Wat does this code do
def generate_realtime_signals(models, feature_df):
    rf_model, gbm_model, rnn_model = models
    
    # Preprocessing input
    scaled_features = scaler.transform(feature_df)
    pca_features = pca.transform(scaled_features)
    
    X_test_array = pca_features.reshape((pca_features.shape[0], 1, pca_features.shape[1]))
    
    rf_preds = rf_model.predict(pca_features)
    gbm_preds = gbm_model.predict(pca_features)
    rnn_preds = (rnn_model.predict(X_test_array) > 0.5).astype(int).flatten()
    
    # Weighted averaging based on validation set performance
    weights = [0.4, 0.4, 0.2]  # Assuming these weights are based on validation performance
    aggregated_preds = np.array([rf_preds, gbm_preds, rnn_preds]).T
    final_preds = [np.round(np.average(preds, weights=weights)).astype(int) for preds in aggregated_preds]
    
    return final_preds[-1]  # Return the latest prediction


### 3. Executing Trades Based on Validated Signals

The execute_trade function will now incorporate additional rules for confirming signals using technical analysis.

### 4. Signal Confirmation Logic

Implement the signal confirmation logic within your trading function:

def confirm_signal_with_technical_analysis(signal, df):
    # Fetch the latest data point
    latest_data = df.iloc[-1]
    
    # Extract necessary indicators for decision making
    rsi = latest_data[‘rsi’]
    sma50 = latest_data[‘sma50’]
    sma200 = latest_data[‘sma200’]
    
    # Define technical analysis criteria
    if signal == 1:
        # Bullish confirmation
        if sma50 > sma200 and rsi < 30:  # Example conditions for oversold/bullish cross
            return True
    elif signal == -1:
        # Bearish confirmation
        if sma50 < sma200 and rsi > 70:  # Example conditions for overbought/bearish cross
            return True
    return False

def execute_trade(signal, symbol, df, current_price, position, balance):
    print(f"Machine Learning Signal: {signal}“)
    
    if confirm_signal_with_technical_analysis(signal, df):
        print(“Signal confirmed by Technical Analysis”)
        
        if signal == 1 and position == 0:  # Buy signal
            print(f"Executing Buy for {symbol}”)
            position = 1
            entry_price = current_price
            balance[‘initial’] = balance[‘current’]  # Track initial balance for comparison
        
        elif signal == -1 and position == 0:  # Sell signal
            print(f"Executing Sell for {symbol}“)
            position = -1
            entry_price = current_price
            balance[‘initial’] = balance[‘current’]
        
        elif signal == 0 and position != 0:  # Close position
            if position == 1:  # Closing long position
                balance[‘current’] += (current_price - entry_price) * balance[‘initial’] / entry_price
            elif position == -1:  # Closing short position
                balance[‘current’] += (entry_price - current_price) * balance[‘initial’] / entry_price
            print(f"Closing position at {current_price}. Updated balance: {balance[‘current’]}”)
            position = 0
    else:
        print(“Signal not confirmed by Technical Analysis. No trade executed.”)
    
    return position, balance

# Initialize balance
balance = {‘initial’: 10000, ‘current’: 10000}

def run_trading_strategy(symbol):
    df = fetch_real_time_data(symbol)
    if df is not None and len(df) > 0:
        feature_df = preprocess_data(df)
        signal = generate_realtime_signals((rf_model, gbm_model, rnn_model), feature_df)
        current_price = df.iloc[-1][‘close’]
        
        global position, balance
        position, balance = execute_trade(signal, symbol, df, current_price, position, balance)
    else:
        print(“No data available to process.”)

In the confirm_signal_with_technical_analysis function, we use simple example conditions. You can modify these to use more sophisticated criteria like Bollinger Bands, MACD, or custom strategies fitting your specific requirements.

### 5. Real-Time Scheduler Setup

The real-time scheduler code remains mostly the same:

if name == “main”:
    symbol = ‘BTC/USDT’
    
    scheduler = BackgroundScheduler()
    scheduler.add_job(func=lambda: run_trading_strategy(symbol), trigger=“interval”, minutes=1)
    scheduler.start()
    
    print(“Scheduler started. Press Ctrl+C to exit.”)
    
    try:
        while True:
            pass
    except (KeyboardInterrupt, SystemExit):
        scheduler.shutdown()
        print(“Scheduler stopped.”)