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
import pandas as pd
import numpy as np
import requests as rq
import pandas_datareader as pdr
from pandas_datareader.google.daily import GoogleDailyReader
from io import StringIO


# hack until pandas_datareader is fixed to a new google API
@property
def url(self):
    return 'http://finance.google.com/finance/historical'


GoogleDailyReader.url = url


# download market benchmark data
# indices: ^dji, ^spx
class MarketData:
    def __init__(self, datafile='', index='^spx'):
        self.datafile = datafile
        self.index = index
        self._date_fmt = '{:%Y%m%d}'

    # convert dates to required format
    def _dates(self, date):
        return self._date_fmt.format(date)

    # startDate, endDate in yyyymmdd format
    def _get_market_index(self, start_date, end_date):
        url = "https://stooq.com/q/d/l/?s={}&d1={}&d2={}&i=d"
        url = url.format(self.index, start_date, end_date)
        # get content
        content = rq.get(url=url, verify=False).content
        df = pd.read_csv(StringIO(content.decode('utf8')))
        # check if empty - e.g. update existing over weekend
        try:
            df['Date'] = pd.to_datetime(df['Date'])
            df.set_index('Date', inplace=True)
        except:
            print('Warning: Market index data is empty!')
            None
        return df

    # returns panel using goodle finance server, pause is required to avoid ban
    def _get_historical_prices(self, tickers, start_date, end_date):
        # pf = data.DataReader(
        #     tickers, "google", start_date, end_date, pause=10)
        # pf = pdr.get_data_google(
        #     tickers, start_date, end_date, pause=10)
        # pf.to_hdf(r'c:\temp\get_data_google.h5', 'key')
        # pf = pdr.get_data_quandl(
        #     tickers, start_date, end_date, pause=10)
        pf = pdr.get_data_morningstar(
            tickers, start_date, end_date, pause=10)
        # pf.to_hdf(r'c:\temp\get_data_morningstar.h5', 'key')
        pf = pf.astype(np.float32)
        pf = pf.to_panel().swapaxes('major', 'minor')
        return pf

    # return all stocks and index in one panel
    def download_save_market_data(self, tickers, start_date, end_date,
                                  update_existing=False):
        start_date_str = self._date_fmt.format(start_date)
        end_date_str = self._date_fmt.format(end_date)
        print("Downloading market data for {}-{}".format(
            start_date_str, end_date_str))

        # add market index
        pf = self._get_historical_prices(
            tickers,
            start_date.date(),
            end_date.date())
        market = self._get_market_index(
            start_date_str, end_date_str)
        pf.loc[:, :, 'market'] = market

        if update_existing:
            new_dict = {}
            pf_old = pd.read_hdf(self.datafile, 'market')
            pf_new = pd.concat([pf_old, pf], axis=1)
            for it in pf_new.items:
                new_dict[it] = pf_new.loc[it].drop_duplicates().sort_index()
            pf = pd.Panel(new_dict)
        else:
            pf.to_hdf(self.datafile, 'market')
        return pf


if __name__ == '__main__':
    print("Testing MarketData")
    md = MarketData(datafile='../data/data.h5')
    df_ord = pd.read_hdf('../data/data.h5', 'orders')
    pf = md.download_save_market_data(
        df_ord.symbol.unique(),
        df_ord.date.min(),
        df_ord.date.max())

```

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

  A) CWE-327: Use of a Broken or Risky Cryptographic Algorithm
  B) CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  C) CWE-215: Insertion of Sensitive Information Into Debugging Code
  D) CWE-611: Improper Restriction of XML External Entity Reference
  E) CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  F) CWE-295: Improper Certificate Validation
Answer: F