I'm seeing this errors from the code below, please review and correct for me
Errors: [{
	"resource": "/D:/Tradin Bot/app.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "\"(\" was not closed",
	"source": "Pylance",
	"startLineNumber": 1,
	"startColumn": 11,
	"endLineNumber": 1,
	"endColumn": 12
},{
	"resource": "/D:/Tradin Bot/app.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Expected expression",
	"source": "Pylance",
	"startLineNumber": 1,
	"startColumn": 12,
	"endLineNumber": 1,
	"endColumn": 16
},{
	"resource": "/D:/Tradin Bot/app.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Expected function or class declaration after decorator",
	"source": "Pylance",
	"startLineNumber": 66,
	"startColumn": 28,
	"endLineNumber": 66,
	"endColumn": 28
},{
	"resource": "/D:/Tradin Bot/app.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"code": {
		"value": "reportUndefinedVariable",
		"target": {
			"$mid": 1,
			"external": "https://github.com/microsoft/pyright/blob/main/docs/configuration.md#reportUndefinedVariable",
			"path": "/microsoft/pyright/blob/main/docs/configuration.md",
			"scheme": "https",
			"authority": "github.com",
			"fragment": "reportUndefinedVariable"
		}
	},
	"severity": 4,
	"message": "\"app\" is not defined",
	"source": "Pylance",
	"startLineNumber": 1,
	"startColumn": 2,
	"endLineNumber": 1,
	"endColumn": 5
}]

The code is below
@app.route(‘/’, methods=[‘GET’, ‘POST’]) 
def index(): 
     if request.method == ‘POST’: 
     api_key = request.form[‘api_key’] 
     api_secret = request.form[‘api_secret’] 
     symbol = request.form[‘symbol’] 
     account_size = float(request.form[‘account_size’]) 
     risk_per_trade = float(request.form[‘risk_per_trade’]) 
     trailing_stop_percent = float(request.form[‘trailing_stop_percent’]) 
    # Validate inputs 
if not all([api_key, api_secret, symbol, account_size, risk_per_trade, trailing_stop_percent]): 
     return “Please fill all the fields.”, 400 
     try: 
     data = fetch_data(api_key, api_secret, symbol, start_date=‘2023-10-10’) 
     except Exception as e: return f"Error fetching data: {e}”, 500 
         if data.empty: 
     return “No data fetched.”, 404 
     # Calculate technical indicators 
     data[‘sma’] = sma(data, window=20) 
     data[‘ema’] = ema(data, window=20) 
     data[‘rsi’] = rsi(data, window=14) 
     data[‘macd’], data[‘signal_line’] = macd(data) data[‘upper_band’], 
     data[‘lower_band’] = bollinger_bands(data) 
     data[‘stochastic’] = stochastic_oscillator(data) 
     # Implement sentiment analysis 
     news_data = fetch_news() 
     # Function to fetch news, needs implementation 
     sentiment_score = sentiment_analysis(news_data) 
     data[‘sentiment’] = sentiment_score 
     # Prepare features and labels for ML models 
     features = data[[‘sma’, ‘ema’, ‘rsi’, ‘macd’, ‘signal_line’, ‘sentiment’]].fillna(0) 
     labels = np.where(features[‘rsi’].shift(-1) > features[‘rsi’], 1, 0) 
     # Example label logic 
     # Optimize and create models 
     dt_model = train_decision_tree_model(features, labels) 
     rf_model = optimize_random_forest(features, labels) 
     lstm_model = train_lstm_model(features.values.reshape((-1, 1, features.shape[1])), labels, n_input=1) 
     # Generate signals using the best models 
     signals = generate_signals(data, dt_model, rf_model, lstm_model) 
     # Backtest the strategy 
     final_balance, open_positions = backtest(data, signals) 
     # Calculate Risk Management values 
     entry_price = signals[‘close’].iloc[-1] 
     stop_loss = entry_price - entry_price * trailing_stop_percent 
     take_profit = entry_price + entry_price * (trailing_stop_percent * 2) 
     position_size = calculate_position_size(account_size, risk_per_trade, entry_price, stop_loss) 
     
     # Plot data 
     fig = go.Figure(data=[go.Candlestick(x=data[‘timestamp’], 
     open=data[‘open’], high=data[‘high’], low=data[‘low’], close=data[‘close’])]) 
     fig.update_layout(title=f’Price Chart for {symbol}', yaxis_title=‘Price’) 
     chart = fig.to_html() 
     
     # Plot backtest results 
     backtest_plot = plot_backtest(data, signals) 
     return render_template(‘index.html’, chart=chart, signals=signals.to_dict(orient=‘records’), 
     entry_price=entry_price, 
     stop_loss=stop_loss, 
     take_profit=take_profit, 
     position_size=position_size, 
     final_balance=final_balance, 
     backtest_plot=backtest_plot) 
     
     return render_template(‘index.html’) 
     if name == ‘main’: 
        app.run(debug=True)