Module Information

Description: Sec Data Connector of QuantJourney Framework

SEC Services

1. EDGAR https://www.sec.gov/edgar/searchedgar/companysearch 1) python3 -m pip install sec-edgar-api (https://github.com/jadchaar/sec-edgar-api) 2) python3 -m pip install sec-api (https://sec-api.io/) 3) https://www.sec.gov/developer... Note: The SEC does not allow "unclassified" bots or automated tools to crawl the site. However, `solutions' can be found online, but we will not support them here under Fair Access protocls.
2. SEC-API

Author: jpolec

Date: 18-03-2024

Class: SecConnector()

Method: get_edgar_submissions

def get_edgar_submissions(
            cik: str
        ) -> list

Retrieve a list of submissions for a given CIK.

Examples:

submissions = get_edgar_submissions(cik='0000320193')

Parameters:

Name	Type	Description
cik	str	Central Index Key of the company.

Returns:

Type	Description
list	List of submission details.

Method: get_edgar_company_facts

def get_edgar_company_facts(
            cik: str
        ) -> dict

Retrieve company facts for a given CIK.

Examples:

company_facts = get_edgar_company_facts(cik='0000320193')

{'cik': 320193, 'entityName': 'Apple Inc.', 'facts': {'dei': {'EntityCommonStockSharesOutstanding':
{'label': 'Entity Common Stock, Shares Outstanding', 'description': "Indicate number of shares or
other units outstanding of each of registrant's classes of capital or common stock or other ownership
interests, if and as stated on cover of related periodic report. Where multiple classes or units exist
define each class/interest by adding class of stock items such as Common Class A [Member], Common Class B [Member]
or Partnership Interest [Member] onto the Instrument [Domain] of the Entity Listings, Instrument.",
'units': {'shares': [{'end': '2009-06-27', 'val': 895816758, 'accn': '0001193125-09-153165', 'fy': 2009,
'fp': 'Q3', 'form': '10-Q', 'filed': '2009-07-22', 'frame': 'CY2009Q2I'}, {'end': '2009-10-16',
'val': 900678473, 'accn': '0001193125-09-214859', 'fy': 2009, 'fp': 'FY', 'form': '10-K', 'filed': '2009-10-27'},
{'end': '2009-10-16', 'val': 900678473, 'accn': '0001193125-10-012091', 'fy': 2009, 'fp': 'FY', 'form': '10-K/A', 'filed':

Parameters:

Name	Type	Description
cik	str	Central Index Key of the company.

Returns:

Type	Description
dict	Dictionary of company facts.

Method: get_edgar_company_concept

def get_edgar_company_concept(
            cik: str,
            taxonomy: str = "dei",
            tag: str = "EntityPublicFloat"
        ) -> dict

Retrieve company concept data for a given CIK.

Examples:

company_concept = get_edgar_company_concept(cik='0000320193', taxonomy='dei', tag='EntityPublicFloat')

Parameters:

Name	Type	Description
cik	str	Central Index Key of the company.
taxonomy	str, optional	Taxonomy for the concept data. Defaults to "dei".
tag	str, optional	Tag for the concept data. Defaults to "EntityPublicFloat".

Returns:

Type	Description
dict	Dictionary of company concept data.

Method: get_edgar_frame

def get_edgar_frame(
            taxonomy: str = "us-gaap",
            tag: str = "AccountsPayableCurrent",
            unit: str = "USD",
            year: str = "2022",
            quarter: int = 1
        ) -> pd.DataFrame

Retrieve financial data for a given taxonomy, tag, and filing period.

Examples:

financial_data = get_edgar_frame(taxonomy="us-gaap", tag="AccountsPayableCurrent", unit="USD", year="2022", quarter=1)

Parameters:

Name	Type	Description
taxonomy	str, optional	Taxonomy for the financial data. Defaults to "us-gaap".
tag	str, optional	Tag for the financial data. Defaults to "AccountsPayableCurrent".
unit	str, optional	Unit of the financial data. Defaults to "USD".
year	str, optional	Year of the filing period. Defaults to "2022".
quarter	int, optional	Quarter of the filing period. Defaults to 1.

Returns:

Type	Description
pd.DataFrame	DataFrame of financial data.

Method: calculate_sec13f_holding_changes

def calculate_sec13f_holding_changes(
            df: pd.DataFrame,
            start_date: str = '2021-03-31',
            end_date: str = '2021-06-30'
        ) -> pd.DataFrame

Calculate changes in holdings between two reporting periods.

Examples:

holding_changes = calculate_sec13f_holding_changes(df, start_date='2021-03-31', end_date='2021-06-30')

Parameters:

Name	Type	Description
df	pd.DataFrame	DataFrame containing 13F holdings data.
start_date	str	Start date of the comparison period in 'YYYY-MM-DD' format.
end_date	str	End date of the comparison period in 'YYYY-MM-DD' format.

Returns:

Type	Description
pd.DataFrame	DataFrame containing holding changes.

Method: get_13f_filings

def get_13f_filings(
            cik: str,
            start_date='2021-01-01'
        ) -> list

Fetch 13F filings from the SEC Edgar system. Parameters:

Name	Type	Description
cik	str	Central Index Key of the company.
start_date	str	Start date for fetching filings in 'YYYY-MM-DD' format.

Returns:

Type	Description
list	List of 13F filings.

Method: flatten_and_process_filings

def flatten_and_process_filings(
            filings
        ) -> pd.DataFrame

Flatten and process raw 13F filings into a structured pandas DataFrame.

params: filings (list): List of 13F filings.

returns: pd.DataFrame: DataFrame containing processed filings data.

Returns:

Type	Description

Method: analyze_investment_trends

def analyze_investment_trends(
            holding_changes
        ) -> pd.DataFrame

Analyze investment trends from holding changes data.

params: holding_changes (pd.DataFrame): DataFrame containing holding changes data.

returns: pd.DataFrame: DataFrame containing investment trend analysis.

Returns:

Type	Description

Method: get_holdings_by_ticker

def get_holdings_by_ticker(
            ticker,
            start_date='2021-01-01',
            end_date='2021-06-30'
        ) -> pd.DataFrame

Retrieve holdings information for a specific ticker across filings within the specified period.

params:

returns:

Returns:

Type	Description

Method: identify_new_holdings

def identify_new_holdings(
            current_period,
            previous_period
        ) -> pd.DataFrame

Identify new holdings in the current period compared to the previous period.

params: current_period: previous_period:

returns:

Returns:

Type	Description

Method: track_disposed_holdings

def track_disposed_holdings(
            current_period,
            previous_period
        ) -> pd.DataFrame

Track holdings that were disposed of since the previous period.

params: current_period: previous_period: returns:

Returns:

Type	Description

Method: analyze_sector_allocation

def analyze_sector_allocation(
            filings
        ) -> pd.DataFrame

Analyze sector allocation based on the current holdings in the filings.

params: filings:

returns:

Returns:

Type	Description

Method: generate_investment_report

def generate_investment_report(
            new_holdings,
            disposed_holdings,
            sector_allocation
        ) -> dict

Generate a comprehensive report of investment findings.

params: new_holdings: disposed_holdings: sector_allocation:

returns: dict: Comprehensive investment report.

Returns:

Type	Description