Pylint fixes for gamestonk_terminal/prediction_techniques/arima.py and fbprophet.py

gamestonk_terminal/prediction_techniques/arima.py

@@ -1,26 +1,29 @@
 import argparse
-from gamestonk_terminal.helper_funcs import *
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 from pandas.plotting import register_matplotlib_converters
-
-register_matplotlib_converters()
 import pmdarima
 from statsmodels.tsa.arima.model import ARIMA
+from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+
+register_matplotlib_converters()
 
 # -------------------------------------------------- ARIMA --------------------------------------------------
+# pylint: disable=unused-argument
 def arima(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
         prog="arima",
-        description="""In statistics and econometrics, and in particular in time
-                                     series analysis, an autoregressive integrated moving average (ARIMA) model
-                                     is a generalization of an autoregressive moving average (ARMA) model. Both
-                                     of these models are fitted to time series data either to better understand
-                                     the data or to predict future points in the series (forecasting).
-                                     ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is
-                                     the order (number of time lags) of the autoregressive model, d is the degree
-                                     of differencing (the number of times the data have had past values subtracted),
-                                     and q is the order of the moving-average model.""",
+        description="""
+            In statistics and econometrics, and in particular in time
+            series analysis, an autoregressive integrated moving average (ARIMA) model
+            is a generalization of an autoregressive moving average (ARMA) model. Both
+            of these models are fitted to time series data either to better understand
+            the data or to predict future points in the series (forecasting).
+            ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is
+            the order (number of time lags) of the autoregressive model, d is the degree
+            of differencing (the number of times the data have had past values subtracted),
+            and q is the order of the moving-average model.
+        """,
     )
 
     parser.add_argument(
@@ -140,7 +143,7 @@ def arima(l_args, s_ticker, s_interval, df_stock):
         plt.axvspan(
             df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
         )
-        xmin, xmax, ymin, ymax = plt.axis()
+        _, _, ymin, ymax = plt.axis()
         plt.vlines(
             df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
         )
@@ -152,5 +155,6 @@ def arima(l_args, s_ticker, s_interval, df_stock):
         print(df_pred.to_string())
         print("")
 
-    except:
+    except Exception as e:
+        print(e)
         print("")

gamestonk_terminal/prediction_techniques/fbprophet.py

@@ -1,26 +1,25 @@
 import argparse
-import numpy as np
-import pandas as pd
+import warnings
 import matplotlib.pyplot as plt
+import pandas as pd
 from pandas.plotting import register_matplotlib_converters
+from fbprophet import Prophet
+from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
 
 register_matplotlib_converters()
-import datetime
-from datetime import datetime
-from alpha_vantage.timeseries import TimeSeries
-from gamestonk_terminal.helper_funcs import *
-from fbprophet import Prophet
-import warnings
 
 warnings.simplefilter("ignore")
 
 # ----------------------------------------------------- FBPROPHET -----------------------------------------------------
+# pylint: disable=unused-argument
 def fbprophet(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
         prog="fbprophet",
-        description="""Facebook Prophet is a forecasting procedure that is fast and provides 
-                                     completely automated forecasts that can be tuned by hand by data scientists and analysts.
-                                     It was developed by Facebook's data science team and is open source. """,
+        description="""
+            Facebook Prophet is a forecasting procedure that is fast and provides
+            completely automated forecasts that can be tuned by hand by data scientists and analysts.
+            It was developed by Facebook's data science team and is open source.
+        """,
     )
 
     parser.add_argument(
@@ -56,9 +55,9 @@ def fbprophet(l_args, s_ticker, s_interval, df_stock):
         close_prices = model.make_future_dataframe(periods=ns_parser.n_days)
         forecast = model.predict(close_prices)
 
-        fig, ax = plt.subplots()
+        _, ax = plt.subplots()
         model.plot(forecast, ax=ax, xlabel="Time", ylabel="Share Price ($)")
-        xmin, xmax, ymin, ymax = ax.axis()
+        _, _, ymin, ymax = ax.axis()
         ax.vlines(
             df_stock["ds"].values[-1],
             ymin,
@@ -87,5 +86,6 @@ def fbprophet(l_args, s_ticker, s_interval, df_stock):
         print(df_pred.to_string())
         print("")
 
-    except:
+    except Exception as e:
+        print(e)
         print("")

gamestonk_terminal/sentiment/reddit_api.py

@@ -241,8 +241,10 @@ def popular_tickers(l_args):
         action="store",
         dest="s_subreddit",
         type=str,
-        help="""subreddits to look for tickers, e.g. pennystocks,stocks.
-                        Default: pennystocks, RobinHoodPennyStocks, Daytrading, StockMarket, stocks, investing, wallstreetbets""",
+        help="""
+            subreddits to look for tickers, e.g. pennystocks,stocks.
+            Default: pennystocks, RobinHoodPennyStocks, Daytrading, StockMarket, stocks, investing, wallstreetbets
+        """,
     )
     parser.add_argument(
         "-d",