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https://github.com/OpenBB-finance/OpenBB.git
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ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
"""Cramer Model"""
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__docformat__ = "numpy"
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import logging
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import os
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import re
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import certifi
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import numpy as np
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import pandas as pd
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import yfinance as yf
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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 request
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logger = logging.getLogger(__name__)
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# Necessary only for installer to identify where SSL certs are
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os.environ["REQUESTS_CA_BUNDLE"] = certifi.where()
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os.environ["SSL_CERT_FILE"] = certifi.where()
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@log_start_end(log=logger)
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def get_cramer_daily(inverse: bool = True) -> pd.DataFrame:
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"""Scrape the daily recommendations of Jim Cramer
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Parameters
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----------
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inverse: bool
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Whether to include inverse
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Returns
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-------
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pd.DataFrame
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Datafreme of daily Cramer recommendations
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"""
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link = (
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"https://madmoney.thestreet.com/screener/index.cfm?showview=stocks&showrows=500"
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)
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r = request(
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link,
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method="POST",
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headers={
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"Content-Type": "application/x-www-form-urlencoded",
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"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
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"AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.3 Safari/605.1.15",
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},
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)
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if r.status_code != 200:
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return pd.DataFrame()
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soup = BeautifulSoup(r.text, "html.parser")
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table = soup.find_all("table")[0]
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trs = table.find_all("tr")
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recs = {
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"1": "Sell",
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"2": "Sell",
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"3": "Hold",
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"4": "Buy",
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"5": "Buy",
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}
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rec = [recs[tr.find_all("td")[3].find("img")["src"][-5]] for tr in trs[1:]]
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df = pd.read_html(r.text)[0]
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df["Symbol"] = df.Company.apply(lambda x: re.findall(r"[\w]+", x)[-1])
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last_price = [
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round(
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yf.download(ticker, period="1d", interval="1h", progress=False)["Close"][
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-1
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],
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2,
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)
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for ticker in df.Symbol
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]
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df["LastPrice"] = last_price
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df["Price"] = df.Price.apply(lambda x: float(x.strip("$")))
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df = df.drop(columns=["Segment", "Call", "Portfolio"])
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df["Change (%)"] = 100 * np.round((df["LastPrice"] - df["Price"]) / df.LastPrice, 4)
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df["Recommendation"] = rec
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df["Company"] = df.apply(lambda x: x.Company.replace(f"({x.Symbol})", ""), axis=1)
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cols = [
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"Date",
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"Company",
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"Symbol",
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"Price",
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"LastPrice",
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"Change (%)",
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"Recommendation",
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]
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if inverse:
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df["InverseCramer"] = df["Recommendation"].apply(
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lambda x: ["Buy", "Sell"][x == "Buy"]
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)
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cols.append("InverseCramer")
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return df[cols]
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@log_start_end(log=logger)
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def get_cramer_ticker(symbol: str) -> pd.DataFrame:
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"""Get cramer recommendations from beginning of year for given ticker
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Parameters
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----------
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symbol: str
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Ticker to get recommendations for
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Returns
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-------
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pd.DataFrame
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Dataframe with dates and recommendations
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"""
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link = "https://raw.githubusercontent.com/jmaslek/InverseCramer/main/AllRecommendations.csv"
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df = pd.read_csv(link, index_col=0)
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df["Date"] = pd.to_datetime(df["Date"].apply(lambda x: x + "/2022"))
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return df[df.Symbol == symbol].reset_index(drop=True)
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