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Recalcul mid_smooth_deriv1 / min expected profit à 0.004

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Jérôme Delacotte
2025-05-12 22:35:16 +02:00
parent 6af7053176
commit db84129421
1 changed files with 14 additions and 18 deletions
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32
Zeus_8_3_2_B_4_2.py
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@@ -494,8 +494,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
def tag_by_derivatives(row):
d1 = row['mid_smooth_deriv1']
d2 = row['mid_smooth_deriv2']
d1_lim_inf = -5
d1_lim_sup = 5
d1_lim_inf = -0.01
d1_lim_sup = 0.01
if d1 >= d1_lim_inf and d1 <= d1_lim_sup: # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
return 'P' # Palier
if d1 == 0.0:
@@ -797,12 +797,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
def calculateTendency(self, dataframe, window=12):
dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
# 2. Calcul du lissage sur 200 bougies par moyenne mobile médiane
dataframe['mid_smooth'] = dataframe['mid'].rolling(window=window, center=True, min_periods=1).median().rolling(
dataframe['mid_smooth'] = dataframe['close'].rolling(window=window, center=True, min_periods=1).median().rolling(
3).mean()
# 2. Dérivée première = différence entre deux bougies successives
dataframe['mid_smooth_deriv1'] = round(100000 * dataframe['mid_smooth'].pct_change(), 2)
dataframe['mid_smooth_deriv1'] = round(100 * dataframe['mid_smooth'].diff() / dataframe['mid_smooth'], 4)
# 3. Dérivée seconde = différence de la dérivée première
dataframe['mid_smooth_deriv2'] = round(100 * dataframe['mid_smooth_deriv1'].pct_change().rolling(3).mean(), 2)
dataframe['mid_smooth_deriv2'] = round(100 * dataframe['mid_smooth_deriv1'].diff().rolling(3).mean(), 4)
dataframe = self.add_tendency_column(dataframe)
return dataframe
@@ -898,6 +898,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# (dataframe["bb_width"] > 0.01)
(dataframe['down_count'].shift(1) < - 1)
& (dataframe['down_count'] == 0)
& (dataframe['mid_smooth_deriv1'] >= -0.01)
# & (dataframe['tendency'] != "B--")
# & (dataframe['tendency'] != "B-")
), ['enter_long', 'enter_tag']] = (1, 'down')
@@ -906,15 +907,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
(
(dataframe['low'] < dataframe['min200'])
& (dataframe['min50'] == dataframe['min50'].shift(3))
# & (dataframe['tendency'] != "B--")
# & (dataframe['tendency'] != "B-")
#
& (dataframe['tendency'] != "B-")
), ['enter_long', 'enter_tag']] = (1, 'low')
# dataframe.loc[
# (
# (dataframe['mid_smooth_deriv1'] > 0)
# & (dataframe['rsi'] < 50)
# & (dataframe['mid_smooth_deriv1'] > dataframe['mid_smooth_deriv1'].shift(1) * 1.5)
# ), ['enter_long', 'enter_tag']] = (1, 'mid')
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
@@ -1079,12 +1075,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
def expectedProfit(self, pair: str, last_candle: DataFrame):
first_price = last_candle['first_price']
first_max = 0.01
if first_price < last_candle['max200']:
first_max = (last_candle['max200'] - first_price) / first_price
# first_price = last_candle['first_price']
# first_max = 0.01
# if first_price < last_candle['max200']:
# first_max = (last_candle['max200'] - first_price) / first_price
expected_profit = min(0.01, first_max)
expected_profit = 0.004 #min(0.01, first_max)
# print(
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")