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Calcul 20240101-20250514 2984.403 220.573$ => 13,21 max 11 mises BTC / 14 mises DOGE

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Jérôme Delacotte
2025-07-15 22:49:10 +02:00
parent d1cf9ab72f
commit 86bcb1240e
2 changed files with 147 additions and 149 deletions
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44
Zeus_8_3_2_B_4_2.py
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@@ -657,8 +657,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
self.calculeDerivees(informative, 'sma5', factor_1=10, factor_2=1)
informative['futur_percent_3'] = 100 * (
(informative['sma5'].shift(-3) - informative['sma5']) / informative['sma5'])
# informative['futur_percent_3'] = 100 * ((informative['sma5'].shift(-3) - informative['sma5']) / informative['sma5'])
# if self.dp.runmode.value in ('backtest'):
# print("##################")
@@ -767,8 +766,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
#
# horizon_h = 24 * 5
# dataframe['futur_percent_1h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-12) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
dataframe['futur_percent_3h'] = 100 * (
(dataframe['close'].shift(-36) - dataframe['close']) / dataframe['close']).rolling(horizon_h).mean()
# dataframe['futur_percent_3h'] = 100 * ((dataframe['close'].shift(-36) - dataframe['close']) / dataframe['close']).rolling(horizon_h).mean()
# dataframe['futur_percent_5h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-60) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
# dataframe['futur_percent_12h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-144) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
#
@@ -781,12 +779,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
abs(dataframe['volume'].shift(1)) + abs(dataframe['volume'].shift(2)))
self.calculeDerivees(dataframe, 'ema_volume', factor_1=10, factor_2=1)
if self.dp.runmode.value in ('backtest'):
print("##################")
print("# STAT DAY vs HOUR")
print("##################")
self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_3h'], indic_1='ema_volume',
indic_2='mid_smooth_1h_deriv1')
# if self.dp.runmode.value in ('backtest'):
# print("##################")
# print("# STAT DAY vs HOUR")
# print("##################")
# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_3h'], indic_1='ema_volume',
# indic_2='mid_smooth_1h_deriv1')
dataframe['proba_hausse'] = dataframe.apply(lambda row: self.getProbaHausse(row), axis=1)
@@ -1253,22 +1251,22 @@ class Zeus_8_3_2_B_4_2(IStrategy):
factors = [1, 1.25, 1.5, 2.0]
if not pair in ('BTC/USDT', 'BTC/USDC'):
factors = [1, 1.2, 1.3, 1.4]
base_stake_amount = base_stake_amount * 0.75
# factors = [1, 1.2, 1.3, 1.4]
adjusted_stake_amount = base_stake_amount
else :
first_price = self.pairs[pair]['first_buy']
if (first_price == 0):
first_price = last_candle['close']
first_price = self.pairs[pair]['first_buy']
if (first_price == 0):
first_price = last_candle['close']
last_max = last_candle['max12_1d']
pct = 5
if last_max > 0:
pct = 100 * (last_max - first_price) / last_max
thresholds = [2, 5, 10, 20]
last_max = last_candle['max12_1d']
pct = 5
if last_max > 0:
pct = 100 * (last_max - first_price) / last_max
thresholds = [2, 5, 10, 20]
factor = self.multi_step_interpolate(pct, thresholds, factors)
factor = self.multi_step_interpolate(pct, thresholds, factors)
adjusted_stake_amount = base_stake_amount * factor # max(base_stake_amount, min(100, base_stake_amount * percent_4))
adjusted_stake_amount = base_stake_amount * factor # max(base_stake_amount, min(100, base_stake_amount * percent_4))
return adjusted_stake_amount