['bb_upperband5'])
#
#
# (dataframe['mid_smooth_1h_deriv1'] >= 0)
# & (dataframe['mid_smooth_1h_deriv1'] >= 0)
& (dataframe['mid_smooth_1h_deriv1'] >= 0)
& (dataframe['mid_smooth_1h_deriv2'] >= 0)
# & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= 0)
# & (dataframe['mid_smooth_1h_deriv1'] >= dataframe['mid_smooth_1h_deriv1'].shift(1))
), ['enter_long', 'enter_tag']] = (1, 'smth')
@@ -1485,7 +1462,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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 * pct_first / 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) - 10 * pct_first / 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:
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
@@ -1541,12 +1519,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
last_lost = self.getLastLost(last_candle, pair)
if (hours > 6
if (current_profit > 0 and hours > 6
# and last_candle['sma60_deriv1'] > 0
and last_candle['rsi_1h'] < 65
and last_candle['rsi_deriv1_1h'] > 0
and last_candle['mid_smooth_5_deriv1_1d'] > 0
and last_candle['sma60_deriv1'] > 0
and last_candle['sma60_deriv2'] > 0
):
try:
stake_amount = self.pairs[pair]['first_amount'] / 2
@@ -1729,7 +1708,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# if last_candle['close'] < max_60:
# pct_to_max = 0.25 * (max_60 - last_candle['close']) / max_60
# pct_to_max = pct_to_max * (2 - pctClose60)
expected_profit = lim * self.pairs[pair]['total_amount'] #min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
expected_profit = lim * self.pairs[pair][
'total_amount'] # min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
self.pairs[pair]['expected_profit'] = expected_profit
# print(
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
@@ -2255,7 +2237,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# if not pair.startswith('BTC'):
dispo = round(self.wallets.get_available_stake_amount())
if self.pairs[pair]['stop'] and last_candle['mid_smooth_5_deriv1_1d'] > -0.9 and last_candle['sma5_deriv1_1d'] > 0 and last_candle['sma5_deriv2_1d'] > 0:
if self.pairs[pair]['stop'] and last_candle['mid_smooth_5_deriv1_1d'] > -0.9 and last_candle[
'sma5_deriv1_1d'] > 0 and last_candle['sma5_deriv2_1d'] > 0:
self.pairs[pair]['stop'] = False
self.log_trade(
last_candle=last_candle,
@@ -2270,7 +2253,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
stake=0
)
else:
if self.pairs[pair]['stop'] == False and (last_candle['sma5_deriv1_1d'] < -0.2 or last_candle['sma5_deriv2_1d'] < -3):
if self.pairs[pair]['stop'] == False and (
last_candle['sma5_deriv1_1d'] < -0.2 or last_candle['sma5_deriv2_1d'] < -3):
self.pairs[pair]['stop'] = True
# if self.pairs[pair]['current_profit'] > 0:
# self.pairs[pair]['force_sell'] = True
@@ -2299,7 +2283,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
if last_candle['sma5_deriv1_1h'] < 0.0 and last_candle['sma5_deriv2_1h'] < -0.0:
return False
if last_candle['mid_smooth_1h_deriv1'] < 0.0 and last_candle['mid_smooth_1h_deriv2'] < -0.0 and last_candle['sma5_deriv2_1h'] < 0:
if last_candle['mid_smooth_1h_deriv1'] < 0.0 and last_candle['mid_smooth_1h_deriv2'] < -0.0 and last_candle[
'sma5_deriv2_1h'] < 0:
return False
# if pair.startswith('BTC'):