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FIX ISSUES

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This commit is contained in:
Jérôme Delacotte
2025-12-04 19:12:59 +01:00
parent 496c4d7827
commit 3efc95e415
13 changed files with 33 additions and 32 deletions
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3
FrictradeLearning.py
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@@ -1061,7 +1061,8 @@ class FrictradeLearning(IStrategy):
# FIN ########################## ALGO ATH
condition = last_candle['hapercent'] > 0 and last_candle['sma24_deriv1'] > 0 \
and last_candle['close'] < self.pairs[pair]['first_buy'] and last_candle['ml_prob'] > 0.65
and last_candle['close'] < self.pairs[pair]['first_buy'] \
and last_candle['ml_prob'] > 0.65
limit_buy = 40
# or (last_candle['close'] <= last_candle['min180'] and hours > 3)
if (decline >= dca_threshold) and condition:

2
GodStraJD3_3.py
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@@ -10,7 +10,7 @@ from freqtrade.persistence import Trade
from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

2
GodStraJD3_6_53.py
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@@ -10,7 +10,7 @@ from freqtrade.persistence import Trade
from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

2
GodStraJD3_6_53_1.py
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@@ -10,7 +10,7 @@ from freqtrade.persistence import Trade
from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

2
GodStraJD3_9.py
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@@ -27,7 +27,7 @@ import numpy as np
from random import shuffle
# TODO: this gene is removed 'MAVP' cuz or error on periods
import user_data.strategies.custom_indicators as csa
# import user_data.strategies.custom_indicators as csa
all_god_genes = {
'Overlap Studies': {

4
Ishimoku_6.py
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@@ -220,10 +220,10 @@ class Ishimoku_6(IStrategy):
def bot_loop_start(self, **kwargs) -> None:
inf_tf = '5m'
pairs = self.dp.current_whitelist()
print("Calcul des pairs informatives")
# print("Calcul des pairs informatives")
for pairname in pairs:
self.stop_buying[pairname] = True
print("Fin Calcul des pairs informatives")
# print("Fin Calcul des pairs informatives")
# def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
# proposed_stake: float, min_stake: float, max_stake: float,

6
Solipsis5.py
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@@ -1,7 +1,7 @@
import numpy as np
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import arrow
# import arrow
from freqtrade.strategy import (IStrategy, merge_informative_pair, stoploss_from_open,
IntParameter, DecimalParameter, CategoricalParameter)
@@ -162,7 +162,7 @@ def SSLChannels(dataframe, length=10, mode='sma'):
df['smaLow'] = df['low'].rolling(length).mean()
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1,
np.where(df['close'] < df['smaLow'], -1, np.NAN))
np.where(df['close'] < df['smaLow'], -1, np.nan))
df['hlv'] = df['hlv'].ffill()
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
@@ -180,7 +180,7 @@ def SSLChannels_ATR(dataframe, length=7):
df['ATR'] = ta.ATR(df, timeperiod=14)
df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.nan))
df['hlv'] = df['hlv'].ffill()
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])

2
Supertrend.py
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@@ -164,7 +164,7 @@ class Supertrend(IStrategy):
df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \
df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] < df['final_lb'].iat[i] else 0.00
# Mark the trend direction up/down
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.nan)
# Remove basic and final bands from the columns
df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)

4
Zeus_5_1.py
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@@ -487,10 +487,10 @@ class Zeus_5_1(IStrategy):
def bot_loop_start(self, **kwargs) -> None:
inf_tf = '5m'
pairs = self.dp.current_whitelist()
print("Calcul des pairs informatives")
# print("Calcul des pairs informatives")
for pairname in pairs:
self.stop_buying[pairname] = True
print("Fin Calcul des pairs informatives")
# print("Fin Calcul des pairs informatives")
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:

4
Zeus_5_2.py
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@@ -491,10 +491,10 @@ class Zeus_5_2(IStrategy):
def bot_loop_start(self, **kwargs) -> None:
inf_tf = '5m'
pairs = self.dp.current_whitelist()
print("Calcul des pairs informatives")
# print("Calcul des pairs informatives")
for pairname in pairs:
self.stop_buying[pairname] = True
print("Fin Calcul des pairs informatives")
# print("Fin Calcul des pairs informatives")
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:

4
Zeus_6.py
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@@ -487,10 +487,10 @@ class Zeus_6(IStrategy):
def bot_loop_start(self, **kwargs) -> None:
inf_tf = '5m'
pairs = self.dp.current_whitelist()
print("Calcul des pairs informatives")
# print("Calcul des pairs informatives")
for pairname in pairs:
self.stop_buying[pairname] = True
print("Fin Calcul des pairs informatives")
# print("Fin Calcul des pairs informatives")
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:

28
Zeus_8_3_2_B_4_2.py
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@@ -1084,23 +1084,23 @@ class Zeus_8_3_2_B_4_2(IStrategy):
dataframe['stop_buying'] = latched
if self.dp.runmode.value in ('backtest'):
if False and self.dp.runmode.value in ('backtest'):
self.trainModel(dataframe, metadata)
short_pair = self.getShortName(pair)
if short_pair == 'BTC':
self.model = joblib.load(f"{short_pair}_rf_model.pkl")
# Préparer les features pour la prédiction
features = dataframe[self.model_indicators].fillna(0)
# Prédiction : probabilité que le prix monte
probs = self.model.predict_proba(features)[:, 1]
# Sauvegarder la probabilité pour l’analyse
dataframe['ml_prob'] = probs
self.inspect_model(self.model)
# if short_pair == 'BTC':
# self.model = joblib.load(f"{short_pair}_rf_model.pkl")
#
# # Préparer les features pour la prédiction
# features = dataframe[self.model_indicators].fillna(0)
#
# # Prédiction : probabilité que le prix monte
# probs = self.model.predict_proba(features)[:, 1]
#
# # Sauvegarder la probabilité pour l’analyse
# dataframe['ml_prob'] = probs
#
# self.inspect_model(self.model)
return dataframe

2
fixed_riskreward_loss.py
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@@ -48,7 +48,7 @@ class FixedRiskRewardLoss(IStrategy):
# using current_time/open_date directly via custom_info_pair[trade.open_daten]
# would only work in backtesting/hyperopt.
# in live/dry-run, we have to search for nearest row before it
open_date_mask = custom_info_pair.index.unique().get_loc(trade.open_date_utc, method='ffill')
open_date_mask = custom_info_pair.index.unique().get_loc(trade.open_date_utc)
open_df = custom_info_pair.iloc[open_date_mask]
# trade might be open too long for us to find opening candle