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Calcul 20250101-20250714 354.190 72.772 28209.62 USDT max 9 XRP

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This commit is contained in:
Jérôme Delacotte
2025-09-22 15:35:22 +02:00
parent 39bd32370a
commit 3b14693536
1 changed files with 63 additions and 28 deletions
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93
Zeus_8_3_2_B_4_2.py
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@@ -303,8 +303,7 @@ 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(
@@ -453,9 +452,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# return 'Can_' + pair_name + '_' + str(count_of_buys)
# if 1 <= count_of_buys <= 3:
if ((before_last_candle_2['mid_smooth_3_deriv1'] <= before_last_candle['mid_smooth_3_deriv1'])
& (before_last_candle['mid_smooth_3_deriv1'] >= last_candle['mid_smooth_3_deriv1'])) \
and (current_profit > expected_profit):
if last_candle['mid_smooth_1h_deriv1'] < 0 and current_profit > expected_profit:
return 'Drv3_' + pair_name + '_' + str(count_of_buys)
# if 4 <= count_of_buys <= 6:
@@ -524,13 +521,12 @@ 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':>10} | {'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|s241h|s242h|smt1h|smt2h|"
f"Tdc|{'val':>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', 'stop',
'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys',
'base_stake_amount', 'stop_buy']
'trade_info', 'last_date', 'expected_profit', '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']
@@ -591,15 +587,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
color_sma20 = GREEN if last_candle['sma20_deriv1_1d'] > 0 else RED
color_sma5 = GREEN if last_candle['sma5_deriv1_1d'] > 0 else RED
color_sma5_2 = GREEN if last_candle['sma5_deriv2_1d'] > 0 else RED
color_sma5_1h = GREEN if last_candle['sma24_deriv1_1h'] > 0 else RED
color_sma5_2h = GREEN if last_candle['sma24_deriv2_1h'] > 0 else RED
color_sma5_1h = GREEN if last_candle['sma5_deriv1_1h'] > 0 else RED
color_sma5_2h = GREEN if last_candle['sma5_deriv2_1h'] > 0 else RED
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(round(self.pairs[pair]['current_profit'], 2)) + '/' + str(profit)
@@ -613,7 +607,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
f"{round(val, 1) or '-' :>6}|"
f"{dist_max:>7}|{color_sma20}{round(last_candle['sma20_deriv1_1d'], 2):>5}{RESET}"
f"|{color_sma5}{round(last_candle['sma5_deriv1_1d'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['sma5_deriv2_1d'], 2):>5}{RESET}"
f"|{color_sma5_1h}{round(last_candle['sma24_deriv1_1h'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma24_deriv2_1h'], 2):>5}{RESET}"
f"|{color_sma5_1h}{round(last_candle['sma5_deriv1_1h'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma5_deriv2_1h'], 2):>5}{RESET}"
f"|{color_smooth_1h}{round(last_candle['mid_smooth_1h_deriv1'], 2):>5}{RESET}|{color_smooth2_1h}{round(last_candle['mid_smooth_1h_deriv2'], 2):>5}{RESET}"
# f"|{last_candle['min60_1d']}|{last_candle['max60_1d']}"
)
@@ -746,7 +740,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# print("##################")
# self.calculateStats(informative, 'sma5_deriv1', 'futur_percent_3')
# self.calculePlateaux(informative, 6, 0.005)
# self.calculePlateaux(informative, 24, 0.01)
macd, macdsignal, macdhist = talib.MACD(
informative['close'],
fastperiod=12,
slowperiod=26,
signalperiod=9
)
informative['macd'] = macd
informative['macdsignal'] = macdsignal
informative['macdhist'] = macdhist
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
@@ -775,6 +779,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1)
self.calculeDerivees(informative, 'sma20', factor_1=10, factor_2=1)
macd, macdsignal, macdhist = talib.MACD(
informative['close'],
fastperiod=12,
slowperiod=26,
signalperiod=9
)
informative['macd'] = macd
informative['macdsignal'] = macdsignal
informative['macdhist'] = macdhist
# informative['futur_percent_3'] = 100 * ((informative['sma5'].shift(-3) - informative['sma5']) / informative['sma5'])
# if self.dp.runmode.value in ('backtest'):
@@ -1120,14 +1135,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
duplicates='drop')
# Affichage formaté pour code Python
# print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
# print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
# Agrégation
grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
# Affichage
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(grouped.round(4))
# Ajout des probabilités de hausse
for col in futur_cols:
df[f"{col}_is_up"] = df[col] > 0
@@ -1358,8 +1372,7 @@ 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 / pct) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
self.adjust_stake_amount(pair, last_candle) - 10 * pct_first / 0.05) # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
self.pairs[trade.pair]['count_of_buys'] += 1
@@ -1449,12 +1462,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
return round((last_candle['max60_1d'] - last_candle['min60_1d']) / last_candle['max60_1d'], 4)
def getPctClose60D(self, pair, last_candle):
if last_candle['close'] > last_candle['max60_1d']:
if last_candle['close'] > last_candle['max12_1d']:
return 1
if last_candle['close'] < last_candle['min60_1d']:
if last_candle['close'] < last_candle['min12_1d']:
return 0
return round(
(last_candle['close'] - last_candle['min60_1d']) / (last_candle['max60_1d'] - last_candle['min60_1d']), 4)
(last_candle['close'] - last_candle['min12_1d']) / (last_candle['max12_1d'] - last_candle['min12_1d']), 4)
def getLimitBuy(self, pair, last_candle, first_pct):
count_of_buys = self.pairs[pair]['count_of_buys']
@@ -2106,6 +2119,18 @@ class Zeus_8_3_2_B_4_2(IStrategy):
limit = 3
if last_candle['sma5_deriv1_1h'] < -0.1:
return False
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:
return False
if pair.startswith('BTC'):
return True # BTC toujours autorisé
# Filtrer les paires non-BTC
non_btc_pairs = [p for p in self.pairs if not p.startswith('BTC')]
@@ -2139,13 +2164,23 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# if (last_candle['mid_smooth_1h_deriv1'] < -0.0 and last_candle['sma24_deriv1_1h'] < -0.0):
# return False
if self.pairs[pair]['count_of_buys'] >= 3:
if (last_candle['sma20_deriv1_1d'] < self.sma20_deriv1_1d_protection.value
and last_candle['sma5_deriv1_1d'] < self.sma5_deriv1_1d_protection.value \
and last_candle['sma24_deriv1_1h'] < self.sma24_deriv1_1h_protection.value):
# or (last_candle['sma5_deriv1_1d'] < -0.1 and last_candle['sma24_deriv1_1h'] < -0.1):
if last_candle['sma5_deriv2_1h'] < -0.2:
return False
if last_candle['mid_smooth_1h_deriv1'] < -0.02: # and last_candle['mid_smooth_1h_deriv2'] > 0):
return False
if self.pairs[pair]['count_of_buys'] >= 3:
if last_candle['sma5_deriv1_1d'] < -0.065 or (last_candle['sma5_deriv1_1d'] < -0.0 and last_candle['sma5_deriv2_1d'] < -0.0):
return False
# if self.pairs[pair]['count_of_buys'] >= 3:
# if (last_candle['sma20_deriv1_1d'] < self.sma20_deriv1_1d_protection.value
# and last_candle['sma5_deriv1_1d'] < self.sma5_deriv1_1d_protection.value \
# and last_candle['sma24_deriv1_1h'] < self.sma24_deriv1_1h_protection.value):
# # or (last_candle['sma5_deriv1_1d'] < -0.1 and last_candle['sma24_deriv1_1h'] < -0.1):
# return False
if pair.startswith('BTC'):
return True # BTC toujours autorisé