def do_download(path, ticker, form):
dl = Downloader(path)
dl.get(form, ticker, after="2015-01-01")
path = os.path.join(path, "sec-edgar-filings", ticker, form)
if not os.path.isdir(path):
return
pattern = re.compile("([0-9]+)")
for filingDir in os.listdir(path):
fullSubmissionFname = os.path.join(path, filingDir,
"full-submission.txt")
htmlFname = os.path.join(path, filingDir, "filing-details.html")
if not os.path.isfile(fullSubmissionFname):
print("skipping ", fullSubmissionFname)
continue
found = False
with open(fullSubmissionFname) as f:
for line in f:
if line.startswith("FILED AS OF DATE"):
date = re.search(pattern, line).group(0)
found = True
if not found:
print("skipping ", filingDir)
continue
url_rewrite(htmlFname, find_filename(path, ticker, form, date))
shutil.rmtree(os.path.join(path, filingDir))
def download_forms(company_index, year: str):
""" Reads index file and downloads 10Ks and 10Qs
"""
dl = Downloader(mypath+'/data/')
for i in company_index.index:
dl.get("10-K", company_index.TICKER[i], after_date=year+'0101', include_amends=False)
dl.get("10-Q", company_index.TICKER[i], after_date=year+'0101', include_amends=False)
def download(tickers):
path = get_filings_folder()
dl = Downloader(path)
n = len(tickers)
for i in range(n):
print_progress(i, n)
if os.path.exists('../Filings/sec_edgar_filings/' +
tickers[i]) == False:
dl.get_10k_filings(tickers[i])
def download_latest_filing(file_type, ticker):
dl = Downloader(os.getcwd())
dl.get(file_type, ticker, amount=1)
dl_path = os.getcwd() + '/sec-edgar-filings/{}/{}/'.format(
ticker, file_type)
inner_most_dir = [x[0] for x in os.walk(dl_path)][1]
html_path = f'{inner_most_dir}/filing-details.html'
txt_path = f'{inner_most_dir}/full-submission.txt'
return (html_path, txt_path)
class FilingsDownloader:
def __init__(self, downloadPath):
self.downloadPath = downloadPath
self.downloader = Downloader(self.downloadPath)
# Download 10 latest 10-K filings for the given company ticker
def downloadFilings(self, ticker, filing_type="10-K", latest=10):
self.downloader.get(filing_type, ticker, latest)
def removefilings(self, path):
shutil.rmtree(path, ignore_errors=True)
def get_files(CIK,x):
save_path = '/Users/yijingtan/Downloads/d'
dl = Downloader(save_path)
dl.get('13F-HR', CIK,include_amends=True,after_date=x)
#CIK = CIK.lstrip("0")
files = os.listdir('/Users/yijingtan/Downloads/d/sec_edgar_filings/' + CIK +'/' +'13F-HR')
data = [parse(file, CIK) for file in sorted(files)]
print(data)
try:
return pd.concat(data)
except ValueError:
print("All Values are None")
return None
def __init__(self, args, log):
self.args = args
self.log = log
self.VERSION = tfds.core.Version(self.args.dataset_version)
self.MANUAL_DOWNLOAD_INSTRUCTIONS = "Dataset already downloaded manually"
super(tfds.core.GeneratorBasedBuilder, self).__init__()
self.dl = Downloader(self.args.download_path)
self.parser = FinancialReportParser()
self.text_processor = get_text_processor(args.model)(args)
def pull_filings(corporations):
print('Pulling filings from SEC...')
dl=Downloader("./sec/filings")
added = []
for corp in corporations:
been_added = set(added)
has_pulled = False
if corp['cik'] in been_added:
has_pulled = True
if corp['main'] != True and has_pulled == False:
dl.get_all_available_filings(corp['cik'])
print(f'Pulled: {corp["name"]}')
added.append(corp['cik'])
def get_files(filing, CIK, number):
save_path = r"C:\Users\smore\Documents\13F"
dl = Downloader(save_path)
dl.get(filing, CIK, number)
CIK = CIK.lstrip("0")
print(CIK)
files = os.listdir(
f"C:/Users/smore/Documents/13F/sec_edgar_filings/{CIK}/{filing}")
data = [parse(file, CIK) for file in sorted(files)]
try:
print(pd.concat(data))
return pd.concat(data)
except ValueError:
print("All Values are None")
return None
def Bulk_extraction(ticker, filetype, date, location):
''' ticker = company ticker or list of tickers
filetype = type of financial doc (8-K,10-K)
date = all filetypes after this date format: year-day-month
location = local directory to store files as string'''
dl = Downloader(str(location))
for company in ticker:
dl.get(str(filetype),
str(company),
after=str(date),
download_details=True)
return "Complete"
# Have to double check to see if all funds are updated for 2019-2020
#Bulk_extraction(holdings,'10-K','2019-01-01',dl)
def test_constructor_relative_path():
dl = Downloader("./Downloads")
expected = Path.cwd().joinpath("Downloads")
assert dl.download_folder == expected
def test_constructor_no_params():
dl = Downloader()
expected = Path.home().joinpath("Downloads")
assert dl.download_folder == expected
def test_constructor_blank_path():
dl = Downloader("")
# pathlib treats blank paths as the current working directory
expected = Path.cwd()
assert dl.download_folder == expected
def downloader(tmpdir):
tmp_dir = Path(tmpdir.mkdir("Downloads"))
dl = Downloader(tmp_dir)
yield dl, tmp_dir
shutil.rmtree(tmp_dir)
def downloader(tmp_path):
tmp_dir = tmp_path / "Downloads"
tmp_dir.mkdir()
dl = Downloader(tmp_dir)
yield dl, tmp_dir
shutil.rmtree(tmp_dir)
# import required packages
import os
import requests
import pandas as pd
import time
from sec_edgar_downloader import Downloader
# api related info.
dl = Downloader('D:/Thesis_data')
tickers = pd.read_excel('D:\\Thesis_data\\cik_ticker.xlsx')
nyse_nas = pd.concat([
tickers[tickers['Exchange'] == 'NYSE'],
tickers[tickers['Exchange'] == 'NASDAQ']
],
axis=0)
n_cik = nyse_nas.shape[0]
error_list = []
print('Number of Stocks: ' + str(n_cik))
n = 0
# 從清單中刪除已下載
def diff(first, second):
n = len(os.listdir('D:\\Thesis_data\\sec_edgar_filings'))
second = set(second)
return [item for item in first if str(item) not in second], n
download_list, n = diff(list(nyse_nas['CIK']),
os.listdir('D:\\Thesis_data\\sec_edgar_filings'))
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 1 12:30:27 2020
@author: Stephen Sigrist
"""
import pandas as pd
from sec_edgar_downloader import Downloader
tickers_data = pd.read_csv("..."\
"...TriggeredEvents\\input files\\equity tickers.csv")
tickers_data.head()
#del tickers_data
dl = Downloader("...TriggeredEvents\\downloaded\\SEC")
for i in range(len(tickers_data)):
print("Begin Downloading " + tickers_data['ticker'][i])
for filing_type in dl.supported_filings:
try:
dl.get(filing_type, tickers_data['ticker'][i], 10)
except:
print("An Error Occured in Downloading Process")
print("Finished Downloading " + tickers_data['ticker'][i])
def form_valid(self, form):
#get user and validate form
form.instance.user = self.request.user
super(Createlisting, self).form_valid(form)
#get Company CIK
tik = form.data['ticker']
URL = 'http://www.sec.gov/cgi-bin/browse-edgar?CIK={}&Find=Search&owner=exclude&action=getcompany'
CIK_RE = re.compile(r'.*CIK=(\d{10}).*')
f = requests.get(URL.format(tik), stream=True)
results = CIK_RE.findall(f.text)
if len(results):
cik = results[0]
cmp_name = self.edgar.getCompanyNameByCik(cik)
#create object in database
company = Company(ticker=tik,
cik=cik,
name=cmp_name,
user=self.request.user)
company.save()
# delete empty database
queryset = Company.objects.filter(name='').delete()
# Creating filename and url structure
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
path = os.path.join(BASE_DIR, 'static')
out_path = path + "/sec_edgar_filings/" + tik + "/10-K/"
# creating object of class with path to the download and downloading the txt file which is too big to load without xml iteration && should look for alternative option than that
dl = Downloader(path)
# download the latest one by adding the 1
dl.get_10k_filings(tik, 1)
#removing the - and .txt to get the number to the current filing submission accession number
file_name = [
f for f in os.listdir(out_path)
if os.path.isfile(os.path.join(out_path, f))
]
switched_filename = file_name[0]
switched_filename = switched_filename.replace('-', '').replace(
'.txt', '/index.json')
# creating base url configuration, i can do a better job than this!!!
bare_url = r"https://www.sec.gov/Archives/edgar/data/"
base_url = r"https://www.sec.gov"
documents_url = bare_url + str(results[0]) + "/" + switched_filename
#retreieve the files and get the summary
content = requests.get(documents_url).json()
for file in content['directory']['item']:
# Grab the filing summary and create a new url leading to the file so we can download it.
if file['name'] == 'FilingSummary.xml':
xml_summary = base_url + content['directory'][
'name'] + "/" + file['name']
#print info
print('-' * 50)
print('File Name: ' + file['name'])
print('File Path: ' + xml_summary)
# define a new base url that represents the filing folder. This will come in handy when we need to download the reports.
base_url = xml_summary.replace('FilingSummary.xml', '')
# request and parse the content
content = requests.get(xml_summary).content
soup = BeautifulSoup(content, 'lxml')
# find the 'myreports' tag because this contains all the individual reports submitted.
reports = soup.find('myreports')
# I want a list to store all the individual components of the report, so create the master list.
master_reports = []
# loop through each report in the 'myreports' tag but except the last one it produces an error.
for report in reports.find_all('report')[:-1]:
# create a dictionary to store all the different parts we need.
report_dict = {}
report_dict['name_short'] = report.shortname.text
report_dict['name_long'] = report.longname.text
report_dict['position'] = report.position.text
report_dict['menu_category'] = report.menucategory.text
report_dict['url'] = base_url + report.htmlfilename.text
# append the dictionary to the master list.
master_reports.append(report_dict)
if report_dict[
'name_short'] == 'Consolidated Statements of Cash Flows':
# print the info.
print('-' * 50)
print(base_url + report.htmlfilename.text)
print(report.longname.text)
print(report.shortname.text)
print(report.menucategory.text)
print(report.position.text)
# creating a holder for the url since a Bug creating a different file path into the database!
redirect_url_to_statement = base_url + report.htmlfilename.text
# in case of multiple statements
statements_url = []
for report_dict in master_reports:
# define the statements we want to look for.
item1 = r"Consolidated Statements of Cash Flows"
# store them in a list.
report_list = [item1]
# if the short name can be found in the report list.
if report_dict['name_short'] in report_list:
# print some info and store it in the statements url.
print('-' * 50)
print(report_dict['name_short'])
print(report_dict['url'])
statements_url.append(report_dict['url'])
statement = Statement(year=2019,
type="CONSOLIDATED STATEMENTS OF CASH FLOWS",
url=redirect_url_to_statement,
company=company)
statement.save()
statements_data = []
# loop through each statement url
for statementUrl in statements_url:
# define a dictionary that will store the different parts of the statement.
statement_data = {}
statement_data['headers'] = []
statement_data['sections'] = []
statement_data['data'] = []
# request the statement file content
content = requests.get(statementUrl).content
report_soup = BeautifulSoup(content, 'html')
# find all the rows, figure out what type of row it is, parse the elements, and store in the statement file list.
for index, row in enumerate(report_soup.table.find_all('tr')):
# first let's get all the elements.
cols = row.find_all('td')
# if it's a regular row and not a section or a table header
if (len(row.find_all('th')) == 0
and len(row.find_all('strong')) == 0):
reg_row = [ele.text.strip() for ele in cols]
statement_data['data'].append(reg_row)
# if it's a regular row and a section but not a table header
elif (len(row.find_all('th')) == 0
and len(row.find_all('strong')) != 0):
sec_row = cols[0].text.strip()
statement_data['sections'].append(sec_row)
# finally if it's not any of those it must be a header
elif (len(row.find_all('th')) != 0):
hed_row = [ele.text.strip() for ele in row.find_all('th')]
statement_data['headers'].append(hed_row)
else:
print('We encountered an error.')
#Creating DAtA into Database
#Creating each header and rotating thru all data values
print("HEADERSSSSS ")
print("Saving Headers...")
for i in range(len(statement_data['headers'][1])):
print(statement_data['headers'][1][i])
statementHeader = Statment_element_headers(
field=statement_data['headers'][1][i], statement=statement)
statementHeader.save()
print("DATAAAAAAAA ")
print("Saving Data Element...")
for j in statement_data['data']:
print(j)
print(j[i + 1])
k = j[i + 1]
# Optimizing the Data Format
if '$' or ',' or '(' in k:
k = k.replace('$', '')
k = k.replace(' ', '')
k = k.replace(',', '.')
k = k.replace('(', '-')
k = k.replace(')', '')
k = float(k)
print(k)
statementData = Statement_element_data(
key=j[0],
value=k,
statement=statement,
company=company,
header=statementHeader)
statementData.save()
print(j)
print("Saving Data Done for Element")
print("Saving Headers Done")
print("SECTIONSSSS ")
print("Saving Headers ...")
for i in statement_data['sections']:
print(i)
statementSections = Statement_element_section(
fieldName=i, statement=statement)
statementSections.save()
print("Saving Sections Done...")
# append it to the master list for future analysis with Panda streams and NLP
statements_data.append(statement_data)
# print(statements_data)
return redirect('home')
def get_links(self):
dl = Downloader(desktop_path + "/13f filings")
# get past 68 13f filings for each company
# for c in CIKs.values():
dl.get_13f_hr_filings('0000807985', 68)
def test_invalid_save_path_constructor():
test_path = str(Path.home().joinpath("Downloads", "invalid_dir"))
with pytest.raises(IOError) as excinfo:
Downloader(test_path)
expected_msg = f"The folder for saving company filings ({test_path}) does not exist."
assert expected_msg in str(excinfo.value)
import pandas as pd
from sec_edgar_downloader import Downloader
x = pd.read_csv(
r"/home/mohit/Dropbox/ra_tasks_ms/bailout_firms/Regular Scheduled SEC scraping/companies.csv"
)
x = x['Column_Name']
dl = Downloader(
r"/home/mohit/Dropbox/ra_tasks_ms/bailout_firms/Regular Scheduled SEC scraping"
)
for i in x:
dl.get("8-K", i, after_date="20200328")
from sec_edgar_downloader import Downloader
path = 'E:\stockdata3\Filings'
dl = Downloader(path)
aapl = dl.get('10-K', 'aapl', 12)
pass
# Edgar SEC 10k extracting
import pandas as pd
from sec_edgar_downloader import Downloader
# importing IBB holdings. csv
file = 'IBB-holdings.csv'
IBB = pd.read_csv(file, skiprows=13)
holdings = list(IBB["Symbol"])
#setting Download location
dl = Downloader('/Users/nick/Documents/cs506/project')
def Bulk_extraction(ticker, filetype, date, location):
''' ticker = company ticker or list of tickers
filetype = type of financial doc (8-K,10-K)
date = all filetypes after this date format: year-day-month
location = local directory to store files as string'''
dl = Downloader(str(location))
for company in ticker:
dl.get(str(filetype),
str(company),
after=str(date),
download_details=True)
return "Complete"
# Have to double check to see if all funds are updated for 2019-2020
#Bulk_extraction(holdings,'10-K','2019-01-01',dl)
import numpy as np
import pandas as pd
from sec_edgar_downloader import Downloader
df = pd.read_csv('data/sp500_list.csv')
drop_column = df.columns[0]
df.drop(columns=drop_column, inplace=True)
df.CIK = df.CIK.astype(str)
df['CIK'] = df['CIK'].str.zfill(10)
dl = Downloader('test_data/')
for i in df.index:
print(f"{df.index[i]}: Pulling 8Ks for {df.COMPANY[i]}")
dl.get("10-K", df.CIK[i], after_date="20190101", include_amends=False)
dl.get("10-Q", df.CIK[i], after_date="20190101", include_amends=False)
print(f"{df.index[i]}: {df.COMPANY[i]} done.")
def test_constructor_user_path():
dl = Downloader("~/Downloads")
expected = Path.home().joinpath("Downloads")
assert dl.download_folder == expected
class FinancialStatementDatasetBuilder(tfds.core.GeneratorBasedBuilder):
def __init__(self, args, log):
self.args = args
self.log = log
self.VERSION = tfds.core.Version(self.args.dataset_version)
self.MANUAL_DOWNLOAD_INSTRUCTIONS = "Dataset already downloaded manually"
super(tfds.core.GeneratorBasedBuilder, self).__init__()
self.dl = Downloader(self.args.download_path)
self.parser = FinancialReportParser()
self.text_processor = get_text_processor(args.model)(args)
def _info(self):
return tfds.core.DatasetInfo(
builder=self,
description=("Financial statements data."),
features=tfds.features.FeaturesDict({
"documents":
tfds.features.Tensor(dtype=tf.string,
shape=(self.args.number_of_periods, )),
"label":
tfds.features.Tensor(dtype=tf.int64, shape=(2, ))
}),
supervised_keys=("documents", "label"),
homepage="https://xxx",
citation=r"""@article{my-awesome-dataset-2020,
author = {Hurubaru, Sebastian},"}""",
)
def _split_generators(self, dl_manager):
# Specify the splits
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
gen_kwargs={
"input_dir": os.path.join(self.args.input_dir, 'train')
},
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
gen_kwargs={
"input_dir": os.path.join(self.args.input_dir, 'dev')
},
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
gen_kwargs={
"input_dir": os.path.join(self.args.input_dir, 'test')
},
)
]
def _generate_examples(self, input_dir):
# Get the content of the dataset file
dataset = tf.data.experimental.make_csv_dataset(
os.path.join(input_dir, self.args.company_files),
batch_size=1,
column_defaults=[tf.string, tf.string, tf.string, tf.int32],
label_name='label',
na_value="?",
num_epochs=1,
ignore_errors=True)
for company_info, label in dataset:
ciks = company_info['cik'].numpy()[0].decode('utf-8').split(';')
ciks.sort(reverse=True, key=lambda cik: int(cik))
end_date = company_info['end_date'].numpy()[0].decode('utf-8')
try:
documents = []
# For multiple CIKs take in the descending order the last args.number_of_periods 10-K reports
for cik in ciks:
cik_folder = os.path.join(
os.path.expanduser(self.args.download_path),
'sec_edgar_filings',
cik.strip().lstrip("0"), '10-K')
# Download if and only if the directories do not exist
if (os.path.exists(cik_folder) is False):
self.dl.get("10-K",
cik,
before_date=end_date,
num_filings_to_download=self.args.
number_of_periods)
for r, d, f in os.walk(cik_folder):
for file in f:
if '.txt' in file:
documents.append(
tf.convert_to_tensor(
self.parser.parse_10K_txt_file(
os.path.join(r, file)),
dtype=tf.string))
if len(documents) < self.args.number_of_periods:
raise Exception(
f'Could not retrieve {self.args.number_of_periods} 10-K records for {cik}'
)
yield cik, {
'documents':
tf.stack(documents)[:self.args.number_of_periods],
'label': [1, 0] if label.numpy()[0] == 0 else [0, 1]
}
except Exception as e:
self.log.error(f'Exception occurred for cik {cik}: {e}')
def _process_text_map_fn(self, text, label):
processed_text, label = tf.py_function(self._process_text,
inp=[text, label],
Tout=(tf.float32, tf.int64))
return processed_text, label
def _process_text(self, text, label):
# To allow debugging in the combined static eager mode
# pydevd.settrace(suspend=True)
# Process the text
processed_text = self.text_processor.process_text(text)
return (processed_text, label)
def test_constructor_custom_path():
custom_path = Path.home().joinpath("Downloads/SEC/EDGAR/Downloader")
dl = Downloader(custom_path)
assert dl.download_folder == custom_path
def downloader(tmp_path):
dl = Downloader(tmp_path)
yield dl, tmp_path
shutil.rmtree(tmp_path)
def download_and_parse(actual_stock, ciks, dict):
"""
This function is the meat and potatoes of downloading the SEC 10-K filings.
It uses the sec_edgar_downloader package to download the 10-K filing.
Then it uses code from https://gist.github.com/anshoomehra/ead8925ea291e233a5aa2dcaa2dc61b2 to parse the 10-K filing for Item 1A.
The code separates Item 1A into sentences and outputs it to a dictionary associated with the CIK value.
:param actual_stock: the stock ticker
:param ciks: CIK - stock ticker dictionary/crosswalk
:param dict: a dictionary to store the 10-K Item 1A sentences
:return: nothing, but it constantly updates/adds to the dictionary
"""
if actual_stock[0] in [
'BF.B', 'BF-B', 'bf-b', 'bf.b', 'HES', 'hes', 'hpe', 'HPE'
]:
print("This stock has no CIK... issue there so I am skipping")
return
cik = convert_ticker_to_cik(actual_stock, ciks)
cik = cik.zfill(10)
dl = Downloader()
#stock_ticker = "0001067983"
dl.get("10-K", cik, after="2015-01-01", download_details=False)
count = 0
for root, dirs, files in os.walk(
"./sec-edgar-filings/{}/10-K/".format(cik)):
# search through each years' 10-k filing
for file in files:
# find the txt document of the 10-K filing
if file == 'full-submission.txt':
try:
year = re.findall(r'\-[0-9][0-9]\-', root)
year = year[len(year) - 1][1:-1]
# certain stocks have issues for certain years. I will include code to exclude them here
# if year == 21 and stock_ticker in ("'APA'", "'ADM'"):
# print("Skipping year {} for ticker {} due to issues...".format(year, stock_ticker))
# read the file
filing_text = read_file(root + '/' + file)
# this code comes from https://gist.github.com/anshoomehra/ead8925ea291e233a5aa2dcaa2dc61b2
doc_start_pattern = re.compile(r'<DOCUMENT>')
doc_end_pattern = re.compile(r'</DOCUMENT>')
# Regex to find <TYPE> tag prceeding any characters, terminating at new line
type_pattern = re.compile(r'<TYPE>[^\n]+')
# Create 3 lists with the span idices for each regex
### There are many <Document> Tags in this text file, each as specific exhibit like 10-K, EX-10.17 etc
### First filter will give us document tag start <end> and document tag end's <start>
### We will use this to later grab content in between these tags
doc_start_is = [
x.end()
for x in doc_start_pattern.finditer(filing_text)
]
doc_end_is = [
x.start()
for x in doc_end_pattern.finditer(filing_text)
]
### Type filter is interesting, it looks for <TYPE> with Not flag as new line, ie terminare there, with + sign
### to look for any char afterwards until new line \n. This will give us <TYPE> followed Section Name like '10-K'
### Once we have have this, it returns String Array, below line will with find content after <TYPE> ie, '10-K'
### as section names
doc_types = [
x[len('<TYPE>'):]
for x in type_pattern.findall(filing_text)
]
document = {}
# Create a loop to go through each section type and save only the 10-K section in the dictionary
for doc_type, doc_start, doc_end in zip(
doc_types, doc_start_is, doc_end_is):
if doc_type == '10-K':
document[doc_type] = filing_text[doc_start:doc_end]
regex = re.compile(
r'(>(Item|ITEM)(\s| | | )(1A|1B)\.{0,1})|(ITEM\s(1A|1B))|(<B>Item</B><B></B><B> 1A</B>)|( Item 1B.)|(Item<font style="font-family:Times New Roman, Times, serif;font-size:10pt;"> 1B)|(Item<font style="font-family: Times New Roman, Times, serif; font-size: 10pt;"> 1B)'
)
matches = regex.finditer(document['10-K'])
test_df = pd.DataFrame([
(x.group(), x.span()[0], x.span()[1]) for x in matches
])
if len(test_df) == 0:
print("error... didn't pick up anything")
break
test_df.columns = ['item', 'start', 'end']
test_df['item'] = test_df.item.str.lower()
test_df.replace(
'<font style="font-family:times new roman, times, serif;font-size:10pt;">',
' ',
regex=True,
inplace=True)
test_df.replace(
'<font style="font-family: times new roman, times, serif; font-size: 10pt;">',
' ',
regex=True,
inplace=True)
test_df.replace(' ', ' ', regex=True, inplace=True)
test_df.replace(' ', ' ', regex=True, inplace=True)
test_df.replace(' ', ' ', regex=True, inplace=True)
test_df.replace(' ', '', regex=True, inplace=True)
test_df.replace('\.', '', regex=True, inplace=True)
test_df.replace('>', '', regex=True, inplace=True)
test_df.replace('<b', '', regex=True, inplace=True)
test_df.replace('</b', '', regex=True, inplace=True)
pos_dat = test_df.sort_values(
'start',
ascending=True).drop_duplicates(subset=['item'],
keep='last')
pos_dat.set_index('item', inplace=True)
# Check conditionals here
if 'item1a' in pos_dat.index and 'item1b' in pos_dat.index:
item_1a_raw = document['10-K'][
pos_dat['start'].loc['item1a']:pos_dat['start'].
loc['item1b']]
item_1a_content = BeautifulSoup(item_1a_raw, 'lxml')
test_df["text"] = item_1a_content.get_text()
test_df.replace(
'([0-9]|[0-9][0-9])(\s{0,3})Table of Contents',
' ',
regex=True,
inplace=True)
test_df.replace('Table of Contents',
' ',
regex=True,
inplace=True)
test_df.replace('\s\s', ' ', regex=True, inplace=True)
test_df.replace('\\u200b',
' ',
regex=True,
inplace=True)
test_df.replace('\\n[0-9]',
' ',
regex=True,
inplace=True)
test_df.replace('[0-9]\\n',
' ',
regex=True,
inplace=True)
test_df.replace('\\xa0', ' ', regex=True, inplace=True)
test_df.replace('\\x92', ' ', regex=True, inplace=True)
test_df.replace('\\x93', ' ', regex=True, inplace=True)
test_df.replace('\\x94', ' ', regex=True, inplace=True)
test_df.replace('\\x95', ' ', regex=True, inplace=True)
test_df.replace('\\n', ' ', regex=True, inplace=True)
test_df.replace('\n', ' ', regex=False, inplace=True)
# output the text to the dict
sentences = nltk.sent_tokenize(str(test_df['text'][0]))
if count == 0:
output_frame = pd.DataFrame(
[[year, sentences]], columns=["year", "text"])
else:
output_frame = output_frame.append(
pd.DataFrame([[year, sentences]],
columns=["year", "text"]),
ignore_index=True)
dict[cik] = output_frame
print(
"finished processing ticker {} ({}) and added to dictionary for year {}"
.format(cik, actual_stock[0], year))
print(75 * '')
count += 1
else:
regex = re.compile(
r'(>(Item|ITEM)(\s| | | )(1A|2)\.{0,1})|(ITEM\s(1A|2))|(<B>Item</B><B></B><B> 1A</B>)|( Item 2.)'
)
matches = regex.finditer(document['10-K'])
test_df = pd.DataFrame([(x.group(), x.span()[0],
x.span()[1])
for x in matches])
if len(test_df) == 0:
print("error... didn't pick up anything")
break
test_df.columns = ['item', 'start', 'end']
test_df['item'] = test_df.item.str.lower()
test_df.replace(' ',
' ',
regex=True,
inplace=True)
test_df.replace(' ',
' ',
regex=True,
inplace=True)
test_df.replace(' ',
' ',
regex=True,
inplace=True)
test_df.replace(' ', '', regex=True, inplace=True)
test_df.replace('\.', '', regex=True, inplace=True)
test_df.replace('>', '', regex=True, inplace=True)
test_df.replace('<b', '', regex=True, inplace=True)
test_df.replace('</b', '', regex=True, inplace=True)
pos_dat = test_df.sort_values(
'start',
ascending=True).drop_duplicates(subset=['item'],
keep='last')
pos_dat.set_index('item', inplace=True)
item_1a_raw = document['10-K'][
pos_dat['start'].loc['item1a']:pos_dat['start'].
loc['item2']]
item_1a_content = BeautifulSoup(item_1a_raw, 'lxml')
test_df["text"] = item_1a_content.get_text()
test_df.replace(
'([0-9]|[0-9][0-9])(\s{0,3})Table of Contents',
' ',
regex=True,
inplace=True)
test_df.replace('Table of Contents',
' ',
regex=True,
inplace=True)
test_df.replace('\s\s', ' ', regex=True, inplace=True)
test_df.replace('\\u200b',
' ',
regex=True,
inplace=True)
test_df.replace('\\n[0-9]',
' ',
regex=True,
inplace=True)
test_df.replace('[0-9]\\n',
' ',
regex=True,
inplace=True)
test_df.replace('\\xa0', ' ', regex=True, inplace=True)
test_df.replace('\\x92', ' ', regex=True, inplace=True)
test_df.replace('\\x93', ' ', regex=True, inplace=True)
test_df.replace('\\x94', ' ', regex=True, inplace=True)
test_df.replace('\\x95', ' ', regex=True, inplace=True)
test_df.replace('\\n', ' ', regex=True, inplace=True)
test_df.replace('\n', ' ', regex=False, inplace=True)
# output the text to the dict
sentences = nltk.sent_tokenize(str(test_df['text'][0]))
if count == 0:
output_frame = pd.DataFrame(
[[year, sentences]], columns=["year", "text"])
else:
output_frame = output_frame.append(
pd.DataFrame([[year, sentences]],
columns=["year", "text"]),
ignore_index=True)
dict[cik] = output_frame
print(
"finished processing ticker {} ({}) and added to dictionary for year {}"
.format(cik, actual_stock[0], year))
print(75 * '')
count += 1
except:
print("error occurred")
from sec_edgar_downloader import Downloader
dl = Downloader('C:\\Users\\willi\\Documents\\Company_filings\\BRK-A\\13F-HR')
# Downloads using CIK of Berkshire hathaway BERK-A
dl.get("13F-HR", "0001067983")
