diff --git a/Zeus_8_3_2_B_4_2.py b/Zeus_8_3_2_B_4_2.py
index ff2dd3d..6f4143b 100644
--- a/Zeus_8_3_2_B_4_2.py
+++ b/Zeus_8_3_2_B_4_2.py
@@ -745,6 +745,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # if self.dp.runmode.value in ('backtest'):
         #     self.test_signal_success(dataframe, 0.005)
 
+        if self.dp.runmode.value in ('backtest'):
+            dataframe.to_feather(f"user_data/data/binance/{metadata['pair'].replace('/', '_')}_df.feather")
+
         return dataframe
 
     def calculateDownAndUp(self, dataframe, limit=0.0001):
diff --git a/tools/mise.py b/tools/mise.py
new file mode 100644
index 0000000..e5fe108
--- /dev/null
+++ b/tools/mise.py
@@ -0,0 +1,20 @@
+def cumulative_loss_on_repeated_dips(initial_price=100, drop_percent=1.5, num_drops=20):
+    price = initial_price
+    total_invested = 0
+    total_value = 0
+
+    print(f"{'Step':>4} | {'Price':>8} | {'Invested':>10} | {'Total Value':>12} | {'Loss %':>8}")
+    print("-" * 52)
+
+    for i in range(1, num_drops + 1):
+        price *= (1 - drop_percent / 100)
+        total_invested += initial_price
+        total_value += price
+        loss_pct = (1 - total_value / total_invested) * 100
+        print(f"{i:>4} | {price:>8.2f} | {total_invested:>10.2f} | {total_value:>12.2f} | {loss_pct:>8.2f}")
+
+    final_loss_pct = (1 - total_value / total_invested) * 100
+    print("\n📉 Perte finale cumulée :", round(final_loss_pct, 2), "%")
+
+# Exemple :
+cumulative_loss_on_repeated_dips(drop_percent=1.5, num_drops=20)
diff --git a/tools/statistique/main.py b/tools/statistique/main.py
new file mode 100644
index 0000000..e69de29