Update app.py

Stock plots 40 days

app.py

@@ -21,6 +21,7 @@ import pandas as pd
 import yfinance as yf
 from datetime import datetime, timedelta
 import pytz
+import math
 
 
 load_dotenv(override=True)
@@ -96,16 +97,20 @@ def stock_list():
             rv += f'{symbol}  {name}\n'
     return rv
 
-def get_stock_news(search_symbol):
+def get_stock_list():
     stock_list = {}
-    fuzzy = True
-    have_symbol = False
-    search_symbol = search_symbol.strip().upper()
     with open(stock_data_path, 'rt') as fp:
         lines = fp.readlines()
         for line in lines:
             (name, symbol, shares) = line.rstrip().split(',')
-            stock_list[symbol.strip()] = name.strip()
+            stock_list[symbol.strip()] = (name.strip(),shares.strip())
+    return stock_list
+
+def get_stock_news(search_symbol):
+    fuzzy = True
+    have_symbol = False
+    search_symbol = search_symbol.strip().upper()
+    stock_list = get_stock_list()
     search_term = search_symbol
     if search_symbol in stock_list.keys():
         have_symbol = True
@@ -155,7 +160,7 @@ def stock_week_df(symbol):
         etime = etz_now()
         if etime.hour >= 16:
             etime = etime + timedelta(days=1)
-        week_ago = etime - timedelta(days=8)
+        week_ago = etime - timedelta(days=40) # was 8
         end = etime.strftime('%Y-%m-%d')
         start = week_ago.strftime('%Y-%m-%d')
         df = yf.download(symbol.upper(),
@@ -169,12 +174,12 @@ def stock_week_df(symbol):
         for i in range(numDays):
             valsTxt.append(vals2d[i][0])
         for val in valsTxt:
-            v = float(val)
+            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'))
+            dates.append(row.strftime('%m-%d'))
         data = {
             "date": dates,
             "value" : values
@@ -263,6 +268,85 @@ def get_last_closing(symbol, offset=0, timeout=10):
     except:
         return (0.0, "0000-00-00")
 
+def get_total_daily_closing_sequence(num_days):
+    try:
+        first_loop = True
+        max_val = 0.0
+        stock_list = get_stock_list()
+        symbols = [s for s in stock_list.keys()]
+        # symbols = symbols[8:10]
+        etime = etz_now()
+        if etime.hour >= 16:
+            etime = etime + timedelta(days=1)
+        end = etime.strftime('%Y-%m-%d')
+        start_time = etime - timedelta(days = num_days)
+        start = start_time.strftime('%Y-%m-%d')
+        df = yf.download(symbols,
+                start = start,
+                end = end,
+                progress = False,
+                )
+        # val2d = df.values.tolist()
+        dates = []
+        for row in df.index:
+            dates.append(row.strftime('%m-%d'))
+        # columns = list(df.columns.values)
+        # cvals = df[columns[0]].tolist()
+
+        for sym in symbols:
+            (name, shares) = stock_list[sym]
+            values = df[('Close', sym)].tolist()
+            n = len(values)
+            for i in range(n):
+                if math.isnan(float(values[i])):
+                    if i == 0:
+                        values[0] = values[1]
+                    else:
+                        values[i] = values[i-1]
+            if first_loop:
+                first_loop = False
+                total_values = values.copy()
+                for i in range(n):
+                    total_values[i] = float(total_values[i]) * float(shares)
+            else:
+                for i in range(n):
+                    total_values[i] += (float(values[i]) * float(shares))
+        for i in range(n):
+            total_values[i] = round(total_values[i], 2)
+            if total_values[i] > max_val:
+                max_val = total_values[i]
+        data = {
+            "date": dates,
+            "value" : total_values
+            }
+        return (pd.DataFrame(data), max_val)
+    except:
+        return (pd.DataFrame(), 0.0)
+
+def get_daily_closing_sequence(symbol, num_days):
+    try:
+        dates = []
+        values = []
+        etime = etz_now()
+        if etime.hour >= 16:
+            etime = etime + timedelta(days=1)
+        end = etime.strftime('%Y-%m-%d')
+        start_time = etime - timedelta(days = num_days)
+        start = start_time.strftime('%Y-%m-%d')
+        df = yf.download(symbol,
+                        start = start,
+                        end = end,
+                        progress = False,
+                        )
+        vals2d = df.values.tolist()
+        valsTxt = []
+        values = [round(float(vals2d[i][0]),2) for i in range(len(vals2d))]
+        for row in df.index:
+            dates.append(row.strftime('%Y-%m-%d'))
+        return(dates, values)
+    except:
+        return([],[])
+
 def create_stock_data_file(txt):
     with open(stock_data_path, 'wt') as fp:
         fp.write(txt)
@@ -496,7 +580,7 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, uploaded_ima
                 if args[1] == 'value':
                     return [past, md(response), None, gptModel, uploaded_image_file, plot]
                 elif args[1] == 'history':
-                    (plot_df, ymax) = stock_history_df(12)
+                    (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,
                            gr.LinePlot(plot_df, x="date", y="value", visible=True,
@@ -506,12 +590,13 @@ def chat(prompt, user_window, pwd_window, past, response, gptModel, uploaded_ima
                     symbol = ' '.join(args[2:])
                     (response, plot_df, ymax) = get_stock_news(symbol)
                     ymax *= 1.1
+                    mdtxt = md(f'News for {symbol}:\n\n' + response)
                     if plot_df.empty:
-                        return [past, md(response), None, gptModel, uploaded_image_file, plot]
+                        return [past, mdtxt, None, gptModel, uploaded_image_file, plot]
                     else:
-                        return [past, md(response), None, gptModel, uploaded_image_file,
+                        return [past, mdtxt, None, gptModel, uploaded_image_file,
                                     gr.LinePlot(plot_df, x="date", y="value", visible=True,
-                                                    y_lim=[0, ymax],label=f"{symbol} Recent Prices")]
+                                                    y_lim=[0, ymax],label=f"{symbol.upper()} Recent Prices")]
                 # elif arg[1] == 'history':
                 #     symbol = arg[2]
                 #     response = 'ok' # get_