add sma and all tickers

app.py

@@ -1,19 +1,26 @@
 
+import pandas as pd
+import panel as pn
+from datetime import datetime
+from datetime import date
+pn.extension('bokeh', template='bootstrap')
+import hvplot.pandas
+
 import pandas as pd
 import yfinance as yf
 import panel as pn
 
 @pn.cache
-def get_df(ticker, startdate , enddate , interval="1d"):
+def get_df(ticker, startdate , enddate , interval="1d",window=50):
     # interval="1d"
     # get_df(ticker ="PG", startdate="2000-01-01" , enddate="2023-09-01" , interval="1d")
     DF = yf.Ticker(ticker).history(start=startdate,end=enddate,interval=interval)
-    DF['SMA50'] = DF.Close.rolling(window=50).mean()
+    DF['SMA'] = DF.Close.rolling(window=window).mean()
     DF = DF.reset_index()
     return DF 
 
-def get_hvplot(ticker , startdate , enddate , interval):
-    DF = get_df(ticker , startdate=startdate , enddate=enddate , interval=interval)
+def get_hvplot(ticker , startdate , enddate , interval,window):
+    DF = get_df(ticker , startdate=startdate , enddate=enddate , interval=interval,window=window)
 
     import hvplot.pandas  # Ensure hvplot is installed (pip install hvplot)
     from sklearn.linear_model import LinearRegression
@@ -36,23 +43,22 @@ def get_hvplot(ticker , startdate , enddate , interval):
 
     # # Step 4: Create a line plot for linear regression
     line_plot = DF.hvplot(x='Date', y='Predicted_Close', kind='line',line_dash='dashed', color='red')
+    line_plot_SMA = DF.hvplot(x='Date', y='SMA', kind='line',line_dash='dashed', color='orange')
 
     # # Step 5: Overlay scatter plot and linear regression line
     # return (scatter_plot * line_plot).opts(width=800, height=600, show_grid=True, gridstyle={ 'grid_line_color': 'gray'})
     # grid_style = {'grid_line_color': 'black'}#, 'grid_line_width': 1.5, 'ygrid_bounds': (0.3, 0.7),'minor_xgrid_line_color': 'lightgray', 'xgrid_line_dash': [4, 4]}
-    return (scatter_plot * line_plot).opts(width=800, height=600, show_grid=True)
+    return (scatter_plot * line_plot *line_plot_SMA).opts(width=800, height=600, show_grid=True)
 
 
-import panel as pn
-from datetime import datetime
-from datetime import date
-pn.extension('bokeh', template='bootstrap')
-import hvplot.pandas
-
-tickers = ['AAPL', 'META', 'GOOG', 'IBM', 'MSFT','NKE','DLTR','DG']
+    
+# tickers = ['AAPL', 'META', 'GOOG', 'IBM', 'MSFT','NKE','DLTR','DG']
+# ticker = pn.widgets.Select(name='Ticker', options=tickers)
 
-ticker = pn.widgets.Select(name='Ticker', options=tickers)
-window = pn.widgets.IntSlider(name='Window Size', value=50, start=1, end=200, step=5)
+tickers = pd.read_csv('tickers.csv').Ticker.to_list()
+ticker = pn.widgets.AutocompleteInput(name='Ticker', options=tickers , placeholder='Write Ticker here همین جا')
+ticker.value = "AAPL"
+window = pn.widgets.IntSlider(name='Window Size', value=200, start=5, end=1000, step=5)
 
 # Create a DatePicker widget with a minimum date of 2000-01-01
 date_start = pn.widgets.DatePicker(
@@ -64,7 +70,7 @@ date_start = pn.widgets.DatePicker(
 date_end = pn.widgets.DatePicker(
     name ="End Date",# value=datetime(2000, 1, 1),
     description='Select a Date',
-    end= date(2023, 9, 1)
+    end= date.today() #date(2023, 9, 1)
 )
 
 date_start.value = date(2010,1,1)
@@ -73,6 +79,7 @@ date_end.value = date.today()
 pn.Row(
     pn.Column( ticker, window , date_start , date_end),
     # pn.panel(pn.bind(get_hvplot, ticker, "2010-01-01","2023-09-01","1d")) #, sizing_mode='stretch_width')
-    pn.panel(pn.bind(get_hvplot, ticker, date_start , date_end,"1d")) #, sizing_mode='stretch_width')
+    pn.panel(pn.bind(get_hvplot, ticker, date_start , date_end,"1d",window)) #, sizing_mode='stretch_width')
 ).servable(title="Under Valued Screener- Linear Regression")
 
+