In my code below, I'm seeing this errors, [{
	"resource": "/D:/Tradin Bot/app.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "\"(\" was not closed",
	"source": "Pylance",
	"startLineNumber": 1,
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},{
	"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,
			"path": "/microsoft/pyright/blob/main/docs/configuration.md",
			"scheme": "https",
			"authority": "github.com",
			"fragment": "reportUndefinedVariable"
		}
	},
	"severity": 4,
	"message": "\"app\" is not defined",
	"source": "Pylance",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Invalid character in identifier",
	"source": "Pylance",
	"startLineNumber": 12,
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Unexpected indentation",
	"source": "Pylance",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Unindent not expected",
	"source": "Pylance",
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	"startColumn": 1,
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Invalid character in identifier",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Unexpected indentation",
	"source": "Pylance",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Unindent not expected",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "\"[\" was not closed",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
	"owner": "_generated_diagnostic_collection_name_#4",
	"severity": 8,
	"message": "Expected expression",
	"source": "Pylance",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
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			"path": "/microsoft/pyright/blob/main/docs/configuration.md",
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	"severity": 4,
	"message": "\"GridSearchCV\" is not defined",
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},{
	"resource": "/D:/Tradin Bot/botlogic.py",
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			"$mid": 1,
			"path": "/microsoft/pyright/blob/main/docs/configuration.md",
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	"severity": 4,
	"message": "\"LSTM\" is not defined",
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			"$mid": 1,
			"path": "/microsoft/pyright/blob/main/docs/configuration.md",
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This is the code that I want you to review and make corrections for the above errors
@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)
###1.Imports and Initialization 
 
from flask import Flask, request, render_template, session
from flask_session import Session
import requests
import plotly.graph_objs as go
import pandas as pd
import numpy as np
from binance.client import Client
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split,   
 GridSearchCV
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import   
 LSTM, Dense
import optuna
from transformers import pipeline
import os
import matplotlib.pyplot as plt
import base64
from io import BytesIO

app = Flask(__name__)
app.secret_key = os.urandom(24)
app.config['SESSION_TYPE'] = 'filesystem'  # Added semicolon here
Session(app)

###2.Technical Indicators 
def sma(data, window): return data[‘close’].rolling(window=window).mean() def ema(data, window): return data[‘close’].ewm(span=window, adjust=False).mean() def rsi(data, window): delta = data[‘close’].diff() gain = (delta.where(delta > 0, 0)).rolling(window=window).mean() loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean() RS = gain / loss return 100 - (100 / (1 + RS)) def macd(data, short_window=12, long_window=26, signal_window=9): short_ema = ema(data, short_window) long_ema = ema(data, long_window) macd_line = short_ema - long_ema signal_line = macd_line.ewm(span=signal_window, adjust=False).mean() return macd_line, signal_line def bollinger_bands(data, window=20, num_std=2): data[‘sma’] = sma(data, window) data[‘upper_band’] = data[‘sma’] + num_std * data[‘close’].rolling(window=window).std() data[‘lower_band’] = data[‘sma’] - num_std * data[‘close’].rolling(window=window).std() return data[‘upper_band’], data[‘lower_band’] def stochastic_oscillator(data, window=14): data[‘low_14’] = data[‘low’].rolling(window=window).min() data[‘high_14’] = data[‘high’].rolling(window=window).max() data[‘stochastic’] = 100 * ((data[‘close’] - data[‘low_14’]) / (data[‘high_14’] - data[‘low_14’])) return data[‘stochastic’] 

###3.Fetch Data 
def fetch_data(api_key, api_secret, symbol, start_date): try: client = Client(api_key, api_secret) data = client.get_historical_klines(symbol, Client.KLINE_INTERVAL_1HOUR, start_date) df = pd.DataFrame(data, columns=[‘timestamp’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’, ‘close_time’, ‘quote_asset_volume’, ‘number_of_trades’, ‘taker_buy_base_asset_volume’, ‘taker_buy_quote_asset_volume’, ‘ignore’]) df[‘close’] = df[‘close’].astype(float) return df except Exception as e: print(f"Error fetching data: {e}“) return pd.DataFrame() 

###4.Machine Learning Models and Hyperparameter Tuning 
def train_decision_tree_model(features, labels):
    model = DecisionTreeClassifier()
    model.fit(features, labels)  # Check if arguments are missing here
    return model

def optimize_random_forest(features, labels):
    param_grid = {
        'n_estimators': [50, 100, 150],
        'max_depth': [None, 10, 20, 30]
    }
    rf = RandomForestClassifier()
    grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, n_jobs=-1, scoring='accuracy')
    grid_search.fit(features, labels)  # Check if arguments are missing here
    return grid_search.best_estimator_

def train_lstm_model(features, labels, n_input):
    model = Sequential()
    model.add(LSTM(100, input_shape=(n_input, features.shape[1])))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])  # Check if arguments are missing here
    model.fit(features, labels, epochs=20, batch_size=32, validation_split=0.2)
    return model

def objective(trial, X_train, y_train, X_test, y_test):
    n_estimators = trial.suggest_int('n_estimators', 50, 150)
    max_depth = trial.suggest_int('max_depth', 10, 30)
    model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth)
    model.fit(X_train, y_train)
    score = model.score(X_test, y_test)
    return score

def optimize_model_bayesian(features, labels):
    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
    study = optuna.create_study(direction='maximize')
    study.optimize(lambda trial: objective(trial, X_train, y_train, X_test, y_test), n_trials=50)   

    best_params = study.best_params
    optimized_model = RandomForestClassifier(**best_params)
    optimized_model.fit(features, labels)
    return optimized_model

###5.Sentiment Analysis 
def fetch_news(): # This is a mock function. Implement your news fetching functionality here. 
    return [{‘content’: ‘Market is looking bullish today.’}, {‘content’: ‘Stock prices are expected to rise.’}] def sentiment_analysis(news_data): sentiment_pipeline = pipeline(“sentiment-analysis”) sentiments = [] for article in news_data: sentiment = sentiment_pipeline(article[‘content’])[0][‘label’] sentiments.append(sentiment) sentiment_score = sentiments.count(‘POSITIVE’) / len(sentiments) return sentiment_score 

###6.Risk Management 
def calculate_position_size(account_size, risk_per_trade, entry_price, stop_loss): risk_amount = account_size * risk_per_trade position_size = risk_amount / abs(entry_price - stop_loss) return position_size def trailing_stop_loss(current_price, trailing_stop_percent): trailing_stop = current_price * (1 - trailing_stop_percent) return trailing_stop 

###7.Signal Generation 
def generate_signals(data, dt_model, rf_model, lstm_model): features = data[[‘sma’, ‘ema’, ‘rsi’, ‘macd’, ‘signal_line’, ‘sentiment’]] # Predict using different models dt_predictions = dt_model.predict(features) rf_predictions = rf_model.predict(features) lstm_predictions = lstm_model.predict(features.reshape((features.shape[0], 1, features.shape[1]))) lstm_predictions = (lstm_predictions > 0.5).astype(int).reshape(-1) # Combining predictions combined_signals = (dt_predictions + rf_predictions + lstm_predictions) / 3 data[‘signal’] = combined_signals.round() return data 

###8.Backtesting 
def backtest(data, signals, initial_balance=10000): balance = initial_balance positions = [] for idx, signal in signals.iterrows(): if signal[‘signal’] == 1: # Buy signal entry_price = data.at[idx, ‘close’] positions.append(entry_price) elif signal[‘signal’] == -1: # Sell signal if positions: entry_price = positions.pop(0) exit_price = data.at[idx, ‘close’] balance += (exit_price - entry_price) * (0.01 * balance / entry_price) return balance, positions def plot_backtest(data, signals): fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(data[‘timestamp’], data[‘close’], label=‘Price’) for idx, signal in signals.iterrows(): if signal[‘signal’] == 1: ax.scatter(data.at[idx, ‘timestamp’], data.at[idx, ‘close’], marker=‘^’, color=‘g’) elif signal[‘signal’] == -1: ax.scatter(data.at[idx, ‘timestamp’], data.at[idx, ‘close’], marker=‘v’, color=‘r’) ax.legend() buf = BytesIO() plt.savefig(buf, format=‘png’) buf.seek(0) return base64.b64encode(buf.read()).decode(‘ascii’)