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Ajout calcul de probabilité fonction de 2 indicateurs / stats calculées depuis 2020

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Jérôme Delacotte
2025-05-15 18:58:37 +02:00
parent 775c950306
commit 1af4fe8460
2 changed files with 100 additions and 43 deletions
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Zeus_8_3_2_B_4_2.py
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@@ -209,22 +209,57 @@ class Zeus_8_3_2_B_4_2(IStrategy):
protection_fibo = IntParameter(1, 10, default=2, space='protection')
sell_allow_decrease = DecimalParameter(0.005, 0.02, default=0.2, decimals=2, space='sell', optimize=True, load=True)
# Probabilité de hausse pour futur_percent_3h (en %):
# mid_smooth_deriv1_1h_bin B5 B4 B3 B2 B1 N0 H1 H2 H3 H4 H5
# sma24_diff_1h_bin
# B5 41.0 47.2 48.1 45.6 74.0 65.9 66.5 83.8 77.8 72.1 81.0
# B4 41.2 35.8 48.4 46.5 59.9 60.2 75.8 79.4 84.6 83.0 78.5
# B3 34.1 39.7 42.8 47.0 63.3 64.5 71.5 80.4 82.0 86.6 76.6
# B2 27.5 27.9 32.3 33.2 61.9 67.1 70.8 79.5 81.3 73.6 81.9
# B1 35.0 26.5 24.4 34.9 50.0 59.2 69.4 72.8 79.8 77.4 69.5
# N0 30.6 19.9 23.6 30.8 41.9 59.2 67.5 70.6 74.0 63.0 75.0
# H1 25.2 28.7 28.6 25.8 35.9 44.2 60.1 68.8 67.7 69.6 80.9
# H2 29.8 20.8 23.9 30.4 34.4 37.5 52.7 66.1 69.8 67.5 62.9
# H3 25.7 29.4 22.7 29.8 37.7 47.1 59.9 68.5 66.5 68.6 66.4
# H4 30.6 27.5 25.1 22.6 30.8 34.1 50.9 59.8 57.0 68.6 63.7
# H5 14.8 21.6 22.2 35.3 19.3 31.6 38.3 59.6 65.2 56.8 59.6
# Données sous forme de dictionnaire
data = {
"B5": [41.0, 41.2, 34.1, 27.5, 35.0, 30.6, 25.2, 29.8, 25.7, 30.6, 14.8],
"B4": [47.2, 35.8, 39.7, 27.9, 26.5, 19.9, 28.7, 20.8, 29.4, 27.5, 21.6],
"B3": [48.1, 48.4, 42.8, 32.3, 24.4, 23.6, 28.6, 23.9, 22.7, 25.1, 22.2],
"B2": [45.6, 46.5, 47.0, 33.2, 34.9, 30.8, 25.8, 30.4, 29.8, 22.6, 35.3],
"B1": [74.0, 59.9, 63.3, 61.9, 50.0, 41.9, 35.9, 34.4, 37.7, 30.8, 19.3],
"N0": [65.9, 60.2, 64.5, 67.1, 59.2, 59.2, 44.2, 37.5, 47.1, 34.1, 31.6],
"H1": [66.5, 75.8, 71.5, 70.8, 69.4, 67.5, 60.1, 52.7, 59.9, 50.9, 38.3],
"H2": [83.8, 79.4, 80.4, 79.5, 72.8, 70.6, 68.8, 66.1, 68.5, 59.8, 59.6],
"H3": [77.8, 84.6, 82.0, 81.3, 79.8, 74.0, 67.7, 69.8, 66.5, 57.0, 65.2],
"H4": [72.1, 83.0, 86.6, 73.6, 77.4, 63.0, 69.6, 67.5, 68.6, 68.6, 56.8],
"H5": [81.0, 78.5, 76.6, 81.9, 69.5, 75.0, 80.9, 62.9, 66.4, 63.7, 59.6]
'B5': [41.2, 41.9, 35.3, 29.5, 26.6, 32.7, 28.8, 30.4, 25.3, 20.2, 14.6],
'B4': [45.2, 40.1, 38.6, 31.2, 30.1, 29.5, 27.1, 24.2, 24.7, 21.6, 18.4],
'B3': [46.7, 41.6, 37.3, 33.4, 27.3, 28.6, 28.8, 24.6, 24.2, 24.8, 23.2],
'B2': [49.4, 47.9, 45.9, 39.8, 34.6, 28.6, 26.2, 28.9, 24.7, 29.7, 28.5],
'B1': [74.1, 61.7, 61.7, 58.2, 50.2, 43.5, 38.1, 32.5, 33.9, 32.8, 25.3],
'N0': [67.8, 58.3, 67.8, 61.7, 58.0, 56.2, 46.7, 42.2, 44.4, 39.7, 30.3],
'H1': [72.6, 66.2, 70.8, 69.1, 70.8, 65.3, 56.3, 51.2, 52.6, 53.9, 48.1],
'H2': [77.2, 78.7, 81.0, 75.9, 73.6, 73.0, 65.4, 63.3, 63.8, 56.9, 52.8],
'H3': [78.8, 76.6, 78.8, 81.2, 76.7, 71.9, 69.2, 67.8, 66.1, 61.3, 58.1],
'H4': [75.5, 79.0, 78.0, 78.5, 73.6, 66.4, 71.2, 63.8, 67.0, 62.6, 59.3],
'H5': [75.1, 78.6, 81.0, 72.3, 70.0, 71.4, 67.8, 66.0, 66.2, 64.5, 59.9]
}
# data = {
# "B5": [41.0, 41.2, 34.1, 27.5, 35.0, 30.6, 25.2, 29.8, 25.7, 30.6, 14.8],
# "B4": [47.2, 35.8, 39.7, 27.9, 26.5, 19.9, 28.7, 20.8, 29.4, 27.5, 21.6],
# "B3": [48.1, 48.4, 42.8, 32.3, 24.4, 23.6, 28.6, 23.9, 22.7, 25.1, 22.2],
# "B2": [45.6, 46.5, 47.0, 33.2, 34.9, 30.8, 25.8, 30.4, 29.8, 22.6, 35.3],
# "B1": [74.0, 59.9, 63.3, 61.9, 50.0, 41.9, 35.9, 34.4, 37.7, 30.8, 19.3],
# "N0": [65.9, 60.2, 64.5, 67.1, 59.2, 59.2, 44.2, 37.5, 47.1, 34.1, 31.6],
# "H1": [66.5, 75.8, 71.5, 70.8, 69.4, 67.5, 60.1, 52.7, 59.9, 50.9, 38.3],
# "H2": [83.8, 79.4, 80.4, 79.5, 72.8, 70.6, 68.8, 66.1, 68.5, 59.8, 59.6],
# "H3": [77.8, 84.6, 82.0, 81.3, 79.8, 74.0, 67.7, 69.8, 66.5, 57.0, 65.2],
# "H4": [72.1, 83.0, 86.6, 73.6, 77.4, 63.0, 69.6, 67.5, 68.6, 68.6, 56.8],
# "H5": [81.0, 78.5, 76.6, 81.9, 69.5, 75.0, 80.9, 62.9, 66.4, 63.7, 59.6]
# }
index_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
matrix_df = pd.DataFrame(data, index=index_labels)
# Récupération des labels ordonnés
ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
label_to_index = {label: i for i, label in enumerate(ordered_labels)}
# Extraction de la matrice numérique
numeric_matrix = matrix_df.reindex(index=ordered_labels, columns=ordered_labels).values
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
@@ -232,7 +267,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
minutes = 0
if self.pairs[pair]['last_date'] != 0:
minutes = round((current_time - self.pairs[pair]['last_date']).total_seconds() / 60,0)
minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
@@ -354,6 +389,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
return None
mid_smooth_label = self.get_mid_smooth_label(last_candle['mid_smooth_deriv1_1h']) # ex. 'B2'
sma24_diff_label = self.get_sma24_diff_label(last_candle['sma24_diff_1h'])
val = self.approx_val_from_bins(row_label=sma24_diff_label, col_label=mid_smooth_label)
if val > 50:
return None
baisse = self.pairs[pair]['max_profit'] - current_profit
mx = self.pairs[pair]['max_profit'] / 5
if (baisse > mx) & (current_profit > expected_profit): #last_candle['min_max200'] / 3):
@@ -500,7 +542,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe['haclose'] = heikinashi['close']
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
dataframe['pct_change'] = dataframe['close'].pct_change(5)
dataframe = self.calculateTendency(dataframe)
dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200)
@@ -652,6 +693,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# dataframe['amount'] = amount
print(f"amount= {amount}")
dataframe['percent_with_previous_day'] = 100 * (dataframe['close'] - dataframe['close_1d']) / dataframe['close']
dataframe['futur_percent_1h'] = 100 * (dataframe['close'].shift(-12) - dataframe['close']) / dataframe['close']
dataframe['futur_percent_3h'] = 100 * (dataframe['close'].shift(-36) - dataframe['close']) / dataframe['close']
dataframe['futur_percent_5h'] = 100 * (dataframe['close'].shift(-60) - dataframe['close']) / dataframe['close']
@@ -707,10 +750,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
(
(dataframe['max200_diff'] >= 0.01)
& (dataframe['percent12'] < -0.002)
# & (dataframe['pct_change'] < 0)
& (dataframe['open'] < dataframe['average_line_288_099'])
& (dataframe['open'] < dataframe['average_line_50'])
# & (dataframe['percent'] >= -0.0005)
& (dataframe['min12'].shift(2) == dataframe['min12'])
& (dataframe['up_count'] > 0)
& (dataframe["bb_width"] > 0.01)
@@ -840,17 +881,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
return None
count_of_buys = trade.nr_of_successful_entries
# if (days_since_first_buy >= 5 and count_of_buys >= 4 and last_candle['sma5_pct_1d'] < 0):
# # print(f"waiting day increase pair {pair}")
# return None
# if 'buy' in last_candle:
# condition = (last_candle['buy'] == 1)
# else:
# condition = False
# self.protection_nb_buy_lost.value
current_time_utc = current_time.astimezone(timezone.utc)
open_date = trade.open_date.astimezone(timezone.utc)
days_since_open = (current_time_utc - open_date).days
@@ -880,7 +910,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
limit_buy = 20
if (count_of_buys < limit_buy) \
and (last_candle['enter_long'] == 1) \
and (pct_max < lim and val > 50):
and (pct_max < lim and val > 50 and last_candle['mid_smooth_deriv1_1d'] > - 1):
try:
max_amount = self.config.get('stake_amount', 100) * 2.5
@@ -1051,7 +1081,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
return df
def get_mid_smooth_label(self, value):
bins = [-2.0622, -0.1618, -0.0717, -0.0353, -0.0135, 0.0, 0.0085, 0.0276, 0.0521, 0.0923, 0.1742, 2.3286]
bins = [-6.6534, -0.1788, -0.0771, -0.0375, -0.0149, 0.0000, 0.0045, 0.0232, 0.0488, 0.0923, 0.1961, 2.7892]
# bins = [-2.0622, -0.1618, -0.0717, -0.0353, -0.0135, 0.0, 0.0085, 0.0276, 0.0521, 0.0923, 0.1742, 2.3286]
labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
for i in range(len(bins) - 1):
if bins[i] <= value < bins[i + 1]:
@@ -1059,17 +1090,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
return labels[-1] # cas limite pour la borne max
def get_sma24_diff_label(self, value):
bins = [-0.84253877, -0.13177195, -0.07485074, -0.04293497, -0.02033502, -0.00215711,
0.01411933, 0.03308264, 0.05661652, 0.09362708, 0.14898214, 0.50579505]
# bins = [-0.84253877, -0.13177195, -0.07485074, -0.04293497, -0.02033502, -0.00215711,
# 0.01411933, 0.03308264, 0.05661652, 0.09362708, 0.14898214, 0.50579505]
bins = [-2.36157394, - 0.14917999, - 0.08269905, - 0.04657017, - 0.02168984, - 0.00339744,
0.01284104, 0.03306543, 0.05950946, 0.09880605, 0.16573819, 1.05922354]
labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
for i in range(len(bins) - 1):
if bins[i] <= value < bins[i + 1]:
return labels[i]
return labels[-1]
import numpy as np
import pandas as pd
def interpolated_val_from_bins(self, row_pos, col_pos):
"""
Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
@@ -1140,19 +1171,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
if row_label not in self.matrix_df.index or col_label not in self.matrix_df.columns:
return np.nan
# Récupération des labels ordonnés
ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
label_to_index = {label: i for i, label in enumerate(ordered_labels)}
# Index correspondant
row_idx = label_to_index.get(row_label)
col_idx = label_to_index.get(col_label)
# Extraction de la matrice numérique
numeric_matrix = self.matrix_df.reindex(index=ordered_labels, columns=ordered_labels).values
row_idx = self.label_to_index.get(row_label)
col_idx = self.label_to_index.get(col_label)
# Approximation directe (aucune interpolation complexe ici, juste une lecture)
return numeric_matrix[row_idx, col_idx]
return self.numeric_matrix[row_idx, col_idx]
# @property