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"""Chartexchange model"""
from __future__ import annotations
import logging
import pandas as pd
import requests
from bs4 import BeautifulSoup
from openbb_terminal.decorators import log_start_end
from openbb_terminal.helper_funcs import get_user_agent
from openbb_terminal.rich_config import console
from openbb_terminal.stocks.options.op_helpers import convert
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def get_option_history(
symbol: str = "GME",
date: str = "2021-02-05",
call: bool = True,
price: str | int | float = "90",
) -> pd.DataFrame:
"""Historic prices for a specific option [chartexchange]
Parameters
----------
symbol : str
Ticker symbol to get historical data from
date : str|int|float
Date as a string YYYYMMDD
call : bool
Whether to show a call or a put
price : str
Strike price for a specific option
Returns
-------
historical : pd.Dataframe
Historic information for an option
"""
url = (
f"https://chartexchange.com/symbol/opra-{symbol.lower()}{date.replace('-', '')}"
)
url += f"{'c' if call else 'p'}{float(price):g}/historical/"
data = requests.get(url, headers={"User-Agent": get_user_agent()}).content
soup = BeautifulSoup(data, "html.parser")
table = soup.find("div", attrs={"style": "display: table; font-size: 0.9em; "})
if table:
rows = table.find_all("div", attrs={"style": "display: table-row;"})
else:
return pd.DataFrame()
clean_rows = []
if rows:
for row in rows[1:]:
item = row.find_all("div")
clean_rows.append([x.text for x in item])
else:
console.print("No data for this option\n")
return pd.DataFrame()
df = pd.DataFrame()
df["Date"] = [x[0] for x in clean_rows]
df["Open"] = [convert(x[2], ",") for x in clean_rows]
df["High"] = [convert(x[3], ",") for x in clean_rows]
df["Low"] = [convert(x[4], ",") for x in clean_rows]
df["Close"] = [convert(x[5], ",") for x in clean_rows]
df["Change"] = [convert(x[6], "%") for x in clean_rows]
df["Volume"] = [convert(x[7], ",") for x in clean_rows]
df["Open Interest"] = [convert(x[8], ",") for x in clean_rows]
df["Change Since"] = [convert(x[9], "%") for x in clean_rows]
return df
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