EMPTy UP AND DOWN

Empty5m.py

@@ -564,7 +564,7 @@ class Empty5m(IStrategy):
                 self.pairs[pair]['force_sell'] = True
                 return 'sma'
 
-        if profit > 0 and \
+        if current_profit > 0.00 and \
                 (baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
                 and last_candle['hapercent'] <0 :
             self.pairs[pair]['force_sell'] = True
@@ -657,7 +657,7 @@ class Empty5m(IStrategy):
         # get access to all pairs available in whitelist.
         pairs = self.dp.current_whitelist()
         informative_pairs = [(pair, '1h') for pair in pairs]
-        # informative_pairs += [(pair, '1h') for pair in pairs]
+        informative_pairs += [(pair, '1d') for pair in pairs]
 
         return informative_pairs
 
@@ -705,10 +705,10 @@ class Empty5m(IStrategy):
         ################### INFORMATIVE 1h
         informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
         # informative = self.populateDataframe(informative, timeframe='1d')
-        # heikinashi = qtpylib.heikinashi(informative)
+        heikinashi = qtpylib.heikinashi(informative)
-        # informative['haopen'] = heikinashi['open']
+        informative['haopen'] = heikinashi['open']
-        # informative['haclose'] = heikinashi['close']
+        informative['haclose'] = heikinashi['close']
-        # informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
+        informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
         informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
         for timeperiod in timeperiods:
             informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
@@ -718,6 +718,7 @@ class Empty5m(IStrategy):
             informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
             self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
 
+        informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=12)
         informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=12)
         informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=12)
 
@@ -743,10 +744,55 @@ class Empty5m(IStrategy):
             else:
                 latched[i] = latched[i - 1]
         informative['stop_buying'] = latched
+        informative = self.calculateDownAndUp(informative, limit=0.0001)
 
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
         # ######################################################################################################
 
+
+        # ######################################################################################################
+        ################### INFORMATIVE 1d
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
+        # informative = self.populateDataframe(informative, timeframe='1d')
+        # heikinashi = qtpylib.heikinashi(informative)
+        # informative['haopen'] = heikinashi['open']
+        # informative['haclose'] = heikinashi['close']
+        # informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
+        informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
+        for timeperiod in timeperiods:
+            informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
+            informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
+            # informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
+            # informative[f"percent{timeperiod}"] = informative['close'].pct_change(timeperiod)
+            informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
+            self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
+
+        for timeperiod in long_timeperiods:
+            informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
+            self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
+
+        informative['rsi'] = talib.RSI(informative['close'], timeperiod=14)
+        self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
+        informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
+        informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
+
+        informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
+        informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
+
+        latched = np.zeros(len(informative), dtype=bool)
+
+        for i in range(1, len(informative)):
+            if informative['stop_buying_deb'].iloc[i]:
+                latched[i] = True
+            elif informative['stop_buying_end'].iloc[i]:
+                latched[i] = False
+            else:
+                latched[i] = latched[i - 1]
+        informative['stop_buying'] = latched
+
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
+        # ######################################################################################################
+
         range_min = dataframe[f"min12_1h"]
         range_max = dataframe[f"max48"]
 
@@ -768,6 +814,9 @@ class Empty5m(IStrategy):
                 / dataframe["sma200_1h"]
         )
 
+        # Compter les baisses / hausses consécutives
+        dataframe = self.calculateDownAndUp(dataframe, limit=0.0001)
+
         # récupérer le dernier trade fermé
         trades = Trade.get_trades_proxy(pair=pair,is_open=False)
         if trades:
@@ -935,7 +984,8 @@ class Empty5m(IStrategy):
         conditions.append(dataframe['close'] <= dataframe['min12_1h'])
         conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
         conditions.append(dataframe['has_cross_min_6'] == 1)
-        # conditions.append(dataframe['min5_1h'] / dataframe['min12_1h'] < 0.03)
+        conditions.append(dataframe['down_count'] <= 5)
+        conditions.append(dataframe['down_count'] <= 5)
         # conditions.append(dataframe['sma12_deriv1'] >= 0)
         dataframe.loc[
             reduce(lambda x, y: x & y, conditions),
@@ -1341,4 +1391,26 @@ class Empty5m(IStrategy):
         # # 3. Dérivée seconde = différence de la dérivée première
         # df[f"{column}_future_{future_offset}_deriv2"] = round(10 * df[f"{column}_future_{future_offset}_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
 
-        return df
+        return df
+
+    def calculateDownAndUp(self, dataframe, limit=0.0001):
+        dataframe['down'] = dataframe['mid_regression'] <= dataframe['mid_regression'].shift(1)
+        dataframe['up'] = dataframe['mid_regression'] >= dataframe['mid_regression'].shift(1)
+        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)
+        # Créer une colonne vide
+        dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
+        dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
+        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