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test pct_max

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Jérôme Delacotte
2025-07-16 10:24:22 +02:00
parent 86bcb1240e
commit f5847ffc25
1 changed files with 26 additions and 39 deletions
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65
Zeus_8_3_2_B_4_2.py
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@@ -267,7 +267,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# if count < 3:
# allow_to_buy = False
if not self.should_enter_trade(pair):
if not self.should_enter_trade(pair, last_candle):
allow_to_buy = False
if allow_to_buy:
@@ -488,11 +488,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
buys = ''
max_touch = '' # round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'],
3) # round(100 * self.pairs[pair]['current_profit'], 1)
pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in
self.pairs.values())) # if not pair in ('BTC/USDT', 'BTC/USDC'))
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
# if trade_type is not None:
# if np.isnan(last_candle['rsi_1d']):
@@ -654,6 +652,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# self.calculeDerivees(informative, 'rsi')
#
informative['sma5'] = talib.SMA(informative, timeperiod=5)
informative['sma14'] = talib.SMA(informative, timeperiod=14)
self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1)
@@ -982,8 +981,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
pct_first = 0
total_counts = sum(
pair_data['count_of_buys'] for pair_data in self.pairs.values() if not pair in ('BTC/USDT', 'BTC/USDC'))
total_counts = sum(pair_data['count_of_buys'] for pair_data in self.pairs.values() if not pair in ('BTC/USDT', 'BTC/USDC'))
if self.pairs[pair]['first_buy']:
pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
@@ -1011,7 +1009,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
print("skip dataframe")
return None
if not self.should_enter_trade(pair):
if not self.should_enter_trade(pair, last_candle):
return None
# if self.dp.runmode.value in ('dry_run'):
@@ -1128,21 +1126,23 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# or ((before_last_candle['mid_re_smooth_3_deriv1'] <= 0) & (last_candle['mid_re_smooth_3_deriv1'] >= 0) & (3 <= count_of_buys < 6)) \
# or ((before_last_candle['mid_smooth_1h_deriv1'] <= 0) & (last_candle['mid_smooth_1h_deriv1'] >= 0) & (6 <= count_of_buys))
limit_buy = 20
limit_buy = 40
if (count_of_buys < limit_buy) and condition and (
pct_max < lim): # and val > self.buy_val_adjust.value and last_candle['mid_smooth_deriv1_1d'] > - 1):
try:
if 6 <= count_of_buys and last_candle['sma24_deriv1_1h'] < 0:
return None
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_first={pct_first:.3f} pct_max={pct_max:.3f} lim={lim:.3f} index={index}")
# self.pairs[trade.pair]['last_palier_index'] = index
# Appel de la fonction
poly_func, x_future, y_future, count = self.polynomial_forecast(
dataframe['mid_smooth_12'],
window=self.buy_horizon_predict_1h.value * 12,
degree=4)
if count < 3:
return None
# poly_func, x_future, y_future, count = self.polynomial_forecast(
# dataframe['mid_smooth_12'],
# window=self.buy_horizon_predict_1h.value * 12,
# degree=4)
#
# if count < 3:
# return None
max_amount = self.config.get('stake_amount') * 2.5
# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
@@ -1170,18 +1170,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
self.pairs[trade.pair]['max_touch'] = last_candle['close']
self.pairs[trade.pair]['last_candle'] = last_candle
# for pair, values in self.pairs.items():
# print(f"\n{pair}")
# for key, value in values.items():
# print(f" {key:20}: {value}")
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']
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']
print(df_filtered)
# 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']
# 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']
#
# print(df_filtered)
return stake_amount
except Exception as exception:
@@ -1790,7 +1785,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
print("Moyenne des valeurs par double-tranche :")
print(pivot_mean.round(2))
def should_enter_trade(self, pair: str) -> bool:
def should_enter_trade(self, pair: str, last_candle) -> bool:
limit = 3
@@ -1810,17 +1805,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
max_pair = p
total_non_btc += self.pairs[p]['count_of_buys']
this_pair_count = self.pairs[p]['count_of_buys']
# if total_non_btc >= 12:
# # print("total_non_btc > 10")
# return False
pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
if max_pair != '':
# if this_pair_count >= limit:
# Si une autre paire non-BTC a aussi >3 trades, blocage
# if max_pair == pair:
# print(f"{pair} Cette paire {max_pair} est la seule avec >=3 / {max_nb_trades}")
return max_pair == pair
return max_pair == pair or pct_max < - 0.25
else:
return True