synchronise HeikinAshi

HammerReversalStrategy.py

@@ -274,7 +274,6 @@ class HammerReversalStrategy(IStrategy):
 
         self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch'])
 
-
         # On ne déclenche le trailing stop que si un profit mini a déjà été atteint
         # and (limit_sell < -0.01)
         if (current_profit > 0.01) and (last_candle['percent12'] < 0) and (last_candle['percent5'] < 0):

HeikinAshi.py

@@ -32,23 +32,14 @@ from ta.utils import dropna
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 from functools import reduce
 import numpy as np
+from scipy.special import binom
+from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
+from ta.momentum import RSIIndicator, StochasticOscillator
 
 
 class HeikinAshi(IStrategy):
     plot_config = {
         "main_plot": {
-            "min12": {
-                "color": "#197260"
-            },
-            'max12': {
-                'color': 'green'
-            },
-            "haclose": {
-                "color": "red"
-            },
-            'haopen': {
-                'color': 'blue'
-            },
             "min288": {
                 "color": "#197260"
             },
@@ -57,13 +48,47 @@ class HeikinAshi(IStrategy):
             },
             'mid288': {
                 'color': 'blue'
-            }
+            },
+            'hasma5': {
+                'color': 'red'
+            },
+            'max48': {
+                'color': 'yellow'
+            },
+            'min48': {
+                'color': 'yellow'
+            },
+            'sma12': {
+                'color': 'pink'
+            },
+            'ema5_1d': {
+                'color': "#74effc"
+            },
+            'ema20_1d': {
+                'color': "cyan"
+            },
         },
         "subplots": {
             "Percent": {
                 "hapercent": {
                     "color": "#74effc"
                 }
+            },
+            'up_down': {
+                'up_pct': {
+                    'color': 'red'
+                },
+                'down_pct': {
+                    'color': 'blue'
+                }
+            },
+            'tag': {
+                'rsi_downtrend': {
+                    'color': 'red'
+                },
+                'ma_downtrend': {
+                    'color': 'blue'
+                }
             }
         }
 
@@ -92,7 +117,7 @@ class HeikinAshi(IStrategy):
     # Optimal timeframe use it in your config
     timeframe = '5m'
     columns_logged = False
-    max_entry_position_adjustment = 20
+    max_entry_position_adjustment = 30
     startup_candle_count = 288
 
     # Trailing stoploss
@@ -100,7 +125,7 @@ class HeikinAshi(IStrategy):
     # trailing_stop_positive = 0.001
     # trailing_stop_positive_offset = 0.015
     # trailing_only_offset_is_reached = True
-    position_adjustment_enable = False
+    position_adjustment_enable = True
 
     pairs = {
         pair: {
@@ -108,12 +133,18 @@ class HeikinAshi(IStrategy):
             "trade_info": {},
             "max_touch": 0.0,
             "last_sell": 0.0,
-            "last_buy": 0.0
+            "last_buy": 0.0,
+            'count_of_buys': 0,
+            'current_profit': 0,
+            'expected_profit': 0,
+            "last_candle": {},
+            "last_trade": None,
+            'base_stake_amount': 0
         }
         for pair in ["BTC/USDT", "ETH/USDT", "DOGE/USDT", "DASH/USDT", "XRP/USDT", "SOL/USDT"]
     }
 
-    decalage = IntParameter(0, 48, default=12, space='buy')
+    decalage = IntParameter(0, 10, default=3, space='buy')
 
     ########################################## END RESULT PASTE PLACE #####################################
     # ------------------------------------------------------------------------------------------------------------------
@@ -125,26 +156,95 @@ class HeikinAshi(IStrategy):
                               **kwargs
                               ) -> Union[Optional[float], Tuple[Optional[float], Optional[str]]]:
 
-
         # ne rien faire si ordre deja en cours
         if trade.has_open_orders:
             return None
+        if (self.wallets.get_available_stake_amount() < 50):  # or trade.stake_amount >= max_stake:
+            return 0
 
         dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
-        last_candle_24 = dataframe.iloc[-25].squeeze()
+        # last_candle_decalage = dataframe.iloc[-1 - self.decalage.value].squeeze()
+        # last_candle_24 = dataframe.iloc[-25].squeeze()
+
+        # if (last_candle['sma5_diff_1d'] < -0.1):
+        #     return None
         # prépare les données
         count_of_buys = trade.nr_of_successful_entries
         current_time = current_time.astimezone(timezone.utc)
         open_date = trade.open_date.astimezone(timezone.utc)
         dispo = round(self.wallets.get_available_stake_amount())
+        hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
 
-        limit_buy = 4
+        # if (current_profit > 0.008) \
+        #         and (last_candle['up_pct'] >= 1)\
+        #         and (last_candle['volume'] >= 250) \
+        #         and (hours >= 1):
+        #     additional_stake = self.config['stake_amount']
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="Gain +",
+        #         dispo=dispo,
+        #         pair=trade.pair,
+        #         rate=current_rate,
+        #         trade_type='Increase',
+        #         profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
+        #         buys=trade.nr_of_successful_entries,
+        #         stake=round(additional_stake, 2)
+        #     )
+        #     # self.pairs[trade.pair]['last_max'] = last_candle['haclose']
+        #     self.pairs[trade.pair]['max_touch'] = last_candle['haclose']
+        #     return additional_stake
+
+        # if (last_candle['percent'] > 0.001) and (current_profit > 0):
+        #         # and (last_candle_decalage['min12'] == last_candle['min12']) \
+        #         # and (last_candle_decalage['close'] < last_candle_decalage['mid288']):
+        #     additional_stake = self.config['stake_amount'] / 10
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="Gain +",
+        #         dispo=dispo,
+        #         pair=trade.pair,
+        #         rate=current_rate,
+        #         trade_type='Increase',
+        #         profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
+        #         buys=trade.nr_of_successful_entries,
+        #         stake=round(additional_stake, 2)
+        #     )
+        #     return additional_stake
+        max_touch = self.pairs[trade.pair]['max_touch']
+        pct_max = - round(100 * (last_candle['close'] - max_touch) / max_touch, 1)
+
+        # if (last_candle['enter_long'] == 1) and (current_profit < - 0.0075 or hours >= 1) and (count_of_buys == 1):
+        #     additional_stake = self.config['stake_amount'] / 2
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="Long",
+        #         dispo=dispo,
+        #         pair=trade.pair,
+        #         rate=current_rate,
+        #         trade_type='Increase',
+        #         profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
+        #         buys=trade.nr_of_successful_entries + 1,
+        #         stake=round(additional_stake, 2)
+        #     )
+        #     self.expectedProfit(trade.pair, last_candle, current_rate)
+        #     self.pairs[trade.pair]['last_buy'] = current_rate
+        #     self.pairs[trade.pair]['max_touch'] = last_candle['close']
+        #     self.pairs[trade.pair]['last_candle'] = last_candle
+        #
+        #     return additional_stake
+
+        limit_buy = 5
         if (count_of_buys < limit_buy) \
-                and (last_candle['min288'] == last_candle_24['min288']) \
-                and (current_profit < -0.01 * count_of_buys) \
-                and (last_candle['close'] < last_candle['mid288']):
-            additional_stake = self.config['stake_amount']
+                and ((last_candle['enter_long'] == 1)) \
+                and (current_profit < -0.015 * count_of_buys):
+            # and (last_candle_decalage['min12'] == last_candle['min12']) \
+            # and (last_candle_decalage['close'] < last_candle_decalage['mid288']):
+            additional_stake = self.calculate_stake(trade.pair, last_candle, 1) # self.config['stake_amount']
             self.log_trade(
                 last_candle=last_candle,
                 date=current_time,
@@ -154,12 +254,39 @@ class HeikinAshi(IStrategy):
                 rate=current_rate,
                 trade_type='Decrease',
                 profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
-                buys=trade.nr_of_successful_entries,
+                buys=trade.nr_of_successful_entries + 1,
                 stake=round(additional_stake, 2)
             )
+            self.expectedProfit(trade.pair, last_candle, current_rate)
+            self.pairs[trade.pair]['last_buy'] = current_rate
+            self.pairs[trade.pair]['max_touch'] = last_candle['close']
+            self.pairs[trade.pair]['last_candle'] = last_candle
+
             return additional_stake
-        if (count_of_buys >= limit_buy) & (current_profit < - 0.03 * count_of_buys):
-            additional_stake = self.config['stake_amount'] * 2
+
+        # if (count_of_buys == limit_buy) & (current_profit < - 0.03 * count_of_buys)\
+        #     and ((last_candle['enter_long'] == 1) or last_candle['percent48'] < - 0.03):
+        #     additional_stake = - trade.stake_amount / 2 #self.config['stake_amount'] * (-current_profit / 0.10)
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="Loss -",
+        #         dispo=dispo,
+        #         pair=trade.pair,
+        #         rate=current_rate,
+        #         trade_type='Decrease',
+        #         profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
+        #         buys=trade.nr_of_successful_entries,
+        #         stake=round(additional_stake, 2)
+        #     )
+        #     # self.pairs[trade.pair]['last_max'] = last_candle['haclose']
+        #     self.pairs[trade.pair]['max_touch'] = last_candle['haclose']
+        #     return additional_stake
+
+        pct_limit = (-0.015 * limit_buy) + (- 0.03 * (count_of_buys - limit_buy))
+        if (count_of_buys >= limit_buy) & (current_profit < pct_limit) \
+                and ((last_candle['enter_long'] == 1) or last_candle['percent48'] < - 0.03):
+            additional_stake = self.calculate_stake(trade.pair, last_candle, 1) * (-current_profit / 0.10)
             self.log_trade(
                 last_candle=last_candle,
                 date=current_time,
@@ -169,15 +296,49 @@ class HeikinAshi(IStrategy):
                 rate=current_rate,
                 trade_type='Decrease',
                 profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
-                buys=trade.nr_of_successful_entries,
+                buys=trade.nr_of_successful_entries + 1,
                 stake=round(additional_stake, 2)
             )
+            self.expectedProfit(trade.pair, last_candle, current_rate)
+            self.pairs[trade.pair]['last_buy'] = current_rate
+            self.pairs[trade.pair]['max_touch'] = last_candle['close']
+            self.pairs[trade.pair]['last_candle'] = last_candle
+
             return additional_stake
 
         return None
 
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            **kwargs) -> float:
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+
+        # Obtenir les données actuelles pour cette paire
+        last_candle = dataframe.iloc[-1].squeeze()
+        stake_amount = self.config['stake_amount']
+        # if last_candle['close'] < last_candle['max5_1d'] * 0.98 :
+        #     stake_amount = 2 * stake_amount
+        # else:
+        #     if last_candle['close'] > last_candle['max5_1d'] * 1.02:
+        #         stake_amount = 0.5 * stake_amount
+        # if last_candle['entry_tag'] == 'buy_hammer':
+        #     stake_amount = stake_amount * 2
+
+        return stake_amount
+
     def calculate_stake(self, pair, last_candle, factor=1):
-        amount = self.config['stake_amount'] * factor #1000 / self.first_stack_factor.value self.protection_stake_amount.value  #
+
+        # if self.pairs[pair]['count_of_buys'] == 1 and factor == 1:
+        #     if last_candle['close'] > last_candle['min5_1d'] + (last_candle['max5_1d'] - last_candle['min5_1d']) / 2:
+        #         factor = 0.5
+        #         amount = self.config['stake_amount'] * factor
+        #     else:
+        #         amount = self.config['stake_amount']
+        #     self.pairs[pair]['base_stake_amount'] = amount
+        # else:
+        #     amount = max(self.config['stake_amount'], self.pairs[pair]['base_stake_amount'])
+
+        amount = self.config['stake_amount']
         return amount
 
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
@@ -185,8 +346,32 @@ class HeikinAshi(IStrategy):
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
         dispo = round(self.wallets.get_available_stake_amount())
+
+        # if (self.pairs[pair]['last_sell'] > 0) and (last_candle['close'] * 1.01 > self.pairs[pair]['last_sell']):
+        #     self.log_trade(
+        #         last_candle=last_candle,
+        #         date=current_time,
+        #         action="CANCEL BUY",
+        #         pair=pair,
+        #         rate=rate,
+        #         dispo=dispo,
+        #         profit=0,
+        #         stake=round(stake_amount, 2)
+        #     )
+        #     return False
+        self.pairs[pair]['last_buy'] = rate
+        self.pairs[pair]['max_touch'] = last_candle['close']
+        self.pairs[pair]['last_max'] = last_candle['close']
+        self.pairs[pair]['last_candle'] = last_candle
+        self.pairs[pair]['count_of_buys'] = 1
+        self.pairs[pair]['current_profit'] = 0
         stake_amount = self.calculate_stake(pair, last_candle, 1)
 
+        # self.columns_logged = False
+        print(
+            f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
+        )
+
         self.log_trade(
             last_candle=last_candle,
             date=current_time,
@@ -195,8 +380,11 @@ class HeikinAshi(IStrategy):
             rate=rate,
             dispo=dispo,
             profit=0,
+            trade_type=entry_tag,
+            buys=1,
             stake=round(stake_amount, 2)
         )
+        self.expectedProfit(pair, last_candle, rate)
         return True
 
     def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
@@ -210,10 +398,9 @@ class HeikinAshi(IStrategy):
         allow_to_sell = (last_candle['percent5'] < -0.00)
         ok = (allow_to_sell) | (exit_reason == 'force_exit')
         if ok:
-            # self.pairs[pair]['last_max'] = 0
-            # self.pairs[pair]['max_touch'] = 0
-            self.pairs[pair]['last_buy'] = 0
             self.pairs[pair]['last_sell'] = rate
+            self.pairs[pair]['last_trade'] = trade
+            self.pairs[pair]['last_candle'] = last_candle
             self.log_trade(
                 last_candle=last_candle,
                 date=current_time,
@@ -224,7 +411,11 @@ class HeikinAshi(IStrategy):
                 dispo=dispo,
                 profit=round(trade.calc_profit(rate, amount), 2)
             )
-            #print(f"Sell {current_time} {exit_reason} rate={rate:.3f} amount={amount} profit={amount * rate:.3f}")
+            self.pairs[pair]['last_max'] = 0
+            self.pairs[pair]['max_touch'] = 0
+            self.pairs[pair]['last_buy'] = 0
+
+            # print(f"Sell {current_time} {exit_reason} rate={rate:.3f} amount={amount} profit={amount * rate:.3f}")
 
         return ok
 
@@ -232,23 +423,94 @@ class HeikinAshi(IStrategy):
 
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
+        before_last_candle = dataframe.iloc[-2].squeeze()
 
-        if (current_profit > 0.004) \
-                & (last_candle['hapercent'] < 0.0) \
-                & (last_candle['percent'] < 0.0):
+        max_touch_before = self.pairs[pair]['max_touch']
+        last_max_before = self.pairs[pair]['last_max']
+        self.pairs[pair]['last_max'] = max(last_candle['haclose'], self.pairs[pair]['last_max'])
+
+        last_lost = (last_candle['close'] - max_touch_before) / max_touch_before
         count_of_buys = trade.nr_of_successful_entries
-            return 'profit_' + str(count_of_buys)
 
-    def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None, last_candle=None):
+        self.pairs[pair]['count_of_buys'] = count_of_buys
+        self.pairs[pair]['current_profit'] = current_profit
+
+        days = (current_time - trade.open_date_utc).days
+        days = max(1, days)
+        factor = 1
+        # if days > 10:
+        #     factor = 1 + days / 10
+        expected_profit = self.pairs[pair]['expected_profit'] / factor
+
+        # print(
+        #     f"{current_time} days={days} expected={expected_profit:.3f} rate={current_rate} max_touch={max_touch_before:.1f} profit={current_profit:.3f} last_lost={last_lost:.3f} buys={count_of_buys} percent={last_candle['percent']:.4f}")
+
+        if (current_profit > expected_profit) \
+                & (last_candle['percent'] < 0.0) \
+                & (last_lost > - current_profit / 5):
+                # & (before_last_candle['hasma5'] < last_candle['hasma5']):
+            # & (last_lost < min(-0.003, - min(0.006, current_profit / 4))):
+            # & (last_candle['up_count'] > 0):
+
+            return 'last_lost_' + str(count_of_buys)
+        self.pairs[pair]['max_touch'] = max(last_candle['haclose'], self.pairs[pair]['max_touch'])
+
+        # if (current_profit > 0.004) \
+        #         & (last_candle['hapercent'] < 0.0) \  
+        #         & (last_candle['percent3'] < - min(0.01, current_profit / 4)):
+        #     return 'profit_' + str(count_of_buys)
+
+    def detect_loose_hammer(self, df: DataFrame) -> DataFrame:
+        """
+        Détection large de marteaux : accepte des corps plus gros, ne vérifie pas le volume,
+        ne demande pas de divergence, juste un pattern visuel simple.
+        """
+
+        body = abs(df['close'] - df['open'])
+        upper_shadow = abs(df['high'] - np.maximum(df['close'], df['open']))
+        lower_shadow = abs(np.minimum(df['close'], df['open']) - df['low'])
+
+        # Critères simplifiés :
+        df['loose_hammer'] = (
+            (lower_shadow > body * 2.5) # mèche basse > 1.5x corps
+            & (upper_shadow < body) # petite mèche haute
+            & (df['low'] < df['bb_lowerband']) # bougie verte (optionnel, on peut prendre aussi les rouges)
+        ).astype(int)
+        df['won_hammer'] = (
+            (upper_shadow > body * 2.5) # mèche basse > 1.5x corps
+            & (lower_shadow < body) # petite mèche haute
+            & (df['high'] > df['bb_upperband']) # bougie verte (optionnel, on peut prendre aussi les rouges)
+        ).astype(int)
+
+        return df
+
+    def expectedProfit(self, pair: str, last_candle, current_rate):
+
+        last_buy = self.pairs[pair]['last_buy']
+        max_touch = self.pairs[pair]['max_touch']
+        last_max = self.pairs[pair]['last_max']
+
+        expected_profit = ((max_touch - last_buy) / max_touch)
+        self.pairs[pair]['expected_profit'] = max(0.004, expected_profit)
+
+        # print(f"expected max_touch={max_touch:.1f} last_buy={last_buy:.1f} expected={expected_profit:.3f} max5_1d={last_candle['max5_1d']:.1f}")
+
+        return expected_profit
+
+    def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
+                  last_candle=None):
         # Afficher les colonnes une seule fois
         if self.config.get('runmode') == 'hyperopt':
             return
         if self.columns_logged % 30 == 0:
+            # print(
+            #     f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
+            # )
             print(
-                f"| {'Date':<16} | {'Action':<10} | {'Pair':<10} | {'Trade Type':<18} | {'Rate':>12} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>5} | {'max7_1d':>11} | {'max_touch':>12} | {'last_max':>12} | {'Buys':>5} | {'Stake':>10} |"
+                f"| {'Date':<16} | {'Action':<10} | {'Pair':<10} | {'Trade Type':<18} | {'Rate':>12} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>5} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>12} | {'Buys':>5} | {'Stake':>10} |"
             )
             print(
-                f"|{'-' * 18}|{'-' * 12}|{'-' * 12}|{'-' * 20}|{'-' * 14}|{'-' * 8}|{'-' * 10}|{'-' * 7}|{'-' * 13}|{'-' * 14}|{'-' * 14}|{'-' * 7}|{'-' * 12}|"
+                f"|{'-' * 18}+{'-' * 12}+{'-' * 12}+{'-' * 20}+{'-' * 14}+{'-' * 8}+{'-' * 10}+{'-' * 7}+{'-' * 13}+{'-' * 14}+{'-' * 14}+{'-' * 7}+{'-' * 12}|"
             )
             self.columns_logged += 1
         date = str(date)[:16] if date else "-"
@@ -279,28 +541,46 @@ class HeikinAshi(IStrategy):
 
         # if action != 'Sell':
         #     profit = round((last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 2)
+        last_lost = round((last_candle['haclose'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
 
         limit_sell = rsi_pct  # round((last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 4)
-        max7_1d = round(self.pairs[pair]['max_touch'], 1) #last_candle['max7_1d'] #round(100 * (last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 1)
-        pct_max = round(100 * (last_candle['close'] - max7_1d) / max7_1d, 1)
+        max_touch = round(self.pairs[pair]['max_touch'],
+                          1)  # last_candle['max7_1d'] #round(100 * (last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 1)
+        pct_max = round(100 * self.pairs[pair]['current_profit'],
+                        1)  # round(100 * (last_candle['close'] - max_touch) / max_touch, 1)
+
+        if trade_type is not None:
+            trade_type = trade_type + " " + str(round(100 * self.pairs[pair]['expected_profit'], 1))
+
         print(
-            f"| {date:<16} | {action:<10} | {pair:<10} | {trade_type or '-':<18} | {rate or '-':>12} | {dispo or '-':>6} | {profit or '-':>8} | {pct_max or '-':>5} | {max7_1d or '-':>11} | {round(self.pairs[pair]['max_touch'], 2) or '-':>12} | {round(self.pairs[pair]['last_max'],2) or '-':>12} | {buys or '-':>5} | {stake or '-':>10} |"
+            f"| {date:<16} | {action:<10} | {pair:<10} | {trade_type or '-':<18} | {rate or '-':>12} | {dispo or '-':>6} | {profit or '-':>8} | {pct_max or '-':>5} | {max_touch or '-':>11} | {last_lost or '-':>12} | {round(self.pairs[pair]['last_max'], 2) or '-':>12} | {self.pairs[pair]['count_of_buys'] or '-':>5} | {stake or '-':>10} |"
         )
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         heikinashi = qtpylib.heikinashi(dataframe)
         dataframe['haopen'] = heikinashi['open']
         dataframe['haclose'] = heikinashi['close']
+        dataframe['hamid'] = dataframe['haclose'] + (dataframe['haopen'] - dataframe['haclose']) / 2
+        dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
+        dataframe['sma12'] = dataframe['mid'].rolling(12).sum() / 12
+
+        dataframe['hasma5'] = dataframe['hamid'].rolling(5).sum() / 5
+        dataframe['hasma5_diff'] = dataframe['hasma5'] - dataframe['hasma5'].shift(1)
         dataframe['halow'] = heikinashi['low']
         dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
         dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
         dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
+        dataframe['min48'] = talib.MIN(dataframe['close'], timeperiod=48)
+        dataframe['max48'] = talib.MAX(dataframe['close'], timeperiod=48)
         dataframe['min288'] = talib.MIN(dataframe['close'], timeperiod=288)
         dataframe['max288'] = talib.MAX(dataframe['close'], timeperiod=288)
         dataframe['mid288'] = dataframe['min288'] + (dataframe['max288'] - dataframe['min288']) / 2
 
         dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
+        dataframe["percent3"] = dataframe['close'].pct_change(3)
         dataframe["percent5"] = dataframe['close'].pct_change(5)
+        dataframe["percent12"] = dataframe['close'].pct_change(12)
+        dataframe["percent48"] = dataframe['close'].pct_change(48)
 
         # Bollinger Bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
@@ -309,26 +589,95 @@ class HeikinAshi(IStrategy):
         dataframe['bb_upperband'] = bollinger['upper']
         dataframe['bb_diff'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_lowerband']
 
+        # Compter les baisses consécutives
+        dataframe['down'] = dataframe['hapercent'] <= 0.001
+        dataframe['up'] = dataframe['hapercent'] >= -0.001
+        dataframe['down_count'] = - dataframe['down'].astype(int) * (
+                dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
+        dataframe['up_count'] = dataframe['up'].astype(int) * (
+                dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
+        dataframe['down_tag'] = (dataframe['down_count'] < -7) 
+        dataframe['up_tag'] = (dataframe['up_count'] > 7)
+
+        # Créer une colonne vide
+        dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
+        dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
+
+        # # ======================================================================================Decrease
+        # ################### INFORMATIVE 1d
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
+        # # Moving Averages
+        # informative['ema5'] = EMAIndicator(informative['close'], window=5).ema_indicator()
+        # informative['ema20'] = EMAIndicator(informative['close'], window=20).ema_indicator()
+        # informative['ma_downtrend'] = (informative['close'] < informative['ema5']) & (informative['ema5'] < informative['ema20'])
+        #
+        # # RSI
+        # informative['rsi'] = RSIIndicator(informative['close'], window=14).rsi()
+        # informative['rsi_downtrend'] = informative['rsi'] < 50
+        informative['max5'] = talib.MAX(informative['close'], timeperiod=5)
+        informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
+        informative['min5'] = talib.MIN(informative['close'], timeperiod=5)
+        informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
+        informative['sma5'] = talib.SMA(informative, timeperiod=25)
+        informative['sma5_diff'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
+        # informative = self.detect_loose_hammer(informative)
+
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
+
+        dataframe = self.detect_loose_hammer(dataframe)
+
         return dataframe
 
+    def calculateUpDownPct(self, dataframe, key):
+        down_pct_values = np.full(len(dataframe), np.nan)
+        # Remplir la colonne avec les bons calculs
+        for i in range(len(dataframe)):
+            shift_value = abs(int(dataframe[key].iloc[i]))  # Récupérer le shift actuel
+            if i - shift_value > 1:  # Vérifier que le shift ne dépasse pas l'index
+                down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
+                                     dataframe['close'].iloc[i - shift_value]
+        return down_pct_values
+
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Buy strategy Hyperopt will build and use.
         """
+        d = self.decalage.value
         # dataframe.loc[
-        #     (dataframe['halow'] <= dataframe['min12'])
+        #     (dataframe['halow'].shift(d)  <= dataframe['min12'].shift(d))
+        #     & (dataframe['min12'].shift(d) == dataframe['min12'])
+        #     # & (dataframe['close'] < dataframe['hasma5'])
+        #     # & (dataframe['bb_diff'] > 0.01)
+        #     ,
+        #     ['enter_long', 'enter_tag']] = [1, 'buy_halow']
+
+        # dataframe.loc[
+        #     (dataframe['hasma5_diff'].shift(2) >= dataframe['hasma5_diff'].shift(1))
+        #     & (dataframe['hasma5_diff'].shift(1) <= dataframe['hasma5_diff'])
+        #     # & (dataframe['bb_diff'] > 0.01)
+        #     ,
+        #     ['enter_long', 'enter_tag']] = [1, 'buy_hasma5_diff']
+
+        # dataframe.loc[
+        #     (dataframe['halow'].shift(decalage) <= dataframe['min288'].shift(decalage))
+        #     # & (dataframe['min288'].shift(decalage) == dataframe['min288'])
         #     # & (dataframe['open'] <= dataframe['bb_middleband'])
         #     # & (dataframe['bb_diff'] > 0.01)
         #     ,
         #     'buy']=1
-        decalage = 3
+
         dataframe.loc[
-            (dataframe['halow'].shift(decalage) <= dataframe['min288'].shift(decalage))
-            & (dataframe['min288'].shift(decalage) == dataframe['min288'])
-            # & (dataframe['open'] <= dataframe['bb_middleband'])
-            # & (dataframe['bb_diff'] > 0.01)
+            (
+                    (dataframe['down_count'].shift(1) <= -8)
+                    | (dataframe['percent12'] <= -0.012)
+            )
+            & (dataframe['down_count'] == 0)
             ,
-            'buy']=1
+            ['enter_long', 'enter_tag']] = [1, 'buy_down']
+
+        dataframe.loc[(dataframe['loose_hammer'] == 1)
+            ,
+            ['enter_long', 'enter_tag']] = [1, 'buy_hammer']
 
         return dataframe
 
@@ -340,3 +689,11 @@ class HeikinAshi(IStrategy):
         #     (qtpylib.crossed_above(dataframe['haclose'], dataframe['haopen'])),
         #     'sell']=1
         return dataframe
+
+    def informative_pairs(self):
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        # informative_pairs += [(pair, '1h') for pair in pairs]
+
+        return informative_pairs

Zeus_8_3_2_B_4_2.py

@@ -39,28 +39,6 @@ def normalize(df):
     df = (df - df.min()) / (df.max() - df.min())
     return df
 
-
-def get_limit_from_config(section, pair):
-    file_path = '/HOME/home/souti/freqtrade2/user_data/strategies/Zeus_8_3_2_B_4_2.txt'
-    # Créez un objet ConfigParser
-    config = configparser.ConfigParser()
-
-    try:
-        # Lisez le fichier avec les valeurs
-        config.read(file_path)
-
-        # Vérifiez si la section existe
-        if config.has_section(section):
-            # Obtenez les valeurs à partir de la section et de la clé (pair)
-            limit = config.get(section, pair)
-            return limit
-        else:
-            raise ValueError(f"La section '{section}' n'existe pas dans le fichier de configuration.")
-    except Exception as e:
-        print(f"Erreur lors de la lecture du fichier de configuration : {e}")
-        return None
-
-
 class Zeus_8_3_2_B_4_2(IStrategy):
     levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
 
@@ -198,14 +176,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     # hard stoploss profit
     sell_allow_decrease = DecimalParameter(0.005, 0.02, default=0.2, decimals=2, space='sell', optimize=True, load=True)
 
-    pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=False, load=True)
-    # profit threshold 1, trigger point, SL_1 is used
-    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=True, load=True)
-    pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=True, load=True)
-
-    # profit threshold 2, SL_2 is used
-    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=True, load=True)
-    pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=True, load=True)
+    # pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=False, load=True)
+    # # profit threshold 1, trigger point, SL_1 is used
+    # pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=True, load=True)
+    # pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=True, load=True)
+    #
+    # # profit threshold 2, SL_2 is used
+    # pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=True, load=True)
+    # pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=True, load=True)
 
     def min_max_scaling(self, series: pd.Series) -> pd.Series:
         """Normaliser les données en les ramenant entre 0 et 100."""
@@ -223,18 +201,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         #     filled_buys = trade.select_filled_orders('buy')
         #     count_buys = len(filled_buys)
 
-        print('entry_tag' + str(entry_tag))
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
         # last_candle_12 = dataframe.iloc[-13].squeeze()
-        limit = get_limit_from_config('Achats', pair)
 
         # allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
         allow_to_buy = True  # (rate <= float(limit)) | (entry_tag == 'force_entry')
-        # allow_to_buy = rate <= dataframe['lbp_3']
         self.trades = list()
         dispo = round(self.wallets.get_available_stake_amount())
-        logger.info(f"BUY {pair} allow_to_buy {allow_to_buy} limit={limit} Buy {entry_tag} {current_time} dispo={dispo}")
+        print(f"BUY {pair} {entry_tag} {current_time} allow_to_buy={allow_to_buy} dispo={dispo}")
 
         return allow_to_buy
 
@@ -242,23 +217,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                            time_in_force: str,
                            exit_reason: str, current_time, **kwargs, ) -> bool:
         # allow_to_sell = (minutes > 30)
-        limit = get_limit_from_config('Ventes', pair)
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
 
-        allow_to_sell = (last_candle['percent'] < 0)  # rate > float(limit)
-        string = ""
+        allow_to_sell = (last_candle['percent'] < 0)
 
         if allow_to_sell:
             self.trades = list()
-            logger.info('Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair + " dispo=" + str(
-                round(self.wallets.get_available_stake_amount()))  # "+ str(amount) + ' ' + str(rate)
-                        + " open_rate=" + str(trade.open_rate) + " rate=" + str(rate) + " profit=" + str(
-                trade.calc_profit(rate, amount))
-                        + " " + string)
-            # del self.max_profit_pairs[pair]
+            dispo= round(self.wallets.get_available_stake_amount())
+            print(f"Sell {pair} {current_time} {exit_reason}  dispo={dispo}  amount={amount} rate={rate} open_rate={trade.open_rate}")
         else:
-            logger.info('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
+            print('Cancel Sell ' + exit_reason + ' ' + str(current_time) + ' ' + pair)
         return (allow_to_sell) | (exit_reason == 'force_exit')
 
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
@@ -269,83 +238,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         current_candle = dataframe.iloc[-1].squeeze()
         adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle)
 
-        logger.info(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
+        # print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
 
         # Use default stake amount.
         return adjusted_stake_amount
-    #
-    # def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-    #                     current_rate: float, current_profit: float, **kwargs) -> float:
-    #
-    #     # # hard stoploss profit
-    #     # HSL = self.pHSL.value
-    #     # PF_1 = self.pPF_1.value
-    #     # SL_1 = self.pSL_1.value
-    #     # PF_2 = self.pPF_2.value
-    #     # SL_2 = self.pSL_2.value
-    #     #
-    #     # # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
-    #     # # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
-    #     # # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
-    #     #
-    #     # if current_profit > PF_2:
-    #     #     sl_profit = SL_2 + (current_profit - PF_2)
-    #     # elif current_profit > PF_1:
-    #     #     sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
-    #     # else:
-    #     #     sl_profit = HSL
-    #
-    #     #print(f"entry_tag={trade.entry_tag} max={trade.max_rate} min={trade.min_rate} ")
-    #     if current_profit > 0.0125:
-    #         sl_profit = 0.75 * current_profit  # 75% du profit en cours
-    #     else:
-    #         sl_profit = self.pHSL.value  # Hard stop-loss
-    #     stoploss = stoploss_from_open(sl_profit, current_profit)
-    #     return stoploss
-
-    #
-    #     dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-    #     last_candle = dataframe.iloc[-1].squeeze()
-    #     limit = get_limit_from_config('Ventes', pair)
-    #
-    #     print(pair + " " + str(current_rate) + " " + str(limit))
-    #
-    #     #stop_loss = self.adjust_stop_loss(dataframe.iloc[-1])
-    #
-    #     #if current_rate < float(limit):
-    #     #    return -1
-    #
-    #     # "pHSL": -0.99,
-    #     # "pPF_1": 0.022,
-    #     # "pSL_1": 0.021,
-    #     # "pPF_2": 0.08,
-    #     # "pSL_2": 0.04,
-    #     #
-    #     # hard stoploss profit
-    #     HSL = self.pHSL.value
-    #     PF_1 = self.pPF_1.value
-    #     SL_1 = self.pSL_1.value
-    #     PF_2 = self.pPF_2.value
-    #     SL_2 = self.pSL_2.value
-    #
-    #     # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
-    #     # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
-    #     # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
-    #
-    #     #                   0.04
-    #     if current_profit > PF_2:
-    #         #           0.04 + (current_profit - 0.08)
-    #         sl_profit = SL_2 + (current_profit - PF_2)
-    #     #                   0.022
-    #     elif current_profit > PF_1:
-    #         #           0.021 + ((current_profit - 0.022) * (0.04 - 0.021) / (0.08 - 0.022))
-    #         sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
-    #     else:
-    #         sl_profit = HSL
-    #
-    #     slfo = stoploss_from_open(sl_profit, current_profit)
-    #     print('current_profit=' + str(current_profit) + ' stop from open=' + str(slfo))
-    #     return slfo
 
     def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
 
@@ -360,12 +256,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # Calcul du prix cible basé sur l'ATR
         atr_take_profit = trade.open_rate + (last_candle['atr'] * 2)  # Prendre profit à 2x l'ATR
 
-        # logger.info(f"{pair} Custom exit atr_take_profit={atr_take_profit:.4f}")
+        # print(f"{pair} Custom exit atr_take_profit={atr_take_profit:.4f}")
         # if current_rate >= atr_take_profit:
         #     return 'sell_atr_take_profit'
 
         if (last_candle['percent3'] < -0.002) & (last_candle['percent12'] < 0) & (
-                current_profit > last_candle['min_max200'] / 2):
+                current_profit > last_candle['min_max200'] / 3):
             self.trades = list()
             return 'min_max200'
         if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
@@ -384,9 +280,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # Add all ta features
         pair = metadata['pair']
 
-        dataframe['achats'] = get_limit_from_config('Achats', pair)
-        dataframe['ventes'] = get_limit_from_config('Ventes', pair)
-
         heikinashi = qtpylib.heikinashi(dataframe)
         dataframe['haopen'] = heikinashi['open']
         dataframe['haclose'] = heikinashi['close']
@@ -546,27 +439,27 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                     dataframe['amount'] = amount
                     print(f"amount= {amount}")
 
-                # trades = Trade.get_trades([Trade.is_open is False]).all()
-                trades = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
-                if trades:
-                    trade = trades[-1]
-                    print('closed trade pair is : ')
-                    print(trade)
-                    dataframe['expected_profit'] = (1 + self.expectedProfit(pair, dataframe.iloc[-1])) * dataframe[
-                        'last_price']
-                    dataframe['lbp'] = dataframe['last_price']
-                    dataframe['lbp_3'] = dataframe['lbp'] * 0.97  # 3
-                    dataframe['lbp_6'] = dataframe['lbp'] * 0.94  # 6
-                    dataframe['lbp_9'] = dataframe['lbp'] * 0.90  # 10
-                    dataframe['lbp_12'] = dataframe['lbp'] * 0.85  # 15
-                    dataframe['lbp_20'] = dataframe['lbp'] * 0.8  # 20
-                    dataframe['fbp'] = trade.open_rate
-                # else:
-                #     last_trade = self.get_trades(pair=pair).order_by('-close_date').first()
-                #     filled_buys = last_trade.select_filled_orders('buy')
-                #     print(last_trade)
-                #     for buy in filled_buys:
-                #         print(filled_buys)
+                # # trades = Trade.get_trades([Trade.is_open is False]).all()
+                # trades = Trade.get_trades_proxy(is_open=False, pair=metadata['pair'])
+                # if trades:
+                #     trade = trades[-1]
+                #     print('closed trade pair is : ')
+                #     print(trade)
+                #     dataframe['expected_profit'] = (1 + self.expectedProfit(pair, dataframe.iloc[-1])) * dataframe[
+                #         'last_price']
+                #     dataframe['lbp'] = dataframe['last_price']
+                #     dataframe['lbp_3'] = dataframe['lbp'] * 0.97  # 3
+                #     dataframe['lbp_6'] = dataframe['lbp'] * 0.94  # 6
+                #     dataframe['lbp_9'] = dataframe['lbp'] * 0.90  # 10
+                #     dataframe['lbp_12'] = dataframe['lbp'] * 0.85  # 15
+                #     dataframe['lbp_20'] = dataframe['lbp'] * 0.8  # 20
+                #     dataframe['fbp'] = trade.open_rate
+                # # else:
+                # #     last_trade = self.get_trades(pair=pair).order_by('-close_date').first()
+                # #     filled_buys = last_trade.select_filled_orders('buy')
+                # #     print(last_trade)
+                # #     for buy in filled_buys:
+                # #         print(filled_buys)
 
         dataframe['buy_level'] = dataframe['lowest_4_average'] * (1 - self.levels[count_buys] / 100)
         # ----------------------------------------------------------
@@ -615,7 +508,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['amplitude_pct_60'] = dataframe['amplitude_pct'].rolling(60).sum()
         # ----------------------------------------------------------
 
-        self.getBinanceOrderBook(pair, dataframe)
+        # self.getBinanceOrderBook(pair, dataframe)
 
         return dataframe
 
@@ -641,9 +534,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         expected_profit = self.expectedProfit(pair, dataframe.iloc[-1])
         # self.getBinanceOrderBook(pair, dataframe)
         last_candle = dataframe.iloc[-1].squeeze()
-        # limit = last_candle['first_price'] * (1 - self.baisse[last_candle['count_buys']] / 100)
 
-        # self.updateLastValue(dataframe, 'expected_profit', expected_profit)
         print("---------------" + pair + "----------------")
         print('adjust stake amount ' + str(self.adjust_stake_amount(pair, dataframe.iloc[-1])))
         # print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
@@ -772,13 +663,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                     & (dataframe['min50'].shift(3) == dataframe['min50'])
                     & (dataframe['close'] <= dataframe['min50'] * 1.002)
             ), ['enter_long', 'enter_tag']] = (1, 'buy_close_02')
-        # dataframe.loc[
-        #     (
-        #             (dataframe['close'] <= dataframe['lowest_4_average'] * 1.002)
-        #             & (dataframe['haopen'] >= dataframe['lbp_3'])
-        #             & (dataframe['haclose'] <= dataframe['lbp_3'])
-        #             & (dataframe['haopen'] < buy_level)
-        #     ), ['enter_long', 'enter_tag']] = (1, 'buy_lbp_3')
+
         dataframe.loc[
             (
                     (dataframe['close'] <= dataframe['lowest_4_average'] * 1.002)
@@ -801,38 +686,26 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         return dataframe
 
-    # def get_buy_level(self, pair, dataframe):
-    #     limit = get_limit_from_config('Achats', pair)
-    #
-    #     filled_buys = {}
-    #     for trade in self.trades:
-    #         if trade.pair != pair:
-    #             continue
-    #         filled_buys = trade.select_filled_orders('enter_long')
-    #     print('populate_buy_trend filled_buys : ' + str(len(filled_buys)))
-    #     # Affichez les valeurs
-    #     print(pair, limit)
-    #     # BUY_LEVELS = {
-    #     #    'BTC/USDT': [int(btc_limit), 42600, 41000, 40000, 39000, 38000, 37000, 36000, 35000],
-    #     #    'ETH/USDT': [int(eth_limit), 2290, 1900, 1800, 1700, 1600, 1500, 1400, 1300],
-    #     #    'ETC/USDT': [int(eth_limit), 2290, 1900, 1800, 1700, 1600, 1500, 1400, 1300],
-    #     #    'DOGE/USDT': [int(eth_limit), 2290, 1900, 1800, 1700, 1600, 1500, 1400, 1300],
-    #     #    # Ajoutez d'autres paires avec leurs niveaux d'achat ici...
-    #     # }
-    #     count_of_buys = len(filled_buys)
-    #     buy_level = dataframe['lbp'] * (1 - self.levels[count_of_buys] / 100)  # float(limit) #BUY_LEVELS.get(pair, [])[0]  #dataframe['lbp_3'] #"
-    #     return buy_level
-
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         return dataframe
 
     def adjust_trade_position(self, trade: Trade, current_time: datetime,
                               current_rate: float, current_profit: float, min_stake: float,
                               max_stake: float, **kwargs):
+        # ne rien faire si ordre deja en cours
+        if trade.has_open_orders:
+            return None
+        if (self.wallets.get_available_stake_amount() < 50):  # or trade.stake_amount >= max_stake:
+            return 0
+
         dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
-        # print(dataframe)
         last_candle = dataframe.iloc[-1].squeeze()
-        last_candle_12 = dataframe.iloc[-13].squeeze()
+        # prépare les données
+        count_of_buys = trade.nr_of_successful_entries
+        current_time = current_time.astimezone(timezone.utc)
+        open_date = trade.open_date.astimezone(timezone.utc)
+        dispo = round(self.wallets.get_available_stake_amount())
+        hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
 
         if (len(dataframe) < 1):
             return None
@@ -851,25 +724,24 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # else:
         #     condition = False
         # self.protection_nb_buy_lost.value
-        # limits = ['lbp_3', 'lbp_6', 'lbp_9', 'lbp_12', 'lbp_20']
-        # limit = last_candle[limits[count_of_buys]]
         limit = last_candle['limit']
         stake_amount = min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
 
-        # print("Adjust " + trade.pair + " time=" + str(current_time) + ' rate=' + str(current_rate) + " buys=" + str(count_of_buys) + " limit=" + str(limit) + " stake=" + str(stake_amount))
-        # logger.info(
-        #     f"Adjust price={trade.pair} buy={condition} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
         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
 
-        if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
+        # if (days_since_open > count_of_buys) & (0 < count_of_buys <= max_buys) & (current_rate <= limit) & (last_candle['enter_long'] == 1):
+        limit_buy = 5
+        if (count_of_buys < limit_buy) \
+                and ((last_candle['enter_long'] == 1) or last_candle['percent48'] < - 0.03) \
+                and (current_profit < -0.015 * count_of_buys) \
+                and (last_candle['enter_long'] == 1):
             try:
 
                 # This then calculates current safety order size
                 # stake_amount = stake_amount * pow(1.5, count_of_buys)
-                # print("Effective Adjust " + trade.pair + " time=" + str(current_time) + ' rate=' + str(current_rate) + " buys=" + str(count_of_buys) + " limit=" + str(limit) + " stake=" + str(stake_amount))
-                logger.info(
+                print(
                     f"Adjust {current_time} price={trade.pair} rate={current_rate:.4f} buys={count_of_buys} limit={limit:.4f} stake={stake_amount:.4f}")
 
                 return stake_amount
@@ -901,7 +773,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # max_min = max_14_days / min_14_days
         # Stack amount ajusté price=2473.47 min_max=0.15058074985054215 percent=0.8379141364642171 amount=20.0
 
-        adjusted_stake_amount = max(base_stake_amount / 2.5, min(75, base_stake_amount * percent_4))
+        adjusted_stake_amount = max(base_stake_amount / 2.5, min(100, base_stake_amount * percent_4))
         # if pair in ('BTC/USDT', 'ETH/USDT'):
         #     if percent_4 > 0.5:
         #         adjusted_stake_amount = 300
@@ -927,17 +799,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     #
     #     return exit_price
 
-    def adjust_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
-                        current_rate: float, current_profit: float, **kwargs) -> float:
-        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
-        # print(dataframe)
-        last_candle = dataframe.iloc[-1].squeeze()
-
-        # Utiliser l'ATR pour ajuster le stoploss
-        atr_stoploss = current_rate - (last_candle['atr'] * 1.5)  # Stoploss à 1.5x l'ATR
-
-        # Retourner le stoploss dynamique en pourcentage du prix actuel
-        return (atr_stoploss / current_rate) - 1
+    # def adjust_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+    #                     current_rate: float, current_profit: float, **kwargs) -> float:
+    #     dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+    #     # print(dataframe)
+    #     last_candle = dataframe.iloc[-1].squeeze()
+    #
+    #     # Utiliser l'ATR pour ajuster le stoploss
+    #     atr_stoploss = current_rate - (last_candle['atr'] * 1.5)  # Stoploss à 1.5x l'ATR
+    #
+    #     # Retourner le stoploss dynamique en pourcentage du prix actuel
+    #     return (atr_stoploss / current_rate) - 1
 
     def expectedProfit(self, pair: str, dataframe: DataFrame):