Update app.py

polyfit stock data for trend

app.py

@@ -22,6 +22,8 @@ import yfinance as yf
 from datetime import datetime, timedelta
 import pytz
 import math
+import numpy as np
+# import matplotlib.pyplot as plt
 
 
 load_dotenv(override=True)
@@ -127,11 +129,11 @@ def get_stock_news(search_symbol):
         rv += f'Link: [URL]({item["link"]})\n\n'
 
     if have_symbol:
-        (plot_df, ymax) = stock_week_df(search_symbol)
+        (plot_df, ymax, pct_delta) = stock_week_df(search_symbol)
     else:
-        (plot_df, ymax) = (pd.DataFrame(), 0.0)
+        (plot_df, ymax, pct_delta) = (pd.DataFrame(), 0.0, 0.0)
 
-    return (rv, plot_df, ymax)
+    return (rv, plot_df, ymax, pct_delta)
 
 def stock_history_df(num_weeks):
     values = []
@@ -152,6 +154,42 @@ 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_week_df(symbol):
     try:
         dates = []
@@ -180,31 +218,17 @@ def stock_week_df(symbol):
                 ymax = v
         for row in df.index:
             dates.append(row.strftime('%Y-%m-%d'))
+        # fit_data = lms_fit(dates, values)
+        pct_delta = lms_fit_trend(dates, values)
         data = {
             "date": dates,
-            "value" : values
+            "value" : values,
+            # "fit" : fit_data
             }
-        return (pd.DataFrame(data), ymax)
+        # fig = make_mp_figure(dates, values, fit_data, ymax)
+        return (pd.DataFrame(data), ymax, pct_delta)
     except:
-        return (pd.DataFrame(), ymax)
-
-
-    for offset in range(num_weeks+1):
-        (value, date) = get_stock_report(False, offset)
-        date = date[5:]
-        values.append(value)
-        dates.append(date)
-        if float(value) > xmax:
-            xmax = float(value)
-    values.reverse()
-    dates.reverse()
-    data = {
-        "date": dates,
-        "value" : values
-        }
-    return (pd.DataFrame(data), f'{int(xmax + 10000)}')
-
-
+        return (pd.DataFrame(), ymax, 0.0)
 
 def get_stock_report(verbose = True, offset = 0):
     try:
@@ -549,6 +573,7 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, uploaded_ima
     if not response:
         response = ''
     plot = gr.LinePlot(visible=False)
+    # plot = gr.Plot(visible=False)
     if user_window == unames[0] and pwd_window == pwdList[0]:
         isBoss = True
         if prompt == 'stats':
@@ -582,21 +607,22 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, uploaded_ima
                 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,
+                    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=[500000, 700000], label="Portfolio Value History")]
             elif num >= 3:
                 if args[1] == 'news':
                     symbol = ' '.join(args[2:])
-                    (response, plot_df, ymax) = get_stock_news(symbol)
+                    (response, plot_df, ymax, pct_delta) = get_stock_news(symbol)
                     ymax *= 1.1
-                    mdtxt = md(f'News for {symbol}:\n\n' + response)
+                    mdtxt = md(f'News for {symbol}:\nTrend = {int(pct_delta)}%\n\n' + response)
                     if plot_df.empty:
                         return [past, mdtxt, None, gptModel, uploaded_image_file, plot]
                     else:
-                        return [past, mdtxt, None, gptModel, uploaded_image_file,
+                        return [past, mdtxt, None, gptModel, uploaded_image_file, #gr.Plot(plot_df, visible=True)]
                                     gr.LinePlot(plot_df, x="date", y="value", visible=True, x_label_angle=270,
-                                                    y_lim=[0, ymax],label=f"{symbol.upper()} Recent Prices")]
+                                                    y_lim=[0, ymax],label=f"{symbol.upper()} Recent Prices",
+                                                    color_map={''})]
                 # elif arg[1] == 'history':
                 #     symbol = arg[2]
                 #     response = 'ok' # get_
@@ -1062,6 +1088,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         with gr.Column():
             image_window = gr.Image(visible=False, label='Generated Image')
     with gr.Row():
+        # plot = gr.Plot(visible=False)
         plot = gr.LinePlot(test_plot_df(), x="month", y="value", visible=False, label="Portfolio Value History")
     submit_button.click(chat,
              inputs=[prompt_window, user_window, password, history, output_window, model, uploaded_image_file],