souti/Freqtrade
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Zeus_8_3_2_B_4_2 current profit / down

Browse Source
This commit is contained in:
Jérôme Delacotte
2025-04-19 09:42:52 +02:00
parent f47836c4b7
commit fef1220183
1 changed files with 6 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
Zeus_8_3_2_B_4_2.py
View File

@@ -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) 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['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() self.trades = list()
return 'mx_' + str(count_of_buys) return 'mx_' + str(count_of_buys)
if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit): 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() dataframe['highest_4_average'] = highest_4.mean()
# Compter les baisses consécutives # Compter les baisses consécutives
dataframe['down'] = dataframe['hapercent'] <= 0.001 dataframe['down'] = dataframe['hapercent'] <= 0.0001
dataframe['up'] = dataframe['hapercent'] >= 0.0001 dataframe['up'] = dataframe['hapercent'] >= 0.0001
dataframe['down_count'] = - dataframe['down'].astype(int) * ( dataframe['down_count'] = - dataframe['down'].astype(int) * (
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1) 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.loc[
( (
(dataframe['max200_diff'] >= 0.025) (dataframe['max200_diff'] >= 0.01)
& (dataframe['percent12'] < -0.002) & (dataframe['percent12'] < -0.002)
# & (dataframe['pct_change'] < 0) # & (dataframe['pct_change'] < 0)
& (dataframe['open'] < dataframe['average_line_288_099']) & (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['percent24'] < -0.022) |
(dataframe['percent48'] < -0.030) (dataframe['percent48'] < -0.030)
) )
& (dataframe['count_buys'] == 0)
& (dataframe['close'] <= dataframe['min50'] * 1.002) & (dataframe['close'] <= dataframe['min50'] * 1.002)
& (dataframe['open'] < dataframe['average_line_50']) & (dataframe['open'] < dataframe['average_line_50'])
& ( & (
@@ -739,9 +738,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
& (dataframe['pct_change'] < 0) & (dataframe['pct_change'] < 0)
& (dataframe['min200'].shift(2) == dataframe['min200']) & (dataframe['min200'].shift(2) == dataframe['min200'])
& (dataframe['close'] < dataframe['lowest_4_average']) & (dataframe['close'] < dataframe['lowest_4_average'])
& (dataframe['count_buys'] == 0 |
((dataframe['count_buys'] > 0) & (dataframe['close'] <= dataframe['limit']))
)
& (dataframe['up_count'] > 0) & (dataframe['up_count'] > 0)
), ['enter_long', 'enter_tag']] = (1, 'min200') ), ['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_1h'] < 70)
# & (dataframe['rsi_diff_1h'] > -5) # & (dataframe['rsi_diff_1h'] > -5)
(dataframe["bb_width"] > 0.01) # (dataframe["bb_width"] > 0.01)
& (dataframe['down_count'].shift(1) < - 6) (dataframe['down_count'].shift(1) < - 6)
& (dataframe['down_count'] == 0) & (dataframe['down_count'] == 0)
& (dataframe['down_pct'].shift(1) <= -0.5) # & (dataframe['down_pct'].shift(1) <= -0.5)
), ['enter_long', 'enter_tag']] = (1, 'down') ), ['enter_long', 'enter_tag']] = (1, 'down')
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)