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Frictrade progression adjust exponentielle / correction trailing

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Jérôme Delacotte
2025-12-07 19:13:55 +01:00
parent e8cdf10488
commit 3cac030f10
1 changed files with 45 additions and 17 deletions
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FrictradeLearning.py
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@@ -27,6 +27,7 @@ import ta
import talib.abstract as talib
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import timezone, timedelta
import mpmath as mp
# Machine Learning
from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
@@ -133,7 +134,7 @@ class FrictradeLearning(IStrategy):
trailing_stop = False
trailing_stop_positive = 0.15
trailing_stop_positive_offset = 0.5
trailing_stop_positive_offset = 1
trailing_only_offset_is_reached = True
# Buy hypers
@@ -246,19 +247,33 @@ class FrictradeLearning(IStrategy):
return allow_to_buy
def calculateStepsDcaThresholds(self, last_candle, pair):
def split_ratio_one_third(n, p):
a = n / (2 * p) # première valeur
d = n / (p * (p - 1)) # incrément
return [round(a + i * d, 3) for i in range(p)]
# def split_ratio_one_third(n, p):
# a = n / (2 * p) # première valeur
# d = n / (p * (p - 1)) # incrément
# return [round(a + i * d, 3) for i in range(p)]
def progressive_parts(total, n, first):
# solve for r
# S = first * (r^n - 1)/(r - 1) = total
# numeric solving
f = lambda r: first * (r ** n - 1) / (r - 1) - total
r = mp.findroot(f, 1.05) # initial guess
parts = [round(first * (r ** k), 4) for k in range(n)]
return parts
# r, parts = progressive_parts(0.4, 40, 0.004)
# print("r =", r)
# print(parts)
if self.pairs[pair]['last_ath'] == 0 :
ath = max(last_candle['mid'], self.get_last_ath_before_candle(last_candle))
self.pairs[pair]['last_ath'] = ath
steps = self.approx_value(last_candle['mid'], self.pairs[pair]['last_ath'])
self.pairs[pair]['dca_thresholds'] = split_ratio_one_third(
self.pairs[pair]['dca_thresholds'] = progressive_parts(
(last_candle['mid'] - (self.pairs[pair]['last_ath'] * (1 - self.allow_decrease_rate))) / last_candle['mid'],
steps)
steps, 0.003)
print(f"val={last_candle['mid']} steps={steps} pct={(last_candle['mid'] - (self.pairs[pair]['last_ath'] * (1 - self.allow_decrease_rate))) / last_candle['mid']}")
print(self.pairs[pair]['dca_thresholds'])
@@ -895,9 +910,11 @@ class FrictradeLearning(IStrategy):
# Buy = prediction > threshold
dataframe["buy"] = 0
dataframe.loc[
(dataframe["ml_prob"].shift(3) < 0.2)
& (dataframe['low'].shift(3) < dataframe['min180'].shift(3))
& (dataframe['min180'].shift(3) == dataframe['min180']),
(dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
& (dataframe['sma24_deriv1'] > 0)
& (dataframe['sma12_deriv1'] > 0)
& (dataframe['open'] < dataframe['max180'] * 0.997),
# & (dataframe['min180'].shift(3) == dataframe['min180']),
['enter_long', 'enter_tag']
] = (1, f"future")
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.003, np.nan)
@@ -1310,6 +1327,8 @@ class FrictradeLearning(IStrategy):
# if current_profit > 0:
# print(f"profit={profit} max_profit={max_profit} current_profit={current_profit}")
if profit < 0:
return None
baisse = 0
if profit > 0:
@@ -1318,11 +1337,11 @@ class FrictradeLearning(IStrategy):
self.pairs[pair]['count_of_buys'] = count_of_buys
self.pairs[pair]['current_profit'] = profit
dispo = round(self.wallets.get_available_stake_amount())
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
days_since_first_buy = (current_time - trade.open_date_utc).days
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
# dispo = round(self.wallets.get_available_stake_amount())
# hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
# days_since_first_buy = (current_time - trade.open_date_utc).days
# hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
# minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
# ----- 2) Mise à jour du max_price -----
self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
@@ -1383,14 +1402,23 @@ class FrictradeLearning(IStrategy):
# ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
if current_trailing_only_offset_is_reached:
# Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
if max_profit < min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))\
and (max_profit > current_trailing_stop_positive_offset): #2 * current_trailing_stop_positive:
if max_profit < min(2, max_profit * current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain'])): #2 * current_trailing_stop_positive:
print(f"{current_time} trailing non atteint trailing_stop={round(trailing_stop,4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
f"{min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))}")
return None # ne pas activer le trailing encore
else:
print(f"{current_time} trailing atteint trailing_stop={round(trailing_stop,4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
f"{min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))}")
# Sinon : trailing actif dès le début
# ----- 6) Condition de vente -----
if 0 < profit <= trailing_stop and last_candle['mid'] < last_candle['sma5']:
self.pairs[pair]['force_buy'] = True
print(
f"{current_time} Condition de vente trailing_stop={round(trailing_stop,4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
f"{round(min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain'])), 4)}")
return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
return None