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FrictradeLearning test condition sur heat_score_1h et sma180_deriv1

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Jérôme Delacotte
2025-12-31 18:31:44 +01:00
parent c26966da45
commit 8210b5c0b3
1 changed files with 16 additions and 4 deletions
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FrictradeLearning.py
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@@ -180,7 +180,7 @@ class FrictradeLearning(IStrategy):
"note": "pic oct. 2025 (source agrégée, à vérifier selon l'exchange)"}
]
def dynamic_trailing_offset(self, pair, stake, price, ath, count_of_buys, max_dca=5):
def dynamic_trailing_offset(self, pair, stake, last_candle, price, ath, count_of_buys, max_dca=5):
# dd_ath = (ath - price) / ath
# dd_ath = max(0.0, min(dd_ath, 0.5))
#
@@ -192,6 +192,9 @@ class FrictradeLearning(IStrategy):
# OFFSET_MIN = self.offset_min.value
# OFFSET_MAX = self.offset_min.value + self.offset_max.value
if last_candle['sma180_deriv1'] < 0.005:
return stake / 200
return stake / 100 # OFFSET_MIN + breathing_score * (OFFSET_MAX - OFFSET_MIN)
def cooldown_from_heat(self, score):
@@ -468,7 +471,8 @@ class FrictradeLearning(IStrategy):
self.printLineLog()
df = pd.DataFrame.from_dict(self.pairs, orient='index')
colonnes_a_exclure = ['last_candle',
'trade_info', 'last_date', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
'trade_info', 'last_date', 'last_count_of_buys',
'base_stake_amount', 'stop_buy', 'mises', 'dca_thresholds']
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
# df_filtered = df_filtered["first_price", "last_max", "max_touch", "last_sell","last_price", 'count_of_buys', 'current_profit']
@@ -890,6 +894,12 @@ class FrictradeLearning(IStrategy):
self.calculeDerivees(dataframe, 'sma12', ema_period=6)
self.calculeDerivees(dataframe, 'sma5', ema_period=3)
dataframe['sma60'] = dataframe['mid'].ewm(span=60, adjust=False).mean()
self.calculeDerivees(dataframe, 'sma60', ema_period=20)
dataframe['sma180'] = dataframe['mid'].ewm(span=180, adjust=False).mean()
self.calculeDerivees(dataframe, 'sma180', ema_period=60)
horizon = 180
self.calculateScores(dataframe, horizon)
@@ -979,7 +989,7 @@ class FrictradeLearning(IStrategy):
total_stake += stake
pct += dca
loss_amount += total_stake * dca_previous
offset = self.dynamic_trailing_offset(pair, total_stake, price=val, ath=ath, count_of_buys=count)
offset = self.dynamic_trailing_offset(pair, total_stake, last_candle, price=val, ath=ath, count_of_buys=count)
if count == self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] - 1:
print(f"next_buy={round(val * (1 - pct),1)} count={count} pct={round(pct, 4)}")
@@ -1059,6 +1069,8 @@ class FrictradeLearning(IStrategy):
& (dataframe['sma5_deriv1'] > 0)
& (dataframe['sma5_deriv2'] > 0)
& (dataframe['rsi'] < 77)
& (dataframe['heat_score_1h'] < 0.5)
& (dataframe['sma180_deriv1'] > 0)
# & (dataframe['open'] < dataframe['max180'] * 0.997)
# & (dataframe['min180'].shift(3) == dataframe['min180'])
, ['enter_long', 'enter_tag']
@@ -1454,7 +1466,7 @@ class FrictradeLearning(IStrategy):
current_trailing_only_offset_is_reached = self.trailing_only_offset_is_reached
current_trailing_stop_positive_offset = self.trailing_stop_positive_offset
current_trailing_stop_positive_offset = self.dynamic_trailing_offset(pair, self.pairs[pair]['total_amount'], price=current_rate,
current_trailing_stop_positive_offset = self.dynamic_trailing_offset(pair, self.pairs[pair]['total_amount'], last_candle, price=current_rate,
ath=self.pairs[pair]['last_ath'],
count_of_buys=count_of_buys)