Update app.py

app.py

@@ -24,7 +24,7 @@ import holoviews as hv
 from utils import load_hf_dataset  # ,upload_to_hf_dataset, download_from_hf_dataset
 
 # Load environment variables from .env file
-# load_dotenv()
+load_dotenv()
 
 # Get the Hugging Face API token from the environment; either set in .env file or in the environment directly in GitHub
 HF_TOKEN = os.getenv("HF_TOKEN")
@@ -40,30 +40,34 @@ def get_tradingview(current_datetime):
     return load_hf_dataset("america.csv", HF_TOKEN, dataset_name_TradingView_input)
 
 
+@pn.cache()
+def get_stock_history(ticker, startdate, enddate, current_datetime):
+  return yf.Ticker(ticker).history(start=startdate, end=enddate)
+  
 
 # Reading gurufocus,finviz from crawling pipelines with GitHub Action
 current_datetime = datetime.now().strftime("%Y-%m-%d")
 DF = get_tradingview(current_datetime)
 # DF.Industry.unique()
 
-def get_plot(tabtype, selected_Industry_or_Sector,  startdate , enddate):
+
+def get_plot_Sector(selected_Sector,  startdate , enddate, num_stocks, method_selector):
   # selected_Industry_or_Sector= "Major Banks"
-  if tabtype == 'Industry':
-    ticker_list = list(DF.query('Industry==@selected_Industry_or_Sector & `Market Capitalization`>100e9').Ticker)
-  else:
-    ticker_list = list(DF.query('Sector==@selected_Industry_or_Sector & `Market Capitalization`>100e9').Ticker)
+  ticker_list = list(DF.query('Sector==@selected_Sector & `Market Capitalization`>100e9').Ticker)
+  ticker_list = list(DF.query('Sector==@selected_Sector').Ticker[0:num_stocks])
   len(ticker_list)
   # ticker_list = ['AAPL', 'AMZN', 'GOOGL','TSLA', 'NVDA' , 'AMD']
 
   # start = datetime(2020, 1, 1)
   # end = datetime.now()
   # end = datetime(2020,1,1)
-  method = 'pearson'#, 'kendall', 'spearman'
+  method = method_selector #'pearson'#, 'kendall', 'spearman'
 
   tickers=[]
   for ticker in ticker_list:     
     if not '/' in ticker:
-      df = yf.Ticker(ticker).history(start=startdate , end=enddate)
+      # df = yf.Ticker(ticker).history(start=startdate , end=enddate)
+      df = get_stock_history(ticker, startdate, enddate, current_datetime)
       df['Ticker'] = ticker    
       tickers.append(df)
   DFtotal = pd.concat(tickers)
@@ -82,7 +86,7 @@ def get_plot(tabtype, selected_Industry_or_Sector,  startdate , enddate):
 
   heatmap = hv.HeatMap(melted_corr, kdims=['Ticker', 'Variable'], vdims='Correlation')
   heatmap = heatmap.opts(
-    title=f'Heat Map correlation based on {method} method for {selected_Industry_or_Sector} sector from {startdate.year} - {enddate.year}',
+    title=f'Heat Map correlation based on {method} method for {selected_Sector} sector from {startdate.year} - {enddate.year}',
     width=800,
     height=600,
     tools=['hover'],
@@ -100,7 +104,19 @@ industry_selector = pn.widgets.Select(
   options=list(DF.Industry.unique()),
   value='Major Banks'
 )
+num_stocks = pn.widgets.IntSlider(
+  name='Number of Stocks',
+  start=5,
+  end=30,
+  value=10,
+  step=1
+)
 
+method_selector = pn.widgets.Select(
+  name='Correlation Method',
+  options=['pearson', 'kendall', 'spearman'],
+  value='pearson'
+)
 # Create dropdown widget for industry selection
 sector_selector = pn.widgets.Select(
   name='Select Sector',
@@ -120,15 +136,15 @@ date_end = pn.widgets.DatePicker(
     end= date.today() #date(2023, 9, 1)
 )
 
-date_start.value = date(2010,1,1)
+date_start.value = date(2020,1,1)
 date_end.value = date.today()
 
 
-tab_Sector_content = pn.Row(pn.Column(date_start, date_end, sector_selector ), pn.bind(get_plot,'Sector', sector_selector, date_start, date_end))
-tab_Industry_content = pn.Row(pn.Column(date_start, date_end, industry_selector ))#, pn.bind(get_plot,'Industry', industry_selector, date_start, date_end))
+tab_Sector_content = pn.Row(pn.Column(date_start, date_end, sector_selector, num_stocks, method_selector ), pn.bind(get_plot_Sector, sector_selector, date_start, date_end, num_stocks, method_selector))
+# tab_Industry_content = pn.Row(pn.Column(date_start, date_end, industry_selector ), pn.bind(get_plot_Industry,industry_selector, date_start, date_end))
 pn.Tabs(
-    ("Tab Sector", tab_Sector_content),
-    ("Tab Industry", tab_Industry_content)
+    ("Sector", tab_Sector_content),
+    # ("Tab Industry", tab_Industry_content)
 ).servable('Stock Correlation Heatmap')
 
 # pn.Row(pn.Column(date_start, date_end, industry_selector ), pn.bind(get_plot,industry_selector, date_start, date_end)).servable('US Stock Correlation Heatmap')