Merge pull request #3 from DidierRLopes/marketbeat

Improve user experience of disc/ratings

ROADMAP.md

@@ -33,6 +33,7 @@
 * [x] Top Losers (@didier) - [PR #171](https://github.com/DidierRLopes/GamestonkTerminal/pull/171)
 * [x] ARK orders (@aia) - [PR #140](https://github.com/DidierRLopes/GamestonkTerminal/pull/140)
 * [x] Analyse sectors/industry performance, overview and valuation from Finviz (@didier) - [PR #315](https://github.com/DidierRLopes/GamestonkTerminal/pull/315)
+* [x] Add top ratings updates (@hinxx) - [PR #350](https://github.com/DidierRLopes/GamestonkTerminal/pull/350)
 
 **NEXT**
 

gamestonk_terminal/README.md

@@ -79,6 +79,7 @@ Command|Description|Source
 `valuation`     |valuation of sectors, industry, country |[Finviz](https://finviz.com)
 `performance`   |performance of sectors, industry, country |[Finviz](https://finviz.com)
 `spectrum`      |spectrum of sectors, industry, country |[Finviz](https://finviz.com)
+`ratings `      |top ratings updates |[MarketBeat](https://marketbeat.com)
 
  
 
@@ -351,4 +352,4 @@ technical      |technical (e.g. Beta, SMA50, 52W Low, RSI, Change)
 signals        |view filter signals (e.g. -s top_gainers)
 
 
- 
+ 

gamestonk_terminal/discovery/README.md

@@ -32,6 +32,8 @@ This menu aims to discover new stocks, and the usage of the following commands a
   * performance of sectors, industry, country [Finviz]
 * [spectrum](#spectrum)
   * spectrum of sectors, industry, country [Finviz]
+* [ratings](#ratings)
+  * top ratings updates [MarketBeat]
 
 
 ## map <a name="map"></a>
@@ -218,3 +220,18 @@ Spectrum of sectors, industry, country. [Source: Finviz]
 * -g : Data group (sector, industry or country). Default: Sector.
 
 ![cntry_spec](https://user-images.githubusercontent.com/25267873/113639067-48fd5e80-9670-11eb-95cf-0931845ddd12.png)
+
+
+## ratings <a name="ratings"></a>
+
+```
+usage: ratings [-t N_THRESHOLD]
+```
+
+Top ratings updates. [Source: MarketBeat]
+
+* -t : Minimum threshold in percentage change between current and target price to show ratings. Default: 100.
+
+<img width="963" alt="ratings" src="https://user-images.githubusercontent.com/25267873/115095983-4544c400-9f1b-11eb-8869-8ec8a0f8eae0.png">
+
+

gamestonk_terminal/discovery/disc_controller.py

@@ -20,6 +20,7 @@ from gamestonk_terminal.discovery import (
     spachero_view,
     unusual_whales_view,
     yahoo_finance_view,
+    marketbeat_view,
 )
 
 
@@ -46,6 +47,7 @@ class DiscoveryController:
         "valuation",
         "performance",
         "spectrum",
+        "ratings",
     ]
 
     def __init__(self):
@@ -87,6 +89,7 @@ class DiscoveryController:
         print("   valuation      valuation of sectors, industry, country [Finviz]")
         print("   performance    performance of sectors, industry, country [Finviz]")
         print("   spectrum       spectrum of sectors, industry, country [Finviz]")
+        print("   ratings        top ratings updates [MarketBeat]")
         print("")
 
     def switch(self, an_input: str):
@@ -187,6 +190,10 @@ class DiscoveryController:
             other_args, "spectrum"
         )
 
+    def call_ratings(self, other_args: List[str]):
+        """Process ratings command"""
+        marketbeat_view.ratings_view(other_args)
+
 
 def menu():
     """Discovery Menu"""

gamestonk_terminal/discovery/marketbeat_model.py

@@ -0,0 +1,93 @@
+""" MarketBeat Model """
+__docformat__ = "numpy"
+
+from typing import List
+import requests
+from bs4 import BeautifulSoup
+
+from gamestonk_terminal.helper_funcs import get_user_agent
+
+
+def get_ratings_html(url_ratings: str) -> str:
+    """Wraps HTTP requests.get for testibility
+
+    Parameters
+    ----------
+    url_ratings : str
+        Ratings URL
+
+    Returns
+    -------
+    str
+        HTML page of ratings
+    """
+    ratings_html = requests.get(
+        url_ratings, headers={"User-Agent": get_user_agent()}
+    ).text
+
+    return ratings_html
+
+
+def get_ratings() -> List[dict]:
+    """Returns a list of ratings
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    list
+        Ratings list
+    """
+
+    ratings = list()
+    url_ratings = "https://www.marketbeat.com/ratings/"
+
+    text_soup_ratings = BeautifulSoup(
+        get_ratings_html(url_ratings),
+        "lxml",
+    )
+
+    for stock_rows in text_soup_ratings.findAll("tr"):
+        tds = stock_rows.findAll("td")
+        if len(tds) == 8:
+            rating = {}
+            # company
+            td = tds[0]
+            company = td.find("div", {"class": "title-area"}).text
+            ticker = td.find("div", {"class": "ticker-area"}).text
+            rating["company"] = company
+            rating["ticker"] = ticker
+            # action
+            td = tds[1]
+            action = td.text
+            rating["action"] = action
+            # brokerage
+            td = tds[2]
+            brokerage = td.find("a").text
+            rating["brokerage"] = brokerage
+            # current price
+            td = tds[3]
+            if td.find("span"):
+                td.span.extract()
+            current_price = td.text
+            rating["current_price"] = current_price
+            # target price
+            td = tds[4]
+            target_price = td.text
+            rating["target_price"] = target_price
+            # new rating
+            td = tds[5]
+            new_rating = td.text
+            rating["rating"] = new_rating
+            # impact on price
+            td = tds[6]
+            if td.find("a"):
+                impact = td.find("a").text
+            else:
+                impact = td.text
+            rating["impact"] = impact
+            ratings.append(rating)
+
+    return ratings

gamestonk_terminal/discovery/marketbeat_view.py

@@ -0,0 +1,116 @@
+""" MarketBeat View """
+__docformat__ = "numpy"
+
+import argparse
+import numpy as np
+import pandas as pd
+from typing import List
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    parse_known_args_and_warn,
+)
+
+from gamestonk_terminal.discovery import marketbeat_model
+
+
+def ratings_view(other_args: List[str]):
+    """Prints top ratings updates
+
+    Parameters
+    ----------
+    other_args : List[str]
+        argparse other args - ["-t", "100"]
+    """
+    parser = argparse.ArgumentParser(
+        add_help=False,
+        prog="ratings",
+        description="""Top ratings updates. [Source: MarketBeat]""",
+    )
+    parser.add_argument(
+        "-t",
+        "--threshold",
+        action="store",
+        dest="n_threshold",
+        type=check_positive,
+        default=100,
+        help="Minimum threshold in percentage change between current and target price to show ratings",
+    )
+
+    if other_args:
+        if "-" not in other_args[0]:
+            other_args.insert(0, "-t")
+
+    ns_parser = parse_known_args_and_warn(parser, other_args)
+    if not ns_parser:
+        return
+
+    try:
+        ratings = marketbeat_model.get_ratings()
+
+        df_ratings = pd.DataFrame(ratings)
+        df_ratings = df_ratings[df_ratings["target_price"] != ""]
+        df_ratings["old_target"] = df_ratings["target_price"].apply(
+            lambda x: x.split(" ➝ ")[0] if "➝" in x else ""
+        )
+        df_ratings["new_target"] = df_ratings["target_price"].apply(
+            lambda x: x.split(" ➝ ")[1] if "➝" in x else x
+        )
+
+        df_ratings["clean_current_price"] = df_ratings["current_price"].apply(
+            lambda x: "".join([c if c.isdigit() or c == "." else "" for c in str(x)])
+        )
+        df_ratings["clean_current_price"] = df_ratings["clean_current_price"].astype(
+            float
+        )
+        df_ratings["clean_target_price"] = df_ratings["new_target"].apply(
+            lambda x: "".join([c if c.isdigit() or c == "." else "" for c in str(x)])
+        )
+        df_ratings["clean_target_price"] = df_ratings["clean_target_price"].astype(
+            float
+        )
+
+        df_ratings["pct_increase"] = (
+            100
+            * (df_ratings["clean_target_price"] - df_ratings["clean_current_price"])
+            / df_ratings["clean_current_price"]
+        )
+        df_ratings["pct_increase"] = df_ratings["pct_increase"].round(2)
+
+        df_ratings["pct_abs"] = np.abs(df_ratings["pct_increase"])
+        df_ratings["pct_abs"] = df_ratings["pct_abs"].round(2)
+
+        df_ratings = df_ratings.sort_values("pct_abs", ascending=False)
+
+        df_ratings_top = df_ratings[df_ratings["pct_abs"] > ns_parser.n_threshold][
+            [
+                "ticker",
+                "brokerage",
+                "rating",
+                "current_price",
+                "old_target",
+                "new_target",
+                "pct_increase",
+            ]
+        ]
+
+        df_ratings_top["pct_increase"] = df_ratings_top["pct_increase"].apply(
+            lambda x: str(x) + " %"
+        )
+
+        print(
+            df_ratings_top.rename(
+                columns={
+                    "ticker": "Ticker",
+                    "brokerage": "Brokerage",
+                    "rating": "Rating",
+                    "current_price": "Current Price",
+                    "old_target": "Old Target",
+                    "new_target": "New Target",
+                    "pct_increase": "Percentage Increase",
+                }
+            ).to_string(index=False)
+        )
+
+        print("")
+    except Exception as e:
+        print(e, "\n")