prom
- Model
- Chart
Get all FINRA ATS data, and parse most promising tickers based on linear regression
Source Code: [link]
openbb.stocks.dps.prom(limit: int = 1000, tier_ats: str = "T1")
Parameters
Name | Type | Description | Default | Optional |
---|---|---|---|---|
limit | int | Number of tickers to filter from entire ATS data based on the sum of the total weekly shares quantity | 1000 | True |
tier_ats | int | Tier to process data from: T1, T2 or OTCE | T1 | True |
Returns
Type | Description |
---|---|
Tuple[pd.DataFrame, Dict] | Dark Pools (ATS) Data, Tickers from Dark Pools with better regression slope |
Display dark pool (ATS) data of tickers with growing trades activity. [Source: FINRA]
Source Code: [link]
openbb.stocks.dps.prom_chart(input_limit: int = 1000, limit: int = 10, tier: str = "T1", export: str = "", sheet_name: Optional[str] = None, external_axes: bool = False)
Parameters
Name | Type | Description | Default | Optional |
---|---|---|---|---|
input_limit | int | Number of tickers to filter from entire ATS data based on the sum of the total weekly shares quantity | 1000 | True |
limit | int | Number of tickers to display from most promising with better linear regression slope | 10 | True |
tier | str | Tier to process data from: T1, T2 or OTCE | T1 | True |
export | str | Export dataframe data to csv,json,xlsx file | True | |
external_axes | bool | Whether to return the figure object or not, by default False | False | True |
Returns
This function does not return anything