From c26966da459fa74757dc4c7731c766aed810914e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Delacotte?= <jerome.delacotte@bappli.com>
Date: Sat, 20 Dec 2025 20:31:09 +0100
Subject: [PATCH] FrictradeLearning

---
 FrictradeLearning.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/FrictradeLearning.py b/FrictradeLearning.py
index 63fd7bf..774c42d 100644
--- a/FrictradeLearning.py
+++ b/FrictradeLearning.py
@@ -73,7 +73,7 @@ RESET = "\033[0m"
 
 
 class FrictradeLearning(IStrategy):
-    startup_candle_count = 200
+    startup_candle_count = 360
     train_model = None
     model_indicators = []
     DEFAULT_PARAMS = {
@@ -634,6 +634,8 @@ class FrictradeLearning(IStrategy):
 
         informative['rsi'] = talib.RSI(informative['mid'], timeperiod=14)
         self.calculeDerivees(informative, 'rsi', ema_period=12)
+        self.calculateScores(informative, 6)
+
         # informative = self.rsi_trend_probability(informative)
 
         # probas = self.calculModelInformative(informative)
@@ -889,13 +891,7 @@ class FrictradeLearning(IStrategy):
         self.calculeDerivees(dataframe, 'sma5', ema_period=3)
 
         horizon = 180
-        dataframe['price_change'] = (dataframe['close'] - dataframe['close'].shift(horizon)) / dataframe['close'].shift(horizon)
-        # dataframe['rsi_delta'] = dataframe['rsi'] - dataframe['rsi'].shift(horizon)
-
-        dataframe['price_score'] = (dataframe['price_change'] / 0.05).clip(0, 2)
-        # dataframe['rsi_score'] = (dataframe['rsi_delta'] / 15).clip(0, 2)
-
-        dataframe['heat_score'] = talib.MAX(dataframe['price_score'], timeperiod=horizon) #+ dataframe['rsi_score']
+        self.calculateScores(dataframe, horizon)
 
         # val = 90000
         # steps = 12
@@ -957,6 +953,7 @@ class FrictradeLearning(IStrategy):
 
         if self.pairs[pair]['count_of_buys']:
             dca_threshold = self.pairs[pair]['dca_thresholds'][min(self.pairs[pair]['count_of_buys'] - 1, len(self.pairs[pair]['dca_thresholds']) - 1)]
+            dataframe[f"next_dca"] = val * (1 - dca_threshold)
             print(f"count_of_buys={self.pairs[pair]['count_of_buys']} dca_threshold={dca_threshold} {self.pairs[pair]['dca_thresholds']}")
 
         print(f"val={val} dca={self.pairs[pair]['dca_thresholds']} ath={self.pairs[pair]['last_ath']} first_price={self.pairs[pair]['first_price']}")
@@ -984,7 +981,7 @@ class FrictradeLearning(IStrategy):
                         loss_amount += total_stake * dca_previous
                         offset = self.dynamic_trailing_offset(pair, total_stake, price=val, ath=ath, count_of_buys=count)
 
-                        if count == self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain']:
+                        if count == self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] - 1:
                             print(f"next_buy={round(val * (1 - pct),1)} count={count} pct={round(pct, 4)}")
                             dataframe[f"next_buy"] = val * (1 - pct)
                         count += 1
@@ -996,6 +993,13 @@ class FrictradeLearning(IStrategy):
 
         return dataframe
 
+    def calculateScores(self, dataframe, horizon):
+        dataframe['price_change'] = (dataframe['close'] - dataframe['close'].shift(horizon)) / dataframe['close'].shift(horizon)
+        # dataframe['rsi_delta'] = dataframe['rsi'] - dataframe['rsi'].shift(horizon)
+        dataframe['price_score'] = (dataframe['price_change'] / 0.05).clip(0, 2)
+        # dataframe['rsi_score'] = (dataframe['rsi_delta'] / 15).clip(0, 2)
+        dataframe['heat_score'] = talib.MAX(dataframe['price_score'], timeperiod=horizon) # + dataframe['rsi_score']
+
     def getOpenTrades(self):
         # if len(self.trades) == 0:
         self.trades = Trade.get_open_trades()
@@ -1059,7 +1063,7 @@ class FrictradeLearning(IStrategy):
             # & (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)
+        dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
 
         return dataframe
 
@@ -2334,6 +2338,9 @@ class FrictradeLearning(IStrategy):
                        and not c.startswith('sma12_deriv1_1h')
                        and not c.startswith('sma12_1h')
                        and not c.startswith('confidence_index')
+                       and not c.startswith('price_change')
+                       and not c.startswith('price_score')
+                       and not c.startswith('heat_score')
                        ]
         # Étape 3 : remplacer inf et NaN par 0
         dataframe[usable_cols] = dataframe[usable_cols].replace([np.inf, -np.inf], 0).fillna(0)