Add Insider Activity over time command from Business Insider

due_diligence/business_insider_api.py

@@ -201,3 +201,116 @@ def estimates(l_args, s_ticker):
     except:
         print("")
         return
+
+
+# ----------------------------------------------- INSIDER_ACTIVITY -----------------------------------------------
+def insider_activity(l_args, df_stock, s_ticker, s_start, s_interval):
+    parser = argparse.ArgumentParser(prog='insider_activity', 
+                                     description="""Insider activity over time [Source: Business Insider API]""")
+    parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=10, help='Number of latest insider activity')
+
+    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
+
+        url_market_business_insider = f"https://markets.businessinsider.com/stocks/{s_ticker.lower()}-stock"
+        text_soup_market_business_insider = BeautifulSoup(requests.get(url_market_business_insider).text, "lxml")
+       
+        d_insider = dict()
+        l_insider_vals = list()
+        for idx, insider_val in enumerate(text_soup_market_business_insider.findAll('td', {'class':"table__td text-center"})):
+            #print(insider_val.text.strip())
+
+            l_insider_vals.append(insider_val.text.strip())
+
+            # Add value to dictionary
+            if (idx+1)%6 == 0:
+                # Check if we are still parsing insider trading activity
+                if '/' not in l_insider_vals[0]:
+                    break
+                d_insider[(idx+1)//6] = l_insider_vals
+                l_insider_vals = list()
+
+        df_insider = pd.DataFrame.from_dict(d_insider, orient='index',
+                                            columns = ['Date', 'Shares Traded', 'Shares Held', 'Price', 'Type', 'Option'])
+
+        df_insider['Date'] = pd.to_datetime(df_insider['Date'])
+        df_insider = df_insider.set_index('Date')
+        df_insider = df_insider.sort_index(ascending=True)
+
+        if s_start:
+            df_insider = df_insider[s_start:]
+
+        pfig, ax = plt.subplots()
+
+        if s_interval == "1440min":
+            plt.plot(df_stock.index, df_stock['5. adjusted close'].values, lw=3)
+        else: # Intraday 
+            plt.plot(df_stock.index, df_stock['4. close'].values, lw=3)
+
+        plt.title(f"{s_ticker.upper()} (Time Series) and Price Target")
+
+        plt.xlabel('Time')
+        plt.ylabel('Share Price ($)')
+
+        df_insider['Trade'] = df_insider.apply(lambda row: (1, -1)[row.Type == 'Sell'] * float(row['Shares Traded'].replace(',','')), axis=1)
+        plt.xlim(df_insider.index[0], df_stock.index[-1])
+        min_price, max_price = ax.get_ylim()
+
+        price_range = max_price - min_price
+        shares_range = df_insider[df_insider['Type'] == 'Buy'].groupby(by=['Date']).sum()['Trade'].max() - df_insider[df_insider['Type'] == 'Sell'].groupby(by=['Date']).sum()['Trade'].min()
+        n_proportion = price_range / shares_range
+
+        for ind in df_insider[df_insider['Type'] == 'Sell'].groupby(by=['Date']).sum().index:
+            if ind in df_stock.index:
+                ind_dt = ind
+            else:
+                ind_dt = get_next_stock_market_days(ind, 1)[0]
+            
+            n_stock_price = 0
+            if s_interval == "1440min":
+                n_stock_price = df_stock['5. adjusted close'][ind_dt]    
+            else:
+                n_stock_price = df_stock['4. close'][ind_dt]
+                
+            plt.vlines(x=ind_dt, 
+                    ymin= n_stock_price + n_proportion * float(df_insider[df_insider['Type'] == 'Sell'].groupby(by=['Date']).sum()['Trade'][ind]), 
+                    ymax= n_stock_price, 
+                    colors='red', ls='-', lw=5)
+            
+        for ind in df_insider[df_insider['Type'] == 'Buy'].groupby(by=['Date']).sum().index:
+            if ind in df_stock.index:
+                ind_dt = ind
+            else:
+                ind_dt = get_next_stock_market_days(ind, 1)[0]
+                
+            n_stock_price = 0
+            if s_interval == "1440min":
+                n_stock_price = df_stock['5. adjusted close'][ind_dt]    
+            else:
+                n_stock_price = df_stock['4. close'][ind_dt]
+                
+            plt.vlines(x=ind_dt, 
+                    ymin=n_stock_price, 
+                    ymax=n_stock_price + n_proportion * float(df_insider[df_insider['Type'] == 'Buy'].groupby(by=['Date']).sum()['Trade'][ind]), 
+                    colors='green', ls='-', lw=5)
+
+        plt.grid(b=True, which='major', color='#666666', linestyle='-')
+        plt.minorticks_on()
+        plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
+        plt.show()
+
+        l_names = list()
+        for s_name in text_soup_market_business_insider.findAll('a', {'onclick':"silentTrackPI()"}):
+            l_names.append(s_name.text.strip())    
+        df_insider['Insider'] = l_names
+
+        print(df_insider.sort_index(ascending=False).head(n=ns_parser.n_num).to_string())
+        print("")
+                
+    except:
+        print("")
+        return

due_diligence/dd_menu.py

@@ -40,6 +40,7 @@ def print_due_diligence(s_ticker, s_start, s_interval):
     print("   rating        rating of the company from strong sell to strong buy [FMP]")
     print("   pt            price targets over time [Business Insider]")
     print("   est           quarter and year analysts earnings estimates [Business Insider]")
+    print("   ins           insider activity over time [Business Insider]")
     print("   insider       insider trading of the company [Finviz]")
     print("   sec           SEC filings [Market Watch]")
     print("   short         short interest [Quandl]")
@@ -57,7 +58,7 @@ def dd_menu(df_stock, s_ticker, s_start, s_interval):
                                            'red', # Reddit
                                            'short', # Quandl
                                            'rating', # FMP
-                                           'pt', 'est', # BI
+                                           'pt', 'est', 'ins', # BI
                                            'insider', 'news', 'analyst', # Finviz
                                            'warnings', 'sec']) # MW
 
@@ -108,6 +109,9 @@ def dd_menu(df_stock, s_ticker, s_start, s_interval):
         elif ns_known_args.cmd == 'est':
             bi_api.estimates(l_args, s_ticker)
 
+        elif ns_known_args.cmd == 'ins':
+            bi_api.insider_activity(l_args, df_stock, s_ticker, s_start, s_interval)
+
         # FINANCIAL MODELING PREP API
         elif ns_known_args.cmd == 'rating':
             fmp_api.rating(l_args, s_ticker)