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Add cost to borrow of stocks. Data from IBKR (#1663)
* add ctb to dps * add test for ctb * reformat using black * fix tests for ctb Co-authored-by: didierlopes.eth <[email protected]> Co-authored-by: jmaslek <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,38 @@ | ||
| """ Interactive Broker Model """ | ||
| __docformat__ = "numpy" | ||
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| import logging | ||
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| import ftplib | ||
| from io import BytesIO | ||
| import pandas as pd | ||
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| from openbb_terminal.decorators import log_start_end | ||
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| logger = logging.getLogger(__name__) | ||
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| @log_start_end(log=logger) | ||
| def get_cost_to_borrow() -> pd.DataFrame: | ||
| """Get stocks with highest cost to borrow [Source: Interactive Broker] | ||
| Returns | ||
| ------- | ||
| pd.DataFrame | ||
| Cost to borrow | ||
| """ | ||
| ftp = ftplib.FTP("ftp3.interactivebrokers.com", "shortstock") | ||
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| flo = BytesIO() | ||
| ftp.retrbinary("RETR usa.txt", flo.write) | ||
| flo.seek(0) | ||
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| data = pd.read_csv(flo, sep="|", skiprows=1) | ||
| data = data[["#SYM", "FEERATE", "AVAILABLE"]] | ||
| data["AVAILABLE"] = data["AVAILABLE"].replace(">10000000", 10000000) | ||
| data.fillna(0, inplace=True) | ||
| data["AVAILABLE"] = data["AVAILABLE"].astype(int) | ||
| data.sort_values(by=["FEERATE"], ascending=False, inplace=True) | ||
| data["FEERATE"] = data["FEERATE"].apply(lambda x: str(x) + "%") | ||
| data.columns = ["Symbol", "Fees", "Available"] | ||
| return data |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,44 @@ | ||
| """ Interactive Broker View """ | ||
| __docformat__ = "numpy" | ||
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| import logging | ||
| import os | ||
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| from openbb_terminal.decorators import log_start_end | ||
| from openbb_terminal.helper_funcs import export_data, print_rich_table | ||
| from openbb_terminal.rich_config import console | ||
| from openbb_terminal.stocks.dark_pool_shorts import ibkr_model | ||
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| logger = logging.getLogger(__name__) | ||
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| @log_start_end(log=logger) | ||
| def display_cost_to_borrow(num_stocks: int, export: str): | ||
| """Display stocks with highest cost to borrow. [Source: Interactive Broker] | ||
| Parameters | ||
| ---------- | ||
| num_stocks: int | ||
| Number of stocks to display | ||
| export : str | ||
| Export dataframe data to csv,json,xlsx file | ||
| """ | ||
| df = ibkr_model.get_cost_to_borrow().head(num_stocks) | ||
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| if df.empty: | ||
| console.print("No data found.") | ||
| else: | ||
| print_rich_table( | ||
| df, | ||
| headers=list(df.columns), | ||
| show_index=False, | ||
| title="Highest Cost to Borrow", | ||
| ) | ||
| console.print("") | ||
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| export_data( | ||
| export, | ||
| os.path.dirname(os.path.abspath(__file__)), | ||
| "cost_to_borrow", | ||
| df, | ||
| ) |
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62 changes: 62 additions & 0 deletions
62
website/content/terminal/stocks/dark_pool_shorts/ctb/_index.md
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,62 @@ | ||
| ``` | ||
| usage: ctb [-n NUM] [--export {csv,json,xlsx}] [-h] | ||
| ``` | ||
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| Request a list of stocks that have the highest cost to borrow [Source: Interactive Broker] | ||
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| ``` | ||
| optional arguments: | ||
| -n NUM, --num NUM Number of the stocks with high cost to borrow to retrieve. (default: 20) | ||
| --export {csv,json,xlsx} | ||
| Export dataframe data to csv,json,xlsx file | ||
| -h, --help show this help message (default: False) | ||
| ``` | ||
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| Example: | ||
| ``` | ||
| 2022 Apr 07, 09:47 (�) /stocks/dps/ $ ctb | ||
| Highest Cost to Borrow | ||
| ┌─────────┬───────────┬───────────┐ | ||
| │ Symbol │ Fees │ Available │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ PIK │ 457.4402% │ 7000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ EVTL │ 413.7294% │ 10000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ CELZ │ 330.9411% │ 100000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ DRCT │ 304.9628% │ 300000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ MYNZ │ 291.8163% │ 3000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ DCFC │ 279.0673% │ 10000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ FGFPP │ 278.422% │ 8000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ ZTEK │ 250.0307% │ 75000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ FRGE │ 245.1883% │ 700 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ SATL │ 240.7341% │ 500 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ ARMR │ 240.5414% │ 5000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ PILL │ 223.538% │ 55000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ ARQQ │ 217.3978% │ 3000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ NRSN │ 216.9888% │ 6000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ ZIONL │ 215.2871% │ 35000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ BLBX │ 213.441% │ 15000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ WINSF │ 212.907% │ 10000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ SKYH │ 206.7746% │ 25000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ SOHON │ 205.1096% │ 40000 │ | ||
| ├─────────┼───────────┼───────────┤ | ||
| │ MH PRA │ 202.1235% │ 100000 │ | ||
| └─────────┴───────────┴───────────┘ | ||
| ``` |
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