TEST calculateMarketState

Empty5m.json

@@ -17,13 +17,13 @@
       "max_open_trades": 20
     },
     "sell": {
-      "b30_indicateur": "sma24",
+      "b30_indicateur": "sma36",
-      "baisse": 0.2,
+      "baisse": 0.3,
-      "sell_force_sell": -0.121,
+      "sell_force_sell": -0.03,
       "sell_indicator": "sma36"
     },
     "protection": {
-      "drop_from_last_entry": 0.0,
+      "drop_from_last_entry": -0.01,
       "mises_bull": 4
     }
   },

Empty5m.py

@@ -19,6 +19,8 @@ from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalP
 import ta
 import talib.abstract as talib
 import freqtrade.vendor.qtpylib.indicators as qtpylib
+from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
+from ta.momentum import RSIIndicator, StochasticOscillator
 
 from functools import reduce
 from random import shuffle
@@ -103,6 +105,7 @@ class Empty5m(IStrategy):
     position_adjustment_enable = True
     use_custom_stoploss = True
 
+    can_short = True
     #max_open_trades = 3
 
     # Optimal ticker interval for the strategy.
@@ -559,12 +562,12 @@ class Empty5m(IStrategy):
                 self.pairs[pair]['force_sell'] = True
                 return 'smaBF'
         else:
-            if current_profit < self.sell_force_sell.value \
+            if current_profit < self.sell_force_sell.value and self.wallets.get_available_stake_amount() < 50 \
                     and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
                 self.pairs[pair]['force_sell'] = True
                 return 'sma'
 
-        if current_profit > 0.00 and \
+        if current_profit > 0.01 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
@@ -694,12 +697,14 @@ class Empty5m(IStrategy):
         dataframe["volume_mean"] = dataframe["volume"].rolling(20).mean()
         dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_mean"]
 
-        dataframe["market_state"] = 0
+        # dataframe["market_state"] = 0
+        #
+        # dataframe.loc[dataframe["percent"] < -0.005, "market_state"] = -1
+        # dataframe.loc[(dataframe["percent3"] < -0.015) & (dataframe["volume_ratio"] > 2), "market_state"] = -2
+        # dataframe.loc[(dataframe["percent"] > 0.003) & (dataframe["volume_ratio"] > 1.5), "market_state"] = 1
+        # dataframe["velocity"] = dataframe["percent"] - dataframe["percent3"]
 
-        dataframe.loc[dataframe["percent"] < -0.005, "market_state"] = -1
+        dataframe = self.calculateMarketState(dataframe, metadata)
-        dataframe.loc[(dataframe["percent3"] < -0.015) & (dataframe["volume_ratio"] > 2), "market_state"] = -2
-        dataframe.loc[(dataframe["percent"] > 0.003) & (dataframe["volume_ratio"] > 1.5), "market_state"] = 1
-        dataframe["velocity"] = dataframe["percent"] - dataframe["percent3"]
 
         # ######################################################################################################
         ################### INFORMATIVE 1h
@@ -746,6 +751,7 @@ class Empty5m(IStrategy):
         informative['stop_buying'] = latched
         informative = self.calculateDownAndUp(informative, limit=0.0001)
 
+        informative = self.calculateMarketState(informative, metadata)
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
         # ######################################################################################################
 
@@ -789,6 +795,7 @@ class Empty5m(IStrategy):
             else:
                 latched[i] = latched[i - 1]
         informative['stop_buying'] = latched
+        informative = self.calculateMarketState(informative, metadata)
 
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
         # ######################################################################################################
@@ -981,17 +988,23 @@ class Empty5m(IStrategy):
         conditions = list()
         # conditions.append(dataframe['mid_regression'].shift(2) > dataframe['mid_regression'].shift(1))
         # conditions.append(dataframe['mid_regression'].shift(1) < dataframe['mid_regression'])
-        conditions.append(dataframe['close'] <= dataframe['min12_1h'])
+        conditions.append(dataframe['close'] <= dataframe['min3_1h'])
-        conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
+        # conditions.append(dataframe['min60'] == dataframe['min60'].shift(5))
         conditions.append(dataframe['has_cross_min_6'] == 1)
-        conditions.append(dataframe['down_count'] <= 5)
+        # 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),
             ['enter_long', 'enter_tag']
         ] = (1, 'Mid')
 
+        dataframe.loc[
+            (
+                (dataframe['rsi'] > 70)  # surachat
+            ),
+            'enter_short'
+        ] = 1
+
         return dataframe
 
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -1414,3 +1427,26 @@ class Empty5m(IStrategy):
                 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 calculateMarketState(self, dataframe, metadata):
+
+        dataframe['ema50'] = EMAIndicator(dataframe['close'], window=50).ema_indicator()
+        dataframe['ema200'] = EMAIndicator(dataframe['close'], window=200).ema_indicator()
+        # RSI
+        dataframe['rsi'] = RSIIndicator(dataframe['close'], window=14).rsi()
+        dataframe["market_state"] = 0
+
+        cond_bull = (
+                (dataframe["rsi"] > 55) &
+                (dataframe["ema50"] > dataframe["ema200"])
+        )
+
+        cond_bear = (
+                (dataframe["rsi"] < 45) &
+                (dataframe["ema50"] < dataframe["ema200"])
+        )
+
+        dataframe.loc[cond_bull, "market_state"] = 1
+        dataframe.loc[cond_bear, "market_state"] = -1
+
+        return dataframe

FrictradeLearning.py

@@ -1267,7 +1267,7 @@ class FrictradeLearning(IStrategy):
         # hours_since_first_price = (current_time - trade.open_date_utc).seconds / 3600.0
         # days_since_first_price = (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
+        minutes = (current_time - trade.date_last_filled_utc).total_seconds() / 60.0
 
         count_of_buys = trade.nr_of_successful_entries
         # current_time_utc = current_time.astimezone(timezone.utc)
@@ -1331,8 +1331,8 @@ class FrictradeLearning(IStrategy):
         increase = - decline
 
         # FIN ########################## ALGO ATH
-        force = hours > 12 #self.hours_force.value and last_candle[self.indic_1h_force_buy.value] > 0
+        force = False #self.hours_force.value and last_candle[self.indic_1h_force_buy.value] > 0
-        condition = last_candle['percent'] > 0 \
+        condition = minutes > 5 and last_candle['percent'] > 0 \
                     and ((count_of_buys <= 4 and last_candle['sma24_deriv1'] > 0) or (count_of_buys > 4 and last_candle['sma60_deriv1'] > 0))\
                     and last_candle['close'] < self.pairs[pair]['first_price']