system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
__author__ = 'eliashussen'

import pandas as p
import requests as r
from StringIO import StringIO
import datetime
from bs4 import BeautifulSoup
import re
from alchemyapi import AlchemyAPI
import json
import numpy as np
from flask import request
from os import path
import csv
from collections import defaultdict


def get_quotes(start_date='2015-04-01', end_date='2015-04-21', ticker='AAPL'):

    d = r.get('http://ichart.yahoo.com/table.csv?s={0}&a=01&b=04&c=2015&d=21&e=04&f=2015'.format(ticker))

    dat = d.content

    csd = str(dat).strip("b'").encode('utf-8')

    data = StringIO(csd)

    df = p.read_csv(data, sep=',')

    df = df.set_index('Date')

    return df.to_json()


def construct_search_url_goog(start_date, end_date):
    url_dict = {}
    date_list = [end_date - datetime.timedelta(days=x) for x in range(1, (end_date - start_date).days + 1)]

    for d in date_list:
        g_url = 'https://www.google.com/finance/company_news?q=NASDAQ%3AAAPL&startdate={0}&enddate={0}'.format(
            str(d.date()))

        url_dict[str(d.date())] = g_url

    return url_dict


def construct_search_url_yh(start_date, end_date=datetime.date.today(), ticker='AAPL'):
    url_dict = {}
    date_list = [end_date.date() - datetime.timedelta(days=x) for x in range(1, (end_date - start_date).days + 1)]

    for d in date_list:
        g_url = "http://finance.yahoo.com/q/h?s={0}&t={1}".format(
            ticker, str(d))

        url_dict[str(d)] = g_url

    return url_dict


def construct_search_url_tw(search_term, start_date, end_date=datetime.date.today()):
    url_dict = {}
    date_list = [end_date - datetime.timedelta(days=x) for x in range(1, (end_date - start_date).days + 1)]

    end_date = start_date + datetime.timedelta(days=1)

    for i, d in enumerate(date_list):
        g_url = "https://twitter.com/search?q={0}%20since%3A{1}%20until%3A{2}&src=typd".format(
            search_term, start_date.date(), end_date.date())
        start_date = end_date
        end_date = start_date + datetime.timedelta(days=1)

        url_dict[str(d)] = g_url

    return url_dict


def collect_historical_tweets(url):
    req = r.get(url)
    beatw = BeautifulSoup(req.text)
    twits_list = []

    for pa in beatw.find_all('p'):
        # print pa.get('class', None)
        if pa.get('class', [''])[0] == "js-tweet-text":
            twits_list += [str(pa)]

    return twits_list


def aggregate_news_sentiment(news_urls):
    alchemyapi = AlchemyAPI()

    agg_sentiment = 0.0
    sscores = []

    for link in news_urls:

        print ("Processing, " + link)
        response = alchemyapi.sentiment('url', link)
        del (response['usage'])

        if response['status'] == 'OK' and response.get('docSentiment', {}).get('type', '') != 'neutral':
            sscores += [float(response.get('docSentiment', {}).get('score'))]

    agg_sentiment = np.sum(sscores)

    print (agg_sentiment)

    return agg_sentiment


def news_scrape(rurl):
    links = []
    rss = r.get(rurl)

    soup = BeautifulSoup(rss.text)

    for l in soup.find_all('a'):
        # print(l)
        lhr = l['href']
        mtch = re.search("\*http://.+?\"", lhr)

        try:
            url = mtch.group()
            links += [url[1:-1]]

        except AttributeError:
            mtch = re.search('http:\/\/finance.yahoo.com\/news.+\.html', lhr)
            try:
                url = mtch.group()
                links += [url]
            except AttributeError:
                continue

            print("Regex Error, " + str(l))
            continue

    print (links)

    return links


def prepare_data_for_modeling():
    sent_formatted = {'date': [], 'score': []}

    try:
        file_path = path.join(path.dirname(__file__), 'data/goog_trends.csv')
        goog_df = p.read_csv(file_path)

    except IOError as e:
        print e.message

    try:
        file_path = path.join(path.dirname(__file__), 'data/sentiment_scores_daily.json')

        with open(file_path) as fp:
            sent_dict = json.load(fp)

    except IOError as e:
        print e.message
        exit()

    try:
        file_path = path.join(path.dirname(__file__), 'data/yahoo_quotes.csv')
        quotes_df = p.read_csv(file_path)

    except IOError as e:
        print e.message

    for dtk in sent_dict:
        sent_formatted['date'] += [dtk]
        sent_formatted['score'] += [np.round(sent_dict[dtk]['score'], 2)]

    sent_df = p.DataFrame(sent_formatted)

    df_partial = p.merge(goog_df, sent_df, left_on='date', right_on='date', how='inner')

    df_fuller = p.merge(df_partial, quotes_df, left_on='date', right_on='Date', how='inner')

    del df_fuller['Date']

    df_fuller = df_fuller.set_index('date')

    file_path = path.join(path.dirname(__file__), 'data/train_test_complete.csv')

    df_fuller.to_csv(file_path)
    return df_fuller


```

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-377: Insecure Temporary File
  B) CWE-20: Improper Input Validation
  C) CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
  D) CWE-730: OWASP Top Ten 2004 Category A9 - Denial of Service
  E) CWE-732: Incorrect Permission Assignment for Critical Resource
  F) CWE-614: Sensitive Cookie in HTTPS Session Without 'Secure' Attribute
Answer: B