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bfc4c021e7
* Report fixes * replace wrong typing * mypy * mypy again Co-authored-by: james <jmaslek11@gmail.com>
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
"""Blockchain Center Model"""
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import json
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import logging
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from datetime import datetime
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from typing import Optional
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import pandas as pd
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import requests
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from bs4 import BeautifulSoup
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import get_user_agent, str_date_to_timestamp
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logger = logging.getLogger(__name__)
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DAYS = [30, 90, 365]
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@log_start_end(log=logger)
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def get_altcoin_index(
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period: int = 30,
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start_date: str = "2010-01-01",
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end_date: Optional[str] = None,
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) -> pd.DataFrame:
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"""Get altcoin index overtime
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[Source: https://blockchaincenter.net]
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Parameters
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----------
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period: int
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Number of days {30,90,365} to check performance of coins and calculate the altcoin index.
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E.g., 365 checks yearly performance, 90 will check seasonal performance (90 days),
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30 will check monthly performance (30 days).
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start_date : str
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Initial date, format YYYY-MM-DD
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end_date : Optional[str]
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Final date, format YYYY-MM-DD
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Returns
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-------
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pd.DataFrame
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Date, Value (Altcoin Index)
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"""
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if end_date is None:
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end_date = datetime.now().strftime("%Y-%m-%d")
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if period not in DAYS:
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return pd.DataFrame()
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soup = BeautifulSoup(
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requests.get(
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"https://www.blockchaincenter.net/altcoin-season-index/",
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headers={"User-Agent": get_user_agent()},
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).content,
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"html.parser",
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)
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script = soup.select_one(f'script:-soup-contains("chartdata[{period}]")')
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string = script.contents[0].strip()
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initiator = string.index(f"chartdata[{period}] = ") + len(f"chartdata[{period}] = ")
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terminator = string.index(";")
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dict_data = json.loads(string[initiator:terminator])
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df = pd.DataFrame(
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zip(dict_data["labels"]["all"], dict_data["values"]["all"]),
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columns=("Date", "Value"),
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)
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df["Date"] = pd.to_datetime(df["Date"])
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df["Value"] = df["Value"].astype(int)
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df = df.set_index("Date")
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ts_start_date = str_date_to_timestamp(start_date)
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ts_end_date = str_date_to_timestamp(end_date)
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df = df[
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(df.index > datetime.fromtimestamp(ts_start_date))
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& (df.index < datetime.fromtimestamp(ts_end_date))
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]
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return df
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