Add command that outputs Short Interest ratio

fundamental_analysis/fa_menu.py

@@ -15,6 +15,7 @@ from fundamental_analysis import financial_modeling_prep_api as fmp_api
 from fundamental_analysis import finviz_api as fvz_api
 from fundamental_analysis import market_watch_api as mw_api
 from fundamental_analysis import reddit_api as r_api
+from fundamental_analysis import quandl_api as q_api
 
 
 # -----------------------------------------------------------------------------------------------------------------------
@@ -36,6 +37,7 @@ def print_fundamental_analysis(s_ticker, s_start, s_interval):
     print("")
     print("   dd            gets due diligence from another user's post [Reddit]")
     print("   warnings      company warnings according to Sean Seah book [Market Watch]")
+    print("   short         short interest [Quandl]")
     print("\nFinviz API")
     print("   screener      screen info about the company")
     print("   insider       insider trading of the company")
@@ -115,7 +117,7 @@ def fa_menu(s_ticker, s_start, s_interval):
 
     # Add list of arguments that the fundamental analysis parser accepts
     fa_parser = argparse.ArgumentParser(prog='fundamental_analysis', add_help=False)
-    fa_parser.add_argument('cmd', choices=['info', 'help', 'q', 'quit', 'more', 'warnings', 'dd',
+    fa_parser.add_argument('cmd', choices=['info', 'help', 'q', 'quit', 'more', 'warnings', 'dd', 'short',
                                            'screener', 'insider', 'news', 'analyst', # Finviz
                                            'income', 'assets', 'liabilities', 'operating', 'investing', 'financing', 'sec']) # MW
 
@@ -153,6 +155,9 @@ def fa_menu(s_ticker, s_start, s_interval):
 
         elif ns_known_args.cmd == 'warnings':
             mw_api.sean_seah_warnings(l_args, s_ticker)
+
+        elif ns_known_args.cmd == 'short':
+            q_api.short_interest(l_args, s_ticker, s_start)
         
         # FINVIZ API
         elif ns_known_args.cmd == 'screener':

fundamental_analysis/quandl_api.py

@@ -0,0 +1,69 @@
+import quandl
+from datetime import datetime
+import config_bot as cfg
+import argparse
+from matplotlib import pyplot as plt
+import numpy as np
+import matplotlib.ticker as ticker
+import pandas as pd
+from stock_market_helper_funcs import *
+
+# -------------------------------------------------------- SHORT_INTEREST --------------------------------------------------------
+def short_interest(l_args, s_ticker, s_start):
+    parser = argparse.ArgumentParser(prog='short', 
+                                     description="""Short interest corresponds to the number of shares that have been sold short 
+                                     but have not yet been covered or closed out. [Source: Quandl API]""")
+
+    parser.add_argument('-n', "--nyse", action="store_true", default=False, dest="b_nyse", help='Data from NYSE')
+    parser.add_argument('-d', "--days", action="store", dest="n_days", type=check_positive, default=10, help='Number of latest days to see data')
+
+    try:
+        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
+
+        if l_unknown_args:
+            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
+            return
+
+        quandl.ApiConfig.api_key = cfg.API_KEY_QUANDL
+
+        if ns_parser.b_nyse:
+            df_short_interest = quandl.get(f"FINRA/FNYX_{s_ticker}")
+        else:
+            df_short_interest = quandl.get(f"FINRA/FNSQ_{s_ticker}")
+
+        df_short_interest = df_short_interest[s_start:]
+        df_short_interest.columns = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_short_interest.columns.tolist()]
+        df_short_interest['% of Volume Shorted'] = round(100*df_short_interest['Short Volume']/df_short_interest['Total Volume'], 2)
+
+        fig, ax = plt.subplots()
+        ax.bar(df_short_interest.index, df_short_interest['Short Volume'], 0.3, color='r')
+        ax.bar(df_short_interest.index, df_short_interest['Total Volume']-df_short_interest['Short Volume'], 0.3, bottom=df_short_interest['Short Volume'], color='b')
+        ax.set_ylabel('Shares')
+        ax.set_xlabel('Date')
+
+        ax.set_title(f"{('NASDAQ', 'NYSE')[ns_parser.b_nyse]} Short Interest on {s_ticker} from {s_start.date()}")
+        
+        ax.legend(labels=['Short Volume', 'Total Volume'])
+        ax.tick_params(axis='both', which='major')
+        ax.yaxis.set_major_formatter(ticker.EngFormatter())
+        ax_twin = ax.twinx()
+        ax_twin.tick_params(axis='y', colors='green')
+        ax_twin.set_ylabel('Percentage of Volume Shorted', color='green')
+        ax_twin.plot(df_short_interest.index, df_short_interest['% of Volume Shorted'], color='green')
+        ax_twin.tick_params(axis='y', which='major', color='green')
+        ax_twin.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f%%'))
+        plt.xlim([df_short_interest.index[0], df_short_interest.index[-1]])
+        
+        df_short_interest['% of Volume Shorted'] = df_short_interest['% of Volume Shorted'].apply(lambda x: f'{x/100:.2%}')
+        df_short_interest = df_short_interest.applymap(lambda x: long_number_format(x)).sort_index(ascending=False)
+
+        pd.set_option('display.max_colwidth', 70)
+        print(df_short_interest.head(n=ns_parser.n_days).to_string())
+        print("")
+
+        plt.show()
+
+    except:
+        print("")
+        return
+