add tools

tools/cuire.py

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+import tkinter as tk
+from tkinter import messagebox
+
+class QuestionResponderApp:
+    def __init__(self, root):
+        self.root = root
+        self.root.title("Question Répondeur")
+
+        # Configurer la taille de la fenêtre
+        self.root.geometry("400x200")
+        self.root.resizable(False, False)
+
+        # Ajout d'un titre
+        self.title_label = tk.Label(
+            root, text="Posez votre question", font=("Helvetica", 16), pady=10
+        )
+        self.title_label.pack()
+
+        # Champ de saisie
+        self.input_box = tk.Entry(root, font=("Helvetica", 14), width=40)
+        self.input_box.pack(pady=10)
+
+        # Bouton pour poser la question
+        self.ask_button = tk.Button(
+            root, text="Poser la question", font=("Helvetica", 14), command=self.respond
+        )
+        self.ask_button.pack(pady=10)
+
+        # Bouton pour quitter
+        self.quit_button = tk.Button(
+            root, text="Quitter", font=("Helvetica", 12), command=root.quit
+        )
+        self.quit_button.pack(side="bottom", pady=10)
+
+    def respond(self):
+        question = self.input_box.get()
+        if question.strip():  # Si la question n'est pas vide
+            messagebox.showinfo("Réponse", "Va te faire cuire le cul.")
+        else:
+            messagebox.showwarning("Erreur", "Veuillez poser une question.")
+
+# Lancer l'application
+if __name__ == "__main__":
+    root = tk.Tk()
+    app = QuestionResponderApp(root)
+    root.mainloop()
+

tools/depth.py

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+import requests
+import pandas as pd
+import matplotlib.pyplot as plt
+import argparse
+
+# Initialisation du parser
+parser = argparse.ArgumentParser(description='Programme qui accepte des paramètres.')
+
+# Ajout d'arguments
+parser.add_argument('--crypto', type=str, help='nom de la crypto (BTC)')
+parser.add_argument('--nb', type=int, help='nombre d\'élément du cumul')
+parser.add_argument('--horizon', type=int, help='nombre d\'élément à lire')
+parser.add_argument('--verbose', action='store_true', help='Mode verbeux')
+
+# Parsing des arguments
+args = parser.parse_args()
+
+# Utilisation des arguments
+print(f"Premier argument : {args.crypto}")
+print(f"Deuxième argument : {args.nb}")
+print(f"Troisième argument : {args.horizon}")
+
+if args.verbose:
+    print("Mode verbeux activé")
+
+# Fetch the API data
+url = "https://api.binance.com/api/v3/depth?symbol=" + args.crypto + "USDT&limit=" + str(args.horizon)
+response = requests.get(url)
+data = response.json()
+
+# Extract bids and asks
+bids = data['bids']
+asks = data['asks']
+
+# Prepare data for plotting
+bid_amounts = [float(bid[0]) for bid in bids]
+bid_volumes = [float(bid[1]) for bid in bids]
+
+ask_amounts = [float(ask[0]) for ask in asks]
+ask_volumes = [float(ask[1]) for ask in asks]
+
+# Convert to pandas DataFrame to apply moving average
+bid_df = pd.DataFrame({'amount': bid_amounts, 'volume': bid_volumes})
+ask_df = pd.DataFrame({'amount': ask_amounts, 'volume': ask_volumes})
+
+# Apply a rolling window to calculate a n-value simple moving average (SMA)
+bid_df['volume_sma'] = bid_df['volume'].rolling(window=args.nb).mean()
+ask_df['volume_sma'] = ask_df['volume'].rolling(window=args.nb).mean()
+
+# Plot the data
+plt.figure(figsize=(10, 6))
+
+# Plot raw bids and asks
+#plt.plot(bid_df['amount'], bid_df['volume'], label='Bids (Raw)', color='green', alpha=0.3)
+#plt.plot(ask_df['amount'], ask_df['volume'], label='Asks (Raw)', color='red', alpha=0.3)
+
+# Plot the moving average
+plt.plot(bid_df['amount'], bid_df['volume_sma'], label='Bids (SMA)', color='darkgreen')
+plt.plot(ask_df['amount'], ask_df['volume_sma'], label='Asks (SMA)', color='darkred')
+
+plt.title(f"Order Book Depth: {args.crypto}/USDT (with n-value SMA)")
+plt.xlabel('Price (USDT)')
+plt.ylabel(f"Volume ({args.crypto})")
+plt.legend()
+plt.grid(True)
+plt.show()
+

tools/sma5.py

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+import pandas as pd
+import yfinance as yf
+
+# Télécharger les données historiques de Bitcoin
+data = yf.download('BTC-USD', start='2021-07-20', end='2022-06-27', interval='1d')
+
+# Calculer la SMA5
+data['SMA5'] = data['Close'].rolling(window=5).mean()
+
+# Calculer la pente (différence quotidienne de la SMA5)
+data['SMA5_slope'] = data['SMA5'].diff()
+
+# Calculer la pente moyenne sur la période
+average_slope = data['SMA5_slope'].mean()
+
+print(f"Pente moyenne de la SMA5 journalière : {average_slope:.6f}")
+