From fef1220183d7461d33fe5b1701be15205b241c85 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Delacotte?= Date: Sat, 19 Apr 2025 09:42:52 +0200 Subject: [PATCH] Zeus_8_3_2_B_4_2 current profit / down --- Zeus_8_3_2_B_4_2.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py index e406f50..7c6881d 100644 --- a/Zeus_8_3_2_B_4_2.py +++ b/Zeus_8_3_2_B_4_2.py @@ -322,7 +322,7 @@ class Zeus_8_3_2_B_4_2(IStrategy): pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) # if (last_candle['rsi_1d'] > 50) & (last_candle['percent12'] < 0.0): - if (last_candle['percent3'] < 0.0) & (current_profit > last_candle['min_max200'] / 3): + if (last_candle['percent3'] < 0.0) & (current_profit > 0.05): #last_candle['min_max200'] / 3): self.trades = list() return 'mx_' + str(count_of_buys) if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit): @@ -491,7 +491,7 @@ class Zeus_8_3_2_B_4_2(IStrategy): dataframe['highest_4_average'] = highest_4.mean() # Compter les baisses consécutives - dataframe['down'] = dataframe['hapercent'] <= 0.001 + dataframe['down'] = dataframe['hapercent'] <= 0.0001 dataframe['up'] = dataframe['hapercent'] >= 0.0001 dataframe['down_count'] = - dataframe['down'].astype(int) * ( dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1) @@ -688,7 +688,7 @@ class Zeus_8_3_2_B_4_2(IStrategy): dataframe.loc[ ( - (dataframe['max200_diff'] >= 0.025) + (dataframe['max200_diff'] >= 0.01) & (dataframe['percent12'] < -0.002) # & (dataframe['pct_change'] < 0) & (dataframe['open'] < dataframe['average_line_288_099']) @@ -706,7 +706,6 @@ class Zeus_8_3_2_B_4_2(IStrategy): (dataframe['percent24'] < -0.022) | (dataframe['percent48'] < -0.030) ) - & (dataframe['count_buys'] == 0) & (dataframe['close'] <= dataframe['min50'] * 1.002) & (dataframe['open'] < dataframe['average_line_50']) & ( @@ -739,9 +738,6 @@ class Zeus_8_3_2_B_4_2(IStrategy): & (dataframe['pct_change'] < 0) & (dataframe['min200'].shift(2) == dataframe['min200']) & (dataframe['close'] < dataframe['lowest_4_average']) - & (dataframe['count_buys'] == 0 | - ((dataframe['count_buys'] > 0) & (dataframe['close'] <= dataframe['limit'])) - ) & (dataframe['up_count'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'min200') @@ -749,10 +745,10 @@ class Zeus_8_3_2_B_4_2(IStrategy): ( # (dataframe['rsi_1h'] < 70) # & (dataframe['rsi_diff_1h'] > -5) - (dataframe["bb_width"] > 0.01) - & (dataframe['down_count'].shift(1) < - 6) + # (dataframe["bb_width"] > 0.01) + (dataframe['down_count'].shift(1) < - 6) & (dataframe['down_count'] == 0) - & (dataframe['down_pct'].shift(1) <= -0.5) + # & (dataframe['down_pct'].shift(1) <= -0.5) ), ['enter_long', 'enter_tag']] = (1, 'down') dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)