# Content Model Catalog
## KR STOCK
### financial
financial
Summary
This dataset provides PIT (point-in-time) financial statements for KR stocks, including various financial metrics and indicators. The data’s index indicates the date the data was included in the database.
Users can choose from 3 types of data frames by adjusting the `preprocess_type` parameter. Note that using `preprocess_type` may slow down data retrieval due to additional transformation logic, potentially taking several seconds per CM:
1. **None**: In this case, the values in the data frame become dictionaries possibly including multiple keys and values. If you want to know all values (including original, restatement, etc.), this option can be a solution.
2. **'default'**: In this case, the values in the data frame become the most recent values at the point in time, not including the fiscal date.
3. **'unpivot'**: In this case, there will be 4 columns (`id`, `pit`, `fiscal`, `value`). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-total_assets", preprocess_type='default')
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | ---------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | Asia/Seoul | 1d |
Item List
Balance Sheet
* krx-spot-advance\_from\_customers: Information on advance payments from customers for Korean stocks
* krx-spot-capital\_stock: Capital information of Korean stocks
* krx-spot-cash\_and\_cash\_equivalent: Information on cash and cash equivalents of Korean stocks
* krx-spot-common\_stock: Information on common stocks in Korea
* krx-spot-construction\_in\_progress: Construction in progress of Korean stocks
* krx-spot-convertible\_bonds: Convertible bonds of Korean stocks
* krx-spot-current\_assets: Current assets of Korean stocks
* krx-spot-current\_financial\_assets: Information on liquid financial assets of Korean stocks
* krx-spot-current\_income\_tax\_liabilities: Current corporate tax liabilities of Korean stocks
* krx-spot-current\_liabilities: Information on current liabilities of Korean stocks
* krx-spot-current\_portion\_of\_long\_term\_debt: Information on the liquidity of long-term debt in Korean stocks
* krx-spot-current\_provisions\_for\_employee\_benefits: Current Employee Benefits Provision for Korean Stocks
* krx-spot-deferred\_tax\_assets: Information on deferred corporate tax assets of Korean stocks
* krx-spot-deferred\_tax\_liabilities: Information on deferred corporate tax liabilities of Korean stocks
* krx-spot-finished\_goods: Products of Korean stocks
* krx-spot-goodwill: Information on goodwill in Korean stocks
* krx-spot-intangible\_assets: Information on intangible assets of Korean stocks
* krx-spot-inventory: Information on inventory assets of Korean stocks
* krx-spot-invested\_capital: Information on investment capital in Korean stocks
* krx-spot-investment\_in\_properties: Investment real estate of Korean stocks
* krx-spot-land: Land of Korean stocks
* krx-spot-liabilities\_included\_in\_disposal\_groups\_classified\_as\_held\_for\_sale: Liabilities included in the group of assets held for sale classified as intended for sale of Korean stocks
* krx-spot-listed\_shares\_comm: The number of listed common stocks in the Korean stock market
* krx-spot-listed\_shares\_pref: The number of listed preferred stocks in the Korean stock market
* krx-spot-loans: Loan information of the Korean stock market
* krx-spot-long\_term\_borrowings: Information on long-term borrowings of Korean stocks
* krx-spot-long\_term\_financial\_instrument: Information on long-term financial products of Korean stocks
* krx-spot-long\_term\_financial\_liabilities: Information on long-term financial liabilities of Korean stocks
* krx-spot-long\_term\_provisions: Information on long-term provisions for Korean stocks
* krx-spot-non\_controlling\_interests\_equity: Information on non-controlling interests in Korean stocks
* krx-spot-non\_current\_biological\_assets: Information on non-current biological assets of Korean stocks
* krx-spot-non\_current\_liabilities: Information on non-current liabilities of Korean stocks
* krx-spot-non\_current\_provisions\_for\_employee\_benefits: Non-current employee benefits provision for Korean stocks
* krx-spot-other\_current\_assets: Other current assets of Korean stocks
* krx-spot-other\_current\_liabilities: Other current liabilities of Korean stocks
* krx-spot-other\_non\_current\_assets: Information on other non-current assets of Korean stocks
* krx-spot-other\_non\_current\_liabilities: Information on other non-current liabilities of Korean stocks
* krx-spot-other\_payables: Information on other liabilities of Korean stocks
* krx-spot-owners\_of\_parent\_equity: Information on the ownership equity of controlling companies in Korean stocks
* krx-spot-paid\_in\_capital\_in\_excess\_of\_par\_value: Paid-in capital in excess of par value of Korean stocks
* krx-spot-paid\_in\_capital\_increase: Information on paid-in capital increase of Korean stocks
* krx-spot-prepaid\_expenses: Information on prepaid expenses for Korean stocks
* krx-spot-preferred\_stock: Information on preferred stocks in Korea
* krx-spot-property\_plant\_and\_equipment: Information on tangible assets of Korean stocks
* krx-spot-receivables: Information on receivables of Korean stocks
* krx-spot-retained\_earnings: Information on retained earnings of Korean stocks
* krx-spot-short\_term\_bonds: Information on short-term bonds in the Korean stock market
* krx-spot-short\_term\_borrowings: Information on short-term borrowings of Korean stocks
* krx-spot-short\_term\_financial\_liabilities: Information on short-term financial liabilities of Korean stocks
* krx-spot-short\_term\_provisions: Information on short-term provisions for Korean stocks
* krx-spot-total\_assets: Total assets of Korean stocks
* krx-spot-total\_equity: Total capital information of Korean stocks
* krx-spot-total\_liabilities: Total liabilities of Korean stocks
* krx-spot-trade\_and\_other\_current\_payables: Accounts payable and other current liabilities of Korean stocks
* krx-spot-trade\_payables: Information on accounts payable for Korean stocks
* krx-spot-trade\_receivables: Information on accounts receivable of Korean stocks
* krx-spot-unearned\_income: Information on forward earnings of Korean stocks
Income Statement
* krx-spot-advertising\_expenses: Information on advertising expenditure for Korean stocks
* krx-spot-amortization: Information on amortization costs of Korean stocks
* krx-spot-cost\_of\_sales: Cost of goods sold information for Korean stocks
* krx-spot-discontinued\_operation\_income: Operating income from discontinued operations in Korean stocks
* krx-spot-ebitda: Information on EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) of Korean stocks
* krx-spot-financial\_income: Financial return information of Korean stocks
* krx-spot-financing\_expenses: Financial costs of the Korean stock market
* krx-spot-gross\_profit: Information on gross profit of Korean stocks
* krx-spot-impairment\_losses\_on\_intangible\_assets: Information on impairment losses of intangible assets in Korean stocks
* krx-spot-impairment\_losses\_on\_property\_plant\_and\_equipment: Impairment loss of tangible assets in Korean stocks
* krx-spot-income\_before\_income\_taxes\_expenses: Information on pre-tax profit of Korean stocks
* krx-spot-income\_taxes\_expenses: Corporate tax expenses of Korean stocks
* krx-spot-interest\_expenses: Interest expense information of Korean stocks
* krx-spot-interest\_income: Information on interest income from Korean stocks
* krx-spot-net\_income: Information on the net income of Korean stocks
* krx-spot-net\_income\_attributed\_to\_non\_controlling\_interest: Net income attributable to non-controlling interests in Korean stocks
* krx-spot-net\_sales: Information on the net sales of Korean stocks
* krx-spot-number\_of\_employees: Information on the number of employees in Korean stocks
* krx-spot-ongoing\_operating\_income: Information on continuing operating profit of Korean stocks
* krx-spot-operating\_income: Operating profit information of Korean stocks
* krx-spot-owners\_of\_parent\_net\_income: Information on the net income attributable to the controlling shareholders of Korean stocks
* krx-spot-owners\_of\_parent\_ongoing\_operating\_income\_or\_loss\_per\_share: Information on the earnings per share of controlling shareholders in Korean stocks
* krx-spot-purchase\_of\_treasury\_stock: Information on share buybacks of Korean stocks
* krx-spot-research\_and\_development: Information on research and development expenses of Korean stocks
* krx-spot-salaries\_and\_wages: Salary information of Korean stocks
* krx-spot-sales: Sales revenue information of Korean stocks
* krx-spot-sales\_of\_treasury\_stock: Profit from the disposal of treasury shares in Korean stocks
* krx-spot-selling\_general\_administrative\_expenses: Information on selling and administrative expenses of Korean stocks
* krx-spot-is\_depreciation: Information on depreciation expenses in the income statement of Korean stocks
Cashflow Statement
* krx-spot-capex: Information on capital expenditures (CAPEX) of Korean stocks
* krx-spot-cash\_payout\_ratio: Cash dividend payout ratio of the Korean stock market
* krx-spot-cashflow\_from\_financial\_activities: Cash flow information resulting from financial activities of Korean stocks
* krx-spot-cashflow\_from\_investing\_activities: Cash flow information resulting from investment activities in Korean stocks
* krx-spot-cashflow\_from\_operating\_activities: Cash flow information from operating activities of Korean stocks
* krx-spot-cf\_depreciation: Information on depreciation expenses in the cash flow statement of Korean stocks
* krx-spot-dividends\_paid: Information on dividend payments for Korean stocks
* krx-spot-fcf1: Information on Free Cash Flow (FCF1) of Korean stocks
* krx-spot-fcf2: Information on Free Cash Flow (FCF2) of Korean stocks
Ratio
* krx-spot-adj\_bps: Adjusted Book Value per Share of Korean Stocks
* krx-spot-enterprise\_value: Information on the corporate value of Korean stocks
* krx-spot-eps: Earnings per Share of Korean Stocks
* krx-spot-ex\_dividend\_date: Ex-dividend date of the Korean stock market
* krx-spot-fiscal\_end: Information on the fiscal year-end date of Korean stocks
* krx-spot-payout\_ratio: Dividend policy of the Korean stock market
* krx-spot-pretax\_income: Pre-tax profit of Korean stocks
* krx-spot-profit\_from\_continuing\_operations: Information on continuing operating profit of Korean stocks
* krx-spot-roic: Return on Invested Capital (ROIC) in the Korean stock market
Dividend
* krx-spot-dividend: Dividend information of Korean stocks
* krx-spot-dividend\_comm\_cash: Cash dividends on common stocks in Korea
* krx-spot-dividend\_pref\_cash: Cash dividends on preferred stocks in South Korea
* krx-spot-dividend\_all-annual: Cash dividends on all(common and preferred) stocks - Annual Only
{% hint style="warning" %}
Note
* Unit : 1 thousand
* If you append "-annual" to an item name, it returns annual data.
{% endhint %}
### market
fund\_center
Summary
This document provides an overview of the Korean fund data available in the data catalog, including various metrics related to fund performance and characteristics.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-fund-nav")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19800101 | 30 10 \* \* 1-5 | | |
Item List
Fund Performance Metrics
* **krx-fund-nav**: Net Asset Value of Korean Funds
* **krx-fund-return**: The return rate of Korean funds
* **krx-fund-excess\_return**: Excess return of Korean funds
* **krx-fund-sharpe**: Sharpe ratio of Korean funds
Fund Characteristics
* **krx-fund-base\_tax\_price**: Taxation benchmark price of Korean funds
* **krx-fund-duration**: Duration of Korean funds
* **krx-fund-fund\_grade**: Rating of Korean Funds
* **krx-fund-preserve**: Principal preservation rate of Korean funds
* **krx-fund-aum**: The size of managed assets in Korean funds
* **krx-fund-base\_price**: The benchmark price of Korean funds price\_volume
Summary
This document provides an overview of the price and volume data available for Korean stocks, including various metrics related to trading and market capitalization.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("price_close")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
Price Data
* **price\_close**: Closing price of Korean stocks
* **price\_open**: The market price of Korean stocks
* **price\_high**: High price of Korean stocks
* **price\_low**: Low prices of Korean stocks
* **price\_base**: The reference price of Korean stocks
Volume Data
* **turnover**: Trading volume of Korean stocks
* **turnover\_all**: Total trading volume of Korean stocks
* **short\_selling\_volume**: Short selling trading volume of Korean stocks
* **short\_selling\_turnover**: Short selling transaction volume of Korean stocks
* **slb\_balance\_volume**: The balance of margin trading in Korean stocks
* **slb\_repay**: Repayment volume of securities lending in the Korean stock market
* **slb\_new\_loan**: New lending volume of securities lending in the Korean stock market
* **volume\_sum**: Trading volume of Korean stocks
Market Capitalization Data
* **mkt\_cap**: Market capitalization of Korean stocks
* **mkt\_cap\_all**: Total market capitalization of Korean stocks
* **mkt\_cap\_index**: Market capitalization index of Korean stocks
* **foreigner\_shares\_ratio**: Foreign ownership ratio of Korean stocks
* **foreigner\_shares**: Number of shares held by foreigners in Korean stocks
* **listed\_shares**: The number of listed shares in the Korean stock market
* **listed\_shares\_to\_be**: Expected number of listed shares in the Korean stock market
* **free\_float**: The number of floating shares in Korean stocks
Margin Trading Data
* **slb\_balance\_amount**: The balance amount of margin trading in Korean stocks cax
Summary
This document provides information on the Korean stock data catalog, specifically focusing on adjustment and dividend factors.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("adjust_factor")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
Adjustment Factors
* **adjust\_factor**: Adjustment factor of Korean stocks
* **dividend\_factor**: Dividend yield of Korean stocks investor\_activity
Summary
This document provides an overview of investor activity data in the Korean stock market, including daily buying and selling amounts segmented by investor type and sector.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("buy_amt_0400")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | Daily |
Item List
Selling Amounts
* **sell\_amt\_0101**: Selling amount of stocks by the institutions
* **sell\_amt\_0102**: Selling amount of stocks by the insurance sector
* **sell\_amt\_0103**: Selling amount of stocks by the investment trust sector
* **sell\_amt\_0104**: Selling amount of stocks by the banking sector
* **sell\_amt\_0105**: Selling amount of stocks by the merchant and savings banks sector
* **sell\_amt\_0106**: Selling amount of stocks by the pension funds and mutual aid associations sector
* **sell\_amt\_0107**: Selling amount of stocks by the private equity funds sector
* **sell\_amt\_0200**: Selling amount of stocks by the aggregated foreign sectors
* **sell\_amt\_0201**: Selling amount of stocks by the foreign investor sector
* **sell\_amt\_0202**: Selling amount of stocks by the other foreign investor sector
* **sell\_amt\_0300**: Selling amount of stocks by the individual sector
* **sell\_amt\_0400**: Selling amount of stocks by the other corporations and government/local authorities sector
* **sell\_amt\_0900**: Total selling amount of stocks by all investors
Buying Amounts
* **buy\_amt\_0101**: Buying amount of stocks by the institutions\&x20;
* **buy\_amt\_0102**: Buying amount of stocks by the insurance sector
* **buy\_amt\_0103**: Buying amount of stocks by the investment trust sector
* **buy\_amt\_0104**: Buying amount of stocks by the banking sector
* **buy\_amt\_0105**: Buying amount of stocks by the merchant and savings banks sector
* **buy\_amt\_0106**: Buying amount of stocks by the pension funds and mutual aid associations sector
* **buy\_amt\_0107**: Buying amount of stocks by the private equity funds sector
* **buy\_amt\_0200**: Buying amount of stocks by the aggregated foreign sectors
* **buy\_amt\_0201**: Buying amount of stocks by the foreign investor sector
* **buy\_amt\_0202**: Buying amount of stocks by the other foreign investor sector
* **buy\_amt\_0300**: Buying amount of stocks by the individual sector
* **buy\_amt\_0400**: Buying amount of stocks by the other corporations and government/local authorities sector
* **buy\_amt\_0900**: Total buying amount of stocks by all investors
Net Buying Amounts
* **net\_buy\_amt\_0101**: Net buying amount of stocks by the institutions\&x20;
* **net\_buy\_amt\_0102**: Net buying amount of stocks by the insurance sector
* **net\_buy\_amt\_0103**: Net buying amount of stocks by the investment trust sector
* **net\_buy\_amt\_0104**: Net buying amount of stocks by the banking sector
* **net\_buy\_amt\_0105**: Net buying amount of stocks by the merchant and savings banks sector
* **net\_buy\_amt\_0106**: Net buying amount of stocks by the pension funds and mutual aid associations sector
* **net\_buy\_amt\_0107**: Net buying amount of stocks by the private equity funds sector
* **net\_buy\_amt\_0200**: Net buying amount of stocks by the aggregated foreign sectors
* **net\_buy\_amt\_0201**: Net buying amount of stocks by the foreign investor sector
* **net\_buy\_amt\_0202**: Net buying amount of stocks by the other foreign investor sector
* **net\_buy\_amt\_0300**: Net buying amount of stocks by the individual sector
* **net\_buy\_amt\_0400**: Net buying amount of stocks by the other corporations and government/local authorities sector
* **net\_buy\_amt\_0900**: Total net buying amount of stocks by all investors
{% hint style="info" %}
Investor Codes:
* 0100: Institutions (Total)
* 0101: Securities & Futures
* 0102: Insurance
* 0103: Investment Trusts
* 0104: Banks
* 0105: Merchant & Savings Banks
* 0106: Pension Funds & Mutual Aid Associations
* 0107: Private Equity Funds
* 0200: Foreigners (Total)
* 0201: Foreign Individuals/Institutions
* 0202: Other Foreigners
* 0300: Individuals
* 0400: Other Corporations & Government/Local Authorities
* 0900: Total
{% endhint %}
Summary This document provides information on the KODEX ETFs available in the KR stock universe, including their weights and metadata.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("kodex200")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 2002-10-14 | 30 00 \* \* 2-6 | | |
Item List
* **kodex200**: KODEX200 ETF Weights
* **kodex\_kosdaq150**: KODEX KOSDAQ 150 ETF Weightsstatus
Summary
This document provides an overview of the status indicators for the Korean stock market, including various metrics related to stock performance and market conditions.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("overheating")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | Daily |
Item List
Market Status Indicators
* overheating: Indicators of overheating in the Korean stock market
* unreliable: Indicator of the lack of reliability in Korean stocks
* illiquid: Indicators of liquidity shortage in the Korean stock market
* abnormal: Indicators of abnormal conditions in the Korean stock market
* alert: Warning status indicator of Korean stocks
* caution: Daily information on stocks of interest in the Korean stock market
Stock Management Information
* administration: Daily information on managed stocks in the Korean stock market
* liquidation: Daily information on liquidation stocks in the Korean stock market
* suspension: Daily information on suspended stocks in the Korean stock market
* borrowing: Loan status indicators of Korean stocks
* list\_yn: Indicator of the listing status of Korean stocks
* market: Daily information on the status of the Korean stock market credit
Summary
This document provides an overview of the credit data items available in the Korean stock market, including their descriptions and usage examples.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("fin_bal_cnt")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 40 23 \* \* 0-4 | | Daily |
Item List
Margin Trading Data
* **fin\_bal\_cnt**: Daily margin trading balance count in the Korean stock market
* **fin\_pay\_cnt**: Daily margin trading repayment count in the Korean stock market
* **fin\_pay\_amt**: Daily margin repayment amount in the Korean stock market
* **fin\_bal\_amt**: Daily margin trading balance amount in the Korean stock market
* **fin\_new\_cnt**: Daily new margin trading cases in the Korean stock market
* **fin\_new\_amt**: Daily new margin trading amount in the Korean stock market
* **fin\_bal\_rt**: Daily margin trading balance rate of the Korean stock market
* **fin\_giv\_rt**: Daily margin lending rate of the Korean stock market
Short Selling Data
* **lend\_new\_cnt**: Daily new short selling cases in the Korean stock market
* **lend\_new\_amt**: Daily new amount of large shareholders in the Korean stock market
* **lend\_pay\_cnt**: Daily short selling repayment count in the Korean stock market
* **lend\_pay\_amt**: Daily short selling repayment amount in the Korean stock market
* **lend\_bal\_cnt**: Daily short selling balance in the Korean stock market
* **lend\_bal\_amt**: Daily short selling balance amount in the Korean stock market
* **lend\_bal\_rt**: Daily short selling balance ratio of the Korean stock market
* **lend\_giv\_rt**: Daily lending rate of major stocks in the Korean stock market calendar
Summary
This data catalog provides information on expiration dates for futures and spread maturity in the Korean stock market.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("expiry_dates-spread")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
Expiration Dates
* **expiry\_dates-spread**: Spread maturity date information
* **expiry\_dates-future**: Futures expiration date information capital
Summary
This document provides information about the listing date of SPAC mergers in the Korean stock market.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-listed_date_of_merger_spac")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
* **krx-spot-listed\_date\_of\_merger\_spac**: Listing date of SPAC mergers in the Korean stock market
### edge
narr
Summary
The data consists of summaries of major English business news and key news, categorized by topic. Each item includes a description that highlights the themes covered, with examples of topics such as 'edge', 'lee', and 'mortality'.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("topenbiznews")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20100101 | 00 6 \* \* \* | | 1m |
Item List
* **topenbiznews**: Summary of major English business news; columns are topic. e.g. 0:\['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len']
* **topnews**: Summary of Key News; columns are topic. e.g. 0:\['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len'] disclosure
Summary
This document provides an overview of the disclosure data related to KOSDAQ and KOSPI listed companies, including various financial metrics and corporate actions.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("buyback_trust", unpivot=True)
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20100101 | 00 17 \* \* \* | | |
Item List
Corporate Actions
* **buyback** = report on decision to acquire treasury shares
* **buyback\_trust** = report on decision to sign a trust agreement for acquiring treasury shares
* **buyback\_disposal** = report on decision to dispose of treasury shares
* **bonus\_issue** = report on decision for bonus (free) issue
* **rights\_issue** = report on decision for rights issue (paid-in capital increase)
* **br\_issue** = report on decision for combined paid-in/bonus issue
* **reduction** = report on decision for capital reduction
* **cb** = report on decision to issue convertible bonds
* **eb** = report on decision to issue exchangeable bonds
* **bw** = report on decision to issue bonds with warrants
* **coco** = report on decision to issue contingent convertible bonds
* **merge** = report on decision for company merger
* **division** = report on decision for company division
* **division\_merge** = report on decision for divisional merger
* **major\_stock** = report on ownership status of securities by executives or major shareholder theme
Summary
This document provides an overview of the Korean stock theme data catalog, including various metrics and scores related to Korean stock companies and themes.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("theme-z_score")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20180101 | 10 22 \* \* \* | | |
Item List
Theme Metrics
* **theme-z\_score**: Z-score of the Korean stock theme
* **theme-ma**: Moving average of the Korean stock theme
* **theme-score**: Score of Korean stock themes
Business Metrics
* **business-ma**: Business moving average of Korean stock companies
* **business-score**: Business score of Korean stock companies
* **business-z\_score**: Business Z-score of Korean stock companies
Company Keyword Metrics
* **company-keyword\_last\_upt\_dt**: Final update date of keyword information for Korean stock companies
* **company-keyword\_score**: Keyword-related scores of Korean stock companies
* **company-keyword\_total\_sales**: Total revenue related to keywords of Korean stock companies
* **company-keyword\_sales\_ratio**: Revenue ratio related to keywords of Korean stock companies
* **company-keyword\_pearson**: Daily keyword Pearson correlation coefficient of Korean stocks
Item Metrics
* **item-z\_score**: Z-score of Korean stock items
* **item-score**: Score of Korean stock items
* **item-ma**: Moving average of Korean stock items ews
Summary
The data consists of various sentiment and early warning system indicators for the Korean stock market, including daily sentiment indices, principal component analysis indicators, and multiple versions of weekly and monthly early warning system indicators. These indicators are designed to provide insights into market sentiment and potential future trends, with some specifically comparing the Korean market to the S\&P 500.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("korea_tone_SP500")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ---------------------------------------------------------------------------- | --------- | -------------- |
| 20050102 | 30 21 \* \* \*
30 23 \* \* \*
30 22 \* \* \*
30 21 \* \* 0-4
| | 1d, 1w, 1m |
Item List
* **korea\_EWS**: Monthly early warning system indicators for the Korean stock market
* **korea\_EWS**: Weekly early warning system indicators for the Korean stock market
* **korea\_EWS\_003**: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003)
* **korea\_EWS\_003\_dcut**: Weekly early warning system indicators for the Korean stock market (Version 003, data cutoff applied)
* **korea\_EWS\_003\_inferenceonly**: Weekly early warning system indicators for the Korean stock market (Version 003, inference only applied)
* **korea\_EWS\_003\_inferenceonly\_dcut**: Weekly early warning system indicators for the Korean stock market (Version 003, inference only applied, data cutoff)
* **korea\_EWS\_003\_sp1**: Weekly Early Warning System Indicator for the Korean Stock Market (Version 003, Special Variation 1)
* **korea\_EWS\_003\_sp1\_dcut**: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 1, Data Cutoff)
* **korea\_EWS\_003\_sp2**: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003, Special Variation 2)
* **korea\_EWS\_003\_sp2\_dcut**: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 2, Data Cutoff)
* **korea\_EWS\_003\_sp3**: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003, Special Variant 3)
* **korea\_EWS\_003\_sp3\_dcut**: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 3, Data Cutoff)
* **korea\_EWS\_003\_sp4**: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003, Special Variant 4)
* **korea\_EWS\_003\_sp4\_dcut**: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 4, Data Cutoff)
* **korea\_EWS\_SP500**: Monthly early warning system indicators for the Korean stock market compared to the S\&P 500
* **korea\_tone**: Daily Sentiment Index for the Korean Stock Market
* **korea\_tone\_SP500**: Daily sentiment index of the Korean stock market compared to the S\&P 500
* **korea\_tone\_neutral**: Daily Neutral Sentiment Index for the Korean Stock Market
* **pca\_proxy**: Daily principal component analysis proxy indicator for the Korean stock market
* **pca\_sentiment**: Daily sentiment indicators based on principal component analysis of the Korean stock market keyword
Summary
This data catalog provides daily keyword trends in the Korean stock market.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-trend")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20160101 | 20 12 \* \* \* | | Daily |
Item List
* **krx-spot-trend**: Daily keyword trends in the Korean stock market emp\_exc
Summary
This data catalog provides information on employee and executive demographics within the Korean stock universe, focusing on gender distribution and salary details.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("exc_tot_woman")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20171113 | 20 18 \* \* 5 | | |
Item List
Executive Data
* **exc\_tot\_woman**: Total number of female executives
* **exc\_tot\_man**: Total number of male executives
* **exc\_tot\_inside\_director**: Total number of internal directors
* **exc\_tot\_outside\_director**: Total number of outside directors
* **exc\_eq\_ceo\_chairman**: Whether the CEO and the Chairman of the Board are the same person
Employee Data
Female Employees
* **emp\_woman\_fulltime**: Number of female regular employees
* **emp\_woman\_parttime**: Number of female non-regular employees
* **emp\_woman\_avg\_year\_of\_service**: Average tenure of female employees
* **emp\_woman\_tot\_year\_salary**: Total annual salary of female employees
Male Employees
* **emp\_man\_fulltime**: Number of male full-time employees
* **emp\_man\_parttime**: Number of male non-regular employees
* **emp\_man\_avg\_year\_of\_service**: Average tenure of male employees
* **emp\_man\_tot\_year\_salary**: Annual total salary of male employees
Data Format
Report Value Table Format
* **Index**: `pit date` (Point in time; Data feeding date)
* **Columns**: Korean stock ticker
* **Values**:\
`{ticker}`: item value
**Index Mapping**
* `idx_r` (Report Type Code):
* `11013`: Q1 Report
* `11012`: Semiannual Report
* `11014`: Q3 Report
* `11011`: Annual Report
* `idx_y` (Report Year):
* The year of the report
### factor
fundamental\_factor
Summary
This document provides a comprehensive overview of various financial metrics and factors related to Korean stocks, including investment strategies and performance indicators.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("kr-gross_profit_to_assets")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
Investment Strategy Factors
* **kr-glenn\_greenberg**: Factors of Korean stocks based on Glen Greenberg's investment strategy
* **kr-cathie\_wood**: Factors of Korean stocks based on Cathie Wood's investment strategy
* **kr-james\_oshaughnessy**: Factors of Korean stocks based on James O'Shaughnessy's investment strategy
* **kr-benjamin\_graham**: Factors of Korean stocks based on Benjamin Graham's investment strategy
* **kr-alex\_sacerdote**: Factors of Korean stocks based on Alex Sasserdo's investment strategy
* **kr-ron\_baron**: Factors of Korean stocks based on Ron Baron’s investment strategy
* **kr-warren\_buffet**: Factors of Korean stocks based on Warren Buffett's investment strategy
* **kr-bill\_ackman**: Factors of Korean stocks based on Bill Ackman's investment strategy
* **kr-david\_dreman**: Korean stock factors based on David Dreman's investment strategy
* **kr-charles\_munger**: Factors of Korean stocks based on Charles Munger's investment strategy
* **kr-brad\_gerstner**: Korean stock factors based on Brad Gerstner's investment strategy
* **kr-colin\_moran**: Korean stock factors based on Colin Moran's investment strategy
* **kr-william\_oneil**: Korean stock factors based on William O'Neil's investment strategy
Profitability Metrics
* **kr-gross\_profit\_to\_assets**: The ratio of gross profit to total assets of Korean stocks
* **kr-op\_12mf\_3m**: The 3-month change rate of the 12-month forecast of operating profit for Korean stocks
* **kr-op\_fy1\_1m**: The one-month change rate of the operating profit forecast for Korean stocks one year later
* **kr-op\_yoy**: The growth rate of operating profit of Korean stocks compared to the previous year
* **kr-return\_on\_asset\_4q**: The cumulative return on total assets for Korean stocks in the fourth quarter
* **kr-return\_on\_equity\_4q**: Cumulative return on equity for Korean stocks in the fourth quarter
* **kr-net\_profit\_margin\_before\_XI**: Net profit margin excluding special items for Korean stocks
* **kr-operating\_profit\_margin\_after\_DP**: Operating profit margin after depreciation of Korean stocks
Valuation Ratios
* **kr-p\_fcf\_24mf**: Expected price/free cash flow ratio of Korean stocks over the next 24 months
* **kr-p\_fcf\_12mf**: 12-month forward P/FCF of Korean stocks
* **kr-pbr\_12mf**: 12-month forward PBR of Korean stocks
* **kr-pcr\_12mf**: 12-month forward PCR of Korean stocks
* **kr-ev\_ebitda\_12mf**: 12-month forward EV/EBITDA of Korean stocks
* **kr-ev\_ebitda\_24mf**: 24-month forward EV/EBITDA of Korean stocks
Investment Ratios
* **kr-dividend\_yield\_fy1**: The dividend yield for Korean stocks in the next fiscal year
* **kr-dividend\_yield\_fy0**: Dividend yield for the current fiscal year of Korean stocks
* **kr-payout\_ratio\_3y**: The 3-year dividend payout ratio of Korean stocks
* **kr-payout\_ratio\_4q**: Dividend payout ratio of Korean stocks in the fourth quarter
Market Performance Metrics
* **kr-sales\_fy1\_3m**: The 3-month change rate of the 1-year revenue forecast for Korean stocks
* **kr-sales\_fy2\_3m**: The 3-month change rate of the 2-year revenue forecast for Korean stocks
* **kr-sales\_yoy**: The revenue growth rate of Korean stocks compared to the previous year
* **kr-sales\_12mf\_3m**: The 3-month change rate of the 12-month forward revenue forecast for Korean stocks
Investment Activity Metrics
* **kr-net\_invest\_ratio\_20d**: 20-day net investment ratio of Korean stocks
* **kr-net\_invest\_ratio\_10d**: 10-day net investment ratio of Korean stocks
* **kr-net\_invest\_sum\_10d**: Total net purchases by investor type in the Korean stock market over the past 10 days
* **kr-net\_invest\_sum\_20d**: Total net purchases by investor type in the Korean stock market over the past 20 days
Risk Metrics
* **kr-beta\_756d**: 756-day beta of Korean stocks
* **kr-beta\_1260d**: 1260-day beta of Korean stocks
* **kr-beta\_yield\_10y\_60m**: 60-month beta regarding the 10-year government bond yield of Korean stocks
* **kr-beta\_yield\_5y\_60m**: 60-month beta related to the 5-year government bond yield of Korean stocks
Other Metrics
* **kr-interest\_coverage\_ratio**: Interest Coverage Ratio of Korean Stocks
* **kr-leverage\_ratio**: Debt ratio of Korean stocks
* **kr-asset\_turnover**: Asset turnover ratio of Korean stocks
* **kr-capex\_growth\_12mf**: 12-month leading capital investment growth rate of Korean stocks
* **kr-capex\_to\_assets**: The ratio of capital expenditures to total assets of Korean stocks
This structure provides a clear and organized way to navigate through the various metrics available for Korean stocks, making it easier for users to find the information they need. descriptor
Summary
This document provides an overview of various financial metrics related to Korean stocks, including their definitions and usage.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("operating-profits-to-assets")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
Company Age Metrics
* **age-found**: The number of years elapsed since the establishment of a company in the Korean stock market.
* **age-listed**: Years elapsed since the listing of Korean stocks.
Profitability Metrics
* **operating-profits-to-assets**: Operating profit margin relative to assets of Korean stocks.
* **profit-margin**: Profitability of Korean stocks.
* **return-on-assets**: Return on Assets (ROA) of Korean stocks.
* **return-on-equity**: Return on Equity (ROE) of Korean stocks.
* **operating-profits-lagged-assets**: Operating profit compared to delayed assets of Korean stocks.
* **operating-profits-lagged-equity**: Operating profit relative to delayed equity in Korean stocks.
* **operating-cashflow-to-price**: Operating cash flow to stock price ratio of Korean stocks.
* **cash-based-operating-profits-to-assets**: Cash-based operating profit ratio relative to assets of Korean stocks.
Investment Metrics
* **change-short-term-investments**: Changes in short-term investment assets of Korean stocks.
* **change-long-term-investments**: Changes in long-term investment assets in Korean stocks.
* **investment-to-assets**: The ratio of total assets to investment in Korean stocks.
* **investment-growth-1-year**: 1-Year Investment Growth Rate of Korean Stocks.
* **investment-growth-2-year**: 2-Year Investment Growth Rate of Korean Stocks.
* **investment-growth-3-year**: 3-Year Investment Growth Rate of Korean Stocks.
Asset and Market Metrics
* **gross-profits-to-assets**: Total return on assets ratio of Korean stocks.
* **assets-to-market**: Asset to Market Value Ratio of Korean Stocks.
* **asset-liquidity-to-assets**: The ratio of total assets to asset liquidity of Korean stocks.
* **asset-liquidity-to-market**: Market capitalization ratio compared to asset liquidity of Korean stocks.
Volatility and Risk Metrics
* **total-volatility**: Total volatility of Korean stocks.
* **idiosyncratic-volatility-per-capm**: Idiosyncratic volatility based on the CAPM model of Korean stocks.
* **idiosyncratic-volatility-per-ff3**: Idiosyncratic volatility based on the Fama-French three-factor model of Korean stocks.
* **market-beta**: Market beta of Korean stocks.
* **frazzini-pedersen-beta**: Fama-French Beta of Korean Stocks.
Financial Ratios
* **debt-to-market**: Debt to Market Value Ratio of Korean Stocks.
* **book-to-market**: The ratio of book value to market value of Korean stocks.
* **book-leverage**: Book leverage of Korean stocks.
* **capital-turnover**: Capital turnover rate of Korean stocks.
* **sales-to-price**: Sales to Price Ratio of Korean Stocks.
Growth and Change Metrics
* **sales-growth**: Revenue growth rate of Korean stocks.
* **sales-growth-quarter**: Quarterly revenue growth rate of Korean stocks.
* **change-return-on-assets**: Changes in the quarterly return on assets (ROA) of Korean stocks.
* **change-return-on-equity**: Changes in the Return on Equity (ROE) of Korean stocks.
Other Metrics
* **ohlson-o-score**: Ohlson O-Score of Korean stocks (bankruptcy probability indicator).
* **altman-z-score**: Altman Z-Score of Korean Stocks (Financial Health Indicator).
* **fundamental-score**: Basic score of Korean stocks.
* **enterprise-multiple**: Valuation multiples of Korean stocks.
* **maximum-five-daily-return**: The maximum daily return of Korean stocks over 5 days.
* **maximum-ten-daily-return**: The maximum daily return of Korean stocks over 10 days.
* **52-week-high**: 52-week high of Korean stocks.
### analysis
consensus
Summary
This data catalog provides consensus data for Korean stocks, including earnings per share (EPS), net profit, cash flows, and other financial metrics.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-eps_q1_up")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | |
Item List
EPS and Earnings Consensus
* **krx-spot-eps2\_up**: Adjustment of EPS2 for Korean stocks
* **krx-spot-eps\_q2\_revision\_ratio\_1m**: Consensus on the 1-month 2nd quarter EPS revision ratio for Korean stocks
* **krx-spot-eps2\_down**: Adjustment of EPS2 downward for Korean stocks
* **krx-spot-new\_eps\_q1**: New Q1 EPS forecast for Korean stocks
* **krx-spot-new\_eps1**: New EPS1 consensus for Korean stocks
* **krx-spot-eps\_q2\_down**: Downgraded consensus for Q2 EPS of Korean stocks
* **krx-spot-eps1\_down**: Downward consensus on EPS1 of Korean stocks
* **krx-spot-eps2\_stay**: The maintenance of EPS2 for Korean stocks
* **krx-spot-eps\_q1\_up**: Upward consensus on Q1 EPS of Korean stocks
* **krx-spot-new\_eps2**: New EPS2 forecast for Korean stocks
* **krx-spot-eps\_q2\_up**: Upward consensus on Q2 EPS of Korean stocks
* **krx-spot-eps\_q1\_stay**: Consensus for maintaining Q1 EPS of Korean stocks
* **krx-spot-eps\_q2\_stay**: Consensus on maintaining Q2 EPS for Korean stocks
* **krx-spot-eps1\_revision\_ratio\_3m**: Consensus on the 3-month EPS1 adjustment ratio for Korean stocks
* **krx-spot-eps\_q1\_revision\_ratio\_1m**: Consensus on the EPS adjustment ratio for Korean stocks for the 1-month 1st quarter
* **krx-spot-eps\_q1\_revision\_ratio\_3m**: The 3-month revision rate of the Q1 EPS forecast for Korean stocks
* **krx-spot-eps2\_revision\_ratio\_1m**: Consensus on the 1-month EPS2 adjustment ratio for Korean stocks
* **krx-spot-eps2\_revision\_ratio\_3m**: Consensus on the 3-month EPS2 adjustment ratio for Korean stocks
* **krx-spot-changed\_ratio\_in\_eps2**: The change rate of EPS2 in Korean stocks
* **krx-spot-changed\_ratio\_in\_eps1**: The change rate of EPS1 in Korean stocks
Financial Metrics
* **krx-spot-net\_debt\_a**: Net debt of Korean stocks
* **krx-spot-financing\_cash\_flows\_a**: Annual financial cash flow consensus of Korean stocks
* **krx-spot-investing\_cash\_flows\_a**: Consensus on annual investment cash flow of Korean stocks
* **krx-spot-operating\_profit\_a**: Annual operating profit consensus of Korean stocks
* **krx-spot-operating\_profit\_q**: Consensus on quarterly operating profit of Korean stocks
* **krx-spot-net\_profit\_a**: Annual net profit consensus of Korean stocks
* **krx-spot-net\_profit\_q**: Consensus on quarterly net profit of Korean stocks
* **krx-spot-sales\_a**: Annual revenue consensus of Korean stocks
* **krx-spot-sales\_q**: Quarterly revenue consensus of Korean stocks
* **krx-spot-ebitda\_a**: Annual EBITDA consensus of Korean stocks
* **krx-spot-evebitda\_a**: Annual EV/EBITDA consensus of Korean stocks
* **krx-spot-fcf\_a**: Consensus on annual free cash flow of Korean stocks
* **krx-spot-capex\_a**: Annual capital expenditure consensus for Korean stocks
* **krx-spot-total\_assets\_a**: Annual total asset consensus of Korean stocks
* **krx-spot-owners\_of\_parent\_equity\_a**: Annual controlling shareholder equity consensus of Korean stocks
* **krx-spot-owners\_of\_parent\_net\_profit\_a**: Annual controlling shareholder net income consensus of Korean stocks
* **krx-spot-owners\_of\_parent\_net\_profit\_q**: Consensus on quarterly controlling shareholder net income of Korean stocks
Dividends and Ratings
* **krx-spot-cash\_dividend\_a**: Annual cash dividend consensus of Korean stocks
* **krx-spot-dividend\_yield\_a**: Consensus on the annual dividend yield of Korean stocks
* **krx-spot-rating**: Consensus on investment opinions for Korean stocks
* **krx-spot-rating\_revision\_ratio\_1m**: Consensus on the revision rate of investment opinions for Korean stocks over one month
* **krx-spot-tp**: Consensus target price for Korean stocks
* **krx-spot-tp\_revision\_ratio\_1m**: Consensus on the revision ratio of the 1-month target price for Korean stocks
* **krx-spot-disparate\_ratio\_tp**: Consensus on the target price deviation rate of Korean stocks
Fiscal Information
* **krx-spot-fiscal**: Information on the fiscal year of Korean stocks somemoney
Summary
The data consists of a collection of stock recommendations categorized under 'somemoney', focusing on various segments of the KOSDAQ and KOSPI markets, including small-cap, mid-cap, and large-cap stocks. Each recommendation is described as a new approach or a specific category, indicating a targeted strategy for investment in these stock markets.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("recommend-n-kosdaq_s1")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20100101 | 30 14 \* \* 1-5 | | 1d |
Item List
* **recommend-kosdaq\_l1**: Recommendation for large-cap stocks in KOSDAQ
* **recommend-kosdaq\_m1**: Recommendation for mid-cap stocks on KOSDAQ
* **recommend-kospi\_l1**: Recommendation for KOSPI Large-cap Stocks
* **recommend-kospi\_m1**: Recommendation for KOSPI mid-cap stocks
* **recommend-n-kosdaq\_l1**: Recommendation for KOSDAQ Large-cap Stocks (New Approach)
* **recommend-n-kosdaq\_m1**: Recommendation for KOSDAQ mid-cap stocks (new approach)
* **recommend-n-kosdaq\_s1**: Recommendation for KOSDAQ small-cap stocks (new approach)
* **recommend-n-kospi\_l1**: Recommendation for KOSPI Large-cap Stocks (New Approach)
* **recommend-n-kospi\_m1**: Recommendation for KOSPI Mid-Cap Stocks (New Approach)
### macro
marketregime
Summary
This document provides an overview of market regime data items available for the Korean stock market, including economic cycle indicators and analyses based on Hidden Markov Models (HMM).
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("hmm")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20110103 | 5 17 \* \* 1-5 | | |
| 20110103 | 0 21 \* \* 1-5 | | |
| 20110103 | 30 10 \* \* \* | | |
| 20110103 | 30 2 \* \* 2-6 | | |
| 20110103 | 30 21 \* \* 0-4 | | Monthly |
| 20110103 | 30 10 \* \* \* | | |
Item List
OECD Economic Cycle Indicators
* **oecd\_world**: OECD Global Economic Cycle Indicators
* **oecd\_china**: OECD China Economic Cycle Indicators
* **oecd\_usa**: OECD U.S. Economic Cycle Indicators
HMM-Based Market Regime Analysis
* **hmm**: Analysis of the market system based on HMM (Hidden Markov Model) in the Korean stock market
* **hmm\_wo\_krx\_etf**: Analysis of market system based on HMM excluding Korean ETFs
* **hmm\_wo\_us\_etf**: Analysis of market system based on HMM excluding US ETFs
* **hmm\_sp500**: HMM-based market regime analysis of the S\&P 500 index
* **hmm\_wo\_krx\_etf (Monthly)**: Analysis of HMM-based market system excluding Korean ETFs (Monthly)
KOSPI 200 Market Status
* **k200\_ms4**: Analysis of the 4-stage market status of the KOSPI 200 index economy
Summary
This document provides information on various economic data items related to the Korean economy, including currency exchange rates, interest rates, and economic indices.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("currency")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 20 14 \* \* 1-5 | | Daily |
Item List
Currency Data
* **currency**: Daily exchange rate information related to the Korean economy
* **interest\_rate**: Daily interest rate information related to the Korean economy
Index Data
* **index\_close**: Daily closing prices of major economic indices in Korea
* **index\_mkt\_cap**: Daily market capitalization of major economic indices in Korea
## KR ETF
### market
Summary This document provides information about the cumulative fundflow data for KR ETFs.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("kr_etf", 20200101, 20200201)
df = cf.get_df("fundflow_cumsum")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000104 | 00 16 \* \* 1-5 | | |
Item List
* **fundflow\_cumsum**: Cumulative fundflow for KR ETFs.
## US ETF
### market
price\_volume
Summary
This document provides an overview of the US ETF price and volume data items available in the data catalog.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("price_close")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19831230 | 30 5 \* \* 2-6 | | Daily |
Item List
Price Data
* **price\_open**: Daily opening price of US ETFs
* **price\_close**: Daily closing price of US ETFs
* **price\_high**: Daily high of US ETFs
* **price\_low**: Daily low of US ETFs
* **indicated\_annual\_dividend**: Annual expected dividends of U.S. ETFs
Volume Data
* **trading\_volume**: Daily trading volume of US ETFs
* **amount**: Trading volume of US ETFs
* **shares\_outstanding**: Daily issuance of shares for U.S. ETFs
Performance Metrics
* **eps**: Earnings Per Share (EPS) of US ETFs
* **adr\_ratio**: ADR ratio of US ETFs
* **ctoc**: Rate of change in closing price compared to the closing price of US ETFs
* **ctoc\_total**: Rate of change in closing price compared to the cumulative closing price of U.S. ETFs cax
Summary
This document provides information on U.S. ETF data items, including total return and adjustment factors.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("total_return_factor")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19840102 | 30 5 \* \* 2-6 | | |
Item List
Total Return Factors
* **total\_return\_factor**: Total return coefficient of U.S. ETFs
Adjustment Factors
* **adjust\_factor**: Adjustment factor of US ETFs Summary This data catalog provides information on cumulative fund flows for US ETFs.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("fundflow_cumsum")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000104 | 30 06 \* \* 2-6 | | |
Item List
* **fundflow\_cumsum**: Cumulative fundflow for US ETFs.
## US STOCK
### financial
financial
Summary
This dataset provides PIT (point-in-time) financial statements for US stocks, including various financial metrics and indicators. The data’s index indicates the date the data was included in the database.
Users can choose from 3 types of data frames by adjusting the ‘mode’ parameter:
1\. **Default behavior**: In this case, the values in the data frame become the most recent values at the point in time, not including the fiscal date.
2\. **Original**: In this case, the values in the data frame become dictionaries possibly including multiple keys and values. If you are considering pre-announced data revisions, this option can be a solution.
3\. **Unpivot**: In this case, there will be 4 columns (‘id’, ‘pit’, ‘fiscal’, ‘value’). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.
**Since the collection of PIT data started recently, data before 2023-08-09, is the same as the research data, not the PIT data.**
If you want to load quarterly period end cm,
This dataset provides 90 days delayed financial statements for US stocks, including various financial metrics and indicators.
Most US companies disclose their IR materials within 90 days after the end of the quarter, but for some that do not, there may be a look-ahead bias. Therefore, it is not recommended to use this data for modeling purposes, but rather for research purposes.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("pit-atq") pit cm
df = cf.get_df("atq") period end cm
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | ---------- | -------------- |
| 1982-01-31 | 30 5 \* \* 2-6 | US/Eastern | 1d |
Item List
Balance Sheet
**Current Assets**
**actq**: Current Assets - Total
**chq**: Cash
**cheq**: Cash and Short-Term Investments
**chsq**: Cash & Deposits - Segregated
**rstcheq**: Restricted Cash & Investments - Current
**ivstq**: Short-Term Investments- Total
**rectq**: Receivables - Total
**rectrq**: Receivables - Trade
**artfsq**: Accounts Receivable/Debtors - Total
**reccoq**: Receivables - Current - Other
**rectoq**: Receivables - Current Other incl Tax Refunds
**recdq**: Receivables - Estimated Doubtful
**recubq**: Unbilled Receivables
**invtq**: Inventories - Total
**invfgq**: Inventory - Finished Goods
**invwipq**: Inventory - Work in Process
**invrmq**: Inventory - Raw Materials
**invoq**: Inventory - Other
**rawmsmq**: Raw Materials, Supplies, and Merchandise
**xppq**: Prepaid Expenses & Accrued Income
**acoq**: Current Assets - Other - Total
**acoxq**: Other Current Assets - Sundry
**deracq**: Derivative Assets - Current
**billexceq**: Billings in Excess of Cost & Earnings
**ceiexbillq**: Cost & Earnings in Excess of Billings
**capcstq:** Capitalized Costs
**Non-Current Assets**
**ancq**: Non-Current Assets - Total
**ppentq**: Property Plant and Equipment - Total (Net)
**ppegtq**: Property, Plant and Equipment - Total (Gross) - Quarterly
**dpactq**: Depreciation, Depletion and Amortization (Accumulated)
**capsftq**: Capitalized Software
**ipq**: Investment Property
**retq**: Total RE Property
**dpacreq**: Accumulated Depreciation of RE Property
**altoq**: Other Long-term Assets
**aoq**: Assets - Other - Total
**aotq**: Assets- Other- Total
**deraltq**: Derivative Assets Long-Term
**rstcheltq**: Long-Term Restricted Cash & Investments
**Intangible Assets**
**intanq**: Intangible Assets - Total
**gdwlq**: Goodwill (net)
**intanoq**: Other Intangibles
**gdwlamq**: Amortization of Goodwill
**Investments**
**ivltq**: Total Long-term Investments
**ivptq**: Investments - Permanent - Total
**ivaeqq**: Investment and Advances - Equity
**ivaoq**: Investment and Advances - Other
**istq**: Investment Securities -Total
**esubq**: Equity in Earnings (I/S) - Unconsolidated Subsidiaries
**Other Assets & Totals**
**atq**: Assets - Total
**anoq**: Assets Netting & Other Adjustments
**aqpl1q**: Assets Level1 (Quoted Prices)
**aol2q**: Assets Level2 (Observable)
**aul3q**: Assets Level3 (Unobservable)
**lseq**: Liabilities and Shareholders' Equity - Total
Current Liabilities
lctq: Current Liabilities - Total
apq: Account Payable/Creditors - Trade
apoq: Accounts Payable - Other
dlcq: Debt in Current Liabilities
dd1q: Long Term Debt Due in One Year
txpq: Income Taxes Payable
accdq: Accrued Expenses & Deferred Income
drcq: Deferred Revenue - Current
derlcq: Derivative Liabilities- Current
lcoq: Current Liabilities - Other - Total
lcoxq: Current Liabilities - Other (Sundry)
npq: Notes Payable Quarterly
Long-Term Liabilities
lltq: Long-Term Liabilities (Total)
dlttq: Long-Term Debt - Total
drltq: Deferred Revenue - Long-term
derlltq: Derivative Liabilities Long-Term
txdbq: Deferred Taxes - Balance Sheet
txdbaq: Deferred Tax Asset - Long Term
txdbcaq: Current Deferred Tax Asset Quarterly
txdbclq: Current Deferred Tax Liability Quarterly
txditcq: Deferred Taxes and Investment Tax Credit
dcomq: Deferred Compensation
Debt
dlttq: Long-Term Debt - Total
dlcq: Debt in Current Liabilities
dd1q: Long Term Debt Due in One Year
Other Liabilities
ltq: Liabilities - Total
ltmibq: Liabilities - Total and Noncontrolling Interest
loq: Liabilities - Other
lsq: Liabilities - Other
loxdrq: Liabilities - Other - Excluding Deferred Revenue
lnoq: Liabilities Netting & Other Adjustments
lol2q: Liabilities Level2 (Observable)
lul3q: Liabilities Level3 (Unobservable)
cltq: Contingent Liabilities- Total
pvtq: Provisions - Total
pvoq: Provisions - Other (Net)
Common Stock & Share Capital
ceqq: Common/Ordinary Equity - Total
cstkq: Common/Ordinary Stock (Capital)
cstkcvq: Common Stock-Carrying Value
cshiq: Common Shares Issued
cshoq: Common Shares Outstanding
capsq: Capital Surplus/Share Premium Reserve
sctq: Total Share Capital
scoq: Share Capital - Other
Preferred Stock
pstkq: Preferred/Preference Stock (Capital) - Total
pstknq: Preferred/Preference Stock - Nonredeemable
pstkrq: Preferred/Preference Stock - Redeemable
prshoq: Redeem Pfd Shares Outs (000)
pnrshoq: Nonred Pfd Shares Outs (000)
dvpq: Dividends Preferred/Preference
esopctq: ESOP Obligation (Common) - Total
esopnrq: Preferred ESOP Obligation - Non-Redeemable
esoprq: Preferred ESOP Obligation - Redeemable
Retained Earnings & Reserves
req: Retained Earnings
reunaq: Unadjusted Retained Earnings
eqrtq: Equity Reserves - Total
eroq: Equity Reserves - Other
rvlrvq: Revaluation Reserve
rvutxq: Reserves - Untaxed
autxrq: Appropriations to Untaxed Reserves
unnpq: Unappropriated Net Profit (Shareholders' Equity)
Treasury Stock
tstkq: Treasury Stock - Total (All Capital)
tstknq: Treasury Stock - Number of Common Shares
cshopq: Total Shares Repurchased
prcraq: Repurchase Price - Average per share Quarter
Accumulated Other Comprehensive Income
rectaq: Accum Other Comp Inc - Cumulative Translation Adjustments
aociderglq: Accum Other Comp Inc - Derivatives Unrealized Gain/Loss
aocisecglq: Accum Other Comp Inc - Unreal G/L Ret Int in Sec Assets
aocipenq: Accum Other Comp Inc - Min Pension Liab Adj
aociotherq: Accum Other Comp Inc - Other Adjustments
msaq: Accum Other Comp Inc - Marketable Security Adjustments
transaq: Cumulative Translation Adjustment
acomincq: Accumulated Other Comprehensive Income (Loss)
Equity Totals
seqq: Stockholders Equity - Parent
teqq: Stockholders Equity - Total
mibtq: Noncontrolling Interests - Total - Balance Sheet
mibnq: Noncontrolling Interests - Nonredeemable - Balance Sheet
mibq: Noncontrolling Interest - Redeemable - Balance Sheet
Participation Rights Certificates
prcq: Participation Rights Certificates
Income Statement\&x20;
Revenue
revtq: Revenue - Total
saleq: Sales/Turnover (Net)
risq: Revenue/Income - Sundry
Operating Income
oiadpq: Operating Income After Depreciation
oibdpq: Operating Income Before Depreciation
gpq: Gross Profit (Loss)
piq: Pretax Income
Net Income
niq: Net Income (Loss)
ibq: Income Before Extraordinary Items
ibcomq: Income Before Extraordinary Items Available for Common
ibmiiq: Income before Extraordinary Items and Noncontrolling Interests
uniamiq: Net Income Before Extraordinary Items and after Noncontrolling Interest
miiq: Noncontrolling Interest (Income Account)
ibadjq: Income Before Extraordinary Items Adjusted for Common Stock Equivalents
ibadj12: Income Before Extra Items - Adj for Common Stock Equivalents - 12MM
Other Income & Discontinued Operations
doq: Discontinued Operations
xiq: Extraordinary Items
xidoq: Extraordinary Items and Discontinued Operations
nopiq: Non-Operating Income (Expense) - Total
nopioq: Other Non-Operating Inc/Expense
ditq: Dividend Income
fcaq: Foreign Exchange Income (Loss)
Cost of Goods Sold
cogsq: Cost of Goods Sold
Operating Expenses
xoprq: Operating Expense- Total
xoproq: Operating Expense - Other
xsgaq: Selling, General and Administrative Expenses
xagtq: Administrative and General Expense - Total
xrdq: Research and Development Expense
xstq: Staff Expense - Wages/Salaries
xstoq: Staff Expense - Other
xsq: Expense - Sundry
stkcoq: Stock Compensation Expense
Interest Expense
xintq: Interest and Related Expense- Total
tieq: Interest Expense - Total (Financial Services)
intcq: Interest Capitalized
intaccq: Interest Accrued
xoiq: Expenses - Other (Insurance)
Taxes
txtq: Income Taxes - Total
txdiq: Income Taxes - Deferred
txwq: Excise Taxes
Depreciation & Amortization
dpq: Depreciation and Amortization - Total
amq: Amortization of Intangibles
dfxaq: Depreciation of Fixed Assets (Tangible)
dpretq: Depr/Amort of Property
Total Expenses
xtq: Expense - Total
Earnings Per Share (EPS)
Note: Some data points may contain NaN or 0 values.
Basic EPS
epspxq: Earnings Per Share (Basic) Excluding Extraordinary Items
epspiq: Earnings Per Share (Basic) Including Extraordinary Items
epsx12: Earnings Per Share (Basic) Excluding Extraordinary Items 12 Months Moving
epspi12: Earnings Per Share (Basic) - Including Extraordinary Items - 12 Months Moving
opepsq: Earnings Per Share from Operations
oeps12: Earnings Per Share from Operations - 12 Months Moving
Diluted EPS
epsfxq: Earnings Per Share (Diluted) Excluding Extraordinary items
epsfiq: Earnings Per Share (Diluted) Including Extraordinary Items
epsf12: Earnings Per Share (Diluted) - Excluding Extraordinary Items - 12 Months Moving
epsfi12: Earnings Per Share (Diluted) - Including Extraordinary Items 12MM
oepsxq: Earnings Per Share Diluted from Operations
oepf12: Earnings Per Share - Diluted - from Operations - 12MM
Shares Outstanding
cshoq: Common Shares Outstanding
cshprq: Common Shares Used to Calculate Earnings Per Share - Basic
cshfdq: Com Shares for Diluted EPS
csh12q: Common Shares Used to Calculate Earnings Per Share - 12 Months Moving
cshfd12: Common Shares Used to Calc Earnings Per Share - Fully Diluted - 12 Months Moving
EPS Adjustments
diladq: Dilution Adjustment
dilavq: Dilution Available - Excluding Extraordinary Items
cstkeq: Common Stock Equivalents Dollar Savings
dvtq: Dividends - Total
Special Items & Adjustments
Note: Some data points may contain NaN or 0 values.
Restructuring & Acquisitions
rcpq: Restructuring Cost Pretax
rcaq: Restructuring Cost After-tax
rcdq: Restructuring Cost Diluted EPS Effect
rcepsq: Restructuring Cost Basic EPS Effect
aqpq: Acquisition/Merger Pretax
aqaq: Acquisition/Merger After-Tax
aqdq: Acquisition/Merger Diluted EPS Effect
aqepsq: Acquisition/Merger Basic EPS Effect
rrpq: Reversal - Restructruring/Acquisition Pretax
rraq: Reversal - Restructruring/Acquisition Aftertax
rra12: Reversal - Restructruring/Acquisition Aftertax 12MM
rrdq: Reversal - Restructuring/Acq Diluted EPS Effect
rrd12: Reversal - Restructuring/Acq Diluted EPS Effect 12MM
rrepsq: Reversal - Restructuring/Acq Basic EPS Effect
rreps12: Reversal - Restructuring/Acq Basic EPS Effect 12MM
Impairments & Writedowns
gdwliaq: Impairment of Goodwill After-tax
gdwlia12: Impairments of Goodwill AfterTax - 12mm
gdwlidq: Impairment of Goodwill Diluted EPS Effect
gdwlid12: Impairments Diluted EPS - 12mm
gdwliepsq: Impairment of Goodwill Basic EPS Effect
gdwlieps12: Impairment of Goodwill Basic EPS Effect 12MM
wdpq: Writedowns Pretax
wdaq: Writedowns After-tax
wddq: Writedowns Diluted EPS Effect
wdepsq: Writedowns Basic EPS Effect
Gains & Losses
glpq: Gain/Loss Pretax
glaq: Gain/Loss After-Tax
gldq: Gain/Loss Diluted EPS Effect
glepsq: Gain/Loss Basic EPS Effect
glivq: Gains/Losses on investments
isgtq: Investment Securities - Gain (Loss) - Total
sretq: Gain/Loss on Sale of Property
derhedglq: Gains/Losses on Derivatives and Hedging
hedgeglq: Gain/Loss on Ineffective Hedges
glcepq: Gain/Loss on Sale (Core Earnings Adjusted) Pretax
glceaq: Gain/Loss on Sale (Core Earnings Adjusted) After-tax
glcea12: Gain/Loss on Sale (Core Earnings Adjusted) After-tax 12MM
glcedq: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS
glced12: Gain/Loss on Sale (Core Earnings Adjusted) Diluted EPS Effect 12MM
glceepsq: Gain/Loss on Sale (Core Earnings Adjusted) Basic EPS Effect
glceeps12: Gain/Loss on Sale (Core Earnings Adjusted) Basic EPS Effect 12MM
Debt Extinguishment & Other Special Items
dtepq: Extinguishment of Debt Pretax
dteaq: Extinguishment of Debt After-tax
dtedq: Extinguishment of Debt Diluted EPS Effect
dteepsq: Extinguishment of Debt Basic EPS Effect
setpq: Settlement (Litigation/Insurance) Pretax
setaq: Settlement (Litigation/Insurance) After-tax
seta12: Settlement (Litigation/Insurance) AfterTax - 12mm
setdq: Settlement (Litigation/Insurance) Diluted EPS Effect
setd12: Settlement (Litigation/Insurance) Diluted EPS Effect 12MM
setepsq: Settlement (Litigation/Insurance) Basic EPS Effect
seteps12: Settlement (Litigation/Insurance) Basic EPS Effect 12MM
rdipq: In Process R\&D
rdipaq: In Process R\&D Expense After-tax
rdipdq: In Process R\&D Expense Diluted EPS Effect
rdipepsq: In Process R\&D Expense Basic EPS Effect
spiq: Special Items
nitq: Net Item - Total
acchgq: Accounting Changes - Cumulative Effect
spiepsq: Other Special Items Basic EPS Effect
spidq: Other Special Items Diluted EPS Effect
spioaq: Other Special Items After-tax
spiopq: Other Special Items Pretax
Core Earnings Adjustments
Note: Some data points may contain NaN or 0 values.
S\&P Core Earnings
spceq: S\&P Core Earnings
spce12: S\&P Core Earnings 12MM
spcepq: S\&P Core Earnings - Preliminary
spcep12: S\&P Core Earnings 12MM - Preliminary
spceepsq: S\&P Core Earnings EPS Basic
spceeps12: S\&P Core Earnings EPS Basic 12MM
spceepspq: S\&P Core Earnings EPS Basic - Preliminary
spcedq: S\&P Core Earnings EPS Diluted
spced12: S\&P Core Earnings EPS Diluted 12MM
spcedpq: S\&P Core Earnings EPS Diluted - Preliminary
spcepd12: S\&P Core Earnings 12MM EPS Diluted - Preliminary
Pension Adjustments
pncq: Core Pension Adjustment
pnc12: Pension Core Adjustment - 12mm
pncpq: Core Pension Adjustment Preliminary
pncepsq: Core Pension Adjustment Basic EPS Effect
pnceps12: Core Pension Adjustment Basic EPS Effect 12MM
pncpepsq: Core Pension Adjustment Basic EPS Effect Preliminary
pncpeps12: Core Pension Adjustment 12MM Basic EPS Effect Preliminary
pncdq: Core Pension Adjustment Diluted EPS Effect
pncd12: Core Pension Adjustment Diluted EPS Effect 12MM
pncpd12: Core Pension Adjustment 12MM Diluted EPS Effect Preliminary
pncipq: Core Pension Interest Adjustment Pretax
pnciaq: Core Pension Interest Adjustment After-tax
pnciapq: Core Pension Interest Adjustment After-tax Preliminary
pnciepsq: Core Pension Interest Adjustment Basic EPS Effect
pncidq: Core Pension Interest Adjustment Diluted EPS Effect
pncidpq: Core Pension Interest Adjustment Diluted EPS Effect Preliminary
pncwipq: Core Pension w/o Interest Adjustment Pretax
pncwiaq: Core Pension w/o Interest Adjustment After-tax
pncwiapq: Core Pension w/o Interest Adjustment After-tax Preliminary
pncwiepsq: Core Pension w/o Interest Adjustment Basic EPS Effect
pncwidq: Core Pension w/o Interest Adjustment Diluted EPS Effect
pncwidpq: Core Pension w/o Interest Adjustment Diluted EPS Effect Preliminary
Post-Retirement Adjustments
prcaq: Core Post Retirement Adjustment
prce12: Core Post Retirement Adjustment 12MM
prcpq: Core Post Retirement Adjustment Preliminary
prcepsq: Core Post Retirement Adjustment Basic EPS Effect
prceps12: Core Post Retirement Adjustment Basic EPS Effect 12MM
prcpepsq: Core Post Retirement Adjustment Basic EPS Effect Preliminary
prcpeps12: Core Post Retirement Adjustment 12MM Basic EPS Effect Preliminary
prcdq: Core Post Retirement Adjustment Diluted EPS Effect
prcd12: Core Post Retirement Adjustment Diluted EPS Effect 12MM
prcpdq: Core Post Retirement Adjustment Diluted EPS Effect Preliminary
prcpd12: Core Post Retirement Adjustment 12MM Diluted EPS Effect Preliminary
Stock Option Expenses
xoptq: Implied Option Expense
xopt12: Implied Option Expense - 12mm
xoptqp: Implied Option Expense Preliminary
xoptepsq: Implied Option EPS Basic
xopteps12: Implied Option EPS Basic 12MM
xoptepsqp: Implied Option EPS Basic Preliminary
xoptepsp12: Implied Option 12MM EPS Basic Preliminary
xoptd12: Implied Option EPS Diluted 12MM
xoptdqp: Implied Option EPS Diluted Preliminary
xoptd12p: Implied Option 12MM EPS Diluted Preliminary
optfvgrq: Options - Fair Value of Options Granted
optdrq: Dividend Rate - Assumption (%)
optlifeq: Life of Options - Assumption (yrs)
optvolq: Volatility - Assumption (%)
As Reported Core
arceq: As Reported Core - After-tax
arcedq: As Reported Core - Diluted EPS Effect
arced12: As Reported Core - Diluted EPS Effect 12MM
arceepsq: As Reported Core - Basic EPS Effect
arceeps12: As Reported Core - Basic EPS Effect 12MM
Comprehensive Income
Note: Some data points may contain NaN or 0 values.
Comprehensive Income Components
ciq: Comprehensive Income - Total
citotalq: Comprehensive Income - Parent
cimiiq: Comprehensive Income - Noncontrolling Interest
cibegniq: Comp Inc - Beginning Net Income
Currency Translation
cicurrq: Comp Inc - Currency Trans Adj
rectaq: Accum Other Comp Inc - Cumulative Translation Adjustments
transaq: Cumulative Translation Adjustment
Derivative & Hedge Adjustments
ciderglq: Comp Inc - Derivative Gains/Losses
aociderglq: Accum Other Comp Inc - Derivatives Unrealized Gain/Loss
derhedglq: Gains/Losses on Derivatives and Hedging
Securities Gains/Losses
cisecglq: Comp Inc - Securities Gains/Losses
aocisecglq: Accum Other Comp Inc - Unreal G/L Ret Int in Sec Assets
Other Comprehensive Income
cipenq: Comp Inc - Minimum Pension Adj
aocipenq: Accum Other Comp Inc - Min Pension Liab Adj
ciotherq: Comp Inc - Other Adj
aociotherq: Accum Other Comp Inc - Other Adjustments
Industry-Specific Items
Note: Some data points may contain NaN or 0 values.
Financial Services (Banking)
dptbq: Deposits - Total - Banks
dptcq: Deposits - Total - Customer
lcabgq: Loans/Claims/Advances - Banks & Government - Total
tiiq: Interest Income - Total (Financial Services)
iditq: Interest Income - Total
niitq: Net Interest Income (Tax Equivalent)
nimq: Net Interest Margin
ncoq: Net Charge-Offs
pllq: Provision for Loan/Asset Losses
pclq: Provision - Credit Losses (Income Account)
rllq: Reserve for Loan/Asset Losses
npatq: Nonperforming Assets - Total
capr1q: Risk-Adjusted Capital Ratio - Tier 1
capr2q: Risk-Adjusted Capital Ratio - Tier 2
capr3q: Risk-Adjusted Capital Ratio - Combined
caprtq: Risk-Adjusted Capital Ratio - Total - Quarterly
icaptq: Invested Capital Total Quarterly
Insurance
bctq: Benefits and Claims - Total (Insurance)
iptiq: Insurance Premiums - Total (Insurance)
iviq: Investment Income - Total (Insurance)
iatiq: Investment Assets - Total (Insurance)
rvtiq: Reserves - Total (Insurance)
ratiq: Reinsurance Assets - Total (Insurance)
rltq: Reinsurance Liabilities - Total
ioiq: Income - Other (Insurance)
xioq: Insurance Expense - Other - Total
dfpacq: Deferred Policy Acquisition Costs
adpacq: Amortization of Deferred Policy Acquisition Costs
dvpdpq: Dividends and Bonuses Paid Policyholders
saaq: Separate Account Assets
salq: Separate Account Liabilities
Broker/Dealer
bdiq: Broker / Dealer Income - Total
ibkiq: Investment Banking Income
ptranq: Principal Transactions
cfbdq: Commissions and Fees (Broker Dealer)
bcefq: Brokerage, Clearing and Exchange Fees
xcomq: Communications Expense (Broker/Dealer)
iobdq: Income - Other (Broker Dealer)
xobdq: Expense - Other (Broker/Dealer)
tdstq: Trading/Dealing Account Securities - Total
tdsgq: Trading/Dealing Securities - Gain (Loss)
ssnpq: Securities Sold Not Yet Purchased
sbdcq: Securities Borrowed and Deposited by Customers
mtlq: Loans From Securities Finance Companies for Margin Transactions
scq: Securities In Custody
feaq: Foreign Exchange Assets
felq: Foreign Exchange Liabilities
Real Estate
retq: Total RE Property
dpacreq: Accumulated Depreciation of RE Property
dpretq: Depr/Amort of Property
reitq: Real Estate Income - Total
dvrreq: Development Revenue (Real Estate)
iireq: Investment Income (Real Estate)
ioreq: Income - Other (Real Estate)
cfereq: Commissions and Fees (Real Estate)
xretq: Real Estate Expense - Total
xdvreq: Expense - Development (Real Estate)
xivreq: Expense - Investment (Real Estate)
xoreq: Expense - Other (Real Estate)
ffoq: Funds From Operations (REIT)
Utilities
ugiq: Gross Income (Income Before Interest Charges)
uopiq: Operating Income - Total - Utility
upstkq: Preferred Stock At Carrying Value - Utility
upstkcq: Preference Stock At Carrying Value - Utility
usubpcvq: Subsidiary Preferred Stock At Carrying Value - Utility
udvpq: Preferred Dividend Requirements
updvpq: Preference Dividend Requirements - Utility
usubdvpq: Subsidiary Preferred Dividends - Utility
upmcstkq: Premium On Common Stock - Utility
upmpfq: Premium On Preferred Stock - Utility
upmpfsq: Premium On Preference Stock - Utility
upmsubpq: Premium On Subsidiary Preferred Stock - Utility
ucapsq: Paid In Capital - Other - Utility
ucconsq: Contributions In Aid Of Construction
uddq: Debt (Debentures) - Utility
udmbq: Debt (Mortgage Bonds)
udoltq: Debt (Other Long-Term)
unnpq: Unappropriated Net Profit (Shareholders' Equity)
uacoq: Current Assets - Other - Utility
uinvq: Inventories
urectq: Receivables (Net) - Utility
ulcoq: Current Liabilities - Other
utemq: Maintenance Expense - Total
uspiq: Special Items - Utility
uniamiq: Net Income Before Extraordinary Items and after Noncontrolling Interest
Finance Division (Captive Finance)
finrevq: Finance Division Revenue
finxintq: Finance Division Interest Expense
finxoprq: Finance Division Operating Expense
finreccq: Finance Division Current Receivables
finrecltq: Finance Division Long-Term Receivables
finivstq: Finance Division Short-Term Investments
finacoq: Finance Division Other Current Assets, Total
finaoq: Finance Division Other Long-Term Assets, Total
finnpq: Finance Division Notes Payable
findlcq: Finance Division Long-Term Debt Current
findltq: Finance Division Debt Long-Term
finlcoq: Finance Division Other Current Liabilities, Total
finltoq: Finance Division Other Long Term Liabilities, Total
Fair Value & Derivatives
tfvaq: Total Fair Value Assets
tfvlq: Total Fair Value Liabilities
tfvceq: Total Fair Value Changes including Earnings
ivtfsq: Financial Services Investment Assets- Total
deracq: Derivative Assets - Current
deraltq: Derivative Assets Long-Term
derlcq: Derivative Liabilities- Current
derlltq: Derivative Liabilities Long-Term
Other Items
caq: Customers' Acceptance
accoq: Acceptances Outstanding
obkq: Order Backlog Quarterly
stkchq: Change in Stocks
oproq: Operating Revenues - Other
cfoq: Commissions and Fees - Other
cfpdoq: Commissions and Fees Paid - Other
{% hint style="warning" %}
Note
* We provide Financial CM converted into USD. According to Compustat’s policy, balance sheet items are converted using a 12-month exchange rate, while income statement and cash flow items are converted using the monthly exchange rate.
* Unit : 1 million
{% endhint %}
### market
classification
Summary
This document provides information about the GICS classification of U.S. stocks available in the data catalog.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("gics")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19831230 | 30 5 \* \* 2-6 | | |
Item List
* gics: GICS classification of U.S. stocks price\_volume
Summary
This document provides an overview of U.S. stock price and volume data items available in the data catalog.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("price_close")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19831230 | 30 5 \* \* 2-6 | | Daily |
Item List
Price Data
* **price\_open**: Daily opening price of U.S. stocks
* **price\_close**: Daily closing price of U.S. stocks
* **price\_high**: Daily high of U.S. stocks
* **price\_low**: Daily low of U.S. stocks
* **ctoc**: Rate of change in closing price compared to the closing price of U.S. stocks
* **ctoc\_total**: Rate of change in closing price compared to the cumulative closing price of U.S. stocks
Volume Data
* **trading\_volume**: Daily trading volume of U.S. stocks
* **amount**: Trading volume of U.S. stocks
* **shares\_outstanding**: Daily number of shares issued for U.S. stocks
Financial Metrics
* **eps**: Earnings per Share of U.S. Stocks
* **mkt\_cap**: Market capitalization of U.S. stocks
* **indicated\_annual\_dividend**: Annual projected dividends of U.S. stocks
* **adr\_ratio**: ADR ratio of U.S. stocks cax
Summary
This document provides information on U.S. stock data items available in the data catalog, including total return and adjustment factors.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory('raw', 20200101, 20251031)
cf.get_df('content.spglobal.compustat.cax.us-stock-total_return_factor.1d')
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19831230 | 30 5 \* \* 2-6 | | |
Item List
* **content.spglobal.compustat.cax.us-stock-total\_return\_factor** : Total return coefficient of U.S. stocks
* **content.spglobal.compustat.cax.us-stock-adjust\_factor :** Adjustment factor of U.S. stocks universe
Summary
The data consists of various categories of US stock market constituents, including those filtered by Shariah compliance, which excludes stocks with Islamic-prohibited GICS and limits total debt to market cap ratios below 33%. Additionally, it includes general constituent stocks of the US market and specific indices such as the NDX and SPX, with mechanisms for generating daily forward-filled data aligned to monthly indices.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("shariah_constituent")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| --------------------------- | -------------------------------------- | --------- | -------------- |
| 19980401
20000101
| 30 5 \* \* 2-6
0 9 \* \* 2-6
| | 1d |
Item List
* **constituent**: Constituent stocks of the US stock market
* **ndx\_constituent**: Generates a daily forward-filled non-NA mask aligned to monthly first valid indices for ndx\_data, saved as content.spglobal.compustat.universe.us-stock-ndx\_constituent.1d.
* **shariah\_constituent**: quantit\_universe shariah filter cm; Excluding Islamic-prohibited GICS, total\_debt / market cap < 33%
* **spx\_constituent**: Generates a daily forward-filled non-NA mask aligned to monthly first valid indices for spx\_data, saved as content.spglobal.compustat.universe.us-stock-spx\_constituent.1d.
* **spx\_shariah\_constituent**: spx shariah filter cm; Excluding Islamic-prohibited GICS, total\_debt / market cap < 33%
### edge
narr
Summary
The data encompasses summaries of major news topics in the United States, specifically focusing on general news and business news. Each summary is organized by topic and includes a list of relevant keywords associated with the news items.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("topnews_us")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20100101 | 00 20 \* \* \* | | 1m |
Item List
* **topenbiznews\_us**: Summary of major English business news in the United States; columns are topic. e.g. 0:\['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len'],
* **topnews\_us**: Summary of Major News in the United States; columns are topic. e.g. 0:\['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len'] ews
Summary
The data consists of various sentiment indices and tone metrics related to the U.S. stock market, including averages, totals, and percentages derived from raw sentiment analysis and business news. These indices are designed to provide insights into market sentiment and trends, utilizing models such as SARIMA and moving averages for analysis.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("raw_sentiment-1_2-mean_sum")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ------------------------------------- | --------- | -------------- |
| 20040101 | 0 9 \* \* \*
30 21 \* \* \*
| | 1d, 1w, 1m |
Item List
* **SP500\_EWS**: S\&P 500 Early Warning System (Monthly)
* **SP500\_EWS**: S\&P 500 Early Warning System (Weekly)
* **SP500\_EWS\_UI\_append**: Monthly UI Additional Sentiment Index for S\&P 500
* **SP500\_EWS\_UI\_append**: Weekly UI Additional Sentiment Index for S\&P 500
* **SP500\_EWS\_biz\_news**: Monthly Business News Sentiment Index for S\&P 500
* **SP500\_EWS\_biz\_news**: Weekly Business News Sentiment Index for S\&P 500
* **SP500\_EWS\_compustat**: S\&P 500 Early Warning System (Based on Compustat, Weekly)
* **SP500\_EWS\_compustat**: S\&P 500 Early Warning System (Compustat-based, Monthly)
* **SP500\_EWS\_origin**: S\&P 500 Early Warning System Original (Monthly)
* **SP500\_EWS\_origin**: S\&P 500 Early Warning System Original (Weekly)
* **SP500\_EWS\_origin\_biz\_news**: Original monthly business news sentiment index for the S\&P 500
* **SP500\_EWS\_origin\_biz\_news**: Original weekly business news sentiment index for the S\&P 500
* **SP500\_EWS\_origin\_compustat**: S\&P 500 Early Warning System Original (Compustat-based, Monthly)
* **SP500\_EWS\_origin\_compustat**: S\&P 500 Early Warning System Original (Compustat-based, Weekly)
* **SP500\_EWS\_origins\_biz\_news**: Original value of the weekly business news sentiment index for the S\&P 500
* **SP500\_EWS\_origins\_biz\_news**: Original values of the monthly business news sentiment index for the S\&P 500
* **SP500\_EWSs\_biz\_news**: Monthly Business News Sentiment Index for S\&P 500
* **SP500\_EWSs\_biz\_news**: Weekly Business News Sentiment Index for S\&P 500
* **post\_sarima\_sent-1\_1-sum\_per**: Percentage of the sentiment index for the US stock market after applying the SARIMA model (alternative version)
* **post\_sarima\_sent-1\_1-sum\_sum**: Total sentiment index of the US stock market after applying the SARIMA model (alternative version)
* **post\_sarima\_sent-1\_2-sum\_per**: Percentage of sentiment index in the US stock market after applying the SARIMA model
* **post\_sarima\_sent-1\_2-sum\_sum**: Total sentiment index of the US stock market after applying the SARIMA model
* **post\_sarima\_sent\_biz\_news-1\_1-sum\_per**: The percentage sum of sentiment analysis results after applying the SARIMA model to business news about the US stock market
* **post\_sarima\_sent\_biz\_news-1\_1-sum\_sum**: The total results of sentiment analysis after applying the SARIMA model to business news about the US stock market
* **post\_sarima\_sent\_biz\_news-1\_2-sum\_per**: The percentage sum of the results of the second type of sentiment analysis after applying the SARIMA model to business news about the US stock market
* **post\_sarima\_sent\_biz\_news-1\_2-sum\_sum**: The total of the second type sentiment analysis results after applying the SARIMA model to business news about the US stock market
* **raw\_sentiment-1\_1-mean\_per**: Average percentage of the U.S. stock market sentiment index
* **raw\_sentiment-1\_1-mean\_sum**: Average Total of the U.S. Stock Market Sentiment Index
* **raw\_sentiment-1\_1-sum\_per**: Percentage of the U.S. Stock Market Sentiment Index
* **raw\_sentiment-1\_1-sum\_sum**: Total Sentiment Index of the U.S. Stock Market
* **raw\_sentiment-1\_2-mean\_per**: Average Percentage of the U.S. Stock Market Sentiment Index (Alternative Version)
* **raw\_sentiment-1\_2-mean\_sum**: Average Total of the U.S. Stock Market Sentiment Index (Alternative Version)
* **raw\_sentiment-1\_2-sum\_per**: U.S. Stock Market Sentiment Index Percentage (Alternative Version)
* **raw\_sentiment-1\_2-sum\_sum**: Total Sentiment Index of the U.S. Stock Market (Alternative Version)
* **raw\_sentiment\_biz\_news-1\_1-mean\_per**: Average percentage of raw sentiment analysis results of business news regarding the US stock market
* **raw\_sentiment\_biz\_news-1\_1-mean\_sum**: The average sum of raw sentiment analysis results of business news regarding the U.S. stock market
* **raw\_sentiment\_biz\_news-1\_1-sum\_per**: The percentage sum of raw sentiment analysis results of business news regarding the US stock market
* **raw\_sentiment\_biz\_news-1\_1-sum\_sum**: The total of raw sentiment analysis results of business news regarding the U.S. stock market
* **raw\_sentiment\_biz\_news-1\_2-mean\_per**: The average percentage of the second type of raw sentiment analysis results for business news related to the U.S. stock market
* **raw\_sentiment\_biz\_news-1\_2-mean\_sum**: The average total of the second type of raw sentiment analysis results for business news related to the U.S. stock market
* **raw\_sentiment\_biz\_news-1\_2-sum\_per**: The second type percentage total of raw sentiment analysis results for business news about the U.S. stock market
* **raw\_sentiment\_biz\_news-1\_2-sum\_sum**: The total of the second type of raw sentiment analysis results for business news regarding the U.S. stock market
* **us\_tone\_m3**: U.S. Stock Market Tone Index (3-Month Moving Average)
* **us\_tone\_m3\_EWS**: U.S. Stock Market Tone Index Early Warning System (3-Month Moving Average, Monthly)
* **us\_tone\_m3\_EWS\_compustat**: U.S. Stock Market Tone Index Early Warning System (3-Month Moving Average, Compustat-Based, Monthly)
* **us\_tone\_m3\_exp**: U.S. Stock Market Tone Index Smoothing (3-Month Moving Average)
* **us\_tone\_m3\_exp\_EWS**: U.S. Stock Market Tone Index Smoothing Early Warning System (3-Month Moving Average, Monthly)
* **us\_tone\_m3\_exp\_EWS\_compustat**: U.S. Stock Market Tone Index Smoothing Early Warning System (3-Month Moving Average, Compustat-Based, Monthly) top\_foreign\_invest
Summary
The data encompasses various metrics related to the SEIBRO Foreign Currency Securities Custody and Settlement, including sums of foreign security sell amounts, total amounts, net buy amounts, and buy amounts. These metrics are designed to track and analyze foreign securities transactions over time, with some values adjusted to account for publication delays.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us_sum_frsec_sell_amt")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20100104 | 0 23 \* \* 1-5 | | 1d |
Item List
* **us\_sum\_frsec\_amt**: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foreign Securities Amount.
* **us\_sum\_frsec\_amt\_shift\_2**: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foreign Securities Amount. Shifted two weekdays to incorporate the gap between the reference and publication dates.
* **us\_sum\_frsec\_buy\_amt**: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Buy Amount.
* **us\_sum\_frsec\_net\_buy\_amt**: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Net Buy Amount.
* **us\_sum\_frsec\_sell\_amt**: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Sell Amount.
* **us\_sum\_frsec\_tot\_amt**: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Total Amount. llm
Summary
This data catalog provides unstructured information related to assets using LLM for the US stock universe.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("asset")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20230901 | 00 3 \* \* 2-6 | | |
Item List
* **asset**: Unstructured information related to assets using LLM. dkg
Summary
This document provides information about the US stock market risk-related knowledge graph data.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory('raw', 20200101, 20251102)
cf.get_df('content.handa.unstructured.dkg.kg_risk_us.1d')
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20100101 | | | |
Item List
* kg\_risk\_us: Knowledge graph information on risks in the U.S. market
### factor
guru\_factor
Summary
This document provides an overview of various US stock strategies based on different investment criteria set by renowned investors.
Value: indicates how many conditions are satisfied.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us-brad_gerstner")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20000101 | 0 15 \* \* 1-5 | | |
Item List
Strategies Based on Specific Investors
* **us-brad\_gerstner**: A US stock strategy based on Brad Gerstner's criteria: Revenue growth with annual revenue growth > 20%, margin expansion with annual gross margin growth > 5%, cash flow with annual FCF growth > 10%, EPS growth with annual EPS growth > 10%, and valuation with P/E < 15.
* **us-charlie\_munger**: A US stock strategy based on Charlie Munger's criteria: Buy and Hold with ROIC > 15%, margin of safety with P/E < 10 & P/B < 1.5, moat holding quality stocks with ROE > 15% & annual revenue growth < 15%, cash strategy with debt-to-equity ratio < 0.5 & ROE > 15%, and risk management with current ratio > 2.
* **us-david\_dreman**: A US stock strategy based on David Dreman's criteria: PER in the bottom 20% or PBR in the bottom 20%, market cap within the top 500, annual net income growth > 7%, ROE in the top one-third among the top 500 by market cap, current ratio > 2, and debt-to-equity ratio < 1.
* **us-alex\_sacerdote**: A US stock strategy based on Alex Sacerdote's criteria: Growth with annual revenue growth > 20% or net income growth > 30%, innovation with R\&D expenditure > 10%, market leadership with EPS growth > 20% or ROE > 10%, macro trends with debt-to-equity ratio < 0.5, and valuation with P/E < 15 or PSR < 2 or EV/EBITDA < 10 or P/CF < 10.
* **us-james\_oshaughnessy**: A US stock strategy based on James O'Shaughnessy's criteria: ROIC > 13% & P/E < 20, P/B < 2, debt-to-equity ratio > 1.5 or FCF > 0, dividend yield > 2%, annual EPS growth > 20%, dividend yield > 4% & debt-to-equity ratio < 1, 12-month price momentum > 0 & annual EPS growth > 20%.
* **us-glenn\_welling**: A US stock strategy based on Glenn Welling's criteria: Activist strategy with EV/EBITDA < 8, operational improvement with ROIC < 10%, spinoff strategy with P/B < 1.5, event-driven strategy with P/S < 1.5, activist focus with P/B < 1, and growth strategy with PEG < 1.
* **us-benjamin**: A US stock strategy based on Benjamin Graham's criteria: Current ratio > 200%, net current assets > long-term debt, EPS growth > 3%, PER < 15, PBR\*PER < 22, and debt-to-equity ratio < 1.
* **us-colin\_moran**: A US stock strategy based on Colin Moran's criteria: High-quality investments with a focus on consistent earnings growth, low leverage with debt-to-equity ratio < 0.5, strong ROE > 15%, valuation with P/E < 20 or P/B < 2, and sustainable free cash flow (FCF > 0).
* **us-warren\_buffet**: A US stock strategy based on Warren Buffett's criteria: ROE > 15%, long-term debt-to-equity ratio < 1, current ratio > 1.5, FCF > 0, PER < 17, P/B < 1.5, debt-to-equity ratio > 1.5, and EPS growth > 10%.
* **us-bill\_ackman**: A US stock strategy based on Bill Ackman's criteria: ROIC > 13% & P/E < 20, activist strategy with P/E < 20 & P/B < 2, debt-to-equity ratio > 1.5 or FCF > 0, and dividend yield > 2%.
* **benjamin\_graham**: No description.
* **us-glenn\_greenberg**: A US stock strategy based on Glenn Greenberg's criteria: Low valuation with P/E < 15, high efficiency with ROIC > 15%, strong financials with debt-to-equity ratio < 0.5, margin expansion with gross margin growth > 3%, and strong cash flow with FCF > 5% of market cap.
* **us-peter\_lynch**: A US stock strategy based on Peter Lynch's criteria: PER < 40, PEG < 1.8, inventory-to-sales ratio < 5%, debt-to-equity ratio < 0.8, ROE > 5%, ROA > 1%, and dividend yield > 3%.
* **us-cathie\_wood**: A US stock strategy based on Cathie Wood's criteria: Innovative companies with PEG < 2, technological transition with PSR < 20, disruptive companies with revenue growth > 20%, and risk management with current ratio > 2.
* **us-william\_oneil**: A US stock strategy based on William O'Neil's criteria: Current quarterly EPS growth > 18%, annual EPS growth > 18%, ROE > 17%, recent stock price > 85% of the 52-week high, and annual stock price growth in the top 20%.
* **us-ron\_baron**: A US stock strategy based on Ron Baron's criteria: Long-term investment with ROE > 15%, valuation strategy with PSR < 1.5 or P/E < 20 or EV/EBITDA < 10 or P/FCF < 15, growth potential with annual revenue growth > 25%, and innovation with R\&D expenditure > 10% of revenue.
fundamental\_factor
Summary
The dataset contains various financial metrics related to U.S. stocks, focusing on growth rates, ratios, and performance indicators over different time frames, such as 1-year and 3-year periods. Key metrics include growth rates for sales, net income, operating assets, and liabilities, as well as ratios like EBITDA to market capitalization, return on equity, and cash to total assets, providing insights into the financial health and performance trends of U.S. stocks.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us-stock_pit-ncol_gr3a")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19820531 | 30 5 \* \* 2-6 | | 1d |
Item List
• at\_gr3 : Asset Growth 3yr
• sale\_gr3 : Sales Growth 3yr
• ca\_gr3 : Current Asset Growth 3yr
• nca\_gr3 : Non-Current Asset Growth 3yr
• lt\_gr3 : Total Liabilities Growth 3yr
• cl\_gr3 : Current Liabilities Growth 3yr
• ncl\_gr3 : Non-Current Liabilities Growth 3yr
• be\_gr3 : Book Equity Growth 3yr
• pstk\_gr3 : Preferred Stock Growth 3yr
• debt\_gr3 : Total Debt Growth 3yr
• cogs\_gr3 : Cost of Goods Sold Growth 3yr
• sga\_gr3 : Selling, General, and Administrative Expenses Growth 3yr
• opex\_gr3 : Operating Expenses Growth 3yr
Growth - Changed Scaled by Total Assets
• gp\_gr1a : Gross Profit Change 1yr (Scaled by Total Assets)
• ocf\_gr1a : Operating Cash Flow Change 1yr (Scaled by Total Assets)
• cash\_gr1a : Cash and Short-Term Investments Change 1yr (Scaled by Total Assets)
• inv\_gr1a : Inventory Change 1yr (Scaled by Total Assets)
• rec\_gr1a : Receivables Change 1yr (Scaled by Total Assets)
• ppeg\_gr1a : Property, Plant, and Equipment Gross Change 1yr
• intan\_gr1a : Intangible Assets Change 1yr
• debtst\_gr1a : Short-Term Debt Change 1yr
• ap\_gr1a : Accounts Payable Change 1yr
• txp\_gr1a : Income Tax Payable Change 1yr
• debtlt\_gr1a : Long-Term Debt Change 1yr
• txditc\_gr1a : Deferred Taxes and Investment Credit Change 1yr
• coa\_gr1a : Current Operating Assets Change 1yr
• col\_gr1a : Current Operating Liabilities Change 1yr
• cowc\_gr1a : Current Operating Working Capital Change 1yr
• ncoa\_gr1a : Non-Current Operating Assets Change 1yr
• ncol\_gr1a : Non-Current Operating Liabilities Change 1yr
• nncoa\_gr1a : Net Non-Current Operating Assets Change 1yr
• oa\_gr1a : Operating Assets Change 1yr
• ol\_gr1a : Operating Liabilities Change 1yr
• noa\_gr1a : Net Operating Assets Change 1yr
• fna\_gr1a : Financial Assets Change 1yr
• fnl\_gr1a : Financial Liabilities Change 1yr
• nfna\_gr1a : Net Financial Assets Change 1yr
• ebitda\_gr1a : Operating Profit before Depreciation Change 1yr
• ebit\_gr1a : Operating Profit after Depreciation Change 1yr
• ope\_gr1a : Operating Earnings to Equity Change 1yr
• ni\_gr1a : Net Income Change 1yr
• dp\_gr1a : Depreciation and Amortization Change 1yr
• nwc\_gr1a : Net Working Capital Change 1yr
• nix\_gr1a : Net Income Including Extraordinary Items Change 1yr
• tax\_gr1a : Effective Tax Rate Change 1yr
• div\_gr1a : Dividend Payout Ratio Change 1yr
• gp\_gr3a : Gross Profit Change 3yr
• ocf\_gr3a : Operating Cash Flow Change 3yr
• cash\_gr3a : Cash and Short-Term Investments Change 3yr
• inv\_gr3a : Inventory Change 3yr
• rec\_gr3a : Receivables Change 3yr
• ppeg\_gr3a : Property, Plant, and Equipment Gross Change 3yr
• lti\_gr3a : Investment and Advances Change 3yr
• intan\_gr3a : Intangible Assets Change 3yr
• debst\_gr3a : Short-Term Debt Change 3yr
• ap\_gr3a : Accounts Payable Change 3yr
• txp\_gr3a : Income Tax Payable Change 3yr
• debtlt\_gr3a : Long-Term Debt Change 3yr
• txditc\_gr3a : Deferred Taxes and Investment Credit Change 3yr
• coa\_gr3a : Current Operating Assets Change 3yr
• col\_gr3a : Current Operating Liabilities Change 3yr
• cowc\_gr3a : Current Operating Working Capital Change 3yr
• ncoa\_gr3a : Non-Current Operating Assets Change 3yr
• nncoa\_gr3a : Net Non-Current Operating Assets Change 3yr
• oa\_gr3a : Operating Assets Change 3yr
• ol\_gr3a : Operating Liabilities Change 3yr
• noa\_gr3a : Net Operating Assets Change 3yr
• fna\_gr3a : Financial Assets Change 3yr
• fnl\_gr3a : Financial Liabilities Change 3yr
• nfna\_gr3a : Net Financial Assets Change 3yr
• ebitda\_gr3a : Operating Profit before Depreciation Change 3yr
• ebit\_gr3a : Operating Profit after Depreciation Change 3yr
• ope\_gr3a : Operating Earnings to Equity Change 3yr
• ni\_gr3a : Net Income Change 3yr
• dp\_gr3a : Depreciation and Amortization Change 3yr
• nwc\_gr3a : Net Working Capital Change 3yr
• inv\_gr3a : Inventory Change 3yr
• ncol\_gr3a : Non-Current Operating Liabilities Change 3yr
• nix\_gr3a : Net Income Including Extraordinary Items Change 3yr
• tax\_gr3a : Effective Tax Rate Change 3yr
• div\_gr3a : Dividend Payout Ratio Change 3yr
Investment
• rd\_at : R\&D scaled by Assets
Non-Recurring Items
• spi\_at : Special Items scaled by Assets
• xido\_at : Extraordinary Items and Discontinued Operations scaled by Assets
• nri\_at : Non-Recurring Items scaled by Assets
Profit Margins
• gp\_sale : Gross Profit Margin
• ebitda\_sale : Operating Profit Margin before Depreciation
• ebit\_sale : Operating Profit Margin after Depreciation
• pi\_sale : Pretax Profit Margin
• nix\_sale : Net Profit Margin
• ocf\_sale : Operating Cash Flow Margin
Return on Assets
• gp\_at : Gross Profit scaled by Assets
• ebitda\_at : Operating Profit before Depreciation scaled by Assets
• ebit\_at : Operating Profit after Depreciation scaled by Assets
• fi\_at : Firm Income scaled by Assets
• cop\_at : Cash Based Operating Profitability scaled by Assets
Return on Book Equity
• ope\_be : Operating Profit to Equity scaled by BE
• ni\_be : Net Income scaled by BE
• nix\_be : Net Income Including Extraordinary Items scaled by BE
• ocf\_be : Operating Cash Flow scaled by BE
Return on Invested Capital
• gp\_bev : Gross Profit scaled by BEV
• ebitda\_bev : Operating Profit before Depreciation scaled by BEV
• ebit\_bev : Operating Profit after Depreciation scaled by BEV
• fi\_bev : Firm Income scaled by BEV
• cop\_bev : Cash Based Operating Profitability scaled by BEV
Return on Physical Capital
• gp\_ppen : Gross Profit scaled by PPEN
• ebitda\_ppen : Operating Profit before Depreciation scaled by PPEN
Equity Payout
• div\_at : Total Dividends scaled by Assets
Accurals
• oaccruals\_at : Operating Accruals
• oaccruals\_ni : Percent Operating Accruals
• taccruals\_at : Total Accruals
• taccruals\_ni : Percent Total Accruals
• noa\_at : Net Operating Asset to Total Assets
Capitalization/Leverage Ratios
• be\_bev : Common Equity scaled by BEV
• debt\_bev : Total Debt scaled by BEV
• cash\_bev : Cash and Short-Term Investments scaled by BEV
• pstk\_bev : Preferred Stock scaled by BEV
• debtlt\_bev : Long-Term Debt scaled by BEV
• debtst\_bev : Short-Term Debt scaled by BEV
Capitalization/Leverage Ratios (Columns gvkeyiid)
• debt\_mev : Total Debt scaled by MEV
• pstk\_mev : Preferred Stock scaled by MEV
• debtlt\_mev : Long-Term Debt scaled by MEV
• debtst\_mev : Short-Term Debt scaled by MEV
Financial Soundness Ratios
• int\_debt : Interest scaled by Total Debt
• int\_debtlt : Interest scaled by Long-Term Debt
• ebitda\_debt : Operating Profit before Depreciation scaled by Total Debt
• profit\_cl : Profit before D\&A scaled by Current Liabilities
• ocf\_cl : Operating Cash Flow scaled by Current Liabilities
• ocf\_debt : Operating Cash Flow scaled by Total Debt
• cash\_lt : Cash Balance scaled by Total Liabilities
• inv\_act : Inventory scaled by Current Assets
• rec\_act : Receivables scaled by Current Assets
• debtst\_debt : Short-Term Debt scaled by Total Debt
• cl\_lt : Current Liabilities scaled by Total Liabilities
• debtlt\_debt : Long-Term Debt scaled by Total Debt
• opex\_at : Operating Leverage
• lt\_ppen : Total Liabilities scaled by Total Tangible Assets
• debtlt\_be : Long-Term Debt to Book Equity
• nwc\_at : Working Capital scaled by Assets
Solvency Ratios
• debt\_at : Debt-to-Assets
• ebit\_int : Interest Coverage Ratio
Liquidity Ratios
• inv\_days : Days Inventory Outstanding
• rec\_days : Days Sales Outstanding
• ap\_days : Days Accounts Payable Outstanding
• cash\_conversion : Cash Conversion Cycle
• cash\_cl : Cash Ratio
• caliq\_cl : Quick Ratio
• ca\_cl : Current Ratio
Activity/Efficiency Ratios
• inv\_turnover : Inventory Turnover
• at\_turnover : Asset Turnover
• rec\_turnover : Receivables Turnover
• ap\_turnover : Account Payables Turnover
Miscellaneous
• sale\_bev : Sales scaled by BEV
• rd\_sale : R\&D scaled by Sales
• sale\_be : Sales scaled by Total Stockholders’ Equity
• div\_ni : Dividend Payout Ratio
• sale\_nwc : Sales scaled by Working Capital
• tax\_pi : Effective Tax Rate
Balance Sheet Fundamental to Market Equity
• be\_me : Book Equity scaled by Market Equity
• at\_me : Total Assets scaled by Market Equity
• cash\_me : Cash and Short-Term Investments scaled by Market Equity
Income Fundamentals to Market Equity
• gp\_me : Gross Profit scaled by ME
• ebitda\_me : Operating Profit before Depreciation scaled by ME
• ebit\_me : Operating Profit after Depreciation scaled by ME
• ope\_me : Operating Earnings to Equity scaled by ME
• ni\_me : Net Income scaled by ME
• sale\_me : Sales scaled by ME
• ocf\_me : Operating Cash Flow scaled by ME
• nix\_me : Net Income Including Extraordinary Items scaled by ME
• cop\_me : Cash Based Operating Profitability scaled by ME
• rd\_me : R\&D scaled by ME
Balance Sheet Fundamentals to Market Enterprise Value
• be\_mev : Book Equity scaled by MEV
• at\_mev : Total Assets scaled by MEV
• cash\_mev : Cash and Short-Term Investments scaled by MEV
• bev\_mev : Book Enterprise Value scaled by MEV
• ppen\_mev : Property, Plant, and Equipment Net scaled by MEV
Equity Payout/Issuance to Market Equity
• div\_me : Total Dividends scaled by ME
Income Fundamentals to Market Enterprise Value
• gp\_mev : Gross Profit scaled by MEV
• ebitda\_mev : Operating Profit before Depreciation scaled by MEV
• ebit\_mev : Operating Profit after Depreciation scaled by MEV
• sale\_mev : Sales scaled by MEV
• ocf\_mev : Operating Cash Flow scaled by MEV
• cop\_mev : Cash Based Operating Profitability scaled by MEV
New Variables not in HXZ
• niq\_saleq\_std : Net Income to Sales Quarterly Volatility
• ni\_at : Net Income scaled by Assets
• ocf\_at : Operating Cash Flow scaled by Assets
• ocf\_at\_chg1 : Operating Cash Flow to Assets 1 yr Change
• roeq\_be\_std : Quarterly ROE Volatility
• roe\_be\_std : ROE Volatility
• gpoa\_ch5 : Gross Product to Assets 5 yr Change
• roe\_ch5 : ROE 5 yr Change
• roa\_ch5 : ROA 5 yr Change
• cfoa\_ch5 : Operating Cash Flow to Assets 5 yr Change
• gmar\_ch5 : Gross Product to Sales 5 yr Change
New Variables from HXZ
• cash\_at : Cash and Short Term Investments scaled by Assets
• ni\_inc8q : Number of Consecutive Earnings Increases
• ppeinv\_gr1a : Change in Property, Plant and Equipment Less Inventories scaled by lagged Assets
• lnoa\_gr1a : Change in Long-Term NOA scaled by average Assets
• sti\_gr1a : Change in Short-Term Investments scaled by Assets
• niq\_be : Quarterly Income scaled by BE
• niq\_be\_chg1 : Change in Quarterly Income scaled by BE
• niq\_at : Quarterly Income scaled by AT
• niq\_at\_chg1 : Change in Quarterly Income scaled by AT
• saleq\_gr1 : Quarterly Sales Growth
• rd5\_at : R\&D Capital-to-Assets
• dsale\_dinv : Change Sales minus Change Inventory
• dsale\_drec : Change Sales minus Change Receivables
• dgp\_dsale : Change Gross Profit minus Change Sales
• dsale\_dsga : Change Sales minus Change SG\&A
• saleq\_su : Earnings Surprise
• niq\_su : Revenue Surprise
• inv\_gr1 : Inventory Change 1 yr
• be\_gr1a : Book Equity Change 1 yr scaled by Assets
• op\_at : Ball Operating Profit to Assets
• pi\_nix : Earnings before Tax and Extraordinary Items to Net Income Including Extraordinary Items
• op\_atl1 : Ball Operating Profit scaled by lagged Assets
• ope\_bel1 : Operating Profit scaled by lagged Book Equity
• gp\_atl1 : Gross Profit scaled by lagged Assets
• cop\_atl1 : Cash Based Operating Profitability scaled by lagged Assets
• at\_be : Book Leverage
• ocfq\_saleq\_std : Operating Cash Flow to Sales Quarterly Volatility
• aliq\_at : Liquidity scaled by lagged Assets
• tangibility : Tangibility
• o\_score : Ohlson O-Score
• earnings\_variability : Earnings Variability
• ni\_ar1 : 1 yr lagged Net Income to Assets
• ni\_ivol : Net Income Idiosyncratic Volatility
New Variables from HXZ (Columns gvkeyiid)
• debt\_me : Total Debt scaled by ME
• netdebt\_me : Net Debt scaled by ME
• aliq\_mat : Liquidity scaled by lagged Market Assets
• eq\_dur : Equity Duration
• z\_score : Altman Z-Score
• kz\_index : Kaplan-Zingales Index
Reference and Formula
[Jensen, Theis Ingerslev, Bryan Kelly, and Lasse Heje Pedersen. "Is there a replication crisis in finance?." *The Journal of Finance* 78.5 (2023): 2465-2518.](https://onlinelibrary.wiley.com/doi/full/10.1111/jofi.13249)
[Jensen, Theis Ingerslev, Bryan Kelly, and Lasse Heje Pedersen. "Global Factor Data Documentation." (2021).](https://jkpfactors.s3.amazonaws.com/documents/Documentation.pdf)
{% hint style="warning" %}
Note
* The paper calculate factor using annual data, but Finterlabs provides factors calculated using quarterly data. Therefore, we do not provide factors for items that do not have quarterly data available.
* For factors calculated using *me\_company(such as \_me, \_mev and etc.)*, their column is provided as *gvkeyiid*.
* In the paper, *me\_company* was used on a monthly basis, but we provide factors calculated using daily *me\_company*.
{% endhint %}
### analysis
analysis
Summary
This document provides information about the HMM-based market system analysis excluding US ETFs.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("hmm_wo_us_etf")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20110103 | 30 6 \* \* 2-6 | | Monthly |
Item List
* **hmm\_wo\_us\_etf**: Analysis of HMM-based market system excluding US ETFs (Monthly)
{% hint style="info" %}
If you would like to use the data, please contact the admin.
{% endhint %}
### macro
macro
Summary
The data set includes various economic indicators related to the U.S. stock market, such as the 30-year Treasury yield, Producer Price Index, and Real Gross Domestic Product (GDP) Growth Rate. It also features metrics like the Consumer Price Index, unemployment rate estimates, and housing market statistics, providing a comprehensive overview of the current economic landscape.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("live-dgs30")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19620102 | 00 10 \* \* \* | | 1d |
Item List
Macroeconomic Indicators
* **live-a261rx1q020sbea**: U.S. real GDP growth rate
* **live-gdp**: Real-time nominal GDP estimate
* **live-gdpc1**: Real-time real (inflation-adjusted) GDP estimate
* **live-cfnai**: Chicago Fed index summarizing overall economic activity
* **live-indpro**: Real-time index of U.S. industrial production
* **live-usalolitoaastsam**: U.S. leading economic indicator estimate
* **live-oecdlolitoaastsam**: OECD leading indicator of global economic trends
* **live-chnlolitoaastsam**: China’s leading economic indicator estimate
* **live-gacdfsa066msfrbphi**: Philadelphia Fed’s U.S. GDP growth projection
* **live-gacdisa066msfrbny**: New York Fed’s GDP forecast (e.g. Nowcast)
* **live-usepuindxd**: Index measuring uncertainty about U.S. economic policy
* **live-usrec**: Indicator signaling current U.S. recession periods
* **live-recprousm156n**: Estimated probability of U.S. recession
Labor Market
* **live-payems**: Total number of non-farm payroll employees
* **live-icsa**: Initial claims for unemployment insurance
* **live-unrate**: Real-time unemployment rate estimate
* **live-awhaeman**: Real-time estimate of average hourly wages
* **live-ulcnfb**: Unit labor cost for the non-farm business sector
* **live-jtsjol**: Total number of job openings
Inflation & Prices
* **live-cpiaucsl**: Real-time Consumer Price Index (CPI)
* **live-cpilfesl**: Core CPI (excludes food and energy)
* **live-pcepi**: PCE Price Index (used by the Fed for inflation targeting)
* **live-pcepilfe**: Core PCE Price Index
* **live-ppiaco**: Producer Price Index (PPI) for all commodities
* **live-ppifis**: PPI for final demand
Monetary Policy & Interest Rates
* **live-fedfunds**: Real-time Federal Funds Rate estimate
* **live-dgs1mo \~ live-dgs30**: U.S. Treasury yields (1 month to 30 years)
* **live-dfii10**: 10-year Treasury Inflation-Protected Securities (TIPS) yield
* **live-t10yie**: 10-year breakeven inflation rate
* **live-t5yie**: 5-year breakeven inflation rate
* **live-t10y2y**: Yield spread between 10Y and 2Y Treasury bonds
* **live-daaa**: Yield on Moody’s Aaa-rated corporate bonds
* **live-dbaa**: Yield on Moody’s Baa-rated corporate bonds
* **live-aaa10y**: Spread between Aaa bonds and 10Y Treasuries
* **live-tedrate**: Spread between 3M LIBOR and 3M Treasury yield
Financial Markets
* **live-sp500**: S\&P 500 stock index
* **live-vixcls**: CBOE Volatility Index (market fear gauge)
* **live-stlfsi2**: St. Louis Fed Financial Stress Index
* **live-bamlh0a0hym2**: High-yield corporate bond spread (ICE BofA)
* **live-bamlhyh0a0hym2triv**: High-yield bond total return index (ICE BofA)
Housing & Real Estate
* **live-houst**: Monthly housing starts
* **live-hsn1f**: New single-family home sales
* **live-permit**: Construction permits for new housing units
* **live-csushpinsa**: S\&P/Case-Shiller U.S. home price index
* **live-comrepusq159n**: Commercial real estate price index
* **live-mortgage30us**: 30-year fixed mortgage rate
* **live-wshotsl**: Inventory of homes for sale
Consumer Activity
* **live-dspic96**: Real disposable personal income
* **live-rpi**: Real personal income
* **live-pcec96**: Real personal consumption expenditures
* **live-rsafs**: Retail sales
* **live-mich**: University of Michigan consumer expectations index
* **live-umcsent**: University of Michigan consumer sentiment index
Business Activity
* **live-dgorder**: New manufacturing orders
* **live-businv**: Total business inventories
* **live-mdsp**: Manufacturing inventory-to-sales ratio
* **live-cdsp**: Retail inventory-to-sales ratio
* **live-whlslrimsa**: Wholesale inventories
* **live-ttlcons**: Total construction spending
Trade & Commodities
* **live-boptexp**: Total exports
* **live-boptimp**: Total imports (or import revenue)
* **live-dcoilwtico**: WTI crude oil price
* **live-pcoppusdm**: Copper price in U.S. dollars
Money Supply & Liquidity
* **live-m1sl**: M1 money supply (cash, demand deposits)
* **live-m2sl**: M2 money supply (M1 + savings, time deposits)
* **live-m2real**: Real M2 (adjusted for inflation)
Credit & Lending
* **live-wdtgal**: Bank lending standards (loan officer survey)
* **live-wlodll**: Bank loan demand (loan officer survey)
Capacity & Utilization
* **live-tcu**: Capacity utilization rate in manufacturing
## VN ETF
### market
market
Summary
This document provides information on Vietnam ETF data items, including their adjustment factors and total return coefficients.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_etf", 20200101, 20200201)
df = cf.get_df("vnm-etf-adjust_factor")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20180101 | 00 22 \* \* 1-5 | | |
Item List
* vnm-etf-adjust\_factor: Adjustment factor of Vietnam ETF
* vnm-etf-total\_return\_factor: Total return coefficient of Vietnam ETF
{% hint style="info" %}
If you would like to use the data, please contact the admin.
{% endhint %}
## VN STOCK
### financial
balance\_sheet
Summary
This document provides an overview of the balance sheet data for Vietnamese stocks, including various financial metrics.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("total_assets")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20140101 | 00 17 \* \* \* | | |
Item List
Assets
* long\_term\_assets: Non-current assets of Vietnamese stocks
* total\_assets: Total assets of Vietnamese stocks
* current\_assets: Liquid assets of Vietnamese stocks
* fixed\_assets: Fixed assets of Vietnamese stocks
* inventories\_net: Net inventory assets of Vietnamese stocks
* cash\_and\_cash\_equivalents: Cash and cash equivalents of Vietnamese stocks
Liabilities
* total\_liabilities: Total debt of Vietnamese stocks
* current\_liabilities: Current liabilities of Vietnamese stocks
* long\_term\_liabilities: Non-current liabilities of Vietnamese stocks
* short\_term\_loans: Short-term borrowings of Vietnamese stocks
* long\_term\_loans: Long-term loan information for Vietnamese stocks
* trade\_accounts\_payable: Accounts payable for Vietnamese stocks
Equity
* owners\_equity: Information on the equity of Vietnamese stocks
* paid\_in\_capital: Information on paid-in capital of Vietnamese stocks
* retained\_earnings: Information on retained earnings of Vietnamese stocks
Other
* total\_resources: Total resource information of Vietnamese stocks cash\_flow
Summary
This document provides an overview of cash flow data items related to Vietnamese stocks, including their descriptions and usage examples.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("net_profit_before_tax")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20140101 | 00 17 \* \* \* | | |
Item List
Cash Flow Information
* **net\_profit\_before\_tax**: Pre-tax net income information of Vietnamese stocks
* **net\_cash\_inflows\_outflows\_from\_operating\_activities**: Information on net cash flow from operating activities of Vietnamese stocks
* **net\_cash\_inflows\_outflows\_from\_financing\_activities**: Information on net cash flow from financing activities of Vietnamese stocks
* **net\_cash\_inflows\_outflows\_from\_investing\_activities**: Information on net cash flow resulting from investment activities in Vietnamese stocks
* **net\_increase\_in\_cash\_and\_cash\_equivalents**: Information on the net increase of cash and cash equivalents in Vietnamese stocks
* **cash\_and\_cash\_equivalents\_at\_the\_beginning\_of\_period**: Information on the basic cash and cash equivalents of Vietnamese stocks
* **cash\_and\_cash\_equivalents\_at\_the\_end\_of\_period**: Information on cash and cash equivalents at the end of the Vietnamese stocks
Investment and Asset Information
* **purchases\_of\_fixed\_assets\_and\_other\_long\_term\_assets**: Information on the purchase of fixed assets and other long-term assets of Vietnamese stocks
* **proceeds\_from\_disposal\_of\_fixed\_assets**: Information on the disposal income of fixed assets in Vietnamese stocks
* **profit\_loss\_from\_liquidating\_fixed\_assets**: Information on the profit and loss from the disposal of fixed assets in Vietnamese stocks
* **investments\_in\_other\_entities**: Investment information on other companies in Vietnamese stocks
* **proceeds\_from\_divestment\_in\_other\_entities**: Information on the return on investment from other companies in Vietnamese stocks
Income and Expense Information
* **dividends\_and\_interest\_received**: Information on dividends and interest income from Vietnamese stocks
* **interest\_income\_and\_dividend**: Information on interest income and dividends from Vietnamese stocks
* **interest\_expense**: Interest expense information of Vietnamese stocks
* **loan\_interests\_already\_paid**: Information on interest payments for loans on Vietnamese stocks
* **payments\_for\_corporate\_income\_tax**: Information on corporate tax payments for Vietnamese stocks
* **business\_income\_tax\_paid**: Information on corporate tax payments for Vietnamese stocks
* **payments\_to\_employees**: Information on employee salary payments for Vietnamese stocks
* **payments\_to\_suppliers**: Information on supplier payments for Vietnamese stocks
* **payments\_for\_share\_returns\_and\_repurchases**: Information on share buybacks and redemption expenditures of Vietnamese stocks
* **other\_payments\_on\_operating\_activities**: Information on other operating expenses of Vietnamese stocks
Cash Flow Adjustments
* **increase\_decrease\_in\_inventories**: Information on the changes in inventory assets of Vietnamese stocks
* **increase\_decrease\_in\_prepaid\_expenses**: Information on the changes in prepaid expenses for Vietnamese stocks
* **increase\_decrease\_in\_payables**: Information on the increase and decrease of accounts payable for Vietnamese stocks
* **increase\_decrease\_in\_receivables**: Information on the changes in accounts receivable of Vietnamese stocks
* **effect\_of\_foreign\_exchange\_differences**: Information on the effects of exchange rate fluctuations on Vietnamese stocks
* **unrealised\_foreign\_exchange\_gain\_loss**: Information on unrealized foreign exchange gains and losses of Vietnamese stocks
Other Financial Information
* **other\_receipts\_from\_operating\_activities**: Information on other operating income from Vietnamese stocks
* **other\_disbursements**: Other expenditure information for Vietnamese stocks
* **other\_gains**: Other income information of Vietnamese stocks
* **gains\_from\_sales\_of\_goods\_and\_service\_provisons\_and\_other\_gains**: Revenue from the sale of products and provision of services related to Vietnamese stocks and other revenue information
* **profit\_loss\_from\_investing\_activities**: Profit and loss information on investment activities in Vietnamese stocks
* **collection\_of\_loans\_proceeds\_from\_sales\_of\_debt\_instruments**: Information on loan recovery and debt product sales revenue from Vietnamese stocks
* **finance\_lease\_principal\_payments**: Information on principal repayment of financial leases for Vietnamese stocks
* **provisions**: Information on provisions for Vietnamese stocks
This structured list provides a comprehensive overview of the cash flow data items available for Vietnamese stocks, making it easier for users to identify and utilize the relevant data. income\_statement
Summary
The data encompasses various financial metrics related to Vietnamese stocks, including revenue sources such as sales, interest expenses, and insurance premiums, as well as expenses like general administrative and selling costs. It also includes profitability indicators such as net profit after tax, gross profit, and earnings per share, providing a comprehensive overview of the income statement for the Vietnamese stock market.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("sales_deductions")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20140101 | 00 17 \* \* \* | | 1d |
Item List
* **business\_tax\_current**: Current corporate tax on Vietnamese stocks
* **business\_tax\_deferred**: Deferred corporate tax on Vietnamese stocks
* **cogs**: Cost of goods sold for Vietnamese stocks
* **eps\_basic**: Basic Earnings Per Share of Vietnamese Stocks
* **eps\_diluted**: Diluted earnings per share of Vietnamese stocks
* **expenses\_banking\_activities**: Banking activity costs of Vietnamese stocks
* **financial\_expense**: Financial costs of Vietnamese stocks
* **financial\_income**: Financial returns of Vietnamese stocks
* **general\_admin\_expense**: General administrative expenses of Vietnamese stocks
* **gross\_insurance\_operating\_profit**: Total profit from insurance operations in Vietnamese stocks
* **gross\_profit**: Gross profit of Vietnamese stocks
* **gross\_written\_premium**: Total insurance premium of Vietnamese stocks
* **income\_banking\_activities**: Banking activity income from Vietnamese stocks
* **income\_reinsurance\_assumed**: The profitability of reinsurance in Vietnamese stocks
* **income\_reinsurance\_ceded**: The revenue from the issuance insurance of Vietnamese stocks
* **increase\_unearned\_premium\_reserve**: Increase in unearned premium reserves for Vietnamese stocks
* **increase\_unearned\_premium\_reserve\_direct**: Increase in direct unearned premium reserves for Vietnamese stocks
* **interest\_expenses**: Interest expenses of Vietnamese stocks
* **investment\_income**: Investment returns on Vietnamese stocks
* **minority\_interest**: Minority shareholder equity in Vietnamese stocks
* **net\_operating\_income\_banking**: Net operating income of banks in Vietnam
* **net\_other\_income\_exp**: Net other income or expenses from Vietnamese stocks
* **net\_profit\_after\_tax**: Net income after tax of Vietnamese stocks
* **net\_profit\_bank\_operation**: Net operating profit of banks in Vietnam
* **net\_profit\_before\_tax**: Pre-tax net income of Vietnamese stocks
* **net\_profit\_insurance\_operation**: Net profit from insurance operations of Vietnamese stocks
* **net\_profit\_parent\_company**: Net income of controlling companies in Vietnam stocks
* **net\_revenue\_insurance\_premium**: Net premium income from Vietnamese stocks
* **net\_sales**: Net sales of Vietnamese stocks
* **net\_sales\_insurance\_business**: Net sales of the insurance business in Vietnam
* **operating\_profit\_loss**: Operating profit or loss of Vietnamese stocks
* **other\_expenses**: Other expenses of Vietnamese stocks
* **other\_income**: Other income from Vietnamese stocks
* **provision\_credit\_losses**: Vietnamese stock allowance for doubtful accounts
* **reinsurance\_premium\_assumed**: Vietnamese stock reinsurance premium
* **reinsurance\_premium\_ceded**: Vietnamese stock issuance insurance premium
* **revenue\_brokerage**: Brokerage commission revenue from Vietnamese stocks
* **revenue\_insurance\_premium**: Insurance premium income from Vietnamese stocks
* **revenue\_investment\_advisory**: Revenue from investment advisory fees for Vietnamese stocks
* **revenue\_issuance\_agency**: Revenue from issuance agency fees for Vietnamese stocks
* **revenue\_securities\_custody**: Custody fee income from Vietnamese stocks
* **revenue\_underwriting**: Acquisition commission revenue from Vietnamese stocks
* **sales**: Revenue of Vietnamese stocks
* **sales\_deductions**: Revenue deduction for Vietnamese stocks
* **selling\_expenses**: Selling costs of Vietnamese stocks
* **tax\_expense**: Total tax cost of Vietnamese stocks ratio
Summary
The data encompasses various financial ratios and metrics related to Vietnamese stocks, including earnings per share (EPS), price-to-earnings ratios, dividend yields, and market capitalization. It also includes growth rates, cash flow metrics, and valuation ratios, providing a comprehensive overview of the financial performance and valuation of stocks within the Vietnamese market.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("EPS_growth_QoQ")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20140101 | 00 17 \* \* \* | | 1d |
Item List
* **BV\_per\_Share**: Book value per share of Vietnamese stocks
* **Beta\_2\_years**: 2-Year Beta Coefficient of Vietnamese Stocks
* **Beta\_6\_months**: 6-month beta coefficient of Vietnamese stocks
* **Cash\_Flow\_per\_Share**: Cash flow per share of Vietnamese stocks
* **Correlation\_between\_price\_and\_market**: The correlation between Vietnamese stock prices and the market
* **Dividend\_Yield\_Paid**: The actual paid dividend yield of Vietnamese stocks
* **Dividend\_Yield\_Plan**: Planned dividend yield of Vietnamese stocks
* **Dividend\_payout\_ratio**: Dividend policy of Vietnamese stocks
* **Dividend\_per\_share**: Dividend per share of Vietnamese stocks
* **Dividend\_yield\_ratio**: Dividend yield of Vietnamese stocks
* **EBITDA\_per\_share**: EBITDA per share of Vietnamese stocks
* **EPS**: Earnings Per Share of Vietnamese Stocks
* **EPS\_Forecast**: EPS forecast for Vietnamese stocks
* **EPS\_diluted**: Diluted earnings per share of Vietnamese stocks
* **EPS\_diluted\_finacial\_statement**: Diluted earnings per share of Vietnamese stocks
* **EPS\_finacial\_statement**: Earnings per Share based on the financial statements of Vietnamese stocks
* **EPS\_growth\_QoQ**: EPS growth rate of Vietnamese stocks compared to the previous quarter
* **EPS\_growth\_YoY**: Year-over-year EPS growth rate of Vietnamese stocks
* **EPS\_preother\_income**: Earnings per share excluding other income from Vietnamese stocks
* **EV**: Corporate value of Vietnamese stocks
* **EV\_EBIT**: Vietnamese stock enterprise value to EBIT ratio
* **EV\_EBITDA**: Vietnamese stock enterprise value to EBITDA ratio
* **EV\_Sales**: The ratio of enterprise value to revenue of Vietnamese stocks
* **Foreign\_investor\_sector**: Information on foreign investor sectors in Vietnamese stocks
* **Free\_cash\_flow\_to\_firm**: Free cash flow of Vietnamese stocks
* **Freeloat\_Shares**: The number of outstanding shares of Vietnamese stocks
* **Graham\_Number**: Graham number of Vietnamese stocks
* **Listing\_Volume**: Number of listed shares in Vietnam stocks
* **Market\_Cap**: Market capitalization of Vietnamese stocks
* **Median\_P\_S\_Value**: The median price-to-sales ratio of Vietnamese stocks
* **Outstanding\_Shares**: Number of issued shares of Vietnamese stocks
* **PEG**: PEG ratio of Vietnamese stocks
* **PEG\_percentage**: PEG ratio (%) of Vietnamese stocks
* **PE\_preother\_income**: Price-to-earnings ratio excluding other income of Vietnamese stocks
* **P\_B**: Price-to-Book Ratio of Vietnamese Stocks
* **P\_CFO**: Price-to-Cash-Flow Ratio of Vietnamese Stocks
* **P\_Cash\_Flow**: Price-to-Cash-Flow Ratio of Vietnamese Stocks
* **P\_DIV**: The price-to-dividend ratio of Vietnamese stocks
* **P\_E**: Price-to-earnings ratio of Vietnamese stocks
* **P\_E\_Forecast**: Forecasted Price-to-Earnings Ratio of Vietnamese Stocks
* **P\_E\_diluted**: Diluted price-to-earnings ratio of Vietnamese stocks
* **P\_FCFE**: The price-to-free cash flow to equity ratio (based on FCFE) of Vietnamese stocks
* **P\_FCFF**: The price-to-free cash flow ratio (based on FCFF) of Vietnamese stocks
* **P\_S**: Price-to-Sales Ratio of Vietnamese Stocks
* **P\_Tangible\_Book**: Price-to-Asset Ratio of Vietnamese Stocks
* **Peter\_Lynch\_Value**: Peter Lynch's value of Vietnamese stocks
* **Return\_of\_market\_in\_2\_years**: 2-Year Return of the Vietnamese Market
* **Return\_of\_stock\_in\_2\_years**: 2-Year Return on Vietnamese Stocks
* **Sales\_per\_Share**: Revenue per share of Vietnamese stocks
* **Tangible\_BV\_per\_Share**: Book value per share of Vietnamese stocks for tangible assets
* **Trading\_value\_compared\_to\_market\_capitalization**: The trading value ratio compared to the market capitalization of Vietnamese stocks
* **Trading\_value\_compared\_to\_the\_market**: The trading value ratio of Vietnamese stocks compared to the market
* **Weighted\_Average\_Diluted\_Outstanding\_Shares**: Weighted average diluted shares outstanding of Vietnamese stocks
* **Weighted\_Average\_Outstanding\_Shares**: Weighted average number of shares issued in Vietnam
* **Weighted\_Average\_Outstanding\_Shares\_in\_the\_period**: Weighted average number of shares outstanding during the period for Vietnamese stocks
### market
classification
Summary
This document provides information about the GICS industry classification of Vietnamese stocks.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory('vn_stock', 20200101, 20251120)
cf.get_df('fiintek-gics')
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20080101 | 00 22 \* \* 1-5 | | |
Item List
* fiintek-gics: GICS industry classification of Vietnamese stocks price\_volume
Summary
This document provides an overview of the Vietnamese stock price and volume data available in the data catalog.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("OpenPrice")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20140101 | 00 22 \* \* 1-5 | | |
Item List
Price Data
* OpenPrice: The market price of Vietnamese stocks
* ClosePrice: Closing price of Vietnamese stocks
* HighestPrice: High price of Vietnamese stocks
* LowestPrice: Low prices of Vietnamese stocks
* AveragePrice: Average prices of Vietnamese stocks
Volume Data
* TotalVolume: volume of Vietnamese stocks
cax
Summary
This document provides information on Vietnamese stock data, including adjustment factors and total return coefficients.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("vnm-stock-adjust_factor")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 20080101 | 00 22 \* \* 1-5 | | |
Item List
Adjustment Factors
* **vnm-stock-adjust\_factor**: Adjustment factor of Vietnamese stocks
Total Return Factors
* **vnm-stock-total\_return\_factor**: Total return coefficient of Vietnamese stocks
{% hint style="info" %}
If you would like to use the data, please contact the admin.
{% endhint %}
## ID STOCK
### financial
balance\_sheet
Summary
This document provides an overview of the balance sheet data items available for the Indonesia stock universe.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1\. **Default behavior**: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2\. **Unpivot**: In this case, there will be 4 columns (‘id’, ‘pit’, ‘fiscal’, ‘value’). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.
**Since the collection of PIT data started recently, data before 2025-05-30, provided with 90 days delayed.**
Example code
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("assets", mode='unpivot')
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | |
Item List
Balance Sheet Items
* **assets**: Total resources owned by the company with future economic value.
* **current\_liabilities**: Obligations due within one year or operating cycle.
* **inventories**: Goods held for sale or used in production.
* **equity**: Residual ownership interest after deducting liabilities from assets.
* **goodwill**: Premium paid over fair value in acquisitions, representing intangible value.
* **current\_inventories**: Inventory expected to be sold within one year.
* **non\_current\_assets**: Long-term assets not expected to convert to cash within one year.
* **current\_assets**: Assets expected to convert to cash within one year.
* **non\_current\_liabilities**: Long-term obligations due beyond one year.
* **treasury\_stocks**: Company's own shares repurchased and held by the company.
* **investments**: Holdings in securities, subsidiaries, or other investment instruments.
* **cash**: Cash on hand and demand deposits readily available.
* **liabilities**: Total financial obligations owed to creditors.
* **property\_plant\_and\_equipment**: Tangible fixed assets used in operations (PP\&E).
* **common\_stocks**: Equity shares representing basic ownership in the company.
* **payments\_for\_acquisition\_of\_intangible\_assets**: Cash outflows for purchasing intangible assets like patents, trademarks, licenses, or software. cash\_flow
Summary
This document provides an overview of the cash flow data items available for the Indonesia stock universe.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1\. **Default behavior**: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2\. **Unpivot**: In this case, there will be 4 columns (‘id’, ‘pit’, ‘fiscal’, ‘value’). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.
**Since the collection of PIT data started recently, data before 2025-05-30, provided with 90 days delayed.**
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401))
df = cf.get_df("cash_flows_from_operating_activities", mode='unpivot')
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | |
Item List
Cash Flow Items
* **cash\_flows\_from\_operating\_activities**: Net cash generated or used by core business operations.
* **cash\_flows\_from\_investing\_activities**: Net cash from buying/selling long-term assets and investments.
* **payments\_for\_acquisition\_of\_property\_plant\_and\_equipment**: Cash outflows for purchasing fixed assets (PP\&E).
* **cash\_flows\_from\_financing\_activities**: Net cash from debt, equity, and dividend transactions.
* **cash\_and\_cash\_equivalents\_cash\_flows**: Net change in cash and highly liquid short-term investments.
* **payments\_for\_acquisition\_of\_intangible\_assets**: Cash outflows for purchasing intangible assets like patents or licenses. income\_statement
Summary
This document provides an overview of the income statement data items available for the Indonesia stock universe.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1\. **Default behavior**: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2\. **Unpivot**: In this case, there will be 4 columns (‘id’, ‘pit’, ‘fiscal’, ‘value’). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.
**Since the collection of PIT data started recently, data before 2025-05-30, provided with 90 days delayed.**
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("interest_income", mode='unpivot')
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | |
Item List
Income Statement Items
* **interest\_income**: Interest earned from loans, deposits, and interest-bearing assets.
* **depreciation\_and\_amortisation\_expenses**: Non-cash allocation of asset costs over their useful lives.
* **profit\_from\_operation**: Profit from core business operations before financing and taxes.
* **finance\_income**: Income from financing activities including interest and lease income.
* **net\_investment\_income**: Net returns from investments after deducting related expenses.
* **non\_operating\_income**: Revenue from activities outside primary business operations.
* **gross\_profit**: Revenue minus cost of goods sold.
* **cost\_of\_sales\_and\_revenue**: Direct costs of producing goods or services sold.
* **profit\_loss\_before\_income\_tax**: The total earnings after all expenses except income tax have been deducted.
ratio
Summary
The data consists of various financial ratios related to Indonesian stocks, including metrics such as price-to-earnings ratio, free cash flow per share, cash ratio, gross profit margin, operating profit margin, asset turnover, price-to-sales ratio, and net profit margin. Each item is associated with a description indicating its relevance to the financial income statement.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1\. **Default behavior**: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2\. **Unpivot**: In this case, there will be 4 columns (‘id’, ‘pit’, ‘fiscal’, ‘value’). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.
**Since the collection of PIT data started recently, data before 2025-08-08, provided with 90 days delayed.**
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("per", mode='unpivot')
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | 1d |
Item List
* **asset\_turnover**: Ratio measuring how efficiently assets generate revenue (Revenue/Total Assets).
* **cash\_ratio**: Liquidity ratio showing ability to cover current liabilities with cash (Cash/Current Liabilities).
* **free\_cashflow\_per\_share**: Free cash flow divided by shares outstanding.
* **gross\_profit\_margin**: Percentage of revenue remaining after cost of sales (Gross Profit/Revenue).
* **net\_profit\_margin**: Percentage of revenue retained as net profit after all expenses.
* **operating\_profit\_margin**: Percentage of revenue from operations before financing and taxes.
* **per**: Price-to-Earnings Ratio, valuation metric (Stock Price/Earnings Per Share).
* **psr**: Price-to-Sales Ratio, valuation metric (Market Cap/Revenue).
### market
***
### hidden: true
classification
price\_volume
Summary
This document provides an overview of the Indonesia stock data catalog, including various price and volume metrics.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20200101, 20200201)
df = cf.get_df("price_close")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | |
Item List
Price Metrics
* **price\_open**: indonesia stock price\_open
* **price\_close**: indonesia stock price\_close
* **price\_high**: indonesia stock price\_high
* **price\_low**: indonesia stock price\_low
Volume Metrics
* **volume\_sum**: indonesia stock volume
Adjustment Factors
* **adjust\_factor**: adjust\_factor calculated by stock\_split event
* **total\_return\_factor**: total\_return\_factor calculated by stock\_split \* dividend\_factor
Stock Information
* **stock\_outstanding**: IDN stock listed shares.
**-> \[CAUTION] 20250101 \~ 20250807 IS FFILL DATA. IT WILL BE FIXED LATER** cax
Summary
This document provides information on the IDN stock data catalog, including sector codes and sharia compliance for stocks.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory('id_stock', 20200101, 20251105)
cf.get_df('sector_code')
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | |
Item List
Sector Information
* **id-stock-sector\_code**: IDN stock sector\_code. sector\_name is on IdTable.
\[CAUTION] 20250101 \~ 20250807 IS FFILL DATA. IT WILL BE FIXED LATER
Sector code list
**A. ENERGY (IDXENERGY)**
**A11 Oil & Gas**
* A111 Oil & Gas Production and Refining
* A112 Oil & Gas Storage and Distribution
**A12 Coal**
* A121 Coal Production
* A122 Coal Distribution
**A13 Oil, Gas & Coal Supports**
* A131 Oil & Gas Drilling Services
* A132 Oil, Gas & Coal Equipment and Services
**A21 Alternative Energy Equipment**
* A211 Alternative Energy Equipment
**A22 Alternative Fuels**
* A221 Alternative Fuels
***
**B. BASIC MATERIALS (IDXBASIC)**
**B11 Chemicals**
* B111 Basic Chemicals
* B112 Agricultural Chemicals
* B113 Specialty Chemicals
**B13 Containers & Packaging**
* B131 Containers & Packaging
**B14 Metals & Minerals**
* B141 Aluminum
* B142 Copper
* B143 Gold
* B144 Steel & Iron
* B145 Precious Metals & Minerals
* B146 Other Metals & Minerals
* B147 Mining Equipment & Services
**B15 Forestry & Paper**
* B151 Wood
* B152 Paper
* B153 Other Forest Products
***
**C. INDUSTRIALS (IDXINDUST)**
**C11 Aerospace & Defense**
* C111 Aerospace & Defense
**C12 Building Products & Fixtures**
* C121 Building Products & Equipment
**C13 Electrical**
* C131 Electrical Components & Equipment
* C132 Heavy Electrical Equipment
**C14 Machinery**
* C141 Construction Machinery & Heavy Vehicles
* C142 Agricultural Machinery
* C143 Industrial Machinery & Components
**C21 Diversified Industrial Trading**
* C211 Industrial Goods Trading
**C22 Commercial Services**
* C221 Commercial Printing
* C222 Environmental & Facility Management Services
* C223 Office Equipment
**C23 Professional Services**
* C224 Business Support Services
* C231 Personnel Services
* C232 Research & Consulting Services
**C31 Multi-sector Holdings**
* C311 Multi-sector Holding Companies
***
**D. CONSUMER NON-CYCLICALS (IDXNONCYC)**
**D11 Food & Staples Retailing**
* D111 Pharmaceutical Retail & Distribution
* D112 Food Retail & Distribution
* D113 Supermarkets
**D21 Beverages**
* D211 Alcoholic Beverages
* D212 Soft Drinks
**D22 Processed Foods**
* D221 Dairy Products
* D222 Processed Foods
**D23 Agricultural Products**
* D231 Fish, Meat & Poultry Products
* D232 Plantations & Food Crops
**D31 Tobacco**
* D311 Cigarettes
**D41 Household Products**
* D411 Household Products
**D42 Personal Care Products**
* D421 Personal Care Products
***
**E. CONSUMER CYCLICALS (IDXCYCILC)**
**E11 Auto Components**
* E111 Automotive Parts
* E112 Tires
**E12 Automobiles**
* E121 Automobile Manufacturers
* E122 Motorcycle Manufacturers
**E21 Household Goods**
* E211 Home Furniture Manufacturers
* E212 Home Appliances
* E213 Household Goods
**E31 Consumer Electronics**
* E311 Consumer Electronics
**E32 Sport Equipment**
* E321 Sports Equipment & Hobby Goods
**E41 Apparel & Luxury Goods**
* E411 Apparel, Accessories & Bags
* E412 Footwear
* E413 Textiles
**E51 Tourism & Recreation**
* E511 Amusement Facilities
* E512 Hotels, Resorts & Cruise Lines
* E513 Travel Agencies
* E514 Recreation & Sports Facilities
* E515 Restaurants
**E52 Education & Support Services**
* E521 Education Services
* E522 Consumer Support Services
**E61 Media**
* E611 Advertising
* E612 Broadcasting
* E613 Pay Broadcasting
* E614 Publishing
**E62 Entertainment & Movie Production**
* E621 Entertainment & Film
**E71 Consumer Distributors**
* E711 Consumer Goods Distribution
**E72 Internet & Homeshop Retail**
* E721 Internet & Home Shopping Retail
**E73 Department Stores**
* E731 Department Stores
**E74 Specialty Retail**
* E741 Apparel & Textile Retail
* E742 Electronics Retail
* E743 Home Goods Retail
* E744 Specialty Stores
* E745 Automotive Retail
***
**F. HEALTHCARE (IDXHEALTH)**
**F11 Healthcare Equipment & Supplies**
* F111 Medical Equipment
* F112 Health Equipment Supply & Distribution
**F12 Healthcare Providers**
* F121 Healthcare Providers
**F21 Pharmaceuticals**
* F211 Pharmaceuticals
**F22 Healthcare Research**
* F221 Health Research
***
**G. FINANCIALS (IDXFINANCE)**
**G11 Banks**
* G111 Banks
**G21 Consumer Financing**
* G211 Consumer Financing
**G22 Business Financing**
* G221 Venture Capital
* G222 Specialized Business Financing
**G31 Investment Services**
* G311 Investment Management
* G312 Investment Banking & Brokerage
* G313 Market Operators
* G314 Investment Service Support
**G41 Insurance**
* G411 Insurance Brokers
* G412 General Insurance
* G413 Life Insurance
* G414 Reinsurance
**G51 Holding & Investment Companies**
* G511 Financial Holding Companies
* G512 Investment Companies
***
**H. PROPERTY & REAL ESTATE (IDXPROPERT)**
**H11 Real Estate Management & Development**
* H111 Real Estate Developers & Operators
* H112 Real Estate Services
***
**I. TECHNOLOGY (IDXTECHNO)**
**I11 Online Applications & Services**
* I111 Internet Applications & Services
**I12 IT Services & Consulting**
* I121 IT Services & Consulting
**I13 Software**
* I131 Software
**I21 Networking Equipment**
* I211 Networking Equipment
**I22 Computer Hardware**
* I221 Computer Equipment
**I23 Electronic Equipment, Instruments & Components**
* I231 Electronic Devices & Instruments
* I232 Electronic Components & Semiconductors
***
**J. INFRASTRUCTURE (IDXINFRA)**
**J11 Transport Infrastructure Operator**
* J111 Airport Operators
* J112 Toll Road & Rail Operators
* J113 Port Operators
**J12 Heavy Construction & Civil Engineering**
* J211 Building Construction
**J31 Telecommunication Service**
* J311 Wired Telecommunications
* J312 Integrated Telecommunications
**J32 Wireless Telecommunication Services**
* J321 Wireless Telecommunications
**J41 Electric Utilities**
* J411 Electric Utilities
**J42 Gas Utilities**
* J421 Gas Utilities
**J43 Water Utilities**
* J431 Water Utilities
***
**K. TRANSPORTATION & LOGISTICS (IDXTRANS)**
**K11 Airlines**
* K111 Airlines
**K12 Passenger Marine Transportation**
* K121 Passenger Sea Transport
**K13 Passenger Land Transportation**
* K131 Railways
* K132 Road Transport
**K21 Logistics & Deliveries**
* K211 Logistics & Delivery
***
**Z. LISTED INVESTMENT PRODUCT**
**Z11 Investment Trusts**
* Z111 Mutual Funds / ETFs
* Z112 Real Estate Investment Funds
* Z113 Infrastructure Investment Funds
**Z21 Bonds**
* Z211 Government Bonds
* Z212 Corporate Bonds
Sharia Compliance
```python
from finter.data import ContentFactory
cf = ContentFactory('id_stock', 20200101, 20251105)
cf.get_df('sharia')
```
* **id-stock-sharia**: IDN stock whether sharia or not;
* history (\~ 20241231) : ISSI constituent stocks
* live (20250807 \~) : daily stock\_master data Summary This document provides information on various index constituents available in the id\_stock universe.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20200101, 20200201)
df = cf.get_df("jii-constituent")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19700101 | 10 22 \* \* 1-5 | | |
Item List
Index Constituents
* jii-constituent: index constituent - JII
* kompas100-constituent: index constituent - KOMPAS100
* lq45-constituent: index constituent - LQ45
* issi-constituent: index constituent - ISSI
## COMMON
### macro
macro
Summary
The data consists of a collection of financial metrics related to the yield curve, specifically including discount factors, zero-coupon rates, and forward rates. These items are updated weekly and have been valid since January 1, 1961, with a future start date set for June 7, 2025.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("common", 20200101, 20200201)
df = cf.get_df("disc_factors")
```
Metadata
| valid from | delivery schedule | time zone | data frequency |
| ---------- | ----------------- | --------- | -------------- |
| 19610101 | 15 22 \* \* \* | | 1d |
Item List
* **disc\_factors**: : updated weekly pit. disc\_factors.
* **fwd\_rates**: : updated weekly pit. fwd\_rates.
* **zeros**: : updated weekly pit. zeros.
{% hint style="info" %}
If you would like to use the data, please contact the admin.
{% endhint %}
## BTCUSDT FUTURE BINANCE
### market
market
Summary
This document provides information on the BTC/USDT futures data available from Binance, including price and volume metrics.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("btcusdt_future_binance", 202512010000, 202512121600)
df = cf.get_df("price_close")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ------------------ | --------- | -------------- |
| 20170101 | 10 0,8,16 \* \* \* | | |
Item List
* **price\_close**: btcusdt usd margin price\_close
* **volume**: btcusdt usd margin volume
## BTCUSDT SPOT BINANCE
### market
market
Summary
This document provides information on the BTC/USDT spot market data available from Binance, including price and volume metrics.
Example code
```python
from finter.data import ContentFactory
cf = ContentFactory("btcusdt_spot_binance", 202512010000, 202512121600)
df = cf.get_df("volume")
```
Metadata
| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ------------------ | --------- | -------------- |
| 20170101 | 10 0,8,16 \* \* \* | | |
Item List
* volume: btcusdt spot volume
* price\_close: btcusdt spot price\_close
## .GITBOOK