Frictrade progression adjust exponentielle / correction trailing

FrictradeLearning.py

@@ -27,6 +27,7 @@ import ta
 import talib.abstract as talib
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 from datetime import timezone, timedelta
+import mpmath as mp
 
 # Machine Learning
 from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
@@ -133,7 +134,7 @@ class FrictradeLearning(IStrategy):
 
     trailing_stop = False
     trailing_stop_positive = 0.15
-    trailing_stop_positive_offset = 0.5
+    trailing_stop_positive_offset = 1
     trailing_only_offset_is_reached = True
 
     # Buy hypers
@@ -246,19 +247,33 @@ class FrictradeLearning(IStrategy):
         return allow_to_buy
 
     def calculateStepsDcaThresholds(self, last_candle, pair):
-        def split_ratio_one_third(n, p):
+        # def split_ratio_one_third(n, p):
-            a = n / (2 * p)  # première valeur
+        #     a = n / (2 * p)  # première valeur
-            d = n / (p * (p - 1))  # incrément
+        #     d = n / (p * (p - 1))  # incrément
-            return [round(a + i * d, 3) for i in range(p)]
+        #     return [round(a + i * d, 3) for i in range(p)]
+        def progressive_parts(total, n, first):
+            # solve for r
+            # S = first * (r^n - 1)/(r - 1) = total
+            # numeric solving
+
+            f = lambda r: first * (r ** n - 1) / (r - 1) - total
+            r = mp.findroot(f, 1.05)  # initial guess
+
+            parts = [round(first * (r ** k), 4) for k in range(n)]
+            return parts
+
+        # r, parts = progressive_parts(0.4, 40, 0.004)
+        # print("r =", r)
+        # print(parts)
 
         if self.pairs[pair]['last_ath'] == 0 :
             ath = max(last_candle['mid'], self.get_last_ath_before_candle(last_candle))
             self.pairs[pair]['last_ath'] = ath
 
         steps = self.approx_value(last_candle['mid'], self.pairs[pair]['last_ath'])
-        self.pairs[pair]['dca_thresholds'] = split_ratio_one_third(
+        self.pairs[pair]['dca_thresholds'] = progressive_parts(
             (last_candle['mid'] - (self.pairs[pair]['last_ath'] * (1 - self.allow_decrease_rate))) / last_candle['mid'],
-            steps)
+            steps, 0.003)
         print(f"val={last_candle['mid']} steps={steps} pct={(last_candle['mid'] - (self.pairs[pair]['last_ath'] * (1 - self.allow_decrease_rate))) / last_candle['mid']}")
         print(self.pairs[pair]['dca_thresholds'])
 
@@ -895,9 +910,11 @@ class FrictradeLearning(IStrategy):
         # Buy = prediction > threshold
         dataframe["buy"] = 0
         dataframe.loc[
-            (dataframe["ml_prob"].shift(3) < 0.2)
+            (dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
-            & (dataframe['low'].shift(3) < dataframe['min180'].shift(3))
+            & (dataframe['sma24_deriv1'] > 0)
-            & (dataframe['min180'].shift(3) == dataframe['min180']),
+            & (dataframe['sma12_deriv1'] > 0)
+            & (dataframe['open'] < dataframe['max180'] * 0.997),
+            # & (dataframe['min180'].shift(3) == dataframe['min180']),
             ['enter_long', 'enter_tag']
         ] = (1, f"future")
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.003, np.nan)
@@ -1310,6 +1327,8 @@ class FrictradeLearning(IStrategy):
 
         # if current_profit > 0:
         #     print(f"profit={profit} max_profit={max_profit} current_profit={current_profit}")
+        if profit < 0:
+            return None
 
         baisse = 0
         if profit > 0:
@@ -1318,11 +1337,11 @@ class FrictradeLearning(IStrategy):
         self.pairs[pair]['count_of_buys'] = count_of_buys
         self.pairs[pair]['current_profit'] = profit
 
-        dispo = round(self.wallets.get_available_stake_amount())
+        # dispo = round(self.wallets.get_available_stake_amount())
-        hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
+        # hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
-        days_since_first_buy = (current_time - trade.open_date_utc).days
+        # days_since_first_buy = (current_time - trade.open_date_utc).days
-        hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
+        # 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
 
         # ----- 2) Mise à jour du max_price -----
         self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
@@ -1383,14 +1402,23 @@ class FrictradeLearning(IStrategy):
         # ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
         if current_trailing_only_offset_is_reached:
             # Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
-            if max_profit < min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))\
+            if max_profit < min(2, max_profit * current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain'])): #2 * current_trailing_stop_positive:
-                    and (max_profit > current_trailing_stop_positive_offset): #2 * current_trailing_stop_positive:
+                print(f"{current_time} trailing non atteint trailing_stop={round(trailing_stop,4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
+                      f"{min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))}")
                 return None  # ne pas activer le trailing encore
+            else:
+                print(f"{current_time} trailing atteint trailing_stop={round(trailing_stop,4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
+                      f"{min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain']))}")
+
         # Sinon : trailing actif dès le début
 
         # ----- 6) Condition de vente -----
         if 0 < profit <= trailing_stop and last_candle['mid'] < last_candle['sma5']:
             self.pairs[pair]['force_buy'] = True
+            print(
+                f"{current_time} Condition de vente trailing_stop={round(trailing_stop,4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
+                f"{round(min(2, current_trailing_stop_positive_offset * (count_of_buys - self.pairs[pair]['has_gain'])), 4)}")
+
             return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
         return None