Analyze this code and tell me what it does
import ccxt
import pandas as pd
import numpy as np
import sqlite3
from apscheduler.schedulers.background import BackgroundScheduler
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import joblib
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout, Bidirectional
import talib

# Initialize exchange
exchange = ccxt.binance()

# Database connection
conn = sqlite3.connect(‘trading_data.db’)

# Load models and scalers
rf_model = joblib.load(‘rf_model.pkl’)
gbm_model = joblib.load(‘gbm_model.pkl’)
rnn_model = Sequential()  # Define your model architecture and load weights
rnn_model.load_weights(‘rnn_model.h5’)
scaler = joblib.load(‘scaler.pkl’)
pca = joblib.load(‘pca.pkl’)

def fetch_real_time_data(symbol, timeframe=‘1m’, limit=100):
    “”“Fetches real-time OHLCV data”“”
    try:
        bars = exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
        df = pd.DataFrame(bars, columns=[‘timestamp’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’])
        df[‘timestamp’] = pd.to_datetime(df[‘timestamp’], unit=‘ms’)
        return df
    except Exception as e:
        print(f"Error fetching real-time data: {e}“)
        return None

def preprocess_data(df):
    “”“Preprocess incoming data for model prediction””“
    df[‘price_change’] = df[‘close’].pct_change()
    df[‘volatility’] = df[‘price_change’].rolling(window=20).std()
    df[‘rsi’] = talib.RSI(df[‘close’], timeperiod=14)
    df[‘sma50’] = talib.SMA(df[‘close’], timeperiod=50)
    df[‘sma200’] = talib.SMA(df[‘close’], timeperiod=200)
    df[‘bollinger_upper’], df[‘bollinger_middle’], df[‘bollinger_lower’] = talib.BBANDS(df[‘close’], timeperiod=20)
    df[‘macd’], df[‘macd_signal’], df[‘macd_hist’] = talib.MACD(df[‘close’])

    df.dropna(inplace=True)

    features = df[[‘price_change’, ‘volatility’, ‘rsi’, ‘sma50’, ‘sma200’, ‘bollinger_upper’, ‘bollinger_middle’, ‘bollinger_lower’, ‘macd’, ‘macd_signal’, ‘macd_hist’]]
    return features


### 2. Model Deployment and Signal Generation

#### Model Prediction and Signal Generation

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


#### Signal Execution with Risk Management

def execute_trade(signal, symbol, current_price, position, balance):
    “””
    Execute trades based on signal, with basic risk management
    “”“
    if signal == 1 and position == 0:  # Buy signal
        print(f"Buying {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"Selling {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
    
    return position, balance

# Load or initialize balance
balance = {‘initial’: 10000, ‘current’: 10000}


### 3. Real-Time Scheduler and Monitoring

#### Scheduler Setup

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, current_price, position, balance)
    else:
        print(“No data available to process.”)

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.”)