import osimport numpy as npimport pandas as pdfrom binance import Clientfrom datetime import datetimeimport tkinter as tkfrom ta.momentum import RSIIndicator, StochasticOscillatorfrom ta.trend import EMAIndicator, MACDfrom ta.volatility import BollingerBandsimport threadingimport timefrom dotenv import load_dotenv class SmartSignalAnalyzer:def __init__(self):load_dotenv()self.client = Client(os.getenv('BINANCE_API_KEY'), os.getenv('BINANCE_API_SECRET'))self.symbol = "EURUSDT"self.interval = Client.KLINE_INTERVAL_1MINUTEself.lookback = 100self.last_update = Noneself.last_price = 0self.setup_gui()self.running = Trueself.start_data_thread() def setup_gui(self):self.root = tk.Tk()self.root.title("Smart Binary Signals")self.root.configure(bg='#1E1E1E')self.root.geometry("800x300") # Main containermain_frame = tk.Frame(self.root, bg='#1E1E1E')main_frame.pack(fill=tk.BOTH, expand=True, padx=20, pady=10) # Price and Time Panelself.setup_header(main_frame)# Signals Panelself.setup_signals_panel(main_frame) def setup_header(self, parent):header = tk.Frame(parent, bg='#2E2E2E')header.pack(fill=tk.X, pady=(0, 10))# Left side - Priceprice_frame = tk.Frame(header, bg='#2E2E2E')price_frame.pack(side=tk.LEFT, padx=20, pady=10)tk.Label(price_frame,text="EUR/USD",font=("Helvetica", 12),bg='#2E2E2E',fg='#888888').pack()self.price_label = tk.Label(price_frame,text="0.00000",font=("Helvetica", 32, "bold"),bg='#2E2E2E',fg='#00FF00')self.price_label.pack()# Right side - Time and Updatetime_frame = tk.Frame(header, bg='#2E2E2E')time_frame.pack(side=tk.RIGHT, padx=20, pady=10)self.time_label = tk.Label(time_frame,text="",font=("Helvetica", 12),bg='#2E2E2E',fg='#888888')self.time_label.pack()self.update_label = tk.Label(time_frame,text="Last Update: Never",font=("Helvetica", 10),bg='#2E2E2E',fg='#666666')self.update_label.pack() def setup_signals_panel(self, parent):signals_frame = tk.LabelFrame(parent,text=" Trading Signals ",font=("Helvetica", 12, "bold"),bg='#2E2E2E',fg='white')signals_frame.pack(fill=tk.X, pady=(0, 10))# Headersheaders = ['Timeframe', 'Signal', 'Confidence', 'Entry', 'Direction']for i, header in enumerate(headers):tk.Label(signals_frame,text=header,font=("Helvetica", 11, "bold"),bg='#2E2E2E',fg='#AAAAAA',width=15).grid(row=0, column=i, padx=5, pady=5)# Signal rowsself.signal_rows = {}timeframes = ['30 Seconds', '1 Minute']for i, timeframe in enumerate(timeframes, 1):row_labels = []for j in range(len(headers)):label = tk.Label(signals_frame,text="-",font=("Helvetica", 12),bg='#2E2E2E',fg='white',width=15)label.grid(row=i, column=j, padx=5, pady=10)row_labels.append(label)self.signal_rows[timeframe] = row_labels def calculate_indicators(self, df):try:df['rsi'] = RSIIndicator(df['close']).rsi()df['stoch_k'] = StochasticOscillator(df['high'], df['low'], df['close']).stoch()macd = MACD(df['close'])df['macd'] = macd.macd()df['macd_signal'] = macd.macd_signal()df['macd_hist'] = macd.macd_diff()for period in [8, 21]:df[f'ema_{period}'] = EMAIndicator(df['close'], period).ema_indicator()bb = BollingerBands(df['close'])df['bb_upper'] = bb.bollinger_hband()df['bb_middle'] = bb.bollinger_mavg()df['bb_lower'] = bb.bollinger_lband()df['volume_sma'] = df['volume'].rolling(20).mean()return dfexcept Exception as e:print(f"Error calculating indicators: {e}")return None def analyze_signals(self, df):try:signals = {}last_row = df.iloc[-1]last_5_rows = df.iloc[-5:]for timeframe in ['30 Seconds', '1 Minute']:# Initialize confidence calculationsignal_strength = 0total_signals = 0# Trend Analysistrend_direction = 1 if last_row['ema_8'] > last_row['ema_21'] else -1trend_strength = abs(last_row['ema_8'] - last_row['ema_21']) / last_row['ema_21'] * 100# RSI Analysisrsi_signal = 0if last_row['rsi'] < 30:rsi_signal = 1elif last_row['rsi'] > 70:rsi_signal = -1# MACD Analysismacd_signal = 0if last_row['macd'] > last_row['macd_signal'] and last_row['macd_hist'] > 0:macd_signal = 1elif last_row['macd'] < last_row['macd_signal'] and last_row['macd_hist'] < 0:macd_signal = -1# Price Positionprice_signal = 0if last_row['close'] < last_row['bb_lower']:price_signal = 1elif last_row['close'] > last_row['bb_upper']:price_signal = -1# Calculate total signal strengthsignals_list = [trend_direction * (trend_strength > 0.05),rsi_signal,macd_signal,price_signal]signal_strength = sum([s for s in signals_list if s != 0])total_signals = sum([1 for s in signals_list if s != 0])# Calculate confidenceconfidence = 0signal = "NEUTRAL"if total_signals > 0:confidence = abs(signal_strength / total_signals) * 100# Adjust confidence based on timeframeif timeframe == '30 Seconds':confidence *= 0.95# Only generate signal if confidence is high enoughif confidence >= 90:signal = "CALL" if signal_strength > 0 else "PUT"signals[timeframe] = {'signal': signal,'confidence': min(confidence, 99),'entry': last_row['close'],'direction': "↑" if signal == "CALL" else "↓" if signal == "PUT" else "-"}return signalsexcept Exception as e:print(f"Error analyzing signals: {e}")return None def update_signal_rows(self, signals):if not signals:returnfor timeframe, signal_data in signals.items():row = self.signal_rows[timeframe]row[0].config(text=timeframe)row[1].config(text=signal_data['signal'],fg='#00FF00' if signal_data['signal'] == 'CALL' else '#FF0000' if signal_data['signal'] == 'PUT' else '#888888')row[2].config(text=f"{signal_data['confidence']:.1f}%",fg='#00FF00' if signal_data['confidence'] >= 95 else '#FFFF00' if signal_data['confidence'] >= 90 else '#888888')row[3].config(text=f"{signal_data['entry']:.5f}")row[4].config(text=signal_data['direction'],fg='#00FF00' if signal_data['direction'] == "↑" else '#FF0000' if signal_data['direction'] == "↓" else '#888888') def get_data(self):try:klines = self.client.get_klines(symbol=self.symbol,interval=self.interval,limit=self.lookback)df = pd.DataFrame(klines, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume','close_time', 'quote_volume', 'trades', 'taker_buy_base','taker_buy_quote', 'ignore'])df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')for col in ['open', 'high', 'low', 'close', 'volume']:df[col] = pd.to_numeric(df[col], errors='coerce')return self.calculate_indicators(df)except Exception as e:print(f"Error fetching data: {e}")return None def start_data_thread(self):def update_loop():while self.running:try:df = self.get_data()if df is not None:signals = self.analyze_signals(df)self.root.after(0, self.update_gui, df, signals)time.sleep(1)except Exception as e:print(f"Update error: {e}")time.sleep(1)self.update_thread = threading.Thread(target=update_loop, daemon=True)self.update_thread.start() def update_gui(self, df, signals):if df is None or signals is None:returncurrent_time = datetime.now()# Update pricecurrent_price = df['close'].iloc[-1]self.price_label.config(text=f"{current_price:.5f}",fg='#00FF00' if current_price > self.last_price else '#FF0000')self.last_price = current_price# Update time labelsself.time_label.config(text=current_time.strftime('%H:%M:%S'))self.update_label.config(text=f"Last Update: {current_time.strftime('%H:%M:%S')}")# Update signalsself.update_signal_rows(signals) def run(self):self.root.mainloop()self.running = False if __name__ == "__main__":app = SmartSignalAnalyzer()app.run()