Zeus_8_3_2_B_4_2 utilisation des tendances

Zeus_8_3_2_B_4_2.py

@@ -250,13 +250,16 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         self.pairs[pair]['current_profit'] = current_profit
         pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
 
+        # if (last_candle['tendency'] in ('H++', 'H--')):
+        #     return None
+
         # if (last_candle['rsi_1d'] > 50) & (last_candle['percent12'] < 0.0):
         if (last_candle['percent3'] < 0.0) & (current_profit > 0.05): #last_candle['min_max200'] / 3):
             self.trades = list()
             return 'mx_' + str(count_of_buys)
         if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
             self.trades = list()
-            return 'profit_' + str(count_of_buys)
+            return 'pft_' + str(count_of_buys)
         if (current_profit >= expected_profit) & (last_candle['percent'] < 0.0) \
             and ((last_candle['rsi'] >= 75) or before_last_candle['rsi'] >= 75)\
             and (count_of_buys < 5):
@@ -360,9 +363,30 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 + " " + str(int(last_candle['rsi_diff_1h']))
 
         print(
-            f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {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} | {buys or '-':>5} | {stake or '-':>10} |"
+            f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} | {rate or '-':>12} | {dispo or '-':>6} "
+            f"| {profit or '-':>8} | {pct_max or '-':>5} | {max_touch or '-':>11} | {last_lost or '-':>12} "
+            f"| {round(self.pairs[pair]['last_max'], 2) or '-':>12} | {buys or '-':>5} | {stake or '-':>10} "
+            f"| {last_candle['tendency'] or '-':>3} | {last_candle['tendency_1h'] or '-':>3} | {last_candle['tendency_1d'] or '-':>3} |"
         )
 
+    def add_tendency_column(self, dataframe: pd.DataFrame) -> pd.DataFrame:
+        def tag_by_derivatives(row):
+            d1 = row['mid_smooth_deriv1']
+            d2 = row['mid_smooth_deriv2']
+    
+            if d1 == 0.0 and d2 == 0.0:
+                return 'P' # Palier
+            if d1 == 0.0:
+                return 'DH' if d2 > 0 else 'DB' #Depart Hausse / Départ Baisse
+            if d1 > 0:
+                return 'H++' if d2 > 0 else 'H--' #Acceleration Hausse / Ralentissement Hausse
+            if d1 < 0:
+                return 'B++' if d2 < 0 else 'B--' # Accéleration Baisse / Ralentissement Baisse
+            return 'indetermine'
+
+        dataframe['tendency'] = dataframe.apply(tag_by_derivatives, axis=1)
+        return dataframe
+
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         # Add all ta features
         pair = metadata['pair']
@@ -375,6 +399,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['close_02'] = dataframe['haclose'] * 1.02
 
         dataframe['pct_change'] = dataframe['close'].pct_change(5)
+        dataframe = self.calculateTendency(dataframe)
+
         dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200)
         dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
 
@@ -410,6 +436,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['sma20_s5'] = dataframe['sma20'].shift(4)
         # print(metadata['pair'])
         dataframe['rsi'] = talib.RSI(dataframe['close'], length=14)
+        dataframe['rsi_diff'] = dataframe['rsi'].diff()
+        dataframe['rsi_diff_2'] = dataframe['rsi_diff'].diff()
 
         # Bollinger Bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
@@ -463,10 +491,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # normalized_close = self.min_max_scaling(dataframe['close'])
         ################### INFORMATIVE 1h
         informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
+        informative = self.calculateTendency(informative)
         informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
         informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
         informative['rsi'] = talib.RSI(informative['close'], length=7)
-        informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1)
+        informative['rsi_diff'] = informative['rsi'].diff()
+        informative['rsi_diff_2'] = informative['rsi_diff'].diff()
 
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
         informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
@@ -474,8 +504,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         ################### INFORMATIVE 1d
         informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
+        informative = self.calculateTendency(informative)
+
         informative['rsi'] = talib.RSI(informative['close'], length=7)
-        informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1)
+        informative['rsi_diff'] = informative['rsi'].diff()
+        informative['rsi_diff_2'] = informative['rsi_diff'].diff()
 
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
         informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
@@ -612,6 +645,18 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         return dataframe
 
+    def calculateTendency(self, dataframe):
+        dataframe['mid'] = dataframe['open'] + (dataframe['close'] - dataframe['open']) / 2
+        # 2. Calcul du lissage sur 200 bougies par moyenne mobile médiane
+        dataframe['mid_smooth'] = dataframe['mid'].rolling(window=12, center=True, min_periods=1).median().rolling(
+            3).mean()
+        # 2. Dérivée première = différence entre deux bougies successives
+        dataframe['mid_smooth_deriv1'] = dataframe['mid_smooth'].diff()
+        # 3. Dérivée seconde = différence de la dérivée première
+        dataframe['mid_smooth_deriv2'] = dataframe['mid_smooth_deriv1'].diff()
+        dataframe = self.add_tendency_column(dataframe)
+        return dataframe
+
     def getOpenTrades(self):
         # if len(self.trades) == 0:
         print('search open trades')
@@ -704,9 +749,17 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 # (dataframe["bb_width"] > 0.01)
                 (dataframe['down_count'].shift(1) < - 6)
                 & (dataframe['down_count'] == 0)
-                # & (dataframe['down_pct'].shift(1) <= -0.5)
+                & (dataframe['tendency'] != "B++")
+                & (dataframe['tendency'] != "B--")
             ), ['enter_long', 'enter_tag']] = (1, 'down')
 
+        dataframe.loc[
+            (
+                (dataframe['low'] < dataframe['min200'])
+                & (dataframe['min50'] == dataframe['min200'].shift(3))
+                & (dataframe['tendency'] != "B++")
+                & (dataframe['tendency'] != "B--")
+            ), ['enter_long', 'enter_tag']] = (1, 'low')
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
 
         return dataframe
@@ -777,6 +830,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                  or ((last_candle['min50'] == last_candle_3['min50']) and (last_candle['low'] <= last_candle['min50']))
             ) \
             and (last_candle['rsi_diff_1h'] >= -5) \
+            and (last_candle['tendency'] in ('P', 'H++', 'DH', 'H--')) \
             and ((pct_max < lim)):
             try:
                 # print(self.adjust_stake_amount(pair, last_candle))