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_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 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)
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)
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
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")
total = total + 1
data_file.seek(0)
curr = 1
positionStr = 'Current company: ' + str(curr).rjust(
5) + ' Total company: ' + str(total).rjust(6)
print(positionStr)
time1 = time.time()
for ticker in csv_reader:
if ticker[0] == "Ticker":
continue
print(ticker[0])
try:
if "1" in ft:
dl.get(filing_type[0],
ticker[0],
after_date=date1,
before_date=date2)
except:
try:
print("Internet error occurred- retrying")
dl.get(filing_type[0],
ticker[0],
after_date=date1,
before_date=date2)
except:
print("Couldn't resolve, moving to next ticker")
try:
if "2" in ft:
dl.get(filing_type[1],
ticker[0],
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.")
from sec_edgar_downloader import Downloader
path = 'E:\stockdata3\Filings'
dl = Downloader(path)
aapl = dl.get('10-K', 'aapl', 12)
pass
from sec_edgar_downloader import Downloader
# Initialize a downloader instance.
# If no argument is passed to the constructor, the package
# will attempt to locate the user's downloads folder.
dl = Downloader("/j/tmp32/edgar")
# Get the latest 10-K filing for Ubiquiti
dl.get("10-K", "UI", 1)
# Get the latest 10-Q filing for Ubiquiti
dl.get("10-Q", "UI", 1)
from sec_edgar_downloader import Downloader
f = open('companies.txt', 'r')
stickers = f.read().splitlines()
f.close()
dl = Downloader(".")
after_date = "20090101"
before_date = "20200427"
for sticker in stickers:
dl.get("10-K", sticker, after_date="20100101", before_date="20200325")
from sec_edgar_downloader import Downloader
# In[8]:
cik = pd.read_csv("FINAL_COMPANY_LIST_SEC_INFO.csv")[['cik']]
cik = cik.values.tolist()
print(cik)
# In[9]:
file_location = "/pylon5/tr5pi7p/suli2020/uspto/rebranding/10K"
cik = cik[:10]
dl = Downloader(file_location)
# In[ ]:
num = 0
for lst in cik:
for c in lst:
print('Started: ' + c)
dl.get("10-K", c, 30)
num += 1
print('Downloaded: ' + c + ', ' + num +' in total')
def getAll10k(company):
dl = Downloader()
# Get all 10-K filings for Microsoft
dl.get("10-K", company, amount=10)
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")
# -*- 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])
