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81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
"""Cramer Model"""
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__docformat__ = "numpy"
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import re
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import pandas as pd
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import requests
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import numpy as np
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import yfinance as yf
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from bs4 import BeautifulSoup
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from gamestonk_terminal.helper_funcs import get_user_agent
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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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cramer_link = "https://madmoney.thestreet.com/screener/index.cfm"
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r = requests.get(cramer_link, headers={"User-Agent": get_user_agent()})
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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 = []
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for tr in trs[1:]:
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rec.append(recs[tr.find_all("td")[3].find("img")["src"][-5]])
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df = pd.read_html("https://madmoney.thestreet.com/screener/index.cfm")[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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for ticker in df.Symbol:
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last_price.append(
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round(
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yf.download(ticker, period="1d", interval="1h", progress=False)[
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"Close"
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][-1],
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2,
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)
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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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