Improve user experience on disc/ratings command

gamestonk_terminal/discovery/disc_controller.py

@@ -89,7 +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        latest ratings [MarketBeat]")
+        print("   ratings        top ratings updates [MarketBeat]")
         print("")
 
     def switch(self, an_input: str):

gamestonk_terminal/discovery/marketbeat_view.py

@@ -2,6 +2,8 @@
 __docformat__ = "numpy"
 
 import argparse
+import numpy as np
+import pandas as pd
 from typing import List
 from gamestonk_terminal.helper_funcs import (
     check_positive,
@@ -17,38 +19,98 @@ def ratings_view(other_args: List[str]):
     Parameters
     ----------
     other_args : List[str]
-        argparse other args - ["-n", "5"]
+        argparse other args - ["-t", "100"]
     """
-
     parser = argparse.ArgumentParser(
         add_help=False,
         prog="ratings",
         description="""Latest ratings. [Source: MarketBeat]""",
     )
-
     parser.add_argument(
-        "-n",
-        "--num",
+        "-t",
+        "--threshold",
         action="store",
-        dest="n_num",
+        dest="n_threshold",
         type=check_positive,
-        default=10,
-        help="number of ratings to print",
+        default=100,
+        help="threshold to show latest ratings that show a drastic increase",
     )
 
+    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
 
-    ratings = marketbeat_model.get_ratings()
-    for idx, rating in enumerate(ratings):
-        print(
-            rating["ticker"],
-            rating["action"].replace(" by", ""),
-            rating["impact"],
-            rating["current_price"],
-            "->",
-            rating["target_price"],
+    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 ""
         )
-        if idx >= ns_parser.n_num - 1:
-            break
+        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")