Update app.py

added stock alerts, changed stock history plot upper limit

app.py

@@ -262,42 +262,6 @@ def stock_history_df(num_weeks):
         }
     return (pd.DataFrame(data), f'{int(xmax + 10000)}')
 
-# def make_mp_figure(dates, values, fit_values, ymax = 0.0):
-#     npdates = np.asarray(dates)
-#     npvals = np.asarray(values)
-#     npfits = np.asarray(fit_values)
-#     plt_format = '-'
-#     fig = plt.figure(layout="constrained", figsize=(6,2))
-#     ax = fig.add_subplot(111)
-#     tics = []
-#     labels = []
-#     i = len(dates) - 1
-#     while i >= 0:
-#         tics.append(i)
-#         labels.append(dates[i])
-#         i -= 5
-#     # tics = list(range(0,len(dates),5))
-#     # labels = dates[0::5]
-#     ax.set_xticks(tics, labels = labels)
-#     ax.plot(npdates, npvals, plt_format)
-#     ax.plot(npdates, npfits, plt_format)
-#     ax.set_ylim(0.0, ymax*1.05)
-#     # ax.set_xlim(dates[0], dates[-1:])
-#     return fig
-
-# def lms_fit_trend(dates, values):
-#     # days = []
-#     # fit_data = []
-#     days = list(range(0, len(dates)))
-#     fit = np.polyfit(days, values, 1)
-#     delta = len(dates) * float(fit[0])
-#     avg = float(fit[1]) + 0.5 * delta
-#     pct_delta = 100 * delta / avg
-#     # for day in days:
-#     #     fit_data.append(float(fit[0]) * day + float(fit[1]) )
-#     # return fit_data
-#     return pct_delta
-
 def stock_deltas(values):
     num = len(values)
     month_end_avg = float(np.average(np.array(values[-3:])))
@@ -351,6 +315,62 @@ def stock_week_df(symbol):
     except:
         return (pd.DataFrame(), ymax, (0.0, 0.0, 0.0))
 
+def stock_recent_delta(symbol):
+    try:
+        dates = []
+        values = []
+        ymax = 0
+        etime = etz_now()
+        if etime.hour >= 16:
+            etime = etime + timedelta(days=1)
+        week_ago = etime - timedelta(days=8)
+        end = etime.strftime('%Y-%m-%d')
+        start = week_ago.strftime('%Y-%m-%d')
+        df = yf.download(symbol.upper(),
+                        start = start,
+                        end = end,
+                        progress = False,
+                        )
+        vals2d = df.values.tolist()
+        valsTxt = []
+        numDays = len(vals2d)
+        for i in range(numDays):
+            valsTxt.append(vals2d[i][0])
+        for val in valsTxt:
+            v = round(float(val),2)
+            values.append(v)
+            if v > ymax:
+                ymax = v
+        for row in df.index:
+            dates.append(row.strftime('%Y-%m-%d'))
+        start_val = float(np.average(np.array(values[:2])))
+        end_val = float(values[len(values)-1])
+        return f'{(end_val/start_val - 1.0)*100:.1f}'
+    except:
+        return 'NA'
+
+def get_alerts():
+    try:
+        rv = ''
+        # stock_data = {}
+        global stock_data_path
+        with open(stock_data_path, 'rt') as fp:
+            lines = fp.readlines()
+        for line in lines:
+            (name, symbol, shares) = line.rstrip().split(',')
+            name = name.strip()
+            symbol = symbol.strip()
+            delta_pct = stock_recent_delta(symbol)
+            if delta_pct == 'NA':
+                rv += f'\n{symbol} ({name})   NA'
+            else:
+                rv += f'\n{symbol} ({name})   {delta_pct}%'
+                if abs(float(delta_pct)) > 3:
+                    rv += ' **\*\*\*** '
+        return 'Stock price changes over last week:\nChanges greater than +/-3% marked by  **\*\*\***\n ' + rv + '\n'
+    except:
+        return "Error getting stock deltas\n"
+
 def get_stock_report(verbose = True, offset = 0):
     try:
         stock_data = {}
@@ -724,15 +744,19 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, uploaded_ima
                 response = stock_list()
                 return [past, md(response), None, gptModel, uploaded_image_file, plot]
             elif num == 2:
-                response = get_stock_report()
-                if args[1] == 'value':
+                if args[1] == 'alerts':
+                    response = get_alerts()
                     return [past, md(response), None, gptModel, uploaded_image_file, plot]
-                elif args[1] == 'history':
-                    (plot_df, ymax) = get_total_daily_closing_sequence(40)   #stock_history_df(12)
-                    # ymax = float(ymax)
-                    return [past, md(response), None, gptModel, uploaded_image_file, # plot]
-                           gr.LinePlot(plot_df, x="date", y="value", visible=True, x_label_angle=270,
-                                      y_lim=[400000, 700000], label="Portfolio Value History")]
+                else:
+                    response = get_stock_report()
+                    if args[1] == 'value':
+                        return [past, md(response), None, gptModel, uploaded_image_file, plot]
+                    elif args[1] == 'history':
+                        (plot_df, ymax) = get_total_daily_closing_sequence(40)   #stock_history_df(12)
+                        # ymax = float(ymax)
+                        return [past, md(response), None, gptModel, uploaded_image_file, # plot]
+                               gr.LinePlot(plot_df, x="date", y="value", visible=True, x_label_angle=270,
+                                          y_lim=[400000, 800000], label="Portfolio Value History")]
             elif num >= 3:
                 if args[1] == 'news':
                     symbol = ' '.join(args[2:])