diff --git a/Zeus_11.py b/Zeus_11.py
index d74298d..b7348cb 100644
--- a/Zeus_11.py
+++ b/Zeus_11.py
@@ -369,7 +369,7 @@ class Zeus_11(IStrategy):
         dataframe["percent48"] = (dataframe["close"] - dataframe["open"].shift(48)) / dataframe["open"].shift(48)
 
         # print(metadata['pair'])
-        dataframe['rsi'] = talib.RSI(dataframe['close'], length=14)
+        dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
 
         # Bollinger Bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
@@ -436,7 +436,7 @@ class Zeus_11(IStrategy):
         # informative["max_profit"] = dataframe["informative"].rolling(n_candles).max()
         # informative["profit_hit"] = dataframe["informative"] >= informative["close"] * (1 + x_percent)
         #
-        informative['rsi'] = talib.RSI(informative['close'], length=7)
+        informative['rsi'] = talib.RSI(informative['close'], timeperiod=7)
         informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1)
 
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
@@ -445,7 +445,7 @@ class Zeus_11(IStrategy):
 
         ################### INFORMATIVE 1d
         informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
-        informative['rsi'] = talib.RSI(informative['close'], length=7)
+        informative['rsi'] = talib.RSI(informative['close'], timeperiod=7)
         informative['rsi_diff'] = informative['rsi'] - informative['rsi'].shift(1)
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
         informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
diff --git a/tools/graph.py b/tools/graph.py
new file mode 100644
index 0000000..5205bd7
--- /dev/null
+++ b/tools/graph.py
@@ -0,0 +1,34 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Charger les fichiers
+signals = pd.read_pickle('backtest-result-2025-04-06_21-30-24_signals.pkl')
+exited = pd.read_pickle('backtest-result-2025-04-06_21-30-24_exited.pkl')
+rejected = pd.read_pickle('backtest-result-2025-04-06_21-30-24_rejected.pkl')  # facultatif
+
+# Choisir un marché pour le graphique (ex : BTC/USDT)
+pair = 'BTC/USDT'
+df_signals = signals[signals['pair'] == pair]
+df_exited = exited[exited['pair'] == pair]
+
+# Charger les données de prix si besoin (par ex. OHLCV depuis Freqtrade)
+# Ici on suppose que vous avez une DataFrame de prix : price_df avec ['date', 'close']
+price_df = df_signals[['date', 'price']].rename(columns={'price': 'close'}).copy()
+price_df.set_index('date', inplace=True)
+
+# Affichage
+plt.figure(figsize=(14, 6))
+plt.plot(price_df.index, price_df['close'], label='Prix', color='gray')
+
+# Ajouter les points d'entrée
+plt.scatter(df_signals['date'], df_signals['price'], color='green', label='Signal', marker='^')
+# Ajouter les points de sortie
+plt.scatter(df_exited['exit_date'], df_exited['exit_price'], color='red', label='Sortie', marker='v')
+
+plt.title(f"Graphique avec signaux pour {pair}")
+plt.xlabel("Date")
+plt.ylabel("Prix")
+plt.legend()
+plt.grid(True)
+plt.tight_layout()
+plt.show()
diff --git a/tools/test_pkl.py b/tools/test_pkl.py
new file mode 100644
index 0000000..2962c2d
--- /dev/null
+++ b/tools/test_pkl.py
@@ -0,0 +1,3 @@
+import pandas as pd
+df = pd.read_pickle("backtest-result-2025-04-06_21-30-24_signals.pkl")
+print(df.head())